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Ribeiro, Ricardo Ferreira. "Face detection on infrared thermal image." Master's thesis, Universidade de Aveiro, 2017. http://hdl.handle.net/10773/23551.
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Mestrado em Engenharia Eletrónica e TelecomunicaçõesInfrared cameras or thermal imaging cameras are devices that use infrared radiation to capture an image. This kind of sensors are being developed for almost a century now. They started to be used in the military environment, but at that time it took too long to create a single image. Nowadays, the infrared sensors have reached a whole new technological level and are used for other than military purposes. These sensors are being used for face detection in this thesis. When comparing the use of thermal images regarding color images, it is possible to see advantages and limitations, such as capture images in total darkness and high price, respectively, which will be explored throughout this document. This work proposes the development or adaptation of several methods for face detection on infrared thermal images. The well known algorithm developed by Paul Viola and Michael Jones, using Haar feature-based cascade classi ers, is used to compare the traditional algorithms developed for visible light images when applied to thermal imaging. Three di erent algorithms for face detection are presented. Face segmentation is the rst step in these methods. A method for the segmentation and ltering of the face in the infrared thermal images resulting in a binary image is proposed. In the rst method, an edge detection algorithm is applied to the binary image and the face detection is based on these contours. In the second method, a template matching method is used for searching and nding the location of a template image with the shape of a human head in the binary image. In the last one, a matching algorithm is used. This algorithm correlates a template with the distance transform of the edge image. This algorithm incorporates edge orientation information resulting in the reduction of false detection and the cost variation is limited. The experimental results show that the proposed methods have promising outcome, but the second method is the most suitable for the performed experiments.
As camaras infravermelhas ou as camaras de imagem termica sao dispositivos que usam radiação infravermelha para capturar uma imagem. Este tipo de sensores estao a ser desenvolvidos há quase um século. Começaram a ser usados para fins militares, mas naquela época demorava demasiado tempo para criar uma única imagem. Hoje em dia, os sensores infravermelhos alcançaram um nível tecnológico totalmente novo e são usados para fins além de militares. Esses sensores estão ser usados para detecção facial nesta dissertação. Comparando o uso de imagens térmicas relativamente a imagens coloridas, é possível ver vantagens e limitações, tal como a captura de imagens na escuridão e o preço elevado, respectivamente, que serão exploradas durante este documento. Este trabalho propõe o desenvolvimento ou adaptação de vários métodos para a detecção facial em imagens térmicas. O conhecido algoritmo desenvolvido por Paul Viola e Michael Jones, que utiliza cascatas de classificadores de Haar baseado em características, é usado para comparar os algoritmos tradicionais desenvolvidos para imagens de luz visível quando aplicados a imagens térmicas. São apresentados três métodos diferentes para a detecção facial. A segmentação do rosto e o primeiro passo nestes métodos. E proposto um método para a segmentação e filtragem do rosto nas imagens térmicas que tem como resultado uma imagem binária. No primeiro método, é aplicado um algoritmo de detecção de contornos a imagem binária e a detecção facial é baseada nesses contornos. No segundo método, é usado um método de correspondência de padrões para pesquisar e encontrar a localização de uma imagem padrão com a forma da cabeça humana na imagem binária. No último, é usado um algoritmo de correspondência. Este algoritmo correlaciona um padrão com a transformada de distância da imagem de contornos. Este algoritmo incorpora informações de orientação de contornos que resulta na redução de falsas detecções e a variação do custo é limitada. Os resultados experimentais mostram que os métodos propostos têm resultados promissores, mas o segundo método é o mais adequado para as experiências realizadas.
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Roman, Matej. "Automatizované měření teploty v boji proti COVID." Master's thesis, Vysoké učení technické v Brně. Fakulta elektrotechniky a komunikačních technologií, 2021. http://www.nusl.cz/ntk/nusl-442439.
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This thesis focuses on the development of an open source software capable of automatic face detection in an image captured by a thermal camera, followed by a temperature measuring. This software is supposed to aid in the COVID-19 pandemics. The developed software is independent of used thermal camera. In this thesis, I am using TIM400 thermal camera. The implementation of the face detection was achieved by an OpenCV module. The methods tested were Template Matching, Eigen Faces, and Cascade Classifier. The last-mentioned had the best results, hence was used in the final version of the software. Cascade Classifier is looking for the eyes and their surrounding area in the image, allowing the software to subsequently measure the temperature on the surface of one's forehead. One can therefore be wearing a face mask or a respirator safely. The temperature measuring works in real time and the software is able to capture several people at once. It then keeps a record of the temperature of each measured individual as well as the time of the measurement. The software as a whole is a part of an installation file compatible with the Windows operating system. The functionality of this software was tested – the video recordings are included in this thesis.
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Mahmood, Muhammad Tariq. "Face Detection by Image Discriminating." Thesis, Blekinge Tekniska Högskola, Avdelningen för för interaktion och systemdesign, 2006. http://urn.kb.se/resolve?urn=urn:nbn:se:bth-4352.
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Human face recognition systems have gained a considerable attention during last few years. There are very many applications with respect to security, sensitivity and secrecy. Face detection is the most important and first step of recognition system. Human face is non rigid and has very many variations regarding image conditions, size, resolution, poses and rotation. Its accurate and robust detection has been a challenge for the researcher. A number of methods and techniques are proposed but due to a huge number of variations no one technique is much successful for all kinds of faces and images. Some methods are exhibiting good results in certain conditions and others are good with different kinds of images. Image discriminating techniques are widely used for pattern and image analysis. Common discriminating methods are discussed.SIPL, Mechatronics, GIST 1 Oryong-Dong, Buk-Gu, Gwangju, 500-712 South Korea tel. 0082-62-970-2997
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Omar, Luma Qassam Abedalqader. "Face liveness detection under processed image attacks." Thesis, Durham University, 2018. http://etheses.dur.ac.uk/12812/.
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Face recognition is a mature and reliable technology for identifying people. Due to high-definition cameras and supporting devices, it is considered the fastest and the least intrusive biometric recognition modality. Nevertheless, effective spoofing attempts on face recognition systems were found to be possible. As a result, various anti-spoofing algorithms were developed to counteract these attacks. They are commonly referred in the literature a liveness detection tests. In this research we highlight the effectiveness of some simple, direct spoofing attacks, and test one of the current robust liveness detection algorithms, i.e. the logistic regression based face liveness detection from a single image, proposed by the Tan et al. in 2010, against malicious attacks using processed imposter images. In particular, we study experimentally the effect of common image processing operations such as sharpening and smoothing, as well as corruption with salt and pepper noise, on the face liveness detection algorithm, and we find that it is especially vulnerable against spoofing attempts using processed imposter images. We design and present a new facial database, the Durham Face Database, which is the first, to the best of our knowledge, to have client, imposter as well as processed imposter images. Finally, we evaluate our claim on the effectiveness of proposed imposter image attacks using transfer learning on Convolutional Neural Networks. We verify that such attacks are more difficult to detect even when using high-end, expensive machine learning techniques.
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Wall, Helene. "Context-Based Algorithm for Face Detection." Thesis, Linköping University, Department of Science and Technology, 2005. http://urn.kb.se/resolve?urn=urn:nbn:se:liu:diva-4171.
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Face detection has been a research area for more than ten years. It is a complex problem due to the high variability in faces and amongst faces; therefore it is not possible to extract a general pattern to be used for detection. This is what makes the face detection problem a challenge.
This thesis gives the reader a background to the face detection problem, where the two main approaches of the problem are described. A face detection algorithm is implemented using a context-based method in combination with an evolving neural network. The algorithm consists of two majors steps: detect possible face areas and within these areas detect faces. This method makes it possible to reduce the search space.
The performance of the algorithm is evaluated and analysed. There are several parameters that affect the performance; the feature extraction method, the classifier and the images used.
This work resulted in a face detection algorithm and the performance of the algorithm is evaluated and analysed. The analysis of the problems that occurred has provided a deeper understanding for the complexity of the face detection problem.
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Yigit, Ahmet. "Thermal And Visible Band Image Fusion For Abandoned Object Detection." Master's thesis, METU, 2010. http://etd.lib.metu.edu.tr/upload/3/12611720/index.pdf.
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Packages that are left unattended in public spaces are a security concern and timely detection of these packages is important for prevention of potential threats. Operators should be always alert to detect abandoned items in crowded environments. However, it is very difficult for operators to stay concentrated for extended periods. Therefore, it is important to aid operators with automatic detection of abandoned items. Most of the methods in the literature define abandoned items as items newly added to the scene and stayed stationary for a predefined time. Hence other stationary objects, such as people sitting on a bench are also detected as suspicious objects resulting in a high number of false alarms. These false alarms could be prevented by discriminating suspicious items as living/nonliving objects. In this thesis, visible band and thermal band cameras are used together to analyze the interactions between humans and other objects. Thermal images help classification of objects using their heat signatures. This way, people and the objects they carry or left behind can be detected separately. Especially, it is aimed to detect abandoned items and discriminate living or nonliving objects
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Tan, Teewoon. "HUMAN FACE RECOGNITION BASED ON FRACTAL IMAGE CODING." University of Sydney. Electrical and Information Engineering, 2004. http://hdl.handle.net/2123/586.
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Human face recognition is an important area in the field of biometrics. It has been an active area of research for several decades, but still remains a challenging problem because of the complexity of the human face. In this thesis we describe fully automatic solutions that can locate faces and then perform identification and verification. We present a solution for face localisation using eye locations. We derive an efficient representation for the decision hyperplane of linear and nonlinear Support Vector Machines (SVMs). For this we introduce the novel concept of $\rho$ and $\eta$ prototypes. The standard formulation for the decision hyperplane is reformulated and expressed in terms of the two prototypes. Different kernels are treated separately to achieve further classification efficiency and to facilitate its adaptation to operate with the fast Fourier transform to achieve fast eye detection. Using the eye locations, we extract and normalise the face for size and in-plane rotations. Our method produces a more efficient representation of the SVM decision hyperplane than the well-known reduced set methods. As a result, our eye detection subsystem is faster and more accurate. The use of fractals and fractal image coding for object recognition has been proposed and used by others. Fractal codes have been used as features for recognition, but we need to take into account the distance between codes, and to ensure the continuity of the parameters of the code. We use a method based on fractal image coding for recognition, which we call the Fractal Neighbour Distance (FND). The FND relies on the Euclidean metric and the uniqueness of the attractor of a fractal code. An advantage of using the FND over fractal codes as features is that we do not have to worry about the uniqueness of, and distance between, codes. We only require the uniqueness of the attractor, which is already an implied property of a properly generated fractal code. Similar methods to the FND have been proposed by others, but what distinguishes our work from the rest is that we investigate the FND in greater detail and use our findings to improve the recognition rate. Our investigations reveal that the FND has some inherent invariance to translation, scale, rotation and changes to illumination. These invariances are image dependent and are affected by fractal encoding parameters. The parameters that have the greatest effect on recognition accuracy are the contrast scaling factor, luminance shift factor and the type of range block partitioning. The contrast scaling factor affect the convergence and eventual convergence rate of a fractal decoding process. We propose a novel method of controlling the convergence rate by altering the contrast scaling factor in a controlled manner, which has not been possible before. This helped us improve the recognition rate because under certain conditions better results are achievable from using a slower rate of convergence. We also investigate the effects of varying the luminance shift factor, and examine three different types of range block partitioning schemes. They are Quad-tree, HV and uniform partitioning. We performed experiments using various face datasets, and the results show that our method indeed performs better than many accepted methods such as eigenfaces. The experiments also show that the FND based classifier increases the separation between classes. The standard FND is further improved by incorporating the use of localised weights. A local search algorithm is introduced to find a best matching local feature using this locally weighted FND. The scores from a set of these locally weighted FND operations are then combined to obtain a global score, which is used as a measure of the similarity between two face images. Each local FND operation possesses the distortion invariant properties described above. Combined with the search procedure, the method has the potential to be invariant to a larger class of non-linear distortions. We also present a set of locally weighted FNDs that concentrate around the upper part of the face encompassing the eyes and nose. This design was motivated by the fact that the region around the eyes has more information for discrimination. Better performance is achieved by using different sets of weights for identification and verification. For facial verification, performance is further improved by using normalised scores and client specific thresholding. In this case, our results are competitive with current state-of-the-art methods, and in some cases outperform all those to which they were compared. For facial identification, under some conditions the weighted FND performs better than the standard FND. However, the weighted FND still has its short comings when some datasets are used, where its performance is not much better than the standard FND. To alleviate this problem we introduce a voting scheme that operates with normalised versions of the weighted FND. Although there are no improvements at lower matching ranks using this method, there are significant improvements for larger matching ranks. Our methods offer advantages over some well-accepted approaches such as eigenfaces, neural networks and those that use statistical learning theory. Some of the advantages are: new faces can be enrolled without re-training involving the whole database; faces can be removed from the database without the need for re-training; there are inherent invariances to face distortions; it is relatively simple to implement; and it is not model-based so there are no model parameters that need to be tweaked.
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Rondahl, Thomas. "Face Detection in Digital Imagery Using Computer Vision and Image Processing." Thesis, Umeå universitet, Institutionen för datavetenskap, 2011. http://urn.kb.se/resolve?urn=urn:nbn:se:umu:diva-51406.
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By adding a failure fault limit to an existing implementation of a face detection system application and a tolerance limit for detection time, a desired throughput for detected objects could be established. The aim of this thesis was to add an increased detection rate for pro le/partial faces while increasing the stability and run-time of the system. The results were obtained through an empirical analysis of test data which was compared between the implementation done for this thesis and the older implementation. The results showed an increase in detected faces (in low sized images) by 10% while also increasing the number of false-positives by 0.725 detections per average image. In large size image cases, an automatic scaling functionality was added, to decrease detection time and decrease false-negatives. The results indicated a decrease in average detection time from (old implementation) 15 seconds to 2 seconds, while still increasing positive detection with 23%, from an average of 42% to 65%. False-positives were also decreased from 5.8 to 0.2 detections per average image used in test.
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Patel, Brindal A. "R-Eye| An image processing-based embedded system for face detection and tracking." Thesis, California State University, Long Beach, 2016. http://pqdtopen.proquest.com/#viewpdf?dispub=10141532.
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The current project presents the development of R-Eye, a face detection and tracking system implemented as an embedded device based on the Arduino microcontroller. The system is programmed in Python using the Viola-Jones algorithm for image processing. Several experiments designed to measure and compare the performance of the system under various conditions show that the system performs well when used with an integrated camera, reaching a 93% face recognition accuracy for a clear face. The accuracy is lower when detecting a face with accessories, such as a pair of eyeglasses (80%), or when a low-resolution low-quality camera is used. Experimental results also show that the system is capable of detecting and tracking a face within a frame containing multiple faces.
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Schwambach, Costa Vítor. "Optimization of a face detection algorithm for real-time mobile phone applications." Universidade Federal de Pernambuco, 2009. https://repositorio.ufpe.br/handle/123456789/2335.
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Previous issue date: 2009Desde equipamentos de vigillância por vídeo a câmeras digitais e telefones celulares, a detecção de rostos e uma funcionalidade que esta rapidamente ganhando peso no projeto de interfaces de usuario mais inteligentes e tornando a interação homem-maquina cada vez mais natural e intuitiva. Com isto em mente, fabricantes de chips estão embarcando esta tecnologia na sua nova geração de processadores de sinal de imagem (ISP) desenvolvidos especificamente para uso em aparelhos celulares. O foco deste trabalho foi analisar um algoritmo para detecção de rostos para suportar a definição da arquitetura mais adequada a ser usada na solução final. Um algoritmo inicial baseado na tecnica de Cascata de Caracteristicas Simples foi usado como base para este trabalho. O algoritmo inicial, como especificado, leva quase quarenta segundos para processar um unico quadro de imagem no processador alvo, tempo este que inviabilizaria o uso desta solução. Focando na implementação de um novo ISP, o algoritmo foi completamente reescrito, otimizado e propriamente mapeado na plataforma alvo, ao ponto onde um fator de aceleração de 167x foi atingido e uma imagem de pior caso agora leva menos de 250 milissegundos para ser processada. Este numero e ainda mais baixo se for considerada a media em um conjunto maior de imagens ou um vídeo, caindo para cerca de 100 milissegundos por quadro de imagem processado. Não obstante, performance não foi o unico alvo, tambem a quantidade de memoria necessaria foi dramaticamente reduzida. Isto tem um impacto direto na area de silicio requerida pelo circuito e conseq uentemente menores custos de producao e consumo de potência, fatores criticos em um sistema para aplicações moveis. E importante ressaltar que a qualidade não foi deixada de lado e em todas as otimizações realizadas, tomou-se o cuidado de verificar que a qualidade de detecção não tinha sido impactada. Este documento apresenta a pesquisa feita e os resultados obtidos. Começa por uma breve introdução ao assunto de Visão Computacional e aos desafios de projetar uma solução de detecção de rostos. Apos esta introdução, o algoritmo que serviu como base para este trabalho e apresentado juntamente com as otimizações mais relevantes ao nivel algoritmico para melhorar a performance. Na sequência, instruções customizadas desenvolvidas para acelerar a execução do algoritmo na solução final são apresentadas e discutidas
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Kripakaran, Rolance. "Face Detection and Facial Feature Localization for multi-pose faces and complex backgroundimages." Thesis, Högskolan Dalarna, Datateknik, 2011. http://urn.kb.se/resolve?urn=urn:nbn:se:du-5705.
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The objective of this thesis work, is to propose an algorithm to detect the faces in a digital image with complex background. A lot of work has already been done in the area of face detection, but drawback of some face detection algorithms is the lack of ability to detect faces with closed eyes and open mouth. Thus facial features form an important basis for detection. The current thesis work focuses on detection of faces based on facial objects. The procedure is composed of three different phases: segmentation phase, filtering phase and localization phase. In segmentation phase, the algorithm utilizes color segmentation to isolate human skin color based on its chrominance properties. In filtering phase, Minkowski addition based object removal (Morphological operations) has been used to remove the non-skin regions. In the last phase, Image Processing and Computer Vision methods have been used to find the existence of facial components in the skin regions.This method is effective on detecting a face region with closed eyes, open mouth and a half profile face. The experiment’s results demonstrated that the detection accuracy is around 85.4% and the detection speed is faster when compared to neural network method and other techniques.
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Kaba, Utku. "Moving Hot Object Detection In Airborne Thermal Videos." Master's thesis, METU, 2012. http://etd.lib.metu.edu.tr/upload/12614532/index.pdf.
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In this thesis, we present an algorithm for vision based detection of moving objects observed by IR sensors on a moving platform. In addition we analyze the performance of different approaches in each step of the algorithm. The proposed algorithm is composed of preprocessing, feature detection, feature matching, homography estimation and difference image analysis steps. First, a global motion estimation based on planar homography model is performed in order to compensate the motion of the sensor and moving platform where the sensors are located. Then, moving objects are identified on difference images of consecutive video frames with global motion suppression. Performance of the proposed algorithm is shown on different IR image sequences.
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Bergenroth, Hannah. "Use of Thermal Imagery for Robust Moving Object Detection." Thesis, Linköpings universitet, Medie- och Informationsteknik, 2021. http://urn.kb.se/resolve?urn=urn:nbn:se:liu:diva-177888.
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This work proposes a system that utilizes both infrared and visual imagery to create a more robust object detection and classification system. The system consists of two main parts: a moving object detector and a target classifier. The first stage detects moving objects in visible and infrared spectrum using background subtraction based on Gaussian Mixture Models. Low-level fusion is performed to combine the foreground regions in the respective domain. For the second stage, a Convolutional Neural Network (CNN), pre-trained on the ImageNet dataset is used to classify the detected targets into one of the pre-defined classes; human and vehicle. The performance of the proposed object detector is evaluated using multiple video streams recorded in different areas and under various weather conditions, which form a broad basis for testing the suggested method. The accuracy of the classifier is evaluated from experimentally generated images from the moving object detection stage supplemented with publicly available CIFAR-10 and CIFAR-100 datasets. The low-level fusion method shows to be more effective than using either domain separately in terms of detection results.Examensarbetet är utfört vid Institutionen för teknik och naturvetenskap (ITN) vid Tekniska fakulteten, Linköpings universitet
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Giunta, Alberto. "Implementazione e analisi comparativa di tecniche di Face Morphing Detection." Master's thesis, Alma Mater Studiorum - Università di Bologna, 2018. http://amslaurea.unibo.it/17029/.
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Il Face Morphing Attack consiste nel presentare ai portali di Automatic Border Control dei passaporti sostanzialmente validi e regolarmente emessi, ma con fotografie e quindi dati biometrici del volto che ne permettano l’uso da parte di più di un solo soggetto, e quindi da parte di soggetti diversi rispetto al legittimo proprietario del documento.
Il campo di ricerca in ambito di Face Morphing Detection è ancora molto giovane e attivo, nonché frammentato: ciascuno studio sull’argomento propone tecniche in qualche modo differenti dalle precedenti e ne verifica l’efficacia su dataset proprietari e costruiti ad hoc da ciascun gruppo di ricercatori.
Con il lavoro proposto in questo lavoro di Tesi si cerca di fare maggiore chiarezza sull’efficacia di diversi metodi di Face Morphing Detection noti in letteratura, applicandoli a situazioni e dataset più fedeli alla realtà e facendone un’estensiva analisi comparativa.
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Bhattarai, Smrity. "Digital Architecture for real-time face detection for deep video packet inspection systems." University of Akron / OhioLINK, 2017. http://rave.ohiolink.edu/etdc/view?acc_num=akron1492787219112947.
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Tallman, Jake T. "SOARNET, Deep Learning Thermal Detection For Free Flight." DigitalCommons@CalPoly, 2021. https://digitalcommons.calpoly.edu/theses/2339.
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Thermals are regions of rising hot air formed on the ground through the warming of the surface by the sun. Thermals are commonly used by birds and glider pilots to extend flight duration, increase cross-country distance, and conserve energy. This kind of powerless flight using natural sources of lift is called soaring. Once a thermal is encountered, the pilot flies in circles to keep within the thermal, so gaining altitude before flying off to the next thermal and towards the destination. A single thermal can net a pilot thousands of feet of elevation gain, however estimating thermal locations is not an easy task. Pilots look for different indicators: color variation on the ground because the difference in the amount of heat absorbed by the ground varies based on the color/composition, birds circling in an area gaining lift, and certain types of cloud formations (cumulus clouds). The above methods are not always reliable enough and pilots study the weather for thermals by estimating solar heating of the ground using cloud cover and time of year and the lapse rate and dew point of the troposphere. In this paper, we present a Machine Learning based solution for assisting in forecasting thermals. We created a custom dataset using flight data recorded and uploaded to public databases by soaring pilots. We determine where and when the pilot encountered thermals to pull weather and satellite images corresponding to the location and time of the flight. Using this dataset we train an algorithm to automatically predict the location of thermals given as input the current weather conditions and terrain information obtained from Google Earth Engine and thermal regions encountered as truth labels. We were able to converge very well on the training and validation set, proving our method with around a 0.98 F1 score. These results indicate success in creating a custom dataset and a powerful neural network with the necessity of bolstering our custom dataset.
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Chenaoua, Kamal S. "Automatic detection of human skin in two-dimensional and complex imagery." Thesis, Queen's University Belfast, 2015. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.680864.
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Cross, Carl Brady. "An Investigation of Thermal Imaging to Detect Physiological Indicators of Stress in Humans." Wright State University / OhioLINK, 2013. http://rave.ohiolink.edu/etdc/view?acc_num=wright1369317509.
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Wang, Dong. "Digital image processing via combination of low-level and high-level approaches." Thesis, University of Bradford, 2011. http://hdl.handle.net/10454/5384.
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With the growth of computer power, Digital Image Processing plays a more and more important role in the modern world, including the field of industry, medical, communications, spaceflight technology etc. There is no clear definition how to divide the digital image processing, but normally, digital image processing includes three main steps: low-level, mid-level and highlevel processing. Low-level processing involves primitive operations, such as: image preprocessing to reduce the noise, contrast enhancement, and image sharpening. Mid-level processing on images involves tasks such as segmentation (partitioning an image into regions or objects), description of those objects to reduce them to a form suitable for computer processing, and classification (recognition) of individual objects. Finally, higher-level processing involves "making sense" of an ensemble of recognised objects, as in image analysis. Based on the theory just described in the last paragraph, this thesis is organised in three parts: Colour Edge and Face Detection; Hand motion detection; Hand Gesture Detection and Medical Image Processing. II In Colour Edge Detection, two new images G-image and R-image are built through colour space transform, after that, the two edges extracted from G-image and R-image respectively are combined to obtain the final new edge. In Face Detection, a skin model is built first, then the boundary condition of this skin model can be extracted to cover almost all of the skin pixels. After skin detection, the knowledge about size, size ratio, locations of ears and mouth is used to recognise the face in the skin regions. In Hand Motion Detection, frame differe is compared with an automatically chosen threshold in order to identify the moving object. For some special situations, with slow or smooth object motion, the background modelling and frame differencing are combined in order to improve the performance. In Hand Gesture Recognition, 3 features of every testing image are input to Gaussian Mixture Model (GMM), and then the Expectation Maximization algorithm (EM)is used to compare the GMM from testing images and GMM from training images in order to classify the results. In Medical Image Processing (mammograms), the Artificial Neural Network (ANN) and clustering rule are applied to choose the feature. Two classifier, ANN and Support Vector Machine (SVM), have been applied to classify the results, in this processing, the balance learning theory and optimized decision has been developed are applied to improve the performance.
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Lopes, Daniel Pedro Ferreira. "Face verication for an access control system in unconstrained environment." Master's thesis, Universidade de Aveiro, 2017. http://hdl.handle.net/10773/23395.
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Mestrado em Engenharia Eletrónica e TelecomunicaçõesO reconhecimento facial tem vindo a receber bastante atenção ao longo dos últimos anos não só na comunidade cientifica, como também no ramo comercial. Uma das suas várias aplicações e o seu uso num controlo de acessos onde um indivíduo tem uma ou várias fotos associadas a um documento de identificação (também conhecido como verificação de identidade). Embora atualmente o estado da arte apresente muitos estudos em que tanto apresentam novos algoritmos de reconhecimento como melhorias aos já desenvolvidos, existem mesmo assim muitos problemas ligados a ambientes não controlados, a aquisição de imagem e a escolha dos algoritmos de deteção e de reconhecimento mais eficazes. Esta tese aborda um ambiente desafiador para a verificação facial: um cenário não controlado para o acesso a infraestruturas desportivas. Uma vez que não existem condições de iluminação controladas nem plano de fundo controlado, isto torna um cenário complicado para a implementação de um sistema de verificação facial. Esta tese apresenta um estudo sobre os mais importantes algoritmos de detecção e reconhecimento facial assim como técnicas de pré-processamento tais como o alinhamento facial, a igualização de histograma, com o objetivo de melhorar a performance dos mesmos. Também em são apresentados dois métodos para a aquisição de imagens envolvendo a seleção de imagens e calibração da câmara. São apresentados resultados experimentais detalhados baseados em duas bases de dados criadas especificamente para este estudo. No uso de técnicas de pré-processamento apresentadas, foi possível presenciar melhorias até 20% do desempenho dos algoritmos de reconhecimento referentes a verificação de identidade. Com os métodos apresentados para os testes ao ar livre, foram conseguidas melhorias na ordem dos 30%.
Face Recognition has been received great attention over the last years, not only on the research community, but also on the commercial side. One of the many uses of face recognition is its use on access control systems where a person has one or several photos associated to an Identi cation Document (also known as identity veri cation). Although there are many studies nowadays, both presenting new algorithms or just improvements of the already developed ones, there are still many open problems regarding face recognition in uncontrolled environments, from the image acquisition conditions to the choice of the most e ective detection and recognition algorithms, just to name a few. This thesis addresses a challenging environment for face veri cation: an unconstrained environment for sports infrastructures access. As there are no controlled lightning conditions nor controlled background, this makes a di cult scenario to implement a face veri cation system. This thesis presents a study of some of the most important facial detection and recognition algorithms as well as some pre-processing techniques, such as face alignment and histogram equalization, with the aim to improve their performance. It also introduces some methods for a more e cient image acquisition based on image selection and camera calibration, specially designed for addressing this problem. Detailed experimental results are presented based on two new databases created speci cally for this study. Using pre-processing techniques, it was possible to improve the recognition algorithms performances up to 20% regarding veri cation results. With the methods presented for the outdoor tests, performances had improvements up to 30%
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Doyle, Jason Emory. "Automatic Dynamic Tracking of Horse Head Facial Features in Video Using Image Processing Techniques." Thesis, Virginia Tech, 2019. http://hdl.handle.net/10919/87582.
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The wellbeing of horses is very important to their care takers, trainers, veterinarians, and owners. This thesis describes the development of a non-invasive image processing technique that allows for automatic detection and tracking of horse head and ear motion, respectively, in videos or camera feed, both of which may provide indications of horse pain, stress, or well-being. The algorithm developed here can automatically detect and track head motion and ear motion, respectively, in videos of a standing horse. Results demonstrating the technique for nine different horses are presented, where the data from the algorithm is utilized to plot absolute motion vs. time, velocity vs. time, and acceleration vs. time for the head and ear motion, respectively, of a variety of horses and ponies. Two-dimensional plotting of x and y motion over time is also presented. Additionally, results of pilot work in eye detection in light colored horses is also presented. Detection of pain in horses is particularly difficult because they are prey animals and have mechanisms to disguise their pain, and these instincts may be particularly strong in the presence of an unknown human, such as a veterinarian. Current state-of-the art for detecting pain in horses primarily involves invasive methods, such as heart rate monitors around the body, drawing blood for cortisol levels, and pressing on painful areas to elicit a response, although some work has been done for humans to sort and score photographs subjectively in terms of a "horse grimace scale." The algorithms developed in this thesis are the first that the author is aware for exploiting proven image processing approaches from other applications for development of an automatic tool for detection and tracking of horse facial indicators. The algorithms were done in common open source programs Python and OpenCV, and standard image processing approaches including Canny Edge detection Hue, Saturation, Value color filtering, and contour tracking were utilized in algorithm development. The work in this thesis provides the foundational development of a non -invasive and automatic detection and tracking program for horse head and ear motion, including demonstration of the viability of this approach using videos of standing horses. This approach lays the groundwork for robust tool development for monitoring horses non-invasively and without the required presence of humans in such applications as post-operative monitoring, foaling, evaluation of performance horses in competition and/or training, as well as for providing data for research on animal welfare, among other scenarios.MS
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Váňa, Jan. "Rozpoznávání termosnímků obličejů." Master's thesis, Vysoké učení technické v Brně. Fakulta informačních technologií, 2009. http://www.nusl.cz/ntk/nusl-236689.
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Images of human face are one of the most used biometric features in automatic identification. This article presents an approach which uses face images in thermal (infrared) spectrum for purpose of important face features (eyes position, head rotation) detection and identification.
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Shi, Yangyu. "Infrared Imaging Decision Aid Tools for Diagnosis of Necrotizing Enterocolitis." Thesis, Université d'Ottawa / University of Ottawa, 2020. http://hdl.handle.net/10393/40714.
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Neonatal necrotizing enterocolitis (NEC) is one of the most severe digestive tract emergencies in neonates, involving bowel edema, hemorrhage, and necrosis, and can lead to serious complications including death. Since it is difficult to diagnose early, the morbidity and mortality rates are high due to severe complications in later stages of NEC and thus early detection is key to the treatment of NEC. In this thesis, a novel automatic image acquisition and analysis system combining a color and depth (RGB-D) sensor with an infrared (IR) camera is proposed for NEC diagnosis. A design for sensors configuration and a data acquisition process are introduced. A calibration method between the three cameras is described which aims to ensure frames synchronization and observation consistency among the color, depth, and IR images. Subsequently, complete segmentation procedures based on the original color, depth, and IR information are proposed to automatically separate the human body from the background, remove other interfering items, identify feature points on the human body joints, distinguish the human torso and limbs, and extract the abdominal region of interest. Finally, first-order statistical analysis is performed on thermal data collected over the entire extracted abdominal region to compare differences in thermal data distribution between different patient groups. Experimental validation in a real clinical environment is reported and shows encouraging results.
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Ramamoorthy, Dhyanesh. "Muscle Fatigue Detection using Infrared Thermography: Image Segmentation to Extract the Region of Interest from Thermograms." University of Cincinnati / OhioLINK, 2018. http://rave.ohiolink.edu/etdc/view?acc_num=ucin1543923019568392.
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Michalík, Marek. "Poloautomatické pořízení rozsáhlé databáze lidských obličejů." Master's thesis, Vysoké učení technické v Brně. Fakulta informačních technologií, 2011. http://www.nusl.cz/ntk/nusl-237025.
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The project is focused on methods of obtaining large number of images of human faces. Such database should then serve as a set of data for face detection and recognition by the means of supervised machine learning. The work deals with the basic principles of supervised machine learning and available data sets for this procedure. Project contains proposals of techniques and implementation of algorithms suitable for acquiring images from video and a concept of user interface for semi-automatic acceptation and annotation of located images.
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Beyan, Cigdem. "Object Tracking For Surveillance Applications Using Thermal And Visible Band Video Data Fusion." Master's thesis, METU, 2010. http://etd.lib.metu.edu.tr/upload/12612743/index.pdf.
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Individual tracking of objects in the video such as people and the luggages they carry is important for surveillance applications as it would enable deduction of higher level information and timely detection of potential threats. However, this is a challenging problem and many studies in the literature track people and the belongings as a single object. In this thesis, we propose using thermal band video data in addition to the visible band video data for tracking people and their belongings separately for indoor applications using their heat signatures. For object tracking step, an adaptive, fully automatic multi object tracking system based on mean-shift tracking method is proposed. Trackers are refreshed using foreground information to overcome possible problems which may occur due to the changes in object&rsquos size, shape and to handle occlusion, split and to detect newly emerging objects as well as objects that leave the scene. By using the trajectories of objects, owners of the objects are found and abandoned objects are detected to generate an alarm. Better tracking performance is also achieved compared a single modality as the thermal reflection and halo effect which adversely affect tracking are eliminated by the complementing visible band data.
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Liao, Wen-Jiao. "Physics-based radiometric signature modeling and detection algorithms of land mines using electro-optical sensors." Columbus, Ohio : Ohio State University, 2003. http://rave.ohiolink.edu/etdc/view?acc%5num=osu1064252075.
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Thesis (Ph. D.)--Ohio State University, 2003.Title from first page of PDF file. Document formatted into pages; contains xxii, 255 p. : ill. (some col.). Advisors: Joel T. Johnson and Brian A. Baertlein, Dept. of Electrical Engineering. Includes bibliographical references (p. 247-255).
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Ye, Getian Information Technology & Electrical Engineering Australian Defence Force Academy UNSW. "Image registration and super-resolution mosaicing." Awarded by:University of New South Wales - Australian Defence Force Academy. School of Information Technology and Electrical Engineering, 2005. http://handle.unsw.edu.au/1959.4/38653.
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This thesis presents new approaches to image registration and super-resolution mosaicing as well as their applications. Firstly, a feature-based image registration method is proposed for a multisensor surveillance system that consists of an optical camera and an infrared camera. By integrating a non-rigid object tracking technique into this method, a novel approach to simultaneous object tracking and multisensor image registration is proposed. Based on the registration and fusion of multisensor information, automatic face detection is greatly improved. Secondly, some extensions of a gradient-based image registration method, called inverse compositional algorithm, are proposed. These extensions include cumulative multi-image registration and the incorporation of illumination change and lens distortion correction. They are incorporated into the framework of the original algorithm in a consistent manner and efficiency can still be achieved for multi-image registration with illumination and lens distortion correction. Thirdly, new super-resolution mosaicing algorithms are proposed for multiple uncompressed and compressed images. Considering the process of image formation, observation models are introduced to describe the relationship between the superresolution mosaic image and the uncompressed and compressed low-resolution images. To improve the performance of super-resolution mosaicing, a wavelet-based image interpolation technique and an approach to adaptive determination of the regularization parameter are presented. For compressed images, a spatial-domain algorithm and a transform-domain algorithm are proposed. All the proposed superresolution mosaicing algorithms are robust against outliers. They can produce superresolution mosaics and reconstructed super-resolution images with improved subjective quality. Finally, new techniques for super-resolution sprite generation and super-resolution sprite coding are proposed. Considering both short-term and long-term motion influences, an object-based image registration method is proposed for handling long image sequences. In order to remove the influence of outliers, a robust technique for super-resolution sprite generation is presented. This technique produces sprite images and reconstructed super-resolution images with high visual quality. Moreover, it provides better reconstructed low-resolution images compared with low-resolution sprite generation techniques. Due to the advantages of the super-resolution sprite, a super-resolution sprite coding technique is also proposed. It achieves high coding efficiency especially at a low bit-rate and produces both decoded low-resolution and super-resolution images with improved subjective quality. Throughout this work, the performance of all the proposed algorithms is evaluated using both synthetic and real image sequences.
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Höll, Karel. "Aplikace metod detekce a rozpoznání obličeje." Master's thesis, Vysoké učení technické v Brně. Fakulta elektrotechniky a komunikačních technologií, 2014. http://www.nusl.cz/ntk/nusl-220909.
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This work deals with image processing and face detection. Includes approaches to the problems of image processing. Furthermore, it focuses mainly on the choice of appropriate libraries and implementation of algorithms able to detect faces from the input image data.
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Trejo, Guerrero Sandra. "Model-Based Eye Detection and Animation." Thesis, Linköping University, Department of Electrical Engineering, 2006. http://urn.kb.se/resolve?urn=urn:nbn:se:liu:diva-7059.
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In this thesis we present a system to extract the eye motion from a video stream containing a human face and applying this eye motion into a virtual character. By the notation eye motion estimation, we mean the information which describes the location of the eyes in each frame of the video stream. Applying this eye motion estimation into a virtual character, we achieve that the virtual face moves the eyes in the same way than the human face, synthesizing eye motion into a virtual character. In this study, a system capable of face tracking, eye detection and extraction, and finally iris position extraction using video stream containing a human face has been developed. Once an image containing a human face is extracted from the current frame of the video stream, the detection and extraction of the eyes is applied. The detection and extraction of the eyes is based on edge detection. Then the iris center is determined applying different image preprocessing and region segmentation using edge features on the eye picture extracted.
Once, we have extracted the eye motion, using MPEG-4 Facial Animation, this motion is translated into the Facial Animation arameters (FAPs). Thus we can improve the quality and quantity of Facial Animation expressions that we can synthesize into a virtual character.
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Sharma, Vinay. "Simultaneous object detection and segmentation using top-down and bottom-up processing." Columbus, Ohio : Ohio State University, 2008. http://rave.ohiolink.edu/etdc/view?acc%5Fnum=osu1196372113.
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Tesař, Martin. "Detekce obličejů ve videu na GPU." Master's thesis, Vysoké učení technické v Brně. Fakulta informačních technologií, 2012. http://www.nusl.cz/ntk/nusl-236454.
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This work deals with task of face detection on graphic card. First part is the introduction to face detection methods focusing on detector proposed by Viola and Jones. Further, this work studies the possibilities of mapping detector's key parts on graphic card. Next part describes implementation details of designed application. The end of work include results and comparison with CPU approach. The last chapter summarizes the whole work and proposes future possibilities of development.
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Chu, Shuyu. "Change Detection and Analysis of Data with Heterogeneous Structures." Diss., Virginia Tech, 2017. http://hdl.handle.net/10919/78613.
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Heterogeneous data with different characteristics are ubiquitous in the modern digital world.
For example, the observations collected from a process may change on its mean or variance.
In numerous applications, data are often of mixed types including both discrete and continuous variables. Heterogeneity also commonly arises in data when underlying models vary across different segments. Besides, the underlying pattern of data may change in different dimensions, such as in time and space. The diversity of heterogeneous data structures makes statistical
modeling and analysis challenging.
Detection of change-points in heterogeneous data has attracted great attention from a variety of application areas, such as quality control in manufacturing, protest event detection in social science, purchase likelihood prediction in business analytics, and organ state change in the biomedical engineering. However, due to the extraordinary diversity of the heterogeneous data structures and complexity of the underlying dynamic patterns, the change-detection and analysis of such data is quite challenging.
This dissertation aims to develop novel statistical modeling methodologies to analyze four types of heterogeneous data and to find change-points efficiently. The proposed approaches have been applied to solve real-world problems and can be potentially applied to a broad range of areas.Ph. D.
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Dubský, Milan. "Simulace biometrických zabezpečovacích systémů pracující na základě rozpoznávání tváře." Master's thesis, Vysoké učení technické v Brně. Fakulta elektrotechniky a komunikačních technologií, 2008. http://www.nusl.cz/ntk/nusl-217325.
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The aim of this work is to realize a system in the Matlab-Simulink environment, which will be able to detect and recognize the human face from the input image. The created model will actually simulate the biometric security systems working on the principle of face recognition. The work is divided into two parts. In the first part, several methods for face detection from image are described. We focused on the symptomatic oriented and color segmentation methods. The pattern matching method is also described and implemented; the advantage ofthe pattern matching that it can be used either for face detection or face recognition. The second part of this work contains a description of the face recognition. Where PCA (Principal Component Analysis) are used for this task, this part of the work also includes experimental results of tests performed on our methods.
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Smith, Phillip R. "Generation of Biomarkers from Anthrax Spores by Catalysis and Analytical Pyrolysis." Diss., CLICK HERE for online access, 2005. http://contentdm.lib.byu.edu/ETD/image/etd1005.pdf.
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Šašinka, Ondřej. "Detekce obličeje." Master's thesis, Vysoké učení technické v Brně. Fakulta informačních technologií, 2009. http://www.nusl.cz/ntk/nusl-236684.
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This MSc Thesis deals with face detection in image. In this approach, facial features (eyes, nose, mouth corners) are detected first and then joined to the whole face. For the facial features detection, classifiers trained with AdaBoost algorithm are used. Haar wavelets are used as features for classification.
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Kocich, Petr. "Extrakce obličejových únavových charakteristik řidiče." Master's thesis, Vysoké učení technické v Brně. Fakulta elektrotechniky a komunikačních technologií, 2011. http://www.nusl.cz/ntk/nusl-219320.
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Bäck, David. "Neural Network Gaze Tracking using Web Camera." Thesis, Linköping University, Department of Biomedical Engineering, 2006. http://urn.kb.se/resolve?urn=urn:nbn:se:liu:diva-5579.
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Gaze tracking means to detect and follow the direction in which a person looks. This can be used in for instance human-computer interaction. Most existing systems illuminate the eye with IR-light, possibly damaging the eye. The motivation of this thesis is to develop a truly non-intrusive gaze tracking system, using only a digital camera, e.g. a web camera.
The approach is to detect and track different facial features, using varying image analysis techniques. These features will serve as inputs to a neural net, which will be trained with a set of predetermined gaze tracking series. The output is coordinates on the screen.
The evaluation is done with a measure of accuracy and the result is an average angular deviation of two to four degrees, depending on the quality of the image sequence. To get better and more robust results, a higher image quality from the digital camera is needed.
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Nascimento, Andréia Vieira do. "Detecção de faces humanas em imagens digitais: um algoritmo baseado em lógica nebulosa." Universidade de São Paulo, 2005. http://www.teses.usp.br/teses/disponiveis/18/18133/tde-19052017-150435/.
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Este trabalho tem como objetivo desenvolver uma metodologia baseada em lógica nebulosa, (KLIR ; YUAN, 1995) para detectar faces humanas em imagens digitais. Considerando que pessoas conseguem reconhecer facilmente as faces humanas, este trabalho prevê a pesquisa da informação relativa a esse reconhecimento utilizando os resultados obtidos, em um esquema \"fuzzy\", para identificação de faces humanas em imagens digitais. É proposto então um algoritmo que classifique automaticamente as regiões de uma imagem em faces humanas ou não. O conhecimento para a construção da base de regras foi obtido através de informações das pessoas por meio de uma pesquisa de campo onde as respostas foram numericamente armazenadas para a geração da classificação nebulosa. Foram gerados desenhos line-draw que de uma maneira global representam as faces humanas. Esses desenhos foram apresentados às pessoas entrevistadas que forneceram subsídios para a montagem das regras \"fuzzy\". O algoritmo foi capaz de a partir daí, identificar faces humanas em imagens digitalizadas. Imagens simples contendo uma face frontal foram submetidas a um algoritmo e ao passarem por processamento (extração de bordas, erosão, binarização, etc...) perderam características, tornando difícil sua identificação. O algoritmo \"fuzzy\" foi capaz de atribuir um grau de pertinência à imagem dentro do conjunto de faces humanas frontais. A lógica nebulosa possui história recente, porém, desde cedo, demonstra sua versatilidade, principalmente por traduzir modelos não lineares ou imprecisos, os quais não apresentam convergência através de modelagem matemática convencional.The present master dissertation aims to develop a methodology based on fuzzy pattern (KLIR; YUAN, 1995) to detect human faces in digital images. Considering that people are easily able to recognize human faces, this study foresees the research of the relative information to this recognition using the acquire results, in a \"fuzzy\" scheme, for the identification of human faces in digital images. It\'s proposed an algorithm which automatically classifies or not the regions of an image in human faces. It is based on the information acquired from people by means of a field research where the answers are stored numerically for the creation of the fuzzy classification. Drawings line-draw were created to represent human faces and were presented to the people interviewed to furnish information for the creation of the fuzzy rules. After that the algorithm was able to identify human faces in digitalized images. The algorithm utilizes simple images containing a frontal face, which lose their characteristics when they are processed (edges extration, erosion, binary image, etc...) and make their identification difficult. The fuzzy algorithm is also able to classify the images within the set of frontal human faces. The fuzzy logic has a recent history, however, it has always demonstrated its versatility, mainly regarding the translation of non-linear or inexact models which do not present conventional mathematical convergence through modeling.
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Rochford, Matthew. "Visual Speech Recognition Using a 3D Convolutional Neural Network." DigitalCommons@CalPoly, 2019. https://digitalcommons.calpoly.edu/theses/2109.
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Main stream automatic speech recognition (ASR) makes use of audio data to identify spoken words, however visual speech recognition (VSR) has recently been of increased interest to researchers. VSR is used when audio data is corrupted or missing entirely and also to further enhance the accuracy of audio-based ASR systems. In this research, we present both a framework for building 3D feature cubes of lip data from videos and a 3D convolutional neural network (CNN) architecture for performing classification on a dataset of 100 spoken words, recorded in an uncontrolled envi- ronment. Our 3D-CNN architecture achieves a testing accuracy of 64%, comparable with recent works, but using an input data size that is up to 75% smaller. Overall, our research shows that 3D-CNNs can be successful in finding spatial-temporal features using unsupervised feature extraction and are a suitable choice for VSR-based systems.
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Vlach, Jan. "Metody a aplikace detekce mrkání očí s využitím číslicového zpracování obrazu." Doctoral thesis, Vysoké učení technické v Brně. Fakulta elektrotechniky a komunikačních technologií, 2009. http://www.nusl.cz/ntk/nusl-233468.
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The thesis deals with eye blink detection, which is part of complex topic of face detection and recognition. The work intents on digital image processing. There is analyse of the topic and description of image databases for testing. Two main chapters describe design of eye blink detection with digital image processing with IR technology and without IR technology.
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Pancisi, Emanuele. "Riconoscimento di volti morphed: un approccio basato su Deep Learning." Master's thesis, Alma Mater Studiorum - Università di Bologna, 2021. http://amslaurea.unibo.it/23154/.
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La pervasività dei sistemi informatici nella vita di ogni giorno richiede di riporre grande attenzione verso il tema della sicurezza informatica, specialmente in tutti quei contesti in cui la violazione di questi sistemi può portare a conseguenze sociali rilevanti. Questo è particolarmente importante nel caso applicativo di controllo automatico degli accessi basato su sistemi di riconoscimento biometrici. Recentemente, gli attacchi basati su tecniche di face morphing hanno suscitato l'interesse della comunità scientifica. É stato dimostrato, infatti, che questi rappresentano una seria e concreta minaccia in varie applicazioni basate sulla verifica automatica dell'identità attraverso sistemi di riconoscimento facciale. Lo scenario considerato è quello dei controlli realizzati nei gate presenti all'interno degli aeroporti internazionali che, per velocizzare la circolazione dei passeggeri, verificano automaticamente se il volto di un soggetto corrisponde a quello contenuto all'interno del suo passaporto elettronico (eMRTD). Attraverso una procedura di morphing due soggetti possono condividere lo stesso documento legale violando il principio fondamentale di collegamento biunivoco tra un individuo e il suo documento identificativo. A questo proposito un soggetto senza precedenti penali potrebbe richiedere, nelle strutture preposte, il passaporto elettronico presentando una foto morphed con il volto di un criminale che successivamente potrà utilizzare il documento per eludere i controlli d'identità. Per questi motivi è forte il bisogno di algoritmi capaci di rilevare in maniera accurata e automatica immagini morphed. L'obiettivo di questo lavoro di tesi è quello di comprendere meglio il problema del face morphing e affrontarlo, nei diversi scenari, proponendo nuovi algoritmi basati su deep learning, ponendo particolare attenzione alla realizzazione di esperimenti rilevanti e sull'analisi critica dei metodi proposti e dei risultati sperimentali ottenuti.
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Ni, Weiyuan. "Recalage d'images de visage." Thesis, Grenoble, 2012. http://www.theses.fr/2012GRENT045/document.
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Etude bibliographique sur le recalage d'images de visage et sur le recalage d'images et travail en collaboration avec Son VuS, pour définir la précision nécessaire du recalage en fonction des exigences des méthodes de reconnaissance de visagesFace alignment is an important step in a typical automatic face recognition system.This thesis addresses the alignment of faces for face recognition applicationin video surveillance context. The main challenging factors of this research includethe low quality of images (e.g., low resolution, motion blur, and noise), uncontrolledillumination conditions, pose variations, expression changes, and occlusions. In orderto deal with these problems, we propose several face alignment methods using differentstrategies. The _rst part of our work is a three-stage method for facial pointlocalization which can be used for correcting mis-alignment errors. While existingalgorithms mostly rely on a priori knowledge of facial structure and on a trainingphase, our approach works in an online mode without requirements of pre-de_nedconstraints on feature distributions. The proposed method works well on images underexpression and lighting variations. The key contributions of this thesis are aboutjoint image alignment algorithms where a set of images is simultaneously alignedwithout a biased template selection. We respectively propose two unsupervised jointalignment algorithms : \Lucas-Kanade entropy congealing" (LKC) and \gradient correlationcongealing" (GCC). In LKC, an image ensemble is aligned by minimizing asum-of-entropy function de_ned over all images. GCC uses gradient correlation coef-_cient as similarity measure. The proposed algorithms perform well on images underdi_erent conditions. To further improve the robustness to mis-alignments and thecomputational speed, we apply a multi-resolution framework to joint face alignmentalgorithms. Moreover, our work is not limited in the face alignment stage. Since facealignment and face acquisition are interrelated, we develop an adaptive appearanceface tracking method with alignment feedbacks. This closed-loop framework showsits robustness to large variations in target's state, and it signi_cantly decreases themis-alignment errors in tracked faces
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Kolman, Aleš. "Detekce obličejů ve videu." Master's thesis, Vysoké učení technické v Brně. Fakulta informačních technologií, 2012. http://www.nusl.cz/ntk/nusl-236583.
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The project is focused on face detection in video. Firstly, it contains a summary of basic color models. Secondly, you can find the description and comparison of the basic methods for detection of human skin with a practical example of implementation of parametric detector. Thirdly, a theoretical basis for face detection and face tracking in a video containing a list of basic concepts and methods of this issue follows. Greater emphasis is placed on the description of machine learning algorithm AdaBoost and description of the possible application of the Kalman filter for the purpose of face tracking. Design, implementation and testing of library accomplished within the master thesis are listed in the final part of this thesis.
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Omaia, Derzu. "Um sistema para detecção e reconhecimento de face em vídeo utilizando a transformada cosseno discreta." Universidade Federal da Paraíba, 2009. http://tede.biblioteca.ufpb.br:8080/handle/tede/6107.
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Previous issue date: 2009-08-27Coordenação de Aperfeiçoamento de Pessoal de Nível Superior
Human face has a very complex and variable pattern, which makes the face detection and recognition operations a challenging problem. The scope of these operations is quite comprehensive, involving mainly security applications, such as authorization for physical and logical access, people tracking, and real time authentication. In addition to security applications, face detection and recognition can also be associated with other applications, such as human-computer interaction and virtual reality. Several studies of face detection and recognition have been proposed and developed by researchers, pursuing greater precision and efficiency. Currently there are face detectors and recognizers with accuracy exceeding 95%. Commercial systems are available as well. This work presents a study on several face detection and recognition methods. Also was discussed the possibility of developing a new face detection method using Prediction by Partial Match (PPM), Entropy and Discrete Cosine Transform (DCT). It is further proposed a new face recognition method based on DCT. Finally, is proposed an architecture for a face detection and recognition system in video. To validate the architecture, the proposed system was implemented using one of the best detectors in the literature and the recognizer produced in this work. Several experiments were performed, and both the face detector used as the recognizer developed were effective, achieving success rates compatible with most current methods
A face humana possui um padrão bastante complexo e variável, o que torna as operações de detecção e reconhecimento de face um problema desafiador. O campo de aplicação dessas operações é bastante abrangente, envolvendo principalmente aplicações de segurança, como autorização de acesso físico e lógico, rastreamento de pessoas e autenticação em tempo real. Além de aplicações de segurança, a detecção e o reconhecimento de faces também pode ser associado a outras aplicações, como interação homem-máquina e realidade virtual. Diversos trabalhos de detecção e reconhecimento de face vêm sendo propostos e desenvolvidos pela comunidade científica, buscando continuamente uma maior precisão e eficiência. Atualmente já estão disponíveis detectores e reconhecedores de face com precisão superior a 95%. Sistemas comerciais também já estão disponíveis no mercado. Este trabalho apresenta um estudo sobre os diversos métodos de detecção e reconhecimento de face existentes. Também foi analisada a possibilidade de desenvolvimento de um novo método de detecção de face utilizando Predição por Casamento Parcial (Prediction by Partial Match, PPM), Entropia e Transformada Cosseno Discreta (Discrete Cosine Transform, DCT). Propõe-se ainda, um novo método de reconhecimento de face baseado na DCT. Por fim, apresenta-se a arquitetura de um sistema de detecção e reconhecimento de face em vídeo. Para validação desta arquitetura, o sistema proposto foi implementado utilizando um dos melhores detectores encontrados na literatura e o reconhecedor produzido neste trabalho. Diversos experimentos foram realizados e tanto o detector de face utilizado, quanto o reconhecedor desenvolvido mostraram-se eficientes, atingindo taxas de acerto compatíveis com os métodos mais atuais.
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Kozák, Roman. "Detekce mimiky obličeje." Master's thesis, Vysoké učení technické v Brně. Fakulta strojního inženýrství, 2015. http://www.nusl.cz/ntk/nusl-232099.
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This master‘s thesis deals with the design and implementation of computer application for facial expression recognition. The first section describes the image processing, especially algorithms used in other parts of this thesis. The next section describes methods used for face detection, particularly the Viola-Jones detector. This is followed by a brief description of the basic facial expressions recognized by the application. The last two sections are dedicated to the design and implementation of the facial expression recognition algorithms and to the evaluation of the results.
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Heideklang, René. "Data Fusion for Multi-Sensor Nondestructive Detection of Surface Cracks in Ferromagnetic Materials." Doctoral thesis, Humboldt-Universität zu Berlin, 2018. http://dx.doi.org/10.18452/19586.
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Ermüdungsrissbildung ist ein gefährliches und kostenintensives Phänomen, welches frühzeitig erkannt werden muss. Weil kleine Fehlstellen jedoch hohe Testempfindlichkeit erfordern, wird die Prüfzuverlässigkeit durch Falschanzeigen vermindert. Diese Arbeit macht sich deshalb die Diversität unterschiedlicher zerstörungsfreier Oberflächenprüfmethoden zu Nutze, um mittels Datenfusion die Zuverlässigkeit der Fehlererkennung zu erhöhen.
Der erste Beitrag dieser Arbeit in neuartigen Ansätzen zur Fusion von Prüfbildern. Diese werden durch Oberflächenabtastung mittels Wirbelstromprüfung, thermischer Prüfung und magnetischer Streuflussprüfung gewonnen. Die Ergebnisse zeigen, dass schon einfache algebraische Fusionsregeln gute Ergebnisse liefern, sofern die Daten adäquat vorverarbeitet wurden. So übertrifft Datenfusion den besten Einzelsensor in der pixelbasierten Falscherkennungsrate um den Faktor sechs bei einer Nutentiefe von 10 μm.
Weiterhin wird die Fusion im Bildtransformationsbereich untersucht. Jedoch werden die theoretischen Vorteile solcher richtungsempfindlichen Transformationen in der Praxis mit den vorliegenden Daten nicht erreicht. Nichtsdestotrotz wird der Vorteil der Fusion gegenüber Einzelsensorprüfung auch hier bestätigt.
Darüber hinaus liefert diese Arbeit neuartige Techniken zur Fusion auch auf höheren Ebenen der Signalabstraktion. Ein Ansatz, der auf Kerndichtefunktionen beruht, wird eingeführt, um örtlich verteilte Detektionshypothesen zu integrieren. Er ermöglicht, die praktisch unvermeidbaren Registrierungsfehler explizit zu modellieren. Oberflächenunstetigkeiten von 30 μm Tiefe können zuverlässig durch Fusion gefunden werden, wogegen das beste Einzelverfahren erst Tiefen ab 40–50 μm erfolgreich auffindet. Das Experiment wird auf einem zweiten Prüfkörper bestätigt.
Am Ende der Arbeit werden Richtlinien für den Einsatz von Datenfusion gegeben, und die Notwendigkeit einer Initiative zum Teilen von Messdaten wird betont, um zukünftige Forschung zu fördern.Fatigue cracking is a dangerous and cost-intensive phenomenon that requires early detection. But at high test sensitivity, the abundance of false indications limits the reliability of conventional materials testing. This thesis exploits the diversity of physical principles that different nondestructive surface inspection methods offer, by applying data fusion techniques to increase the reliability of defect detection. The first main contribution are novel approaches for the fusion of NDT images. These surface scans are obtained from state-of-the-art inspection procedures in Eddy Current Testing, Thermal Testing and Magnetic Flux Leakage Testing. The implemented image fusion strategy demonstrates that simple algebraic fusion rules are sufficient for high performance, given adequate signal normalization. Data fusion reduces the rate of false positives is reduced by a factor of six over the best individual sensor at a 10 μm deep groove. Moreover, the utility of state-of-the-art image representations, like the Shearlet domain, are explored. However, the theoretical advantages of such directional transforms are not attained in practice with the given data. Nevertheless, the benefit of fusion over single-sensor inspection is confirmed a second time. Furthermore, this work proposes novel techniques for fusion at a high level of signal abstraction. A kernel-based approach is introduced to integrate spatially scattered detection hypotheses. This method explicitly deals with registration errors that are unavoidable in practice. Surface discontinuities as shallow as 30 μm are reliably found by fusion, whereas the best individual sensor requires depths of 40–50 μm for successful detection. The experiment is replicated on a similar second test specimen. Practical guidelines are given at the end of the thesis, and the need for a data sharing initiative is stressed to promote future research on this topic.
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Tureček, Martin. "Detekce obličejů v obraze z kamery na mobilním telefonu s WM." Master's thesis, Vysoké učení technické v Brně. Fakulta informačních technologií, 2010. http://www.nusl.cz/ntk/nusl-235553.
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This thesis deals with a face detection on mobile phones. It especially focuses on Windows Mobile platform. The introduction is therefore devoted to this operating system and alternatives of working with the camera. The next part of the text refers to general problems of the face detection in the image considering the weak performance of the target device. Another part of this thesis is a description of the acquisition of images from the camera using DirectShow multimedia framework and creation of a custom transformation filter for the face detection. Achieved results are summarized in the conclusion. It takes a form of tests examining different mobile devices. All difficulties arising during Windows Mobile developing are also mentioned.
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Knapo, Peter. "Vývoj algoritmů pro digitální zpracování obrazu v reálním čase v DSP procesoru." Master's thesis, Vysoké učení technické v Brně. Fakulta elektrotechniky a komunikačních technologií, 2009. http://www.nusl.cz/ntk/nusl-217872.
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Rozpoznávanie tvárí je komplexný proces, ktorého hlavným ciežom je rozpoznanie žudskej tváre v obrázku alebo vo video sekvencii. Najčastejšími aplikáciami sú sledovacie a identifikačné systémy. Taktiež je rozpoznávanie tvárí dôležité vo výskume počítačového videnia a umelej inteligencií. Systémy rozpoznávania tvárí sú často založené na analýze obrazu alebo na neurónových sieťach. Táto práca sa zaoberá implementáciou algoritmu založeného na takzvaných „Eigenfaces“ tvárach. „Eigenfaces“ tváre sú výsledkom Analýzy hlavných komponent (Principal Component Analysis - PCA), ktorá extrahuje najdôležitejšie tvárové črty z originálneho obrázku. Táto metóda je založená na riešení lineárnej maticovej rovnice, kde zo známej kovariančnej matice sa počítajú takzvané „eigenvalues“ a „eigenvectors“, v preklade vlastné hodnoty a vlastné vektory. Tvár, ktorá má byť rozpoznaná, sa premietne do takzvaného „eigenspace“ (priestor vlastných hodnôt). Vlastné rozpoznanie je na základe porovnania takýchto tvárí s existujúcou databázou tvárí, ktorá je premietnutá do rovnakého „eigenspace“. Pred procesom rozpoznávania tvárí, musí byť tvár lokalizovaná v obrázku a upravená (normalizácia, kompenzácia svetelných podmienok a odstránenie šumu). Existuje mnoho algoritmov na lokalizáciu tváre, ale v tejto práci je použitý algoritmus lokalizácie tváre na základe farby žudskej pokožky, ktorý je rýchly a postačujúci pre túto aplikáciu. Algoritmy rozpoznávania tváre a lokalizácie tváre sú implementované do DSP procesoru Blackfin ADSP-BF561 od Analog Devices.
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Šujan, Miroslav. "Detekce částí obličeje v termografickém spektru." Master's thesis, Vysoké učení technické v Brně. Fakulta elektrotechniky a komunikačních technologií, 2011. http://www.nusl.cz/ntk/nusl-219293.
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Master´s thesis deals with current problems of face detection and its parts in the infrared thermographic spectrum. Most previously published literature deals with the detection in the visible spectrum, making the thermographic detection range an interesting alternative. The work deals with the processing of image signals, images and faces in thermographic spectrum, selected methods of face detection and its parts and also deals with practical system design for detecting facial parts in this spectrum and its subsequent testing.