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Your search keyword '"Timofte, R."' showing total 4 results
Start Over Author "Timofte, R." Topic 0202 electrical engineering, electronic engineering, information engineering
4 results on '"Timofte, R."'

1. The Seventh Visual Object Tracking VOT2019 Challenge Results

Author

Kristan, M., Matas, J., Leonardis, A., Felsberg, M., Pflugfelder, R., Kämäräinen, J.-K., Čehovin Zajc, L., Drbohlav, O., Lukežič, A., Berg, A., Eldesokey, A., Käpylä, J., Fernández, G., Gonzalez-Garcia, A., Memarmoghadam, A., Lu, A., He, A., Varfolomieiev, A., Chan, A., Shekhar Tripathi, A., Smeulders, A., Suraj Pedasingu, B., Chen, B.X., Zhang, B., Wu, B., Li, B., He, B., Yan, B., Bai, B., Kim, B.H., Ma, C., Fang, C., Qian, C., Chen, C., Li, C., Zhang, C., Tsai, C.-Y., Luo, C., Micheloni, C., Tao, D., Gupta, D., Song, D., Wang, D., Gavves, E., Yi, E., Khan, F.S., Zhang, F., Wang, F., Zhao, F., De Ath, G., Bhat, G., Chen, G., Wang, G., Li, G., Cevikalp, H., Du, H., Zhao, H., Saribas, H., Jung, H.M., Bai, H., Hu, H., Peng, H., Lu, H., Li, H., Li, J., Fu, J., Chen, J., Gao, J., Zhao, J., Tang, J., Wu, J., Liu, J., Wang, J., Qi, J., Zhang, J., Tsotsos, J.K., Lee, J.H., van de Weijer, J., Kittler, J., Zhuang, J., Zhang, K., Wang, K., Dai, K., Chen, L., Liu, L., Guo, L., Zhang, L., Wang, L., Zhou, L., Zheng, L., Rout, L., Van Gool, L., Bertinetto, L., Danelljan, M., Dunnhofer, M., Ni, M., Kim, M.Y., Tang, M., Yang, M.-H., Paluru, N., Martinel, N., Xu, P., Zhang, P., Zheng, P., Torr, P.H.S., Zhang, Q., Wang, Q., Guo, Q., Timofte, R., Gorthi, R.K., Everson, R., Han, R., Zhang, R., You, S., Zhao, S.-C., Zhao, S., Li, S., Ge, S., Bai, S., Guan, S., Xing, T., Xu, T., Yang, T., Zhang, T., Vojíř, T., Feng, W., Hu, W., Wang, W., Tang, W., Zeng, W., Liu, W., Chen, X., Qiu, X., Bai, X., Wu, X.-J., Yang, X., Li, X., Sun, X., Tian, X., Tang, X., Zhu, X.-F., Huang, Y., Chen, Y., Lian, Y., Gu, Y., Liu, Y., Zhang, Y., Xu, Y., Wang, Y., Li, Y., Zhou, Y., Dong, Y., Wang, Z., Luo, Z., Zhang, Z., Feng, Z.-H., He, Z., Song, Z., Chen, Z., Wu, Z., Xiong, Z., Huang, Z., Teng, Z., Ni, Z., and Intelligent Sensory Information Systems (IVI, FNWI)

Subjects
Source code, Computer science, business.industry, media_common.quotation_subject, Object tracking, Performance evaluation, VOT challenge, 020206 networking & telecommunications, 02 engineering and technology, Visualization, Datorseende och robotik (autonoma system), Robustness (computer science), Video tracking, 0202 electrical engineering, electronic engineering, information engineering, RGB color model, 020201 artificial intelligence & image processing, Computer vision, Artificial intelligence, business, Computer Vision and Robotics (Autonomous Systems), media_common
Abstract

The Visual Object Tracking challenge VOT2019 is the seventh annual tracker benchmarking activity organized by the VOT initiative. Results of 81 trackers are presented; many are state-of-the-art trackers published at major computer vision conferences or in journals in the recent years. The evaluation included the standard VOT and other popular methodologies for short-term tracking analysis as well as the standard VOT methodology for long-term tracking analysis. The VOT2019 challenge was composed of five challenges focusing on different tracking domains: (i) VOT-ST2019 challenge focused on short-term tracking in RGB, (ii) VOT-RT2019 challenge focused on "real-time" short-term tracking in RGB, (iii) VOT-LT2019 focused on long-term tracking namely coping with target disappearance and reappearance. Two new challenges have been introduced: (iv) VOT-RGBT2019 challenge focused on short-term tracking in RGB and thermal imagery and (v) VOT-RGBD2019 challenge focused on long-term tracking in RGB and depth imagery. The VOT-ST2019, VOT-RT2019 and VOT-LT2019 datasets were refreshed while new datasets were introduced for VOT-RGBT2019 and VOT-RGBD2019. The VOT toolkit has been updated to support both standard short-term, long-term tracking and tracking with multi-channel imagery. Performance of the tested trackers typically by far exceeds standard baselines. The source code for most of the trackers is publicly available from the VOT page. The dataset, the evaluation kit and the results are publicly available at the challenge website(1). Funding Agencies|Slovenian research agencySlovenian Research Agency - Slovenia [J2-8175, P2-0214, P2-0094]; Czech Science Foundation Project GACR [P103/12/G084]; MURI project - MoD/DstlMURI; EPSRCEngineering & Physical Sciences Research Council (EPSRC) [EP/N019415/1]; WASP; VR (ELLIIT, LAST, and NCNN); SSF (SymbiCloud); AIT Strategic Research Programme; Faculty of Computer Science, University of Ljubljana, Slovenia

Published
2019

2. NTIRE 2019 Challenge on Video Super-Resolution: Methods and Results

Author

Shuhang Gu, Greg Shakhnarovich, Fatih Porikli, Kyoung Mu Lee, Zhongyuan Wang, Yulun Zhang, Seokil Hong, M. Akin Yilmaz, Kuldeep Purohit, Si Miao, A. S. Mandal, Yapeng Tian, A. Murat Tekalp, Norimichi Ukita, Sanghyun Son, Junjun Jiang, Yun Fu, A.N. Rajagopalan, Chenliang Xu, Gyeongsik Moon, Sungyong Baik, Ankit Shukla, Zhe Hu, Chen Change Loy, Manoj Sharma, Chao Li, Ding Yukang, Dong Un Kang, Yongxin Zhu, Santanu Chaudhury, Ratheesh Kalarot, Hang Dong, Avinash Upadhyay, Muhammad Haris, Ke Yu, Thomas S. Huang, Megh Makwana, Wang Xintao, Xinyi Zhang, Peng Yi, Jiahui Yu, Kwanyoung Kim, Kui Jiang, Chao Dong, Xiao Huo, Rudrabha Mukhopadhyay, Yuchen Fan, Dongliang He, Ding Liu, Se Young Chun, Shilei Wen, Ajay Pratap Singh, Xiao Liu, Kelvin C.K. Chan, Cansu Korkmaz, Jiayi Ma, Radu Timofte, Anuj Badhwar, Seungjun Nah, Dheeraj Khanna, Tekalp, Ahmet Murat (ORCID 0000-0003-1465-8121 & YÖK ID 26207), Nah, S., Timofte, R., Gu, S., Baik, S., Hong, S., Moon, G., Son, S., Lee, K.M., Wang, X., Chan, K.C.K., Yu, K., Dong, C., Loy, C.C., Fan, Y., Yu, J., Liu, D., Huang, T.S., Liu, X., Li, C., He, D., DIng, Y., Wen, S., Porikli, F., Kalarot, R., Haris, M., Shakhnarovich, G., Ukita, N., Yi, P., Wang, Z., Jiang, K., Jiang, J., Ma, J., Dong, H., Zhang, X., Hu, Z., Kim, K., Kang, D.U., Chun, S.Y., Purohit, K., Rajagopalan, A.N., Tian, Y., Zhang, Y., Fu, Y., Xu, C., Yılmaz, M.A., Korkmaz, C., Sharma, M., Makwana, M., Badhwar, A., Singh, A.P., Upadhyay, A., Mukhopadhyay, R., Shukla, A., Khanna, D., Mandal, A.S., Chaudhury, S., Miao, S., Zhu, Y., Huo, X., College of Engineering, and Department of Electrical and Electronics Engineering

Subjects
Computer science, business.industry, ComputingMethodologies_IMAGEPROCESSINGANDCOMPUTERVISION, 02 engineering and technology, Image resolution, Image restoration, Video signal processing, Track (rail transport), Superresolution, Optical resolving power, Image super, 0202 electrical engineering, electronic engineering, information engineering, Bicubic interpolation, 020201 artificial intelligence & image processing, Computer vision, Artificial intelligence, Focus (optics), business
Abstract

This paper reviews the first NTIRE challenge on video super-resolution (restoration of rich details in low-resolution video frames) with focus on proposed solutions and results. A new REalistic and Diverse Scenes dataset (REDS) was employed. The challenge was divided into 2 tracks. Track 1 employed standard bicubic downscaling setup while Track 2 had realistic dynamic motion blurs. Each competition had 124 and 104 registered participants. There were total 14 teams in the final testing phase. They gauge the state-of-the-art in video super-resolution., NA

Published
2019

3. NTIRE 2019 challenge on image enhancement: Methods and results

Author

Ling Shao, Liang Lin, Flavio Piccoli, Xingguang Zhou, Dongwon Park, Syed Waqas Zamir, Lai-Kuan Wong, Greg Shakhnarovich, Cheolkon Jung, Hongzhi Zhang, Andrey Ignatov, Xiaochao Qu, Pengxu Wei, Zhiwei Zhong, Zheng Hui, Kazutoshi Akita, Jinghui Qin, Xinbo Gao, Pablo Navarrete Michelini, Wushao Wen, Jingdong Liu, Radu Timofte, Jie Liu, Jiye Liu, Salman Khan, Norimichi Ukita, Hanwen Liu, Wangmeng Zuo, Muhammad Haris, Yukai Shi, Debin Zhao, Fahad Shahbaz Khan, Pengfei Wan, Ganapathy Krishnamurthi, Xianming Liu, Se Young Chun, Simone Bianco, Tomoki Yoshida, Ting Liu, Xiumei Wang, Kai Zhang, Junjun Jiang, Claudio Cusano, John See, Nelson Chong Ngee Bow, Lishan Huang, Pengju Liu, Raimondo Schettini, Mahendra Khened, Kanti Kumari, Aditya Arora, Vikas Kumar Anand, Dan Zhu, Ignatov, A, Timofte, R, Qu, X, Zhou, X, Liu, T, Wan, P, Zamir, S, Arora, A, Khan, S, Khan, F, Shao, L, Park, D, Chun, S, Michelini, P, Liu, H, Zhu, D, Zhong, Z, Liu, X, Jiang, J, Zhao, D, Haris, M, Akita, K, Yoshida, T, Shakhnarovich, G, Ukita, N, Liu, J, Jung, C, Schettini, R, Bianco, S, Cusano, C, Piccoli, F, Liu, P, Zhang, K, Zhang, H, Zuo, W, Bow, N, Wong, L, See, J, Qin, J, Huang, L, Shi, Y, Wei, P, Wen, W, Lin, L, Hui, Z, Wang, X, Gao, X, Kumari, K, Anand, V, Khened, M, and Krishnamurthi, G

Subjects
0209 industrial biotechnology, Computer science, Image quality, Structural similarity, business.industry, media_common.quotation_subject, image enhancement, image quality, tone adjustment, cameras, smartphones, task analysis, visualization, computer vision, ComputingMethodologies_IMAGEPROCESSINGANDCOMPUTERVISION, 02 engineering and technology, Visualization, Image (mathematics), 020901 industrial engineering & automation, Perception, Metric (mathematics), 0202 electrical engineering, electronic engineering, information engineering, Contrast (vision), 020201 artificial intelligence & image processing, Computer vision, Artificial intelligence, business, Focus (optics), Image resolution, media_common
Abstract

This paper reviews the first NTIRE challenge on perceptual image enhancement with the focus on proposed solutions and results. The participating teams were solving a real-world photo enhancement problem, where the goal was to map low-quality photos from the iPhone 3GS device to the same photos captured with Canon 70D DSLR camera. The considered problem embraced a number of computer vision subtasks, such as image denoising, image resolution and sharpness enhancement, image color/contrast/exposure adjustment, etc. The target metric used in this challenge combined PSNR and SSIM scores with solutions' perceptual results measured in the user study. The proposed solutions significantly improved baseline results, defining the state-of-the-art for practical image enhancement.

Published
2019

4. NTIRE 2019 challenge on real image denoising: Methods and results

Author

Kazutoshi Akita, Thomas S. Huang, Simone Zini, Raimondo Schettini, Jae-Ryun Chung, Bumjun Park, Chuan Wang, Sang-Won Lee, Seung-Won Jung, Simone Bianco, Lei Zhang, Yiyun Zhao, Yuchen Fan, Yifan Ding, Greg Shakhnarovich, Se Young Chun, Hongwei Yong, Ling Shao, Deyu Meng, Wangmeng Zuo, Chi Li, Salman Khan, Tomoki Yoshida, Chang Chen, Ding Liu, Dongwon Park, Wenyi Tang, Zhiwei Xiong, Syed Waqas Zamir, Yuqian Zhou, Norimichi Ukita, Haoqiang Fan, Seung-Wook Kim, Jue Wang, Zhiguo Cao, Yuzhi Wang, Radu Timofte, Dong-Wook Kim, Sung-Jea Ko, Fahad Shahbaz Khan, Magauiya Zhussip, Dong-Pan Lim, Seo-Won Ji, Yang Wang, Muhammad Haris, Aditya Arora, Michael S. Brown, Shakarim Soltanayev, Jiaming Liu, Qin Xu, Abdelrahman Abdelhamed, Shaofan Cai, Kai Zhang, Jechang Jeong, Chi-Hao Wu, Songhyun Yu, Yue Lu, Pengliang Tang, Abdelhamed, A, Timofte, R, Brown, M, Yu, S, Park, B, Jeong, J, Jung, S, Kim, D, Chung, J, Liu, J, Wang, Y, Wu, C, Xu, Q, Wang, C, Cai, S, Ding, Y, Fan, H, Wang, J, Zhang, K, Zuo, W, Zhussip, M, Park, D, Soltanayev, S, Chun, S, Xiong, Z, Chen, C, Haris, M, Akita, K, Yoshida, T, Shakhnarovich, G, Ukita, N, Zamir, S, Arora, A, Khan, S, Khan, F, Shao, L, Ko, S, Lim, D, Kim, S, Ji, S, Lee, S, Tang, W, Fan, Y, Zhou, Y, Liu, D, Huang, T, Meng, D, Zhang, L, Yong, H, Zhao, Y, Tang, P, Lu, Y, Schettini, R, Bianco, S, Zini, S, Li, C, and Cao, Z

Subjects
Noise measurement, Computer science, business.industry, Noise reduction, sRGB, ComputingMethodologies_IMAGEPROCESSINGANDCOMPUTERVISION, 0211 other engineering and technologies, INF/01 - INFORMATICA, 02 engineering and technology, Color space, Real image, Image denoising, 0202 electrical engineering, electronic engineering, information engineering, RGB color model, 020201 artificial intelligence & image processing, Computer vision, Artificial intelligence, Focus (optics), business, 021101 geological & geomatics engineering
Abstract

This paper reviews the NTIRE 2019 challenge on real image denoising with focus on the proposed methods and their results. The challenge has two tracks for quantitatively evaluating image denoising performance in (1) the Bayer-pattern raw-RGB and (2) the standard RGB (sRGB) color spaces. The tracks had 216 and 220 registered participants, respectively. A total of 15 teams, proposing 17 methods, competed in the final phase of the challenge. The proposed methods by the 15 teams represent the current state-of-the-art performance in image denoising targeting real noisy images.

Published
2019

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