Damon CHANDLER Professor
- 教員データベース
- ビジュアルインフォメーションエンジニアリング研究室
- 所属学部
- 情報理工学部
- 職位
- Professor
- 専門
- 視覚情報工学
- 主な担当科目
- 信号処理、AI、プログラミング
- 研究分野・テーマ
- Visual Information Engineering, Computational Perception, Image and Video Quality Assessment
- 過去の部活動
- Golf
- 得意な科目
- Computer science
- 苦手な科目
- Physics
Message
What are the appealing and interesting points of Information Science and Engineering?
The amount of digital information that is generated and these days is overwhelming. Information Science and Engineering plays a critical role in not only ensuring that this information is readily available, but also in harnessing the information to make automated decisions that can help our everyday lives. Information science and engineering is particularly appealing because it lies at the intersection of computer science, engineering, and mathematics; and can thus cater to students from a wide variety of backgrounds.
How will the knowledge gained in the College of Information Science and Engineering be useful for students after graduation?
The ability to design software/system to make intelligent decisions from digital information is an extremely valuable skill that is highly sought in industry and research. Consequently, students in the College of Information Science and Engineering will be able to use the knowledge and skills gained during their time at Ritsumeikan to support almost any future technical task.
Please describe your major research, activities, and current research themes.
Research in the Visual Information Engineering lab broadly concerns analysis, coding, and processing of visual information. Our research explores how visual information can be utilized to help society.
We research and develop software and systems to: (1) make fast and reliable decisions from visual sources, and/or (2) to assess/improve the appearance, security, and usefulness of the visual content. Our key research topics include:
・Image/video enhancement, restoration, and compression via perceptually guided and/or machine-learning based methods
・Quality assessment of natural and synthetic images, video, 3D content
・Traditional and AI-based analysis, including detection, segmentation, and classification
・Computational modeling of the human visual system using natural-scene statistics and visual psychophysics
・Real-time analysis and processing
Some applications of our work including automatic detection and scoring of streamed visual content, perceptually lossless compression and watermarking, visual guidance for the blind, and detection, segmentation, and correction of driving video.
Please describe technologies that you find interesting now.
Artificial intelligence and machine learning have been very influential over the last 10 years, particularly in the field of image and video processing and analysis. The ability of AI to make intelligent decisions from complex visual scenes has made amazing progress due to the availability of both data and processing power. I believe future research in image/video processing should harness this technology to enhance (but not replace) traditional approaches.