9th International Conference on Computational Vision and Robotics (ICCVR-2019)
(Date June 15-16, 2019 )Bhubaneswar
Organized by: I.I.M.T., Bhubaneswar, Odisha, India
Programme Committee
Bendjenna, Hakim, University of Larbi Tebessi, Algeria
Bhatia, Sanjiv K., University of Missouri - St. Louis, USA
Chaudhuri, Debasis, Defence Electronics Applications Laboratory, India
Damper, R.I., University of Southampton, UK
DeSouza, Guilherme N., University of Western Australia, Australia
Gini, Maria, University of Minnesota, USA
Governi, Lapo, University of Florence, Italy
Han, Wang, Nanyang Technological University, Singapore
Koshizen, Takamasa, Honda Research Institute Japan Co. Ltd., Japan
Kothari, Ravi, IBM Research - India, India
Kountchev, Roumen Kirilov, Technical University of Sofia, Bulgaria
Krim, Hamid, North Carolina State University, USA
Lee, Chan-Su, Yeungnam University, South Korea
Lima, Pedro U., Instituto Superior Técnico, Puerto Rico
Mata, Mario, Universidad Europea de Madrid, Spain
Michalewicz, Zbigniew, University of Adelaide, Australia
Moreno, Luis, University of Carlos III, Spain
Nahvi, Ali, K.N. Toosi University of Technology, Iran
Ranjan, Jayanthi, Institute of Management Technology (IMT), India
Ruichek, Yassine, University of Technology of Belfort-Montbéliard, France
Samal, Ashok, University of Nebraska-Lincoln, USA
Sethi, Ishwar, Oakland University, USA
Shafazand, M. Hassan, Shahid Bahonar University of Kerman, Iran
Sharma, Rajeev, Pennsylvania State University, USA
Sun, Yu, Michigan State University, USA
Tan, Kok Kiong, National University of Singapore, Singapore
Wren, Christopher R., Mitsubishi Electric Research Laboratories, USA
Yang, Yeon-Mo, Kumoh National Institute of Technology, South Korea
Yeasin, Mohammed, University of Memphis, USA
Yezzi, Anthony, Georgia Institute of Technology, USA
Yin, Peng-Yeng, National Chi Nan University, Taiwan
Bhatia, Sanjiv K., University of Missouri - St. Louis, USA
Chaudhuri, Debasis, Defence Electronics Applications Laboratory, India
Damper, R.I., University of Southampton, UK
DeSouza, Guilherme N., University of Western Australia, Australia
Gini, Maria, University of Minnesota, USA
Governi, Lapo, University of Florence, Italy
Han, Wang, Nanyang Technological University, Singapore
Koshizen, Takamasa, Honda Research Institute Japan Co. Ltd., Japan
Kothari, Ravi, IBM Research - India, India
Kountchev, Roumen Kirilov, Technical University of Sofia, Bulgaria
Krim, Hamid, North Carolina State University, USA
Lee, Chan-Su, Yeungnam University, South Korea
Lima, Pedro U., Instituto Superior Técnico, Puerto Rico
Mata, Mario, Universidad Europea de Madrid, Spain
Michalewicz, Zbigniew, University of Adelaide, Australia
Moreno, Luis, University of Carlos III, Spain
Nahvi, Ali, K.N. Toosi University of Technology, Iran
Ranjan, Jayanthi, Institute of Management Technology (IMT), India
Ruichek, Yassine, University of Technology of Belfort-Montbéliard, France
Samal, Ashok, University of Nebraska-Lincoln, USA
Sethi, Ishwar, Oakland University, USA
Shafazand, M. Hassan, Shahid Bahonar University of Kerman, Iran
Sharma, Rajeev, Pennsylvania State University, USA
Sun, Yu, Michigan State University, USA
Tan, Kok Kiong, National University of Singapore, Singapore
Wren, Christopher R., Mitsubishi Electric Research Laboratories, USA
Yang, Yeon-Mo, Kumoh National Institute of Technology, South Korea
Yeasin, Mohammed, University of Memphis, USA
Yezzi, Anthony, Georgia Institute of Technology, USA
Yin, Peng-Yeng, National Chi Nan University, Taiwan
Submission Guidelines
- The submissions may be of any form out of the following:
- New algorithms with empirical, theoretical, psychological, or biological justification.
- Experimental and/or theoretical studies yielding new insight into the design and behavior of learning in intelligent systems.
- Applications of existing techniques that shed light on the strengths and weaknesses of the methods.
- New learning tasks (e.g., in the context of new applications) and of methods for assessing performance on those tasks.
- Development of new analytical frameworks that advance theoretical studies of practical learning methods.
- Computational models of data from natural learning systems at the behavioral or neural level; or extremely well-written surveys of existing work.
Sponsored by
IIMT, Bhubaneswar (INDIA)
IRNet (INDIA)
Important Dates
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