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2015, ISBN 1482205378, 1 v. (various pagings)
Background modeling and foreground detection are important steps in video processing used to detect robustly moving objects in challenging environments. This... 
Optical pattern recognition | Video surveillance
Book
IEEE Transactions on Pattern Analysis and Machine Intelligence, ISSN 0162-8828, 01/2018, Volume 40, Issue 1, pp. 20 - 33
Journal Article
IEEE Transactions on Image Processing, ISSN 1057-7149, 04/2016, Volume 25, Issue 4, pp. 1834 - 1848
Journal Article
Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), ISSN 0302-9743, 2016, Volume 9909, pp. 318 - 333
Conference Proceeding
2008 IEEE Conference on Computer Vision and Pattern Recognition, ISSN 1063-6919, 06/2008, pp. 1 - 8
This paper presents three novel methods that enable bilateral filtering in constant time O(1) without sampling. Constant time means that the computation time... 
Histograms | Smoothing methods | Runtime | Convolution | Nonlinear filters | Filtering algorithms | Taylor series | Polynomials | Kernel | Surface treatment
Conference Proceeding
2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR'05), ISSN 1063-6919, 2005, Volume 1, pp. 829 - 836 vol. 1
We present a novel method, which we refer as an integral histogram, to compute the histograms of all possible target regions in a Cartesian data space. Our... 
Search methods | Image processing | Statistics
Conference Proceeding
IEEE Transactions on Image Processing, ISSN 1057-7149, 11/2017, Volume 26, Issue 11, pp. 5506 - 5518
Journal Article
IEEE Transactions on Image Processing, ISSN 1057-7149, 11/2016, Volume 25, Issue 11, pp. 5025 - 5034
Journal Article
Image and Vision Computing, ISSN 0262-8856, 04/2017, Volume 60, pp. 4 - 21
Understanding human actions in visual data is tied to advances in complementary research areas including object recognition, human dynamics, domain adaptation... 
Human action recognition | Survey | Deep networks | Motion recognition | MOVEMENTS | REPRESENTATION | CLASSIFICATION | MODEL | COMPUTER SCIENCE, ARTIFICIAL INTELLIGENCE | ENGINEERING, ELECTRICAL & ELECTRONIC | COMPUTER SCIENCE, SOFTWARE ENGINEERING | MOTION | COMPUTER SCIENCE, THEORY & METHODS | SCALE | OPTICS
Journal Article
Journal Article
IEEE Transactions on Image Processing, ISSN 1057-7149, 11/2018, Volume 27, Issue 11, pp. 5652 - 5667
Journal Article
IEEE Transactions on Pattern Analysis and Machine Intelligence, ISSN 0162-8828, 04/2019, Volume 41, Issue 4, pp. 985 - 998
Journal Article
IEEE Transactions on Circuits and Systems for Video Technology, ISSN 1051-8215, 02/2018, Volume 28, Issue 2, pp. 314 - 326
Conventional tracking-by-detection approaches for visual object tracking often assume that the task at hand is a binary foreground-versus-background... 
Contextual clustering | fine-grained model | support vector machine (SVM) | tracking by detection | VISUAL TRACKING | OBJECT TRACKING | ENGINEERING, ELECTRICAL & ELECTRONIC | Image segmentation | Usage | Artificial intelligence | Visualization (Computers) | Analysis | Detectors | Clusters | Classifiers | Tracking
Journal Article
Proceedings of SPIE - The International Society for Optical Engineering, ISSN 0277-786X, 04/2010, Volume 7698
We present a computationally efficient track-before-detect algorithm that achieves more than 50% true detection at 10 super(-6) false alarm rate for pixel... 
Picking | False alarms | Images | Needles | Queues | Computational efficiency | Sampling | Temporal logic
Journal Article
IEEE Transactions on Neural Networks and Learning Systems, ISSN 2162-237X, 09/2018, Volume 29, Issue 9, pp. 4339 - 4346
Journal Article
2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), ISSN 1063-6919, 06/2016, Volume 2016-, pp. 943 - 951
Most tracking-by-detection methods employ a local search window around the predicted object location in the current frame assuming the previous location is... 
Support vector machines | Adaptation models | Tracking | Image edge detection | Heating | Search problems | Proposals
Conference Proceeding
IEEE Transactions on Circuits and Systems for Video Technology, ISSN 1051-8215, 06/2018, Volume 28, Issue 6, pp. 1358 - 1368
Compared with other applications in computer vision, convolutional neural networks (CNNs) have underperformed on pedestrian detection. A breakthrough was made... 
Training | ensemble model | pedestrian detection | Detectors | Object detection | Convolutional feature map (CFM) | Feature extraction | Labeling | Proposals | Australia | ENGINEERING, ELECTRICAL & ELECTRONIC | Datasets | Occlusion | Computer vision | State of the art | Feature maps | Optical flow (image analysis) | Machine learning | Artificial neural networks
Journal Article
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