No abstract available.
Cited By
- Wang C, Xu L and Liu L (2023). Structure–texture image decomposition via non-convex total generalized variation and convolutional sparse coding, The Visual Computer: International Journal of Computer Graphics, 39:3, (1121-1136), Online publication date: 1-Mar-2023.
- Zhou F, Chen Q, Liu B and Qiu G (2020). Structure and Texture-Aware Image Decomposition via Training a Neural Network, IEEE Transactions on Image Processing, 29, (3458-3473), Online publication date: 1-Jan-2020.
- Ding L, Huang H and Zang Y (2017). Image Quality Assessment Using Directional Anisotropy Structure Measurement, IEEE Transactions on Image Processing, 26:4, (1799-1809), Online publication date: 1-Apr-2017.
- Márquez-Valle P, Gil D and Hernàndez-Sabaté A Error analysis for lucas-kanade based schemes Proceedings of the 9th international conference on Image Analysis and Recognition - Volume Part I, (184-191)
- Yu Y, Mann G and Gosine R (2010). An object-based visual attention model for robotic applications, IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics, 40:5, (1398-1412), Online publication date: 1-Oct-2010.
- Heindlmaier M, Yu L and Diepold K The impact of nonlinear filtering and confidence information on optical flow estimation in a Lucas & Kanade framework Proceedings of the 16th IEEE international conference on Image processing, (1573-1576)
- Ren W, Singh S, Singh M and Zhu Y (2009). State-of-the-art on spatio-temporal information-based video retrieval, Pattern Recognition, 42:2, (267-282), Online publication date: 1-Feb-2009.
- Preusser T, Scharr H, Krajsek K and Kirby R (2008). Building Blocks for Computer Vision with Stochastic Partial Differential Equations, International Journal of Computer Vision, 80:3, (375-405), Online publication date: 1-Dec-2008.
- Olsen O and Nielsen M (2006). The Generic Structure of the Optic Flow Field, Journal of Mathematical Imaging and Vision, 24:1, (37-53), Online publication date: 1-Jan-2006.
- Scharr H Diffusion-Like reconstruction schemes from linear data models Proceedings of the 28th conference on Pattern Recognition, (51-60)
- Arseneau S and Cooperstock J An improved representation of junctions through asymmetric tensor diffusion Proceedings of the Second international conference on Advances in Visual Computing - Volume Part I, (363-372)
- Agrawal A, Raskar R, Nayar S and Li Y Removing photography artifacts using gradient projection and flash-exposure sampling ACM SIGGRAPH 2005 Papers, (828-835)
- Gil D, Hernandez A, Carol A, Rodriguez O and Radeva P A deterministic-statistic adventitia detection in IVUS images Proceedings of the Third international conference on Functional Imaging and Modeling of the Heart, (65-74)
- Agrawal A, Raskar R, Nayar S and Li Y (2005). Removing photography artifacts using gradient projection and flash-exposure sampling, ACM Transactions on Graphics, 24:3, (828-835), Online publication date: 1-Jul-2005.
- Scharr H Towards a multi-camera generalization of brightness constancy Proceedings of the 1st international conference on Complex motion, (78-90)
- Scharr H Optimal filters for extended optical flow Proceedings of the 1st international conference on Complex motion, (14-29)
- Jähne B Complex motion in environmental physics and live sciences Proceedings of the 1st international conference on Complex motion, (91-103)
- Ngo C, Pong T and Zhang H (2019). Motion-Based Video Representation for Scene Change Detection, International Journal of Computer Vision, 50:2, (127-142), Online publication date: 1-Nov-2002.
- Yu W, Sommer G, Beauchemin S and Daniilidis K (2002). Oriented Structure of the Occlusion Distortion, IEEE Transactions on Pattern Analysis and Machine Intelligence, 24:9, (1286-1290), Online publication date: 1-Sep-2002.
- López A, Lumbreras F, Serrat J and Villanueva J (1999). Evaluation of Methods for Ridge and Valley Detection, IEEE Transactions on Pattern Analysis and Machine Intelligence, 21:4, (327-335), Online publication date: 1-Apr-1999.
- Bab-Hadiashar A and Suter D (2019). Robust Optic Flow Computation, International Journal of Computer Vision, 29:1, (59-77), Online publication date: 1-Aug-1998.
Recommendations
Spatio-temporal image registration for respiratory motion correction in pet imaging
ISBI'09: Proceedings of the Sixth IEEE international conference on Symposium on Biomedical Imaging: From Nano to MacroPositron emission tomography (PET) is a molecular imaging technique which is now widely established as a powerful tool for diagnosing a variety of cancers. However, PET images are substantially degraded by respiratory motion to the extent that this may ...
Motion analysis and segmentation through spatio-temporal slices processing
This paper presents new approaches in characterizing and segmenting the content of video. These approaches are developed based upon the pattern analysis of spatio-temporal slices. While traditional approaches to motion sequence analysis tend to ...
Spatio-Temporal Segmentation Using 3D Morphological Tools
ICPR '00: Proceedings of the International Conference on Pattern Recognition - Volume 3In this paper, we present a novel morphological approach to image simplification by flat zones which, applied to a temporal image sequence, leads to a method for spatio-temporal segmentation. Our approach considers a sequence as a single 3-dimensional ...