Dissertations / Theses on the topic 'Edge Detection'
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Hasanaj, Enis, Albert Aveler, and William Söder. "Cooperative edge deepfake detection." Thesis, Jönköping University, JTH, Avdelningen för datateknik och informatik, 2021. http://urn.kb.se/resolve?urn=urn:nbn:se:hj:diva-53790.
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Deepfakes are an emerging problem in social media and for celebrities and political profiles, it can be devastating to their reputation if the technology ends up in the wrong hands. Creating deepfakes is becoming increasingly easy. Attempts have been made at detecting whether a face in an image is real or not but training these machine learning models can be a very time-consuming process. This research proposes a solution to training deepfake detection models cooperatively on the edge. This is done in order to evaluate if the training process, among other things, can be made more efficient with this approach. The feasibility of edge training is evaluated by training machine learning models on several different types of iPhone devices. The models are trained using the YOLOv2 object detection system. To test if the YOLOv2 object detection system is able to distinguish between real and fake human faces in images, several models are trained on a computer. Each model is trained with either different number of iterations or different subsets of data, since these metrics have been identified as important to the performance of the models. The performance of the models is evaluated by measuring the accuracy in detecting deepfakes. Additionally, the deepfake detection models trained on a computer are ensembled using the bagging ensemble method. This is done in order to evaluate the feasibility of cooperatively training a deepfake detection model by combining several models. Results show that the proposed solution is not feasible due to the time the training process takes on each mobile device. Additionally, each trained model is about 200 MB, and the size of the ensemble model grows linearly by each model added to the ensemble. This can cause the ensemble model to grow to several hundred gigabytes in size.
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Nes, Preben Gråberg. "Edge-Detection in Signals using the Continuous Wavelet-Transform. : Edge-Detection in Medical UltraSound Images." Thesis, Norwegian University of Science and Technology, Department of Mathematical Sciences, 2006. http://urn.kb.se/resolve?urn=urn:nbn:no:ntnu:diva-9498.
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Today, UltraSound (US) images are often used in medical examination and surgery. An improvement of the quality of these US-images will lead to many advantages, which is a big motivation for research on this field. One obstacle in improving the quality of the images is the presence of noise and texture. In order to distinguish this unwanted information from the interesting objects, different techniques can be used. Characteristic features, such as the ability to find vague contours, small objects or edges of small strength, decides if the technique is suitable for analysing noisy signals. This thesis presents different techniques for finding objects in US-images by using the continuous wavelet-transform. One observation from the analysis is that for edge-detectors using the wavelet-transform at a single scale, there is a compromise between accuracy and reliability. One has to choose between detecting small objects or vague contours. At fine scales one is able to detect small objects, but not objects with a vague contour without including redundant information. At coarse scales one is able to detect vague contours without including redundant information, but one will not detect small objects. The Lipschitz-regularity and the length of a maxima-line in the time-scale plane works well to find the points where the signal changes with a long duration, but is less suitable to find small objects and to remove unwanted information. By using the value of the wavelet-transform at several scales, it is possible to find vague contours in images, small objects, and edges of small strength compared to the strength of the noise. Another important observation from the analysis is that use of the circumference of objects is appropriate in order to find the most important objects in an image. Using this information has been very useful with respect to the analysis of US-images. Medical ultra-sound images are in general of varying quality. In addition the quality of a US-image will typically change within the signal, and changes with respect to the quality of the contour of objects and the influence of noise. The technique which in general is most reliable and produces the best representations of the US-images analysed in this thesis, uses information about the amplitude of the wavelet-transform both within and across scales, in addition to information about the circumference of the objects. This combined edge-detector is reliable with respect to represent the important objects in the image, and this representation is often easily obtained by the edge-detector.
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Ciftci, Serdar. "Improving Edge Detection Using Intersection Consistency." Master's thesis, METU, 2011. http://etd.lib.metu.edu.tr/upload/12613846/index.pdf.
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Edge detection is an important step in computer vision since edges are utilized by the successor visual processing stages including many tasks such as motion estimation, stereopsis, shape representation and matching, etc. In this study, we test whether a local consistency measure based on image orientation (which we call Intersection Consistency - IC), which was previously shown to improve detection of junctions, can be used for improving the quality of edge detection of seven different detectorsnamely, Canny, Roberts, Prewitt, Sobel, Laplacian of Gaussian (LoG), Intrinsic Dimensionality, Line Segment Detector (LSD). IC works well on images that contain prominent objects which are different in color from their surroundings. IC give good results on natural images that have especially cluttered background. On images involving human made objects, IC leads to good results as well. But, depending on the amount of clutter, the loss of true positives might be more crucial. Through our comprehensive investigation, we show that approximately 21% increase in f-score is obtained whereas some important edges are lost. We conclude from our experiments that IC is suitable for improving the quality of edge detection in some detectors such as Canny, LoG and LSD.
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Ganugapati, Seshu Srilakshmi. "Edge detection methods for speckled images." Thesis, National Library of Canada = Bibliothèque nationale du Canada, 1997. http://www.collectionscanada.ca/obj/s4/f2/dsk2/ftp04/mq23137.pdf.
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Stephens, David A. "Bayesian edge-detection in image processing." Thesis, University of Nottingham, 1990. http://eprints.nottingham.ac.uk/11723/.
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Problems associated with the processing and statistical analysis of image data are the subject of much current interest, and many sophisticated techniques for extracting semantic content from degraded or corrupted images have been developed. However, such techniques often require considerable computational resources, and thus are, in certain applications, inappropriate. The detection localised discontinuities, or edges, in the image can be regarded as a pre-processing operation in relation to these sophisticated techniques which, if implemented efficiently and successfully, can provide a means for an exploratory analysis that is useful in two ways. First, such an analysis can be used to obtain quantitative information relating to the underlying structures from which the various regions in the image are derived about which we would generally be a priori ignorant. Secondly, in cases where the inference problem relates to discovery of the unknown location or dimensions of a particular region or object, or where we merely wish to infer the presence or absence of structures having a particular configuration, an accurate edge-detection analysis can circumvent the need for the subsequent sophisticated analysis. Relatively little interest has been focussed on the edge-detection problem within a statistical setting. In this thesis, we formulate the edge-detection problem in a formal statistical framework, and develop a simple and easily implemented technique for the analysis of images derived from two-region single edge scenes. We extend this technique in three ways; first, to allow the analysis of more complicated scenes, secondly, by incorporating spatial considerations, and thirdly, by considering images of various qualitative nature. We also study edge reconstruction and representation given the results obtained from the exploratory analysis, and a cognitive problem relating to the detection of objects modelled by members of a class of simple convex objects. Finally, we study in detail aspects of one of the sophisticated image analysis techniques, and the important general statistical applications of the theory on which it is founded.
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Ramalho, Mário António da Silva Neves. "Edge detection using neural network arbitration." Thesis, University of Nottingham, 1996. http://eprints.nottingham.ac.uk/12883/.
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A human observer is able to recognise and describe most parts of an object by its contour, if this is properly traced and reflects the shape of the object itself. With a machine vision system this recognition task has been approached using a similar technique. This prompted the development of many diverse edge detection algorithms. The work described in this thesis is based on the visual observation that edge maps produced by different algorithms, as the image degrades. Display different properties of the original image. Our proposed objective is to try and improve the edge map through the arbitration between edge maps produced by diverse (in nature, approach and performance) edge detection algorithms. As image processing tools are repetitively applied to similar images we believe the objective can be achieved by a learning process based on sample images. It is shown that such an approach is feasible, using an artificial neural network to perform the arbitration. This is taught from sets extracted from sample images. The arbitration system is implemented upon a parallel processing platform. The performance of the system is presented through examples of diverse types of image. Comparisons with a neural network edge detector (also developed within this thesis) and conventional edge detectors show that the proposed system presents significant advantages.
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Jirwe, Marcus. "Online Anomaly Detection on the Edge." Thesis, KTH, Skolan för elektroteknik och datavetenskap (EECS), 2021. http://urn.kb.se/resolve?urn=urn:nbn:se:kth:diva-299565.
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The society of today relies a lot on the industry and the automation of factory tasks is more prevalent than ever before. However, the machines taking on these tasks require maintenance to continue operating. This maintenance is typically given periodically and can be expensive while sometimes requiring expert knowledge. Thus it would be very beneficial if one could predict when a machine needs maintenance and only employ maintenance as necessary. One method to predict when maintenance is necessary is to collect sensor data from a machine and analyse it for anomalies. Anomalies are usually an indicator of unexpected behaviour and can therefore show when a machine needs maintenance. Due to concerns like privacy and security, it is often not allowed for the data to leave the local system. Hence it is necessary to perform this kind of anomaly detection in an online manner and in an edge environment. This environment imposes limitations on hardware and computational ability. In this thesis we consider four machine learning anomaly detection methods that can learn and detect anomalies in this kind of environment. These methods are LoOP, iForestASD, KitNet and xStream. We first evaluate the four anomaly detectors on the Skoltech Anomaly Benchmark using their suggested metrics as well as the Receiver Operating Characteristic curves. We also perform further evaluation on two data sets provided by the company Gebhardt. The experimental results are promising and indicate that the considered methods perform well at the task of anomaly detection. We finally propose some avenues for future work, such as implementing a dynamically changing anomaly threshold.Dagens samhälle är väldigt beroende av industrin och automatiseringen av fabriksuppgifter är mer förekommande än någonsin. Dock kräver maskinerna som tar sig an dessa uppgifter underhåll för att forsätta arbeta. Detta underhåll ges typiskt periodvis och kan vara dyrt och samtidigt kräva expertkunskap. Därför skulle det vara väldigt fördelaktigt om det kunde förutsägas när en maskin behövde underhåll och endast göra detta när det är nödvändigt. En metod för att förutse när underhåll krävs är att samla in sensordata från en maskin och analysera det för att hitta anomalier. Anomalier fungerar ofta som en indikator av oväntat beteende, och kan därför visa att en maskin behöver underhåll. På grund av frågor som integritet och säkerhet är det ofta inte tillåtet att datan lämnar det lokala systemet. Därför är det nödvändigt att denna typ av anomalidetektering genomförs sekventiellt allt eftersom datan samlas in, och att detta sker på nätverkskanten. Miljön som detta sker i påtvingar begränsningar på både hårdvara och beräkningsförmåga. I denna avhandling så överväger vi fyra anomalidetektorer som med användning av maskininlärning lär sig och upptäcker anomalier i denna sorts miljö. Dessa metoder är LoOP, iForestASD, KitNet och xStream. Vi analyserar först de fyra anomalidetektorerna genom Skoltech Anomaly Benchmark där vi använder deras föreslagna mått samt ”Receiver Operating Characteristic”-kurvor. Vi genomför även vidare analys på två dataset som vi har tillhandhållit av företaget Gebhardt. De experimentella resultaten är lovande och indikerar att de övervägda metoderna presterar väl när det kommer till detektering av anomalier. Slutligen föreslår vi några idéer som kan utforskas för framtida arbete, som att implementera en tröskel för anomalidetektering som anpassar sig dynamiskt.
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Wang, Ziqing. "Fuzzy neural network for edge detection and Hopfield network for edge enhancement." Thesis, National Library of Canada = Bibliothèque nationale du Canada, 1999. http://www.collectionscanada.ca/obj/s4/f2/dsk1/tape7/PQDD_0005/MQ42458.pdf.
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Sun, Xiaofang. "Learning optimal linear filters for edge detection." Thesis, University of British Columbia, 1991. http://hdl.handle.net/2429/30347.
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Edge detection is important both for its practical applications to computer vision as well as its relationship to early processing in the visual cortex. We describe experiments in which the back-propagation learning algorithm was used to learn sets of linear filters for the task of determining the orientation and location of edges to sub-pixel accuracy. A model of edge formation was used to generate novel input-output pairs for each iteration of the training process. The desired output included determining the interpolated location and orientation of the edge. The linear filters that result from this optimization process bear a close resemblance to oriented Gabor or derivative-of-Gaussian filters that have been found in primary visual cortex. In addition, the edge detection results appear to be superior to the existing standard edge detectors and may prove to be of considerable practical value in computer vision.Science, Faculty of
Computer Science, Department of
Graduate
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Gruber, Stephen S. "Optimizing detection efficiency for transition edge sensors." Connect to online resource, 2007. http://gateway.proquest.com/openurl?url_ver=Z39.88-2004&rft_val_fmt=info:ofi/fmt:kev:mtx:dissertation&res_dat=xri:pqdiss&rft_dat=xri:pqdiss:1442954.
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Giovanny, Giron Amaya Edwin. "Non-parametric edge detection in speckled imagery." Universidade Federal de Pernambuco, 2008. https://repositorio.ufpe.br/handle/123456789/6193.
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Previous issue date: 2008Coordenação de Aperfeiçoamento de Pessoal de Nível Superior
Este trabalho propõe uma técnica não-paramétrica para detecção de bordas em imagens speckle. As imagens SAR ("Synthetic aperture Radar"), sonar, B-ultrasound e laser são corrompidas por um ruído não aditivo chamado speckle. Vários modelos estatísticos foram propostos para desrever este ruído, levando ao desenvolvimento de técnicas especiais para melhoramento e análise de imagens. A distribuição G0 é um modelo estatístico que consegue descrever uma ampla gama de áreas, como, por exemplo, em dados SAR, pastos (lisos), florestas (rugosos) e áreas urbanas (muito rugosos). O objetivo deste trabalho é estudar ténicas alternativas na detecção de imagens speckled, tomando como ponto de partida Gambini et al. (2006, 2008). Um novo detector de borda baseado no teste de Kruskal Wallis é proposto. Os nossos resultados numéricos mostram que esse detector é uma alternativa atraente ao detector de M. Gambini, que é baseado na função de verossimilhançaa. Neste trabalho fornecemos evidências de que a técnica de M. Gambini pode ser substituída om sucesso pelo método Kruskal Wallis. O ganho reside em ter um algoritmo 1000 vezes mais rápido, sem omprometer a qualidade dos resultados
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Beattie, R. J. "Edge detection for semantically based early visual processing." Thesis, University of Edinburgh, 1985. http://hdl.handle.net/1842/26281.
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Yan, Bowen. "Edge prediction and community detection in complex networks." Thesis, University of Bristol, 2013. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.616868.
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Many complex systems can be represented as networks, with vertices for individuals and edges denoting relations between them. The study of the structure and properties of a network can help to understand the behaviour of elements in the network in order to improve the productivity and quality of life of humans. This thesis aims at exploring the structure of complex networks and the impact of the structure on their behaviour. It is motivated by two problems in network analysis: community detection and edge prediction. In this thesis, we develop a series of techniques for predicting missing edges and detecting communities in complex networks. One of the significant findings is that some existing techniques in these two areas can be used in complementary ways. For example, missing edges are more likely to be found within communities than between different communities, and the community structure can be discovered by extra information on edges, for example, weights, by using the feature of vertex similarity. We also analysed the influence of different types of missing edges on network analysis methods. Another hypothesis is that a community can be defined as a clique with missing edges, inspired a new community detection algorithm. Finally, we extended a popular sampling method in epidemiology to allow the recovery of the structure, especially the community structure, of a network from samples. Another interesting finding is that we can even use key vertices found from samples to control the spread of an infection in the original network.
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Wilbee, Aaron J. "A Framework For Learning Scene Independent Edge Detection." Thesis, Rochester Institute of Technology, 2015. http://pqdtopen.proquest.com/#viewpdf?dispub=1589662.
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In this work, a framework for a system which will intelligently assign an edge detection filter to an image based on features taken from the image is introduced. The framework has four parts: the learning stage, image feature extraction, training filter creation, and filter selection training. Two prototypes systems of this framework are given. The learning stage for these systems is the Berkeley Segmentation Database coupled with the Baddelay Delta Metric. Feature extraction is performed using a GIST methodology which extracts color, intensity, and orientation information. The set of image features are used as the input to a single hidden layer feed forward neural network trained using back propagation. The system trains against a set of linear cellular automata filters which are determined to best solve the edge image according to the Baddelay Delta Metric. One system uses cellular automata augmented with a fuzzy rule. The systems are trained and tested against the images from the Berkeley Segmentation Database. The results from the testing indicate that systems built on this framework can perform better than standard methods of edge detection on average across many types of images.
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Ling, Jue. "Application of fluorescent molecular logic to edge detection." Thesis, Queen's University Belfast, 2016. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.707813.
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This is the first time that small fluorescent sensors performing complicated computational functions, edge detection, which can be regarded as a new area of research in form of image processing. With a combination of florescent sensor, photo acid generator and sodium carbonate spreading on a filter paper, clear and visualized edges of 1-2 mm width were delivered, based on the light dose-driven ‘off-on-off fluorescent behaviour. The fluorescent intensity of sensor molecules in the irradiated region gradually enhanced upon irradiation of 254 nm UV light, due to generation of protons by photo-decomposition of the photo acid generator in that area. Then, it was slowly quenched by a strong quencher produced along with protons; whereas the fluorescence at the edge of irradiated and unirradiated regions was switched ‘on’ by the diffusion of protons neutralizing the pH there. A number of parameters, such as the pKa value of the fluorescent sensors, the light intensity from the UV lamp at two wavelengths, the number of molecules involved in producing the edge of the object and the values of Ksv between sensors and quenchers were determined. The proof of light dose-driven ‘off-on-off fluorescent behaviour of the molecules in solution and the development of a minimal model were built.
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Dong, Weixiao. "Event Detection in the Terrain Surface." Thesis, Virginia Tech, 2016. http://hdl.handle.net/10919/71792.
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Event Detection is a process of identifying terrain flatness from which localized events such as potholes in the terrain surface can be found and is an important tool in pavement health monitoring and vehicle performance inspection. Repeated detection of terrain surfaces over an extended period of time can be used by highway engineers for long term road health monitoring. An accurate terrain map can allow maintenance personnel for identifying deterioration in road surface for immediate correction. Additionally, knowledge of the events in terrain surface can be used to predict the performance the vehicles would experience while traveling over it.
Event detection is composed of two processes: event edging and stitching edges to events. Edge detection is a process of identifying significant localized changes in the terrain surface. Many edge detection methods have been designed capable of capturing edges in terrain surfaces. Gradient searches are frequently used in image processing to recover useful information from images. The issue with using a gradient search method is that it returns deterministic values resulting in edges which are less precise. In order to predict the precision of the terrain surface, the individual nodal probability densities must be quantified and finally combined for the precision of terrain surface. A Comparative Nodal Uncertainty Method is developed in this work to detect edges based on the probability distribution of the nodal heights within some local neighborhood. Edge stitching is developed to group edges to events in a correct sequence from which an event can be determined finally.Master of Science
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Parekh, Siddharth Avinash. "A comparison of image processing algorithms for edge detection, corner detection and thinning." University of Western Australia. Centre for Intelligent Information Processing Systems, 2004. http://theses.library.uwa.edu.au/adt-WU2004.0073.
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Image processing plays a key role in vision systems. Its function is to extract and enhance pertinent information from raw data. In robotics, processing of real-time data is constrained by limited resources. Thus, it is important to understand and analyse image processing algorithms for accuracy, speed, and quality. The theme of this thesis is an implementation and comparative study of algorithms related to various image processing techniques like edge detection, corner detection and thinning. A re-interpretation of a standard technique, non-maxima suppression for corner detectors was attempted. In addition, a thinning filter, Hall-Guo, was modified to achieve better results. Generally, real time data is corrupted with noise. This thesis also incorporates few smoothing filters that help in noise reduction. Apart from comparing and analysing algorithms for these techniques, an attempt was made to implement correlation-based optic flow
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Tydén, Amanda, and Sara Olsson. "Edge Machine Learning for Animal Detection, Classification, and Tracking." Thesis, Linköpings universitet, Reglerteknik, 2020. http://urn.kb.se/resolve?urn=urn:nbn:se:liu:diva-166572.
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A research field currently advancing is the use of machine learning on camera trap data, yet few explore deep learning for camera traps to be run in real-time. A camera trap has the purpose to capture images of bypassing animals and is traditionally based only on motion detection. This work integrates machine learning on the edge device to also perform object detection. Related research is brought up and model tests are performed with a focus on the trade-off regarding inference speed and model accuracy. Transfer learning is used to utilize pre-trained models and thus reduce training time and the amount of training data. Four models with slightly different architecture are compared to evaluate which model performs best for the use case. The models tested are SSD MobileNet V2, SSD Inception V2, and SSDLite MobileNet V2, SSD MobileNet V2 quantized. Since the client-side usage of the model, the SSD MobileNet V2 was finally selected due to a satisfying trade-off between inference speed and accuracy. Even though it is less accurate in its detections, its ability to detect more images per second makes it outperform the more accurate Inception network in object tracking. A contribution of this work is a light-weight tracking solution using tubelet proposal. This work further discusses the open set recognition problem, where just a few object classes are of interest while many others are present. The subject of open set recognition influences data collection and evaluation tests, it is however left for further work to research how to integrate support for open set recognition in object detection models. The proposed system handles detection, classification, and tracking of animals in the African savannah, and has potential for real usage as it produces meaningful events
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Haugsdal, Kari. "Edge and line detection of complicated and blurred objects." Thesis, Norwegian University of Science and Technology, Department of Computer and Information Science, 2010. http://urn.kb.se/resolve?urn=urn:nbn:no:ntnu:diva-9122.
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This report deals with edge and line detection in pictures with complicated and/or blurred objects. It explores the alternatives available, in edge detection, edge linking and object recognition. Choice of methods are the Canny edge detection and Local edge search processing combined with regional edge search processing in the form of polygon approximation.
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Madhvesh, Ashok. "Crucial edge detection in sensor system under energy constraints." Thesis, Wichita State University, 2009. http://hdl.handle.net/10057/2507.
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Wireless sensor nodes are usually deployed in remote locations for various applications that require
monitoring of certain interesting events. Due to this remote operational feature the longevity
of the sensor node's lifetime has been a primary concern. Although the sensor nodes available
today may be equipped with rechargeable batteries, the minimal energy capacity of such batteries
and low recharge rates degrade the sensor's lifetime and achievable performance. Hence,
operational algorithms are needed to guarantee high performance with efficient utilization of energy
available. In this thesis, considering temporally correlated event phenomena, the important
question answered is: "How long should the sensor sleep, and for how long should the sensor
stay active?". To achieve this, a sensor activation/deactivation algorithm has been developed that
achieves high performance with efficient energy utilization.
A sensor loses energy predominantly because of redundant transmissions of sensed data. To
avoid this, a sensor was modeled to transmit only the changes sensed in the event-occurrence
process, referred to as Crucial Edges or Transitions. In addition, the system model allows the
transmission of transitions that are detected late. Several intuitive decision-making policies were
compared and the results compared in order to determine the best policy for this problem. This
policy was later analyzed usingMarkov chain analysis techniques to derive upper and lower bounds
on the achievable performance. The proposed policy achieves high performance under energy
balancing constraints, and is deterministic, simple and easy to implement on a sensor node.Thesis (M.S.)--Wichita State University, College of Engineering, Dept. of Electrical Engineering and Computer Science
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Yang, Horng-Chang. "Multiresolution neural networks for image edge detection and restoration." Thesis, University of Warwick, 1994. http://wrap.warwick.ac.uk/66740/.
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One of the methods for building an automatic visual system is to borrow the properties of the human visual system (HVS). Artificial neural networks are based on this doctrine and they have been applied to image processing and computer vision. This work focused on the plausibility of using a class of Hopfield neural networks for edge detection and image restoration. To this end, a quadratic energy minimization framework is presented. Central to this framework are relaxation operations, which can be implemented using the class of Hopfield neural networks. The role of the uncertainty principle in vision is described, which imposes a limit on the simultaneous localisation in both class and position space. It is shown how a multiresolution approach allows the trade off between position and class resolution and ensures both robustness in noise and efficiency of computation. As edge detection and image restoration are ill-posed, some a priori knowledge is needed to regularize these problems. A multiresolution network is proposed to tackle the uncertainty problem and the regularization of these ill-posed image processing problems. For edge detection, orientation information is used to construct a compatibility function for the strength of the links of the proposed Hopfield neural network. Edge detection 'results are presented for a number of synthetic and natural images which show that the iterative network gives robust results at low signal-to-noise ratios (0 dB) and is at least as good as many previous methods at capturing complex region shapes. For restoration, mean square error is used as the quadratic energy function of the Hopfield neural network. The results of the edge detection are used for adaptive restoration. Also shown are the results of restoration using the proposed iterative network framework.
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Riste-Smith, Robert. "Edge detection and knowledge based segmentation of medical radiographs." Thesis, University of Portsmouth, 1990. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.303486.
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Sharman, Rebecca J. "Cue combination of colour and luminance in edge detection." Thesis, University of Nottingham, 2014. http://eprints.nottingham.ac.uk/14029/.
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Much is known about visual processing of chromatic and luminance information. However, less is known about how these two signals are combined. This thesis has three aims to investigate how colour and luminance are combined in edge detection. 1) To determine whether presenting colour and luminance information together improves performance in tasks such as edge localisation and blur detection. 2) To investigate how the visual system resolves conflicts between colour and luminance edge information. 3) To explore whether colour and luminance edge information is always combined in the same way. It is well known that the perception of chromatic blur can be constrained by sharp luminance information in natural scenes. The first set of experiments (Chapter 3) quantifies this effect and demonstrates that it cannot be explained by poorer acuity in processing chromatic information, higher contrast of luminance information or differences in the statistical structure of colour and luminance information in natural scenes. It is therefore proposed that there is a neural mechanism that actively promotes luminance information. Chapter 4 and Experiments 5.1 and 5.3 aimed to investigate whether the presence of both chromatic and luminance information improves edge localisation performance. Participant performance in a Vernier acuity (alignment) task was compared to predictions from three models; ‘winner takes all’, unweighted averaging and maximum likelihood estimation (a form of weighted averaging). Despite several attempts to differentiate the models we failed to increase the differences in model predictions sufficiently and it was not possible to determine whether edge localisation was enhanced by the presence of both cues. In Experiment 5.4 we investigated how edges are localised when colour and luminance cues conflict, using the method of adjustment. Maximum likelihood estimation was used to make predictions based on measurements of each cue in isolation. These predictions were then compared to observed data. It was found that, whilst maximum likelihood estimation captured the pattern of the data, it consistently over-estimated the weight of the luminance component. It is suggested that chromatic information may be weighted more heavily than predicted as it is more useful for detecting object boundaries in natural scenes. In Chapter 6 a novel approach, perturbation discrimination, was used to investigate how the spatial arrangement of chromatic and luminance cues, and the type of chromatic and luminance information, can affect cue combination. Perturbation discrimination requires participants to select the grating stimulus that contains spatial perturbation. If one cue dominated over the other it was expected that this would be reflected by masking and increased perturbation detection thresholds. We compared perturbation thresholds for chromatic and luminance defined line and square-wave gratings in isolation and when presented with a mask of the other channel and other grating type. For example, the perturbation threshold for a luminance line target alone was compared to the threshold for a luminance line target presented with a chromatic square-wave target. The introduction of line masks caused masking for both combinations. Introduction of an achromatic square-wave mask had no effect on perturbation thresholds for chromatic line targets. However, the introduction of a chromatic square-wave mask to luminance line targets improved perturbation discrimination performance. This suggests that the perceived location of the chromatic edges is determined by the location of the luminance lines. Finally, in Chapter 7, we investigated whether chromatic blur is constrained by luminance information in bipartite edges. Earlier in the thesis we demonstrated that luminance information constrains chromatic blur in natural scenes, but also that chromatic information has more influence than expected when colour and luminance edges conflict. This difference may be due to differences in the stimuli or due to differences in the task. The luminance masking effect found using natural scenes was replicated using bipartite edges. Therefore, the finding that luminance constrains chromatic blur is not limited to natural scene stimuli. This suggests that colour and luminance are combined differently for blur discrimination tasks and edge localisation tasks. Overall we can see that luminance often dominates in edge perception tasks. For blur discrimination this seems to be because the mechanisms differ. For edge localisation it might be simply that luminance cues are often higher contrast and, when this is equated, chromatic cues are actually a good indicator of edge location.
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Bodén, Rikard, and Jonathan Pernow. "SORTED : Serial manipulator with Object Recognition Trough Edge Detection." Thesis, KTH, Skolan för industriell teknik och management (ITM), 2019. http://urn.kb.se/resolve?urn=urn:nbn:se:kth:diva-264513.
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Today, there is an increasing demand for smart robots that can make decisions on their own and cooperate with humans in changing environments. The application areas for robotic arms with camera vision are likely to increase in the future of artificial intelligence as algorithms become more adaptable and intelligent than ever. The purpose of this bachelor’s thesis is to develop a robotic arm that recognises arbitrarily placed objects with camera vision and has the ability to pick and place the objects when they appear in unpredictable positions. The robotic arm has three degrees of freedom and the construction is modularised and 3D-printed with respect to maintenance, but also in order to be adaptive to new applications. The camera vision sensor is integrated in an external camera tripod with its field of view over the workspace. The camera vision sensor recognises objects through colour filtering and it uses an edge detection algorithm to return measurements of detected objects. The measurements are then used as input for the inverse kinematics, that calculates the rotation of each stepper motor. Moreover, there are three different angular potentiometers integrated in each axis to regulate the rotation by each stepper motor. The results in this thesis show that the robotic arm is able to pick up to 90% of the detected objects when using barrel distortion correction in the algorithm. The findings in this thesis is that barrel distortion, that comes with the camera lens, significantly impacts the precision of the robotic arm and thus the results. It can also be stated that the method for barrel distortion correction is affected by the geometry of detected objects and differences in illumination over the workspace. Another conclusion is that correct illumination is needed in order for the vision sensor to differentiate objects with different hue and saturation.Idag ökar efterfrågan på smarta robotar som kan ta egna beslut och samarbeta med människor i föränderliga miljöer. Tillämpningsområdena för robotar med kamerasensorer kommer sannolikt att öka i en framtid av artificiell intelligens med algoritmer som blir mer intelligenta och anpassningsbara än tidigare. Syftet med detta kandidatexamensarbete är att utveckla en robotarm som, med hjälp av en kamerasensor, kan ta upp och sortera godtyckliga objekt när de uppträder på oförutsägbara positioner. Robotarmen har tre frihetsgrader och hela konstruktionen är 3D-printad och modulariserad för att vara underhållsvänlig, men också anpassningsbar för nya tillämpningsområden. Kamerasensorn ¨ar integrerad i ett externt kamerastativ med sitt synfält över robotarmens arbetsyta. Kamerasensorn detekterar objekt med hjälp av en färgfiltreringsalgoritm och returnerar sedan storlek, position och signatur för objekten med hjälp av en kantdetekteringsalgoritm. Objektens storlek används för att kalibrera kameran och kompensera för den radiella förvrängningen hos linsen. Objektens relativa position används sedan till invers kinematik för att räkna ut hur mycket varje stegmotor ska rotera för att erhålla den önskade vinkeln på varje axel som gör att gripdonet kan nå det detekterade objektet. Robotarmen har även tre olika potentiometrar integrerade i varje axel för att reglera rotationen av varje stegmotor. Resultaten i denna rapport visar att robotarmen kan detektera och plocka upp till 90% av objekten när kamerakalibrering används i algoritmen. Slutsatsen från rapporten är att förvrängningen från kameralinsen har störst påverkan på robotarmens precision och därmed resultatet. Det går även att konstatera att metoden som används för att korrigera kameraförvrängningen påverkas av geometrin samt orienteringen av objekten som ska detekteras, men framför allt variationer i belysning och skuggor över arbetsytan. En annan slutsats är att belysningen över arbetsytan är helt avgörande för om kamerasensorn ska kunna särskilja objekt med olika färgmättad och nyans.
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Hildreth, Ellen C. "Edge Detection." 1985. http://hdl.handle.net/1721.1/6429.
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The goal of vision is to recover physical properties of objects in a scene, such as the location of object boundaries and the structure, color, and texture of object surfaces, from the two-dimensional image that is projected onto the eye or camera. The first clues about the physical properties of the scene are provided by the changes of intensity in the image. The importance of intensity changes and edges in early visual processing has led to extensive research on their detection, description, and use, both in computer and biological vision systems. This article reviews some of the theory that underlies the detection of edges and the methods used to carry out this analysis.
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Xu, Wei. "Multi-channel edge detection /." 2005.
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Thesis (M.Sc.)--York University, 2005. Graduate Programme in Computer Science.Typescript. Includes bibliographical references (leaves 112-117). Also available on the Internet. MODE OF ACCESS via web browser by entering the following URL: http://gateway.proquest.com/openurl?url%5Fver=Z39.88-2004&res%5Fdat=xri:pqdiss &rft_val_fmt=info:ofi/fmt:kev:mtx:dissertation&rft_dat=xri:pqdiss:MR11930
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CHEN, JUN-SHENG, and 陳俊勝. "Thresholding for edge detection." Thesis, 1990. http://ndltd.ncl.edu.tw/handle/92488543130129325075.
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Weng, Yin-Lai, and 翁銀來. "Image Edge Detection Research." Thesis, 2007. http://ndltd.ncl.edu.tw/handle/67633512629659485443.
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碩士義守大學
電子工程學系碩士班
95
Edge detection is the digital image processing for the most important topics. Using computer digital image processing can produce the purpose for human observation and identification, more capable of automatic identification and understanding of images. After several decades of development, and Roberts, Sobel, Kirsch, Canny, Marr and pull-type edge detection device research, the study of various methods were compared calculus, analysis and then figure out a better edge detection algorithm, the algorithm to reduce the software complexity of computation time, right edge detection of a large number of imaging results, the algorithm is simple but a very good performance, Edge detection is a good choice
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Hsu, Yao-Wen, and 許耀文. "A Wavelet-based Multiresolution Edge Tracking for Edge Detection." Thesis, 2001. http://ndltd.ncl.edu.tw/handle/85552067826771510535.
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碩士國立中央大學
資訊工程研究所
89
A novel edge detection approach based on the wavelet transformation and edge tracking is proposed. Wavelet transform provides multiresolution representation of images for robust tracking. The proposed approach consists of four modules: (i) image preprocessing, (ii) starting point extraction and purgation for tracking, (iii) wavelet decomposition, and (iv) multiresolution edge tracking. Image preprocessing includes band-pass and high-pass filterings. The band-pass filter is used to remove noise and eliminate regular and violent textures; the high-pass filtering is used to generate a gradient image for multiresolution tracking. The starting points may affect the performance and tracking results. The results is dependent on applications; thus the starting points are extracted from the gradient image by specifying threshold values or using default values for a specified application as user’s desire. Before tracking, the gradient image is decomposed twice by a wavelet transform to generate two coarser-scaled gradient images for multiresolution tracking. The proposed approach doesn’t need post-processing. Experiments with several commonly used images and medical images are conducted to evaluate performance of the proposed approach. Based on the human visual inspection, the proposed approach always generates the proper results.
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Chaney, Ronald D. "Feature Extraction Without Edge Detection." 1993. http://hdl.handle.net/1721.1/6794.
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Information representation is a critical issue in machine vision. The representation strategy in the primitive stages of a vision system has enormous implications for the performance in subsequent stages. Existing feature extraction paradigms, like edge detection, provide sparse and unreliable representations of the image information. In this thesis, we propose a novel feature extraction paradigm. The features consist of salient, simple parts of regions bounded by zero-crossings. The features are dense, stable, and robust. The primary advantage of the features is that they have abstract geometric attributes pertaining to their size and shape. To demonstrate the utility of the feature extraction paradigm, we apply it to passive navigation. We argue that the paradigm is applicable to other early vision problems.
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Hsu, Heng-chia, and 許恆嘉. "PCB Edge Detection with DWT." Thesis, 2001. http://ndltd.ncl.edu.tw/handle/16419932987493613598.
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碩士國立交通大學
電機與控制工程系
89
To develop an appropriate algorithm for PCB (Printed Circuit Board) image edge detection is the main subject of this thesis. The algorithm proposed in this thesis can be applied only on digital gray-level images of PCBs. Instead of the conventional idea of difference, DWT (Discrete Wavelet Transform), an alternative and novel method for basis change despite Fourier Transform, is employed. In this thesis, digital gray-level images of PCB are considered as vectors existing in the vector space constructed by selected basis that are the dilations and translations of Harr function and box function. By using DWT, image-edge-related information in digital gray-level images can be extracted easily. Not only DWT but also optimization algorithms are involved. Two well-known optimization algorithms, gradient method and one-dimensional Newton's method, are adopted for tuning the result into the best condition. The purpose of adopting two-stage optimization algorithm, gradient method and one-dimensional Newton's method, is to diminish the elapsing time in etermining the weighting vectors by searching the maximum step-size in the determined descent direction. Step-size and descent direction are determined by one-dimensional Newton's method and gradient method, respectively. When the weighting vectors are obtained, the two-stage optimization can be abandoned. Only are the weighting vectors and DWT used for PCB image edge detection in the rest of PCBs.
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Chang, Albert H. S., and 張宏碩. "Edge Detection On Infrared Images." Thesis, 1993. http://ndltd.ncl.edu.tw/handle/78985359336644229466.
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Wang, Chyi-Cheng, and 王麒程. "Edge detection of noisy blurred image using fuzzy-edge-operator." Thesis, 1994. http://ndltd.ncl.edu.tw/handle/20397846252447662560.
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碩士中原大學
電子工程學系
82
In this paper, we propose a new edge operator for edge detection of noisy blurred images.Our new operator "fuzzy-edge- operator" based on the theory of fuzzy sets performs edge detection of noisy blurred images faster and more perfectly than other existed operators in both speed and resolution. In the edge detection of a noisy blurred image, the conventional edge operators may require noise elimination and noise suppressing processes which may lose some original important information of edge. On the other hand, edge detection systems using our fuzzy-edge-operator can detect edge directly without preprocessing noise. Also, the results of edge detection using our fuzzy-edge-operator in both speed and cost are much better because the edge detection system using fuzzy-edge-operator is composed of coincidence, XOR and comparators only. Experimental results of noisy blurred images are given.From our experimental results, it shows that the performance of fuzzy-edge-operator for edge detection is much more satisfactory than that of other operators in considerations of noise tolerance, speed and cost.
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Chiu, Chin-Chi, and 邱敬棋. "Using Edge Detection Combined with Feature Detection for Moving Object Detection." Thesis, 2017. http://ndltd.ncl.edu.tw/handle/b32ygd.
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碩士國立臺灣師範大學
機電工程學系
105
This thesis is detecting object for moving images. Nowadays, there are many methods for moving object detection on surveillance, and the method used is to find features and then to use the motion of those features between images to calculate features points moving. But the feature points sometimes are more difficult to define because the objects moving are easy to make images blur. Especially, when the objects may not be known in advance. In this thesis, using SURF algorithm defines the features of motional images because it detecting speed is faster than SIFT. But whether it is SIFT or SURF when the detected object moves, the matching result is not as good as expected because the objects may have incorrect feature points on moving. In the thesis, we provide edge and feature detection to combine for increasing the feature matching. In addition, this study we use a lot of different detection to detect and calculate the correct feature points to analyze. In experiment, we can further understand our methods getting the better ability to identify compared to the traditional methods.
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Poggio, Tomaso, Harry Voorhees, and Alan Yuille. "A Regularized Solution to Edge Detection." 1985. http://hdl.handle.net/1721.1/5618.
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We consider edge detection as the problem of measuring and localizing changes of light intensity in the image. As discussed by Torre and Poggio (1984), edge detection, when defined in this way, is an ill-posed problem in the sense of Hadamard. The regularized solution that arises is then the solution to a variational principle. In the case of exact data, one of the standard regularization methods (see Poggio and Torre, 1984) leads to cubic spline interpolation before differentiation. We show that in the case of regularly-spaced data this solution corresponds to a convolution filter---to be applied to the signal before differentiation -- which is a cubic spline. In the case of non-exact data, we use another regularization method that leads to a different variational principle. We prove (1) that this variational principle leads to a convolution filter for the problem of one-dimensional edge detection, (2) that the form of this filter is very similar to the Gaussian filter, and (3) that the regularizing parameter $lambda$ in the variational principle effectively controls the scale of the filter.
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Geiger, Davi, and Tomaso Poggio. "An Optimal Scale for Edge Detection." 1988. http://hdl.handle.net/1721.1/6499.
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Many problems in early vision are ill posed. Edge detection is a typical example. This paper applies regularization techniques to the problem of edge detection. We derive an optimal filter for edge detection with a size controlled by the regularization parameter $\\ lambda $ and compare it to the Gaussian filter. A formula relating the signal-to-noise ratio to the parameter $\\lambda $ is derived from regularization analysis for the case of small values of $\\lambda$. We also discuss the method of Generalized Cross Validation for obtaining the optimal filter scale. Finally, we use our framework to explain two perceptual phenomena: coarsely quantized images becoming recognizable by either blurring or adding noise.
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LIAO, RONG-SHENG, and 廖榮勝. "Clutter edge detection by linear prediction." Thesis, 1989. http://ndltd.ncl.edu.tw/handle/35482985908913722238.
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Hung, Jing-Yao, and 洪竟堯. "Design of Edge Defect Detection Algorithm." Thesis, 2010. http://ndltd.ncl.edu.tw/handle/58219765364218632469.
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碩士國立交通大學
工業工程與管理學系
98
There are plenty of consumers using contact lenses to correct vision in recent years. Due to the contact lens will contact with users eyeball directly, the quality of contact lens has to be assured not to influence users eye health. Contact lens was generally inspected by quality inspectors in the past. However, owing to the diversity of individual subjective judgment and fatigue situation, there is no consistent inspection standard. The lens quality cannot always be guaranteed. Recently, with the development of auto-optical inspection (AOI) technology, several researches has been done on contact lens auto inspection. However, the available algorithms for edge defect detection still left room for improvement. This thesis proposed a robust edge defect detection algorithm based on surplus filter and LOG filter. The proposed algorithm performed extreme well for false positive detection of uneven yet qualified lenses and false negative detection of defective lenses with thinner edge. Experimentations showed that the proposed algorithm had a great improvement in the accuracy rate and reduction in the error rate of edge defect detection.
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DU, RONG-SHU, and 杜榮樹. "Edge detection and its performance evaluation." Thesis, 1986. http://ndltd.ncl.edu.tw/handle/88908423940982951079.
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Srilakshmi, Ganugapati Seshu. "Edge detection methods for speckled images /." 1996.
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Tseng, Pin-hsien, and 曾繽賢. "Edge Detection on Underwater Laser Spot." Thesis, 2007. http://ndltd.ncl.edu.tw/handle/93236656110965178035.
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Wu, Yung-Fa, and 吳泳發. "Edge Detection Based SWIR Image Bad Pixel Detection and Correction." Thesis, 2011. http://ndltd.ncl.edu.tw/handle/29936203362904049955.
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碩士國立交通大學
電控工程研究所
100
In this thesis, we first use edge detection and fuzzy rules to find bad pixel map of a SWIR sensor. Then we employ two median filters sequentially to correct them. Moreover, we apply two-point correction method to correct non-uniformity among pixels of SWIR sensor. To enhance the tools for bad pixel correction, we have also proposed a new color edge detector based on vector order statistics. The proposed detector consists of two stages. In the first stage, we use fuzzy gradient to estimate the direction of the gradient for every processing pixel in the image and adjust the corresponding processing window according to this detected direction for reliable edge detection setup. The second stage computes the vector mean distance (VMD) based on vector order statistics. Hence, the proposed detector, which integrates vector order statistics and fuzzy gradient, can provide more robust response for edge detection. Furthermore, we also combine the edge detector to our proposed thresholding method, which can automatically determine an optimal threshold and be adaptive to different image contents without manual intervention. Thus, the excellent results by our proposed edge detection scheme demonstrate that it is very user friendly and confident. This edge detection scheme could also be promising for better detecting bad pixels of a SWIR image sensor.
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Wang, Tzu-chʻing. "Fuzzy neural network for edge detection and Hopfield network for edge enhancement /." 1999.
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Zhang, Jian. "Edge detection in a pixel array circuit." Thesis, 2004. http://spectrum.library.concordia.ca/8101/1/MQ94718.pdf.
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Edge detection is commonly used to extract important features of an image, while providing a compression of image data, and thus it is one of the most important operations in image processing systems. Such a detection is usually implemented in a system of data processor(s) with its input image signals provided by a camera. However, in many applications, in which the constraints of speed, power and size of the hard-ware are critical, it will be favorable to implement the edge detection in a smart sensor, i.e. a pixel array circuit, in which each pixel operates in parallel with other to perform signal acquisition and processing. In this thesis, the design of a CMOS pixel array for edge detection is presented. The work of this thesis is in the two aspects, detection algorithms and circuit implementations. A new version of the algorithm for edge detection has been proposed, aiming at facilitating its implementation in an integrated circuit with simple inter-pixel connections and processing units in the pixels. It has been demonstrated that the proposed algorithm can result in a quality of the detection as good as the most commonly used detection algorithms. To implement this algorithm, a pixel array circuit has been designed with simple current-mode modules and logic gates in each pixel. The research effect in the circuit aspect is on solving the problems of the transistor mismatch, which makes identically-designed units behave non-uniformly, and charge injection in the current-mode circuits. With special compensation schemes implemented in the pixel circuit, the uniformity of the processing units integrated in the pixel array increased more than ten times, which improves significantly the quality of the operations in the process of the detection in the circuits. The pixel array circuit can be easily implemented with a standard CMOS technology
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Lin, Ei Jn, and 林益正. "Self-Organization Neural Network for Edge Detection." Thesis, 1993. http://ndltd.ncl.edu.tw/handle/78170069012831048647.
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Wang, Yen-Chi, and 王彥棋. "Edge-based Vehicle Detection in Satellite Images." Thesis, 2004. http://ndltd.ncl.edu.tw/handle/43960973418186493275.
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碩士國立中央大學
資訊工程研究所
92
It is very important to collect the information of enemy in modern war. If we can know how the enemy disposes their forces, then we can make a good decision and prevent the happening of worst situation. Recently, due to the fast and mature development of satellite technologies, the satellite scanning resolution has been uplifted higher. Moreover, the scanning range covering by satellites almost has no forbidden area. It can be easily applied for military purpose. In response to this need, a novel method is proposed to detect vehicles in satellite images. There are many approaches presented to detect objects in satellite images. In this thesis, an edge-based approach is proposed using the Canny’s edge detection method to detect object’s edges in satellite images. Then, the related edge-map can be found from object’s edges. Simultaneously, original satellite images are also preprocessed to reduce some unwanted situations, and then use image processing techniques to find out the ROI (region of interest) which contains the vehicles. Finally, we can successfully detect all vehicles in satellite images from the edge-map together with the ROI image. Experiments were conducted on various satellite images and the results show that our proposed method is feasible and effective in detecting vehicles presented in satellite images. Furthermore, we can utilize this approach to detect other objects in satellite images.
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Chang, Yao-Ming, and 張耀明. "The Image Edge Detection Using Grey Theory." Thesis, 1999. http://ndltd.ncl.edu.tw/handle/69395528747238747919.
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碩士中原大學
電子工程學系
87
In the thesis, the concept of the grey prediction theory is introduced into the process of the edge detection on gray image. What is image edge? There it exists different gray level characteristics between two ranges. In the particularity, the concept in many researches on traditional edge detection is the operation of gradient. In the thesis''s research methods, the first is converting the pixels of image into gray sequence. The second is finding out the regularity of sequence operated by accumulated generating operation. The third is converting the sequence into differential equation. Finally, it can create the grey GM(1,1) model in abstractly developing system. Thus, we can actually use this method to detect the image edge through the combination between homogenous growing quality in GM(1,1) model and true image edge''s characteristics. We can clearly find out the edge points of gray image form using the research way. The mathematics operation of the creating grey prediction model in the thesis is the process of some algebraic operation. In accordance with the algorithmic flows in our research, we can reduce operation time of software and interference on noise of image. According to experimental results in chapter five, the research method is better than the Laplacian of Gaussian operator on anti-noise
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Lu, De-Sian, and 盧德賢. "Edge Detection Improvement Using Ant Colony Optimization." Thesis, 2006. http://ndltd.ncl.edu.tw/handle/91818470030889298650.
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碩士玄奘大學
資訊管理學系碩士班
94
Edge detection is important in further analyzing image content. However, traditional edge detection approaches always accompany with broken pieces and some important edges may then lose. In this paper, we propose an ant colony optimization based mechanism to compensate broken edges. The proposed procedure adopts several moving policies to reduce the computation time. Remainders of pheromone as compensable edges are acquired after finite iterations. Experimental results demonstrate the efficiency of our proposed edge detection improvement approach.
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Lee, Tzu-Chia, and 李梓嘉. "The edge detection of wireless sensor network." Thesis, 2005. http://ndltd.ncl.edu.tw/handle/85130560290503523738.
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碩士國立清華大學
通訊工程研究所
93
Edge detection is a very important application in the conventional image processing area. In real life we may need to face this critical technique, for example, understanding how to use wireless sensors to detect the correct extended range of greasiness on the sea. In the distributed wireless sensor network environment, we utilize the technique of Cellular Neural Network to reach the goal of edge detection. Using this technique, we can make each sensor only receive the information of adjacent sensors to identify itself as edge sensor or not. First of all, we propose an algorithm, that is, according to the detecting edge results of sensors under different communication abilities, we determine the most efficient communication range of sensors and make the simulations in different communication qualities. Moreover, we discuss the other two edge detection methods which are also used in the distributed wireless sensor network: one is the statistical-based approach and the other is the filter-based approach applied in the image processing. Comparing the edge detecting simulation results of this two methods and the method of Cellular Neural Network, we find that in the low SNR environment, the results of Cellular Neural Network we gained might be better than the ones of the other two. Third, we discuss in advance the mathematical model combined by the Cellular Neural Network and wireless sensor network, and then, utilize the numerical recipes to obtain the edge detection's approximate analytical results, the probability of detection, and the probability of false alarm. Finally, we compare the analytical results' probability of detection and false alarm with the ones of simulation results which belong to the edge detection found by the technique of the combination of wireless sensor network and Cellular Neural Network.
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Chang, Shen-Ming, and 張賢明. "Application of Fractal Coding to Edge Detection." Thesis, 1999. http://ndltd.ncl.edu.tw/handle/11890497488893717094.
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碩士國立交通大學
電機與控制工程系
87
According to the previous researches, fractal coding applied to data compression achieves high compression ratio, which accordingly results in reduction in memory capacity and improvement on transmission efficiency. This research work does not aim at the data compression aspect. Instead the fractal coding approach is applied to edge detection. When applying fractal coding to image compression, a smaller mean square error（MSE）between the original and decompressed images is usually obtained in the smoothing region; a larger MSE is observed for the region containing edge or strong contrast attribute. Hence, we utilize this property to identify and extract the edges. The reason that fractal coding has not been widely used is because of the complexity of the compression process, which requires a large amount of operating time. It is the same problem encountered in our research study. This thesis presents a new method to reduce the arithmetic complexity and operating time. Moreover, the method has better noise immunization ability compared with some widely used methods.