Journal articles: 'Segmentation; Feature tracking; Computer vision'

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Kushwah, Chandra Pal. "Review on Semantic Segmentation of Satellite Images Using Deep Learning." International Journal for Research in Applied Science and Engineering Technology 9, no. VII (July 31, 2021): 3820–29. http://dx.doi.org/10.22214/ijraset.2021.37204.

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Image segmentation for applications like scene understanding, medical image analysis, robotic vision, video tracking, improving reality, and image compression is a key subject of image processing and image evaluation. Semantic segmentation is an integral aspect of image comprehension and is essential for image processing tasks. Semantic segmentation is a complex process in computer vision applications. Many techniques have been developed, from self-sufficient cars, human interaction, robotics, medical science, agriculture, and so on, to tackle the issue.In a short period, satellite imagery will provide a lot of large-scale knowledge about the earth's surfaces, saving time. With the growth & development of satellite image sensors, the recorded object resolution was improved with advanced image processing techniques. Improving the performance of deep learning models in a broad range of vision applications, important work has recently been carried out to evaluate approaches for deep learning models in image segmentation.In this paper,a detailed overview provides onImage segmentation and describes its techniques likeregion, edge, feature, threshold, and model-based. Also, provide Semantic Segmentation, Satellite imageries, and Deep learning & its Techniques like-DNN, CNN, RNN, RBM, and so on.CNN is one of the efficient deep learning techniques among all of them that can be usedwith the U-net model in further work.

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KONWAR, LAKHYADEEP, ANJAN KUMAR TALUKDAR, and KANDARPA KUMAR SARMA. "Robust Real Time Multiple Human Detection and Tracking for Automatic Visual Surveillance System." WSEAS TRANSACTIONS ON SIGNAL PROCESSING 17 (August 6, 2021): 93–98. http://dx.doi.org/10.37394/232014.2021.17.13.

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Detection of human for visual surveillance system provides most important rule for advancement in the design of future automation systems. Human detection and tracking are important for future automatic visual surveillance system (AVSS). In this paper we have proposed a flexible technique for proper human detection and tracking for the design of AVSS. We used graph cut for segment human as a foreground image by eliminating background, extract some feature points by using HOG, SVM classifier for proper classification and finally we used particle filter for tracking those of detected human. Our system can easily detect and track humans in poor lightening conditions, color, size, shape, and clothing due to the use of HOG feature descriptor and particle filter. We use graph cut based segmentation technique, therefore our system can handle occlusion at about 88%. Due to the use of HOG to extract features our system can properly work in indoor as well as outdoor environments with 97.61% automatic human detection and 92% automatic human detection and tracking accuracy of multiple human

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Zhang, Yiqing, Jun Chu, Lu Leng, and Jun Miao. "Mask-Refined R-CNN: A Network for Refining Object Details in Instance Segmentation." Sensors 20, no. 4 (February 13, 2020): 1010. http://dx.doi.org/10.3390/s20041010.

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With the rapid development of flexible vision sensors and visual sensor networks, computer vision tasks, such as object detection and tracking, are entering a new phase. Accordingly, the more challenging comprehensive task, including instance segmentation, can develop rapidly. Most state-of-the-art network frameworks, for instance, segmentation, are based on Mask R-CNN (mask region-convolutional neural network). However, the experimental results confirm that Mask R-CNN does not always successfully predict instance details. The scale-invariant fully convolutional network structure of Mask R-CNN ignores the difference in spatial information between receptive fields of different sizes. A large-scale receptive field focuses more on detailed information, whereas a small-scale receptive field focuses more on semantic information. So the network cannot consider the relationship between the pixels at the object edge, and these pixels will be misclassified. To overcome this problem, Mask-Refined R-CNN (MR R-CNN) is proposed, in which the stride of ROIAlign (region of interest align) is adjusted. In addition, the original fully convolutional layer is replaced with a new semantic segmentation layer that realizes feature fusion by constructing a feature pyramid network and summing the forward and backward transmissions of feature maps of the same resolution. The segmentation accuracy is substantially improved by combining the feature layers that focus on the global and detailed information. The experimental results on the COCO (Common Objects in Context) and Cityscapes datasets demonstrate that the segmentation accuracy of MR R-CNN is about 2% higher than that of Mask R-CNN using the same backbone. The average precision of large instances reaches 56.6%, which is higher than those of all state-of-the-art methods. In addition, the proposed method requires low time cost and is easily implemented. The experiments on the Cityscapes dataset also prove that the proposed method has great generalization ability.

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Zhang, Xinyu, Hongbo Gao, Chong Xue, Jianhui Zhao, and Yuchao Liu. "Real-time vehicle detection and tracking using improved histogram of gradient features and Kalman filters." International Journal of Advanced Robotic Systems 15, no. 1 (January 1, 2018): 172988141774994. http://dx.doi.org/10.1177/1729881417749949.

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Intelligent transportation systems and safety driver-assistance systems are important research topics in the field of transportation and traffic management. This study investigates the key problems in front vehicle detection and tracking based on computer vision. A video of a driven vehicle on an urban structured road is used to predict the subsequent motion of the front vehicle. This study provides the following contributions. (1) A new adaptive threshold segmentation algorithm is presented in the image preprocessing phase. This algorithm is resistant to interference from complex environments. (2) Symmetric computation based on a traditional histogram of gradient (HOG) feature vector is added in the vehicle detection phase. Symmetric HOG feature with AdaBoost classification improves the detection rate of the target vehicle. (3) A motion model based on adaptive Kalman filter is established. Experiments show that the prediction of Kalman filter model provides a reliable region for eliminating the interference of shadows and sharply decreasing the missed rate.

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Yao, Li Feng, and Jian Fei Ouyang. "Catching Data from Displayers by Machine Vision." Advanced Materials Research 566 (September 2012): 124–29. http://dx.doi.org/10.4028/www.scientific.net/amr.566.124.

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With the emergence of eHealth, the importance of keeping digital personal health statistics is quickly rising in demand. Many current health assessment devices output values to the user without a method of digitally saving the data. This paper presents a method to directly translate the numeric displays of the devices into digital records using machine vision. A wireless-based machine vision system is designed to image the display and a tracking algorithm based on SIFT (Scale Invariant Feature Transform) is developed to recognize the numerals from the captured images. First, a local camera captures an image of the display and transfers it wirelessly to a remote computer, which generates the gray-scale and binary figures of the images for further processing. Next, the computer applies the watershed segmentation algorithm to divide the image into regions of individual values. Finally, the SIFT features of the segmented images are picked up in sequence and matched with the SIFT features of the ten standard digits from 0 to 9 one by one to recognize the digital numbers of the device’s display. The proposed approach can obtain the data directly from the display quickly and accurately with high environmental tolerance. The numeric recognition converts with over 99.2% accuracy, and processes an image in less than one second. The proposed method has been applied in the E-health Station, a physiological parameters measuring system that integrates a variety of commercial instruments, such as OMRON digital thermometer, oximeter, sphygmomanometer, glucometer, and fat monitor, to give a more complete physiological health measurement.

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Khalid, Nida, Munkhjargal Gochoo, Ahmad Jalal, and Kibum Kim. "Modeling Two-Person Segmentation and Locomotion for Stereoscopic Action Identification: A Sustainable Video Surveillance System." Sustainability 13, no. 2 (January 19, 2021): 970. http://dx.doi.org/10.3390/su13020970.

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Due to the constantly increasing demand for automatic tracking and recognition systems, there is a need for more proficient, intelligent and sustainable human activity tracking. The main purpose of this study is to develop an accurate and sustainable human action tracking system that is capable of error-free identification of human movements irrespective of the environment in which those actions are performed. Therefore, in this paper we propose a stereoscopic Human Action Recognition (HAR) system based on the fusion of RGB (red, green, blue) and depth sensors. These sensors give an extra depth of information which enables the three-dimensional (3D) tracking of each and every movement performed by humans. Human actions are tracked according to four features, namely, (1) geodesic distance; (2) 3D Cartesian-plane features; (3) joints Motion Capture (MOCAP) features and (4) way-points trajectory generation. In order to represent these features in an optimized form, Particle Swarm Optimization (PSO) is applied. After optimization, a neuro-fuzzy classifier is used for classification and recognition. Extensive experimentation is performed on three challenging datasets: A Nanyang Technological University (NTU) RGB+D dataset; a UoL (University of Lincoln) 3D social activity dataset and a Collective Activity Dataset (CAD). Evaluation experiments on the proposed system proved that a fusion of vision sensors along with our unique features is an efficient approach towards developing a robust HAR system, having achieved a mean accuracy of 93.5% with the NTU RGB+D dataset, 92.2% with the UoL dataset and 89.6% with the Collective Activity dataset. The developed system can play a significant role in many computer vision-based applications, such as intelligent homes, offices and hospitals, and surveillance systems.

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Et. al., Mohan kumar Shilpa ,. "An Effective Framework Using Region Merging and Learning Machine for Shadow Detection and Removal." Turkish Journal of Computer and Mathematics Education (TURCOMAT) 12, no. 2 (April 10, 2021): 2506–14. http://dx.doi.org/10.17762/turcomat.v12i2.2098.

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Moving cast shadows of moving objects significantly degrade the performance of many high-level computer vision applications such as object tracking, object classification, behavior recognition and scene interpretation. Because they possess similar motion characteristics with their objects, moving cast shadow detection is still challenging. In this paper, the foreground is detected by background subtraction and the shadow is detected by combination of Mean-Shift and Region Merging Segmentation. Using Gabor method, we obtain the moving targets with texture features. According to the characteristics of shadow in HSV space and texture feature, the shadow is detected and removed to eliminate the shadow interference for the subsequent processing of moving targets. Finally, to guarantee the integrity of shadows and objects for further image processing, a simple post-processing procedure is designed to refine the results, which also drastically improves the accuracy of moving shadow detection. Extensive experiments on publicly common datasets that the performance of the proposed framework is superior to representative state-of-the-art methods.

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Kim, Byung-Gyu, and Dong-Jo Park. "Unsupervised video object segmentation and tracking based on new edge features." Pattern Recognition Letters 25, no. 15 (November 2004): 1731–42. http://dx.doi.org/10.1016/j.patrec.2004.07.009.

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Abdulghafoor, Nuha, and Hadeel Abdullah. "Enhancement Performance of Multiple Objects Detection and Tracking for Real-time and Online Applications." International Journal of Intelligent Engineering and Systems 13, no. 6 (December 31, 2020): 533–45. http://dx.doi.org/10.22266/ijies2020.1231.47.

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Multi-object detection and tracking systems represent one of the basic and important tasks of surveillance and video traffic systems. Recently. The proposed tracking algorithms focused on the detection mechanism. It showed significant improvement in performance in the field of computer vision. Though. It faced many challenges and problems, such as many blockages and segmentation of paths, in addition to the increasing number of identification keys and false-positive paths. In this work, an algorithm was proposed that integrates information on appearance and visibility features to improve the tracker's performance. It enables us to track multiple objects throughout the video and for a longer period of clogging and buffer a number of ID switches. An effective and accurate data set, tools, and metrics were also used to measure the efficiency of the proposed algorithm. The experimental results show the great improvement in the performance of the tracker, with high accuracy of more than 65%, which achieves competitive performance with the existing algorithms.

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Volkov, Vladimir Yu, Oleg A. Markelov, and Mikhail I. Bogachev. "IMAGE SEGMENTATION AND OBJECT SELECTION BASED ON MULTI-THRESHOLD PROCESSING." Journal of the Russian Universities. Radioelectronics 22, no. 3 (July 2, 2019): 24–35. http://dx.doi.org/10.32603/1993-8985-2019-22-3-24-35.

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Introduction. Detection, isolation, selection and localization of variously shaped objects in images are essential in a variety of applications. Computer vision systems utilizing television and infrared cameras, synthetic aperture surveillance radars as well as laser and acoustic remote sensing systems are prominent examples. Such problems as object identification, tracking and matching as well as combining information from images available from different sources are essential. Objective. Design of image segmentation and object selection methods based on multi-threshold processing. Materials and methods. The segmentation methods are classified according to the objects they deal with, including (i) pixel-level threshold estimation and clustering methods, (ii) boundary detection methods, (iii) regional level, and (iv) other classifiers, including many non-parametric methods, such as machine learning, neural networks, fuzzy sets, etc. The keynote feature of the proposed approach is that the choice of the optimal threshold for the image segmentation among a variety of test methods is carried out using a posteriori information about the selection results. Results. The results of the proposed approach is compared against the results obtained using the well-known binary integration method. The comparison is carried out both using simulated objects with known shapes with additive synthesized noise as well as using observational remote sensing imagery. Conclusion. The article discusses the advantages and disadvantages of the proposed approach for the selection of objects in images, and provides recommendations for their use.

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Chen, Dong, Fan Tang, Weiming Dong, Hanxing Yao, and Changsheng Xu. "SiamCPN: Visual tracking with the Siamese center-prediction network." Computational Visual Media 7, no. 2 (April 5, 2021): 253–65. http://dx.doi.org/10.1007/s41095-021-0212-1.

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AbstractObject detection is widely used in object tracking; anchor-free object tracking provides an end-to-end single-object-tracking approach. In this study, we propose a new anchor-free network, the Siamese center-prediction network (SiamCPN). Given the presence of referenced object features in the initial frame, we directly predict the center point and size of the object in subsequent frames in a Siamese-structure network without the need for perframe post-processing operations. Unlike other anchor-free tracking approaches that are based on semantic segmentation and achieve anchor-free tracking by pixel-level prediction, SiamCPN directly obtains all information required for tracking, greatly simplifying the model. A center-prediction sub-network is applied to multiple stages of the backbone to adaptively learn from the experience of different branches of the Siamese net. The model can accurately predict object location, implement appropriate corrections, and regress the size of the target bounding box. Compared to other leading Siamese networks, SiamCPN is simpler, faster, and more efficient as it uses fewer hyperparameters. Experiments demonstrate that our method outperforms other leading Siamese networks on GOT-10K and UAV123 benchmarks, and is comparable to other excellent trackers on LaSOT, VOT2016, and OTB-100 while improving inference speed 1.5 to 2 times.

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Reddy Gurunatha Swamy, P., and B. Ananth Reddy. "Human Pose Estimation in Images and Videos." International Journal of Engineering & Technology 7, no. 3.27 (August 15, 2018): 1. http://dx.doi.org/10.14419/ijet.v7i3.27.17640.

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Estimation of human poses is an interesting and challenging topic in the field of Computer vision. It includes some un-noticed challenges like background effect, the color of the dress, skin tones and many other unpredictable challenges. This is a workable concept because it can be used in sign language recognition, correlating various pose styles from different parts of the world and in medical applications. A deep structure which can represent a man’s body in different models will help in improved recognition of body parts and the spatial correlation between them. For hand detection, features based on hand shape and representation of geometrical details are derived with the help of hand contour. An adaptive and unsupervised approach based on Voronoi region is primarily used for the color image segmentation problem. This process includes identification of key points of the body, which may include body joints and parts. The identification parts will be tough due to small joints and occlusions. Identification of Image features is described in this paper with the help of Box Model Based Estimation, Speed up robust features and finally with Optical flow tracking algorithm. In Optical flow tracking algorithm, we have used Horn-Schunk algorithm to determine featural changes in the images.

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Shariff, Aabid, Joshua Kangas, Luis Pedro Coelho, Shannon Quinn, and Robert F. Murphy. "Automated Image Analysis for High-Content Screening and Analysis." Journal of Biomolecular Screening 15, no. 7 (May 20, 2010): 726–34. http://dx.doi.org/10.1177/1087057110370894.

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The field of high-content screening and analysis consists of a set of methodologies for automated discovery in cell biology and drug development using large amounts of image data. In most cases, imaging is carried out by automated microscopes, often assisted by automated liquid handling and cell culture. Image processing, computer vision, and machine learning are used to automatically process high-dimensional image data into meaningful cell biological results. The key is creating automated analysis pipelines typically consisting of 4 basic steps: (1) image processing (normalization, segmentation, tracing, tracking), (2) spatial transformation to bring images to a common reference frame (registration), (3) computation of image features, and (4) machine learning for modeling and interpretation of data. An overview of these image analysis tools is presented here, along with brief descriptions of a few applications.

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Feng, Zhanshen. "An Image Detection Method Based on Parameter Optimization of Support Vector Machine." International Journal of Circuits, Systems and Signal Processing 15 (April 8, 2021): 306–14. http://dx.doi.org/10.46300/9106.2021.15.35.

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With the progress and development of multimedia image processing technology, and the rapid growth of image data, how to efficiently extract the interesting and valuable information from the huge image data, and effectively filter out the redundant data, these have become an urgent problem in the field of image processing and computer vision. In recent years, as one of the important branches of computer vision, image detection can assist and improve a series of visual processing tasks. It has been widely used in many fields, such as scene classification, visual tracking, object redirection, semantic segmentation and so on. Intelligent algorithms have strong non-linear mapping capability, data processing capacity and generalization ability. Support vector machine (SVM) by using the structural risk minimization principle constructs the optimal classification hyper-plane in the attribute space to make the classifier get the global optimum and has the expected risk meet a certain upper bound at a certain probability in the entire sample space. This paper combines SVM and artificial fish swarm algorithm (AFSA) for parameter optimization, builds AFSA-SVM classification model to achieve the intelligent identification of image features, and provides reliable technological means to accelerate sensing technology. The experiment result proves that AFSA-SVM has better classification accuracy and indicates that the algorithm of this paper can effectively realize the intelligent identification of image features.

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Prahara, Adhi, Murinto Murinto, and Dewi Pramudi Ismi. "Bottom-up visual attention model for still image: a preliminary study." International Journal of Advances in Intelligent Informatics 6, no. 1 (March 31, 2020): 82. http://dx.doi.org/10.26555/ijain.v6i1.469.

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The philosophy of human visual attention is scientifically explained in the field of cognitive psychology and neuroscience then computationally modeled in the field of computer science and engineering. Visual attention models have been applied in computer vision systems such as object detection, object recognition, image segmentation, image and video compression, action recognition, visual tracking, and so on. This work studies bottom-up visual attention, namely human fixation prediction and salient object detection models. The preliminary study briefly covers from the biological perspective of visual attention, including visual pathway, the theory of visual attention, to the computational model of bottom-up visual attention that generates saliency map. The study compares some models at each stage and observes whether the stage is inspired by biological architecture, concept, or behavior of human visual attention. From the study, the use of low-level features, center-surround mechanism, sparse representation, and higher-level guidance with intrinsic cues dominate the bottom-up visual attention approaches. The study also highlights the correlation between bottom-up visual attention and curiosity.

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RAUTARAY, SIDDHARTH S., and ANUPAM AGRAWAL. "VISION-BASED APPLICATION-ADAPTIVE HAND GESTURE RECOGNITION SYSTEM." International Journal of Information Acquisition 09, no. 01 (March 2013): 1350007. http://dx.doi.org/10.1142/s0219878913500071.

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With the increasing role of computing devices, facilitating natural human computer interaction (HCI) will have a positive impact on their usage and acceptance as a whole. For long time, research on HCI has been restricted to techniques based on the use of keyboard, mouse, etc. Recently, this paradigm has changed. Techniques such as vision, sound, speech recognition allow for much richer form of interaction between the user and machine. The emphasis is to provide a natural form of interface for interaction. Gestures are one of the natural forms of interaction between humans. As gesture commands are found to be natural for humans, the development of gesture control systems for controlling devices have become a popular research topic in recent years. Researchers have proposed different gesture recognition systems which act as an interface for controlling the applications. One of the drawbacks of present gesture recognition systems is application dependence which makes it difficult to transfer one gesture control interface into different applications. This paper focuses on designing a vision-based hand gesture recognition system which is adaptive to different applications thus making the gesture recognition systems to be application adaptive. The designed system comprises different processing steps like detection, segmentation, tracking, recognition, etc. For making the system as application-adaptive, different quantitative and qualitative parameters have been taken into consideration. The quantitative parameters include gesture recognition rate, features extracted and root mean square error of the system while the qualitative parameters include intuitiveness, accuracy, stress/comfort, computational efficiency, user's tolerance, and real-time performance related to the proposed system. These parameters have a vital impact on the performance of the proposed application adaptive hand gesture recognition system.

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Ha, In Young, Matthias Wilms, and Mattias Heinrich. "Semantically Guided Large Deformation Estimation with Deep Networks." Sensors 20, no. 5 (March 4, 2020): 1392. http://dx.doi.org/10.3390/s20051392.

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Deformable image registration is still a challenge when the considered images have strong variations in appearance and large initial misalignment. A huge performance gap currently remains for fast-moving regions in videos or strong deformations of natural objects. We present a new semantically guided and two-step deep deformation network that is particularly well suited for the estimation of large deformations. We combine a U-Net architecture that is weakly supervised with segmentation information to extract semantically meaningful features with multiple stages of nonrigid spatial transformer networks parameterized with low-dimensional B-spline deformations. Combining alignment loss and semantic loss functions together with a regularization penalty to obtain smooth and plausible deformations, we achieve superior results in terms of alignment quality compared to previous approaches that have only considered a label-driven alignment loss. Our network model advances the state of the art for inter-subject face part alignment and motion tracking in medical cardiac magnetic resonance imaging (MRI) sequences in comparison to the FlowNet and Label-Reg, two recent deep-learning registration frameworks. The models are compact, very fast in inference, and demonstrate clear potential for a variety of challenging tracking and/or alignment tasks in computer vision and medical image analysis.

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Wu, Bing-Fei, Chih-Chung Kao, Ying-Feng Li, and Min-Yu Tsai. "A Real-Time Embedded Blind Spot Safety Assistance System." International Journal of Vehicular Technology 2012 (April 22, 2012): 1–15. http://dx.doi.org/10.1155/2012/506235.

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This paper presents an effective vehicle and motorcycle detection system in the blind spot area in the daytime and nighttime scenes. The proposed method identifies vehicle and motorcycle by detecting the shadow and the edge features in the daytime, and the vehicle and motorcycle could be detected through locating the headlights at nighttime. First, shadow segmentation is performed to briefly locate the position of the vehicle. Then, the vertical and horizontal edges are utilized to verify the existence of the vehicle. After that, tracking procedure is operated to track the same vehicle in the consecutive frames. Finally, the driving behavior is judged by the trajectory. Second, the lamps in the nighttime are extracted based on automatic histogram thresholding, and are verified by spatial and temporal features to against the reflection of the pavement. The proposed real-time vision-based Blind Spot Safety-Assistance System has implemented and evaluated on a TI DM6437 platform to perform the vehicle detection on real highway, expressways, and urban roadways, and works well on sunny, cloudy, and rainy conditions in daytime and night time. Experimental results demonstrate that the proposed vehicle detection approach is effective and feasible in various environments.

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King, A. P., P. J. Edwards, C. R. Maurer, D. A. de Cunha, R. P. Gaston, M. Clarkson, D. L. G. Hill, et al. "Stereo Augmented Reality in the Surgical Microscope." Presence: Teleoperators and Virtual Environments 9, no. 4 (August 2000): 360–68. http://dx.doi.org/10.1162/105474600566862.

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This paper describes the MAGI (microscope-assisted guided interventions) augmented-reality system, which allows surgeons to view virtual features segmented from preoperative radiological images accurately overlaid in stereo in the optical path of a surgical microscope. The aim of the system is to enable the surgeon to see in the correct 3-D position the structures that are beneath the physical surface. The technical challenges involved are calibration, segmentation, registration, tracking, and visualization. This paper details our solutions to these problems. As it is difficult to make reliable quantitative assessments of the accuracy of augmented-reality systems, results are presented from a numerical simulation, and these show that the system has a theoretical overlay accuracy of better than 1 mm at the focal plane of the microscope. Implementations of the system have been tested on volunteers, phantoms, and seven patients in the operating room. Observations are consistent with this accuracy prediction.

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Ghose, Tandra, Yannik Schelske, Takeshi Suzuki, and Andreas Dengel. "Low-level pixelated representations suffice for aesthetically pleasing contrast adjustment in photographs." Psihologija 50, no. 3 (2017): 239–70. http://dx.doi.org/10.2298/psi1703239g.

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Today?s web-based automatic image enhancement algorithms decide to apply an enhancement operation by searching for ?similar? images in an online database of images and then applying the same level of enhancement as the image in the database. Two key bottlenecks in these systems are the storage cost for images and the cost of the search. Based on the principles of computational aesthetics, we consider storing task-relevant aesthetic summaries, a set of features which are sufficient to predict the level at which an image enhancement operation should be performed, instead of the entire image. The empirical question, then, is to ensure that the reduced representation indeed maintains enough information so that the resulting operation is perceived to be aesthetically pleasing to humans. We focus on the contrast adjustment operation, an important image enhancement primitive. We empirically study the efficacy of storing a pixelated summary of the 16 most representative colors of an image and performing contrast adjustments on this representation. We tested two variants of the pixelated image: a ?mid-level pixelized version? that retained spatial relationships and allowed for region segmentation and grouping as in the original image and a ?low-level pixelized-random version? which only retained the colors by randomly shuffling the 50 x 50 pixels. In an empirical study on 25 human subjects, we demonstrate that the preferred contrast for the low-level pixelized-random image is comparable to the original image even though it retains very few bits and no semantic information, thereby making it ideal for image matching and retrieval for automated contrast editing. In addition, we use an eye tracking study to show that users focus only on a small central portion of the low-level image, thus improving the performance of image search over commonly used computer vision algorithms to determine interesting key points.

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Watt, R. J. "Feature-based image segmentation in human vision." Spatial Vision 1, no. 3 (1986): 243–56. http://dx.doi.org/10.1163/156856886x00043.

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Dorini, Leyza Baldo, and Siome Klein Goldenstein. "Unscented feature tracking." Computer Vision and Image Understanding 115, no. 1 (January 2011): 8–15. http://dx.doi.org/10.1016/j.cviu.2010.07.009.

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Shih, Frank Y., and Xin Zhong. "Automated Counting and Tracking of Vehicles." International Journal of Pattern Recognition and Artificial Intelligence 31, no. 12 (September 17, 2017): 1750038. http://dx.doi.org/10.1142/s0218001417500380.

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A robust traffic surveillance system is crucial in improving the control and management of traffic systems. Vehicle flow processing primarily involves counting and tracking vehicles; however, due to complex situations such as brightness changes and vehicle partial occlusions, traditional image segmentation methods are unable to segment and count vehicles correctly. This paper presents a novel framework for vision-based vehicle counting and tracking, which consists of four main procedures: foreground detection, feature extraction, feature analysis, and vehicles counting/tracking. Foreground detection intends to generate regions of interest in an image, which are used to produce significant feature points. Vehicles counting and tracking are achieved by analyzing clusters of feature points. As for testing on recorded traffic videos, the proposed framework is verified to be able to separate occluded vehicles and count the number of vehicles accurately and efficiently. By comparing with other methods, we observe that the proposed framework achieves the highest occlusion segment rate and the counting accuracy.

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Dankers, Andrew, Nick Barnes, and Alex Zelinsky. "MAP ZDF segmentation and tracking using active stereo vision: Hand tracking case study." Computer Vision and Image Understanding 108, no. 1-2 (October 2007): 74–86. http://dx.doi.org/10.1016/j.cviu.2006.10.013.

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Poling, Bryan, and Gilad Lerman. "Enhancing feature tracking with gyro regularization." Image and Vision Computing 50 (June 2016): 42–58. http://dx.doi.org/10.1016/j.imavis.2016.01.004.

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Harmanci, Yunus Emre, Zhilu Lai, Utku Gülan, Markus Holzner, and Eleni Chatzi. "Computer Vision Aided Structural Identification: Feature Tracking Using Particle Tracking Velocimetry versus Optical Flow." Proceedings 4, no. 1 (November 14, 2018): 33. http://dx.doi.org/10.3390/ecsa-5-05750.

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Recent advances in computer vision techniques allow to obtain information on the dynamic behaviour of structures using commercial grade video recording devices. The advantage of such schemes lies in the non-invasive nature of video recording and the ability to extract information at a high spatial density utilizing structural features. This creates an advantage over conventional contact sensors since constraints such as cabling and maximum channel availability are alleviated. In this study, two such schemes are explored, namely Particle Tracking Velocimetry (PTV) and the optical flow algorithm. Both are validated against conventional sensors for a lab-scale shear frame and compared. In cases of imperceptible motion, the recently proposed Phase-based Motion Magnification (PBMM) technique is employed to obtain modal information within frequency bands of interest and further used for modal analysis. The optical flow scheme combined with (PBMM) is further tested on a large-scale post-tensioned concrete beam and validated against conventional measurements, as a transition from lab- to outdoor field applications.

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Zhang, Peng, Tao Zhuo, Lei Xie, and Yanning Zhang. "Deformable object tracking with spatiotemporal segmentation in big vision surveillance." Neurocomputing 204 (September 2016): 87–96. http://dx.doi.org/10.1016/j.neucom.2015.07.149.

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Reid, Ian, and Keith Connor. "Multiview segmentation and tracking of dynamic occluding layers." Image and Vision Computing 28, no. 6 (June 2010): 1022–30. http://dx.doi.org/10.1016/j.imavis.2009.09.007.

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Heber, Markus, Martin Godec, Matthias Rüther, Peter M. Roth, and Horst Bischof. "Segmentation-based tracking by support fusion." Computer Vision and Image Understanding 117, no. 6 (June 2013): 573–86. http://dx.doi.org/10.1016/j.cviu.2013.02.001.

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Xu, Binbin, Andrew J. Davison, and Stefan Leutenegger. "Deep Probabilistic Feature-Metric Tracking." IEEE Robotics and Automation Letters 6, no. 1 (January 2021): 223–30. http://dx.doi.org/10.1109/lra.2020.3039216.

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Li, Qianwen, Zhihua Wei, and Wen Shen. "Selective Feature Fusion Based Adaptive Image Segmentation Algorithm." Advances in Multimedia 2018 (September 9, 2018): 1–10. http://dx.doi.org/10.1155/2018/4724078.

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Image segmentation is an essential task in computer vision and pattern recognition. There are two key challenges for image segmentation. One is to find the most discriminative image feature set to get high-quality segments. The other is to achieve good performance among various images. In this paper, we firstly propose a selective feature fusion algorithm to choose the best feature set by evaluating the results of presegmentation. Specifically, the proposed method fuses selected features and applies the fused features to region growing segmentation algorithm. To get better segments on different images, we further develop an algorithm to change threshold adaptively for each image by measuring the size of the region. The adaptive threshold can achieve better performance on each image than fixed threshold. Experimental results demonstrate that our method improves the performance of traditional region growing by selective feature fusion and adaptive threshold. Moreover, our proposed algorithm obtains promising results and outperforms some popular approaches.

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Huerta, Ivan, Michael B. Holte, Thomas B. Moeslund, and Jordi Gonzàlez. "Chromatic shadow detection and tracking for moving foreground segmentation." Image and Vision Computing 41 (September 2015): 42–53. http://dx.doi.org/10.1016/j.imavis.2015.06.003.

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Nichol, David, and Merrilyn Fiebig. "Tracking multiple moving objects by binary object forest segmentation." Image and Vision Computing 9, no. 6 (December 1991): 362–71. http://dx.doi.org/10.1016/0262-8856(91)90003-8.

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Fusiello, A., E. Trucco, T. Tommasini, and V. Roberto. "Improving Feature Tracking with Robust Statistics." Pattern Analysis & Applications 2, no. 4 (November 1, 1999): 312–20. http://dx.doi.org/10.1007/s100440050039.

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Lomakina-Rumyantseva, E., P. Voronin, D. Kropotov, D. Vetrov, and A. Konushin. "Video tracking and behaviour segmentation of laboratory rodents." Pattern Recognition and Image Analysis 19, no. 4 (December 2009): 616–22. http://dx.doi.org/10.1134/s1054661809040075.

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Takada, Chika, and Yasuyuki Sugaya. "Incorrect Feature Tracking Detection by Affine Space Fitting." IPSJ Transactions on Computer Vision and Applications 1 (2009): 174–82. http://dx.doi.org/10.2197/ipsjtcva.1.174.

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Wang, Zelun, Jinjun Wang, Shun Zhang, and Yihong Gong. "Visual tracking based on online sparse feature learning." Image and Vision Computing 38 (June 2015): 24–32. http://dx.doi.org/10.1016/j.imavis.2015.04.005.

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du Buf, J. M. H., M. Kardan, and M. Spann. "Texture feature performance for image segmentation." Pattern Recognition 23, no. 3-4 (January 1990): 291–309. http://dx.doi.org/10.1016/0031-3203(90)90017-f.

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Chu, Tao, Wenjie Cai, and Qiong Liu. "Learning panoptic segmentation through feature discriminability." Pattern Recognition 122 (February 2022): 108240. http://dx.doi.org/10.1016/j.patcog.2021.108240.

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Konwar, Lakhyadeep, Anjan Kumar Talukdar, Kandarpa Kumar Sarma, Navajit Saikia, and Subhash Chandra Rajbangshi. "Segmentation and Selective Feature Extraction for Human Detection to the Direction of Action Recognition." International Journal of Circuits, Systems and Signal Processing 15 (September 8, 2021): 1371–86. http://dx.doi.org/10.46300/9106.2021.15.147.

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Detection as well as classification of different object for machine vision application is a challenging task. Similar to the other object detection and classification task, human detection concept provides a major role for the ad- vancement in the design of an automatic visual surveillance system (AVSS). For the future automation system if it is possible to include human detection and tracking, human action recognition, usual as well as unusual event recognition etc. concept for future AVSS, it will be a greater success in the transformable world. In this paper we have proposed a proper human detection and tracking technique for human action recognition toward the design of AVSS. Here we use median filter for noise removal, graph cut for segment the human images, mathematical morphology to refine the segmentation mask, extract selective feature points by sing HOG, classify human objects by using SVM with polynomial ker- nel and finally particle filter for tracking those of detected human. Due to the above mentioned combinations our system can independent to the variations of lightening conditions, color, shape, size, clothing etc. and can handle the occlusion. Our system can easily detect and track human in different indoor as well as outdoor environ- ment with a automatic multiple human detection rate of 97:61% and total multiple human detection and tracking accuracy is about 92% for AVSS. Due to the use of HOG to extract features af- ter graph cut segmentation operation, our system requires less memory for store the trained data therefore processing speed as well as accuracy of detection and tracking will be better than other techniques which can be suitable for action classification task.

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Zhong, Bineng, Shengnan Pan, Cheng Wang, Tian Wang, Jixiang Du, Duansheng Chen, and Liujuan Cao. "Robust Individual-Cell/Object Tracking via PCANet Deep Network in Biomedicine and Computer Vision." BioMed Research International 2016 (2016): 1–15. http://dx.doi.org/10.1155/2016/8182416.

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Tracking individual-cell/object over time is important in understanding drug treatment effects on cancer cells and video surveillance. A fundamental problem of individual-cell/object tracking is to simultaneously address the cell/object appearance variations caused by intrinsic and extrinsic factors. In this paper, inspired by the architecture of deep learning, we propose a robust feature learning method for constructing discriminative appearance models without large-scale pretraining. Specifically, in the initial frames, an unsupervised method is firstly used to learn the abstract feature of a target by exploiting both classic principal component analysis (PCA) algorithms with recent deep learning representation architectures. We use learned PCA eigenvectors as filters and develop a novel algorithm to represent a target by composing of a PCA-based filter bank layer, a nonlinear layer, and a patch-based pooling layer, respectively. Then, based on the feature representation, a neural network with one hidden layer is trained in a supervised mode to construct a discriminative appearance model. Finally, to alleviate the tracker drifting problem, a sample update scheme is carefully designed to keep track of the most representative and diverse samples during tracking. We test the proposed tracking method on two standard individual cell/object tracking benchmarks to show our tracker's state-of-the-art performance.

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McFarlane, N. J. B., and C. P. Schofield. "Segmentation and tracking of piglets in images." Machine Vision and Applications 8, no. 3 (May 1995): 187–93. http://dx.doi.org/10.1007/bf01215814.

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Li, Houjie, Shuangshuang Yin, Fuming Sun, and Fasheng Wang. "Face Tracking via Content Aware Correlation Filter." International Journal of Circuits, Systems and Signal Processing 15 (July 20, 2021): 677–89. http://dx.doi.org/10.46300/9106.2021.15.76.

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Face tracking is an importance task in many computer vision based augment reality systems. Correlation filters (CFs) have been applied with great success to several computer vision problems including object detection, classification and tracking, but few CF-based methods are proposed for face tracking. As an essential research direction in computer vision, face tracking is very important in many human-computer applications. In this paper, we present a content aware CF for face tracking. In our work, face content refers to the locality sensitive histogram based foreground feature and the learning samples extracted from complex background. It means that both foreground and background information are considered in constructing the face tracker. The foreground feature is introduced into the objective function which could learn an efficient model to adapt to the face appearance variation. For evaluating the proposed face tracker, we build a dataset which contains 97 video sequences covering the 11 challenging attributes of face tracking. Extensive experiments are conducted on the dataset and the results demonstrate that the proposed face tracker shows superior performance to several state-of-the-art tracking algorithms.

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Han, J., G. Awad, and A. Sutherland. "Automatic skin segmentation and tracking in sign language recognition." IET Computer Vision 3, no. 1 (2009): 24. http://dx.doi.org/10.1049/iet-cvi:20080006.

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Gao, Terry. "Detection and Tracking Cows by Computer Vision and Image Classification Methods." International Journal of Security and Privacy in Pervasive Computing 13, no. 1 (January 2021): 1–45. http://dx.doi.org/10.4018/ijsppc.2021010101.

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In this paper, the cow recognition and traction in video sequences is studied. In the recognition phase, this paper does some discussion and analysis which aim at different classification algorithms and feature extraction algorithms, and cow's detection is transformed into a binary classification problem. The detection method extracts cow's features using a method of multiple feature fusion. These features include edge characters which reflects the cow body contour, grey value, and spatial position relationship. In addition, the algorithm detects the cow body through the classifier which is trained by Gentle Adaboost algorithm. Experiments show that the method has good detection performance when the target has deformation or the contrast between target and background is low. Compared with the general target detection algorithm, this method reduces the miss rate and the detection precision is improved. Detection rate can reach 97.3%. In traction phase, the popular compressive tracking (CT) algorithm is proposed. The learning rate is changed through adaptively calculating the pap distance of image block. Moreover, the update for target model is stopped to avoid introducing error and noise when the classification response values are negative. The experiment results show that the improved tracking algorithm can effectively solve the target model update by mistaken when there are large covers or the attitude is changed frequently. For the detection and tracking of cow body, a detection and tracking framework for the image of cow is built and the detector is combined with the tracking framework. The algorithm test for some video sequences under the complex environment indicates the detection algorithm based on improved compressed perception shows good tracking effect in the changing and complicated background.

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Zhang, Tiedong, Shuwei Liu, Xiao He, Hai Huang, and Kangda Hao. "Underwater Target Tracking Using Forward-Looking Sonar for Autonomous Underwater Vehicles." Sensors 20, no. 1 (December 23, 2019): 102. http://dx.doi.org/10.3390/s20010102.

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In the scenario where autonomous underwater vehicles (AUVs) carry out tasks, it is necessary to reliably estimate underwater-moving-target positioning. While cameras often give low-precision visibility in a limited field of view, the forward-looking sonar is still an attractive method for underwater sensing, which is especially effective for long-range tracking. This paper describes an online processing framework based on forward-looking-sonar (FLS) images, and presents a novel tracking approach based on a Gaussian particle filter (GPF) to resolve persistent multiple-target tracking in cluttered environments. First, the character of acoustic-vision images is considered, and methods of median filtering and region-growing segmentation were modified to improve image-processing results. Second, a generalized regression neural network was adopted to evaluate multiple features of target regions, and a representation of feature subsets was created to improve tracking performance. Thus, an adaptive fusion strategy is introduced to integrate feature cues into the observation model, and the complete procedure of underwater target tracking based on GPF is displayed. Results obtained on a real acoustic-vision AUV platform during sea trials are shown and discussed. These showed that the proposed method is feasible and effective in tracking targets in complex underwater environments.

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Li, Ruoxiang, Dianxi Shi, Yongjun Zhang, Ruihao Li, and Mingkun Wang. "Asynchronous event feature generation and tracking based on gradient descriptor for event cameras." International Journal of Advanced Robotic Systems 18, no. 4 (July 1, 2021): 172988142110270. http://dx.doi.org/10.1177/17298814211027028.

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Recently, the event camera has become a popular and promising vision sensor in the research of simultaneous localization and mapping and computer vision owing to its advantages: low latency, high dynamic range, and high temporal resolution. As a basic part of the feature-based SLAM system, the feature tracking method using event cameras is still an open question. In this article, we present a novel asynchronous event feature generation and tracking algorithm operating directly on event-streams to fully utilize the natural asynchronism of event cameras. The proposed algorithm consists of an event-corner detection unit, a descriptor construction unit, and an event feature tracking unit. The event-corner detection unit addresses a fast and asynchronous corner detector to extract event-corners from event-streams. For the descriptor construction unit, we propose a novel asynchronous gradient descriptor inspired by the scale-invariant feature transform descriptor, which helps to achieve quantitative measurement of similarity between event feature pairs. The construction of the gradient descriptor can be decomposed into three stages: speed-invariant time surface maintenance and extraction, principal orientation calculation, and descriptor generation. The event feature tracking unit combines the constructed gradient descriptor and an event feature matching method to achieve asynchronous feature tracking. We implement the proposed algorithm in C++ and evaluate it on a public event dataset. The experimental results show that our proposed method achieves improvement in terms of tracking accuracy and real-time performance when compared with the state-of-the-art asynchronous event-corner tracker and with no compromise on the feature tracking lifetime.

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Umeda, Takayuki, Kosuke Sekiyama, and Toshio Fukuda. "Vision-Based Object Tracking by Multi-Robots." Journal of Robotics and Mechatronics 24, no. 3 (June 20, 2012): 531–39. http://dx.doi.org/10.20965/jrm.2012.p0531.

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This paper proposes a cooperative visual object tracking by a multi-robot system, where robust cognitive sharing is essential between robots. Robots identify the object of interest by using various types of information in the image recognition field. However, the most effective type of information for recognizing an object accurately is the difference between the object and its surrounding environment. Therefore we propose two evaluation criteria, called ambiguity and stationarity, in order to select the best information. Although robots attempt to select the best available feature for recognition, it will lead a failure of recognition if the background scene contains very similar features with the object of concern. To solve this problem, we introduce a scheme that robots share the relation between the landmarks and the object of interest where landmark information is generated autonomously. The experimental results show the effectiveness of the proposed multi-robot cognitive sharing.

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Schnorr, Andrea, Dirk N. Helmrich, Dominik Denker, Torsten W. Kuhlen, and Bernd Hentschel. "Feature Tracking by Two-Step Optimization." IEEE Transactions on Visualization and Computer Graphics 26, no. 6 (June 1, 2020): 2219–33. http://dx.doi.org/10.1109/tvcg.2018.2883630.

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Katz, Sagi, George Leifman, and Ayellet Tal. "Mesh segmentation using feature point and core extraction." Visual Computer 21, no. 8-10 (September 2005): 649–58. http://dx.doi.org/10.1007/s00371-005-0344-9.

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Journal articles on the topic 'Segmentation; Feature tracking; Computer vision'

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