Pub Date : 2020-07-01DOI: 10.1109/tpami.2020.2993888
Ayan Chakrabarti, Kalyan Sunkavalli, D. Forsyth
The nine papers in this special section focus on computational photography. The development of increasingly successful visual inference algorithms has driven progress in a number of different application domains—ranging from photography to autonomous vehicles to graphics and virtual reality systems. As we continue to extend the capabilities of these computational algorithms, a complementary research direction lies in asking what the right visual measurements are for these algorithms to operate on. In computational photography, we seek to investigate both components—computational and sensory—of intelligent visual systems in synergy, to build measurement schemes and inference algorithms that are jointly optimal for a desired task, and thus create functionalities that go beyond what is possible with traditional cameras and computational tools. The call for papers for this section was co-ordinated with the 2020 IEEE International Conference on Computational Photography (ICCP) that was held from April 24-26, 2020.
{"title":"Guest Editors' Introduction to the Special Section on Computational Photography","authors":"Ayan Chakrabarti, Kalyan Sunkavalli, D. Forsyth","doi":"10.1109/tpami.2020.2993888","DOIUrl":"https://doi.org/10.1109/tpami.2020.2993888","url":null,"abstract":"The nine papers in this special section focus on computational photography. The development of increasingly successful visual inference algorithms has driven progress in a number of different application domains—ranging from photography to autonomous vehicles to graphics and virtual reality systems. As we continue to extend the capabilities of these computational algorithms, a complementary research direction lies in asking what the right visual measurements are for these algorithms to operate on. In computational photography, we seek to investigate both components—computational and sensory—of intelligent visual systems in synergy, to build measurement schemes and inference algorithms that are jointly optimal for a desired task, and thus create functionalities that go beyond what is possible with traditional cameras and computational tools. The call for papers for this section was co-ordinated with the 2020 IEEE International Conference on Computational Photography (ICCP) that was held from April 24-26, 2020.","PeriodicalId":13207,"journal":{"name":"IEEE Trans. Pattern Anal. Mach. Intell.","volume":"112 1","pages":"1545-1546"},"PeriodicalIF":0.0,"publicationDate":"2020-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"84163360","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2020-05-01DOI: 10.1109/tpami.2020.2971291
Sergio Escalera, H. Escalante, Xavier Baró, Isabelle M Guyon, Meysam Madadi, Jun Wan, S. Ayache, Yağmur Güçlütürk, Umut Güçlü
The papers in this special issue comprise all aspects of computer vision and pattern recognition devoted to image and video inpainting, including related tasks like denoising, debluring, sampling, super-resolutkon enhancement, restoration, hallucination, etc. The special issue was associated to the 2018 Chalearn Looking at People Satellite ECCV Workshop1 and the 2018 ChaLearn Challenges on Image and Video Inpainting.
{"title":"Guest Editorial: Image and Video Inpainting and Denoising","authors":"Sergio Escalera, H. Escalante, Xavier Baró, Isabelle M Guyon, Meysam Madadi, Jun Wan, S. Ayache, Yağmur Güçlütürk, Umut Güçlü","doi":"10.1109/tpami.2020.2971291","DOIUrl":"https://doi.org/10.1109/tpami.2020.2971291","url":null,"abstract":"The papers in this special issue comprise all aspects of computer vision and pattern recognition devoted to image and video inpainting, including related tasks like denoising, debluring, sampling, super-resolutkon enhancement, restoration, hallucination, etc. The special issue was associated to the 2018 Chalearn Looking at People Satellite ECCV Workshop1 and the 2018 ChaLearn Challenges on Image and Video Inpainting.","PeriodicalId":13207,"journal":{"name":"IEEE Trans. Pattern Anal. Mach. Intell.","volume":"103 1","pages":"1021-1024"},"PeriodicalIF":0.0,"publicationDate":"2020-05-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"85078624","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2000-03-01DOI: 10.1109/TPAMI.2000.10001
H. Freeman
{"title":"Obituary Jean Claude Simon 1924 to 2000","authors":"H. Freeman","doi":"10.1109/TPAMI.2000.10001","DOIUrl":"https://doi.org/10.1109/TPAMI.2000.10001","url":null,"abstract":"","PeriodicalId":13207,"journal":{"name":"IEEE Trans. Pattern Anal. Mach. Intell.","volume":"53 1","pages":"226"},"PeriodicalIF":0.0,"publicationDate":"2000-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"79638532","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 1998-01-01DOI: 10.1109/TPAMI.1998.10001
Stefano Soatto, P. Perona
A number of methods have been proposed in the literature for estimating scene-structure and ego-motion from a sequence of images using dynamical models. Despite the fact that all methods may be derived from a “natural” dynamical model within a unified framework, from an engineering perspective there are a number of trade-offs that lead to different strategies depending upon the applications and the goals one is targeting. We want to characterize and compare the properties of each model such that the engineer may choose the one best suited to the specific application. We analyze the properties of filters derived from each dynamical model under a variety of experimental conditions, assess the accuracy of the estimates, their robustness to measurement noise, sensitivity to initial conditions and visual angle, effects of the bas-relief ambiguity and occlusions, dependence upon the number of image measurements and their sampling rate.
{"title":"Correction to: \"Reducing 'Structure From Motion': A General Framework for Dynamic Vision Part 2: Implementation and Experimental Assessment\"","authors":"Stefano Soatto, P. Perona","doi":"10.1109/TPAMI.1998.10001","DOIUrl":"https://doi.org/10.1109/TPAMI.1998.10001","url":null,"abstract":"A number of methods have been proposed in the literature for estimating scene-structure and ego-motion from a sequence of images using dynamical models. Despite the fact that all methods may be derived from a “natural” dynamical model within a unified framework, from an engineering perspective there are a number of trade-offs that lead to different strategies depending upon the applications and the goals one is targeting. We want to characterize and compare the properties of each model such that the engineer may choose the one best suited to the specific application. We analyze the properties of filters derived from each dynamical model under a variety of experimental conditions, assess the accuracy of the estimates, their robustness to measurement noise, sensitivity to initial conditions and visual angle, effects of the bas-relief ambiguity and occlusions, dependence upon the number of image measurements and their sampling rate.","PeriodicalId":13207,"journal":{"name":"IEEE Trans. Pattern Anal. Mach. Intell.","volume":"25 1","pages":"1117"},"PeriodicalIF":0.0,"publicationDate":"1998-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"74555935","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
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