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5 results on '"Kazutoshi, Akita"'

1. Context-Aware Region-Dependent Scale Proposals for Scale-Optimized Object Detection Using Super-Resolution

Author

Kazutoshi Akita and Norimichi Ukita

Subjects
Object detection, object scales, region-dependent scale proposals, super-resolution, tiny object detection, Electrical engineering. Electronics. Nuclear engineering, TK1-9971
Abstract

Image scaling techniques such as Super-Resolution (SR) are useful for object detection, especially for detecting small objects. However, we found that scaling by an inappropriate factor tends to induce false-positive detections. This paper presents a Region-Dependent Scale-Proposal (RDSP) network that estimates the appropriate scale factors for each image region depending on its contextual information. In our detection framework, images are appropriately scaled by SR according to the estimations of the RDSP network, and fed into the scale-specific object detectors. While previous works have proposed models for scale proposal, our RDSP extracts regions where objects could potentially exist based on scene structure, regardless of whether actual objects are present, because small objects are often too small to determine their presence accurately. Additionally, while existing approaches have fused object detection and SR in an end-to-end manner, scale proposals for SR are not provided or are performed independently. Qualitative and quantitative experiments show that our RDSP network provides appropriate SR scales and improve detection accuracy on highly challenging dataset, captured by real car-mounted cameras with size-varied objects, including extremely small objects. Our code is available at https://github.com/kakitamedia/RDSP.

Published
2023
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2. CALN1 hypomethylation as a biomarker for high-risk bladder cancer

Author

Kimiaki Takagi, Azumi Naruse, Kazutoshi Akita, Yuka Muramatsu-Maekawa, Kota Kawase, Takuya Koie, Masanobu Horie, and Arizumi Kikuchi

Subjects
Bladder cancer, CALN1, Methylation analysis, Methylation-sensitive restriction enzyme (MSRE), Molecular diagnosis technique, Transurethral resection of bladder tumor (TURBT), Diseases of the genitourinary system. Urology, RC870-923
Abstract

Abstract Background DNA methylation in cancer is considered a diagnostic and predictive biomarker. We investigated the usefulness of the methylation status of CALN1 as a biomarker for bladder cancer using methylation-sensitive restriction enzyme (MSRE)-quantitative polymerase chain reaction (qPCR). Methods Eighty-two bladder cancer fresh samples were collected via transurethral resection of bladder tumors. Genomic DNA was extracted from the samples, and MSRE-qPCR was performed to determine the CALN1 methylation percentage. Reverse transcription-qPCR was performed to assess the correlation between CALN1 methylation and mRNA expression. The association between CALN1 methylation percentage and clinicopathological variables of all cases and intravesical recurrence of non-muscle-invasive bladder cancer (non-MIBC) cases were analyzed. Results Of the 82 patients, nine had MIBC and 71 had non-MIBC who had not undergone total cystectomy. The median CALN1 methylation percentage was 79.5% (interquartile range: 51.1–92.6%). The CALN1 methylation percentage had a negative relationship with CALN1 mRNA expression (Spearman’s ρ = − 0.563 and P = 0.012). Hypomethylation of CALN1 was associated with advanced tumor stage (P = 0.0007) and histologically high grade (P = 0.018). Furthermore, multivariate analysis revealed that CALN1 hypomethylation was an independent risk factor for intravesical recurrence in non-MIBC patients (hazard ratio 3.83, 95% confidence interval; 1.14–13.0, P = 0.031). Conclusion Our findings suggest that CALN1 methylation percentage could be a useful molecular biomarker for bladder cancer.

Published
2022
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3. AIM 2020 Challenge on Video Extreme Super-Resolution: Methods and Results

Author

Shanshan Zhao, Salah Karout, Zhiwu Huang, Xuan Xu, Kazutoshi Akita, Aryan Esfandiari, Haoyu Chen, Dan Xia, Wenhao Zhang, Shuhang Gu, Zhi Zhang, Arnau Raventos, Jinjin Gu, C. V. Jiji, Dongyang Zhang, Tongtong Zhao, Pablo Navarrete Michelini, Xin Li, Hanwei Zhu, P. S. Hrishikesh, Norimichi Ukita, Xin Xiong, Jinge Wang, Dario Fuoli, Radu Timofte, Densen Puthussery, Bartoli, Adrien, and Fusiello, Andrea

Subjects
Ground truth, Pixel, Computer science, business.industry, media_common.quotation_subject, ComputingMethodologies_IMAGEPROCESSINGANDCOMPUTERVISION, Fidelity, Contrast (statistics), Task (project management), Domain (software engineering), Perception, ComputingMilieux_COMPUTERSANDEDUCATION, Computer vision, Artificial intelligence, Set (psychology), business, media_common
Abstract

Lecture Notes in Computer Science, 12538, ISSN:0302-9743, ISSN:1611-3349, Computer Vision – ECCV 2020 Workshops Glasgow, UK, August 23–28, 2020, Proceedings, Part IV, ISBN:978-3-030-66822-8, ISBN:978-3-030-66823-5

Published
2020

4. 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

5. 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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