13:00 | Opening | |
13:05 | Keynote speaker | |
14:00 | Adrian Galdran, Javier Vazquez-Corral, David Pardo, Marcelo Bertalmío | A Variational Framework for Single Image Dehazing |
14:20 | Homayoun Bagherinia, Roberto Manduchi | Color Barcode Decoding in the Presence of Specular Reflection |
14:40 | Panagiotis-Alexandro Bokaris, Michele Gouiffes, Christian Jacquemin, Jean-Marc Chomaz | Photometric Compensation to Dynamic Surfaces in a Projector-Camera System |
15:00 | Keynote speaker | |
16:00 | Fabien Pierre, Jean-François Aujol, Aurélie Bugeau, Vinh-Thong Ta | A Unified Model for Image Colorization |
16:20 | Tomas F Yago Vicente, Dimitris Samaras | Single Image Shadow Removal via Neighbor Based Region Relighting |
16:40 | Michael Weinmann, Reinhard Klein | Material Recognition for Efficient Acquisition of Geometry and Reflectance |
17:00 | Graham Finlayson, Christopher Powell | Shape in a Box |
17:20 | Concluding Remarks |
When light interacts with surfaces and participating media, it is altered in terms of its spectrum, polarization state, and spatial and angular distributions. Modeling and analyzing these processes has a long history in vision, and it has deepened our understanding of biological vision systems and enabled the development of a variety of computational tools for analyzing and organizing visual data.
Over the last decade, with the acceleration of digital photography and the advances in appearance scanners, image sensors, and displays, we have seen explosive growth in the amount of visual data that is available, and equally explosive growth in the opportunities for image understanding by machines.
This workshop will leverage this growth and exploit these opportunities by providing new insights for the understanding of color and photometry in computer vision. As color and photometry are shared among various research fields, this workshop places them at the junctions of different areas, including color science, applied optics, computational photography, computer vision, computer graphics, and machine learning. It seeks to enable knowledge discovery using area-specific expertise and cross-understanding.
We encourage researchers to formulate innovative color theories, color representations, and color processing techniques, and to evaluate their effectiveness. We also encourage new theories and processes for organizing images and inferring scene information from images through analysis of photometry and/or color that is motivated by perception, physics, and phenomenology. We are soliciting original contributions that address a wide range of theoretical and practical issues including, but not limited to: