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Computer Vision Lab
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A lab that uses computers to study visual information processing of images.
AboutAbout the lab
Education and research in visual information processing.
Prof. Hara's lab specializes in computer vision and pattern information processing, and works on education and research in visual information processing.
Its research themes include processing wide-field-of-view images such as spherical images, image noise removal, and distinguishing CG images from natural images.
Prof. Hara is a faculty member of the Media Design Course in the School of Design.
Key terms
- Superpixel
- A small region in an image that groups together pixels with similar properties. It serves as a unit for processing that divides an image into regions
- Denoising
- Processing that removes noise mixed into an image. It is also called noise removal
- Natural scene statistics
- Statistical properties commonly found in images of natural scenery
ResearchResearch themes
Prof. Hara's lab centers on computer vision and pattern information processing, and studies methods for processing and distinguishing images. Its research themes span spherical image processing, noise removal, distinguishing CG images from natural images, and wide-field-of-view image processing.
*Illustrative imageSuperpixel segmentation of spherical images
The lab studies methods for dividing spherical images, which capture all directions, into superpixels, which are groups of similar pixels. The lab has been working on this theme since April 2024, and its keyword is the Yin-Yang grid.
*Illustrative imageParameter optimization for noise removal
The lab studies methods for determining optimal parameters in denoising, the process of removing noise from images. Its keyword is natural scene statistics.
*Illustrative imageDistinguishing CG images from natural images
The lab studies methods for telling computer-generated CG images apart from natural images. It uses an approach that models image properties with a generalized Gaussian distribution.
*Illustrative imageProcessing of wide-field-of-view images
This research deals with the processing of wide-field-of-view images such as omnidirectional panoramic images. With the Yin-Yang grid as a keyword, it covers topics such as feature extraction and foreground magnification.
MembersFaculty
- ProfessorKenji Hara
VoicesStudent voices
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Write a lab reviewAnonymous · Your draft is saved if you stopRelatedLabs similar to Computer Vision Lab
Computer Vision Lab (Kenji Hara)School of Design, Department of DesignComputer vision, pattern information processing
Pattern Recognition LabSchool of Design, Department of DesignPattern recognition, image processing
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Computer Graphics LabSchool of Design, Department of DesignComputer graphicsThe information on this page is compiled by the LabLog team from the lab's public information and interviews. For corrections or removal requests, please contact LabLog, not the lab.
Report an errorAbout the images
The top and research theme images are illustrative and were not taken at the lab. They are cropped from Wikimedia Commons images. Cropped CC BY-SA images are provided under the same CC BY-SA license as the original (version shown on each line).
- Top image: “Polarlicht 2 kmeans 16 large” Senior Airman Joshua Strang, derivative work by King of Hearts / Public domain (cropped)
- Superpixel segmentation of spherical images: “Dent de Vaulion - 360 degree panorama” Alexandre Duret-Lutz from Paris, France / CC BY-SA 2.0 (cropped)
- Parameter optimization for noise removal: “CH.ZG.Zug View from Guggi 01 16x9+R 8192x4608 Br058 gp denoising Br075” NOTE: This image is a panorama consisting of multiple frames that were merged or stitched in software. As a result, this image necessarily underwent some form of digital manipulation. These manipulations may include blending, blurring, cloning, and color and perspective adjustments. As a result of these adjustments, the image content may be slightly different from reality at the points where multiple images were combined. This manipulation is often required due to lens, perspective, and parallax distortions. / CC BY-SA 4.0 (cropped)
- Distinguishing CG images from natural images: “Rendering techniques example, path tracing, high quality, Blender Cycles” KaiaVintr / CC BY-SA 4.0 (cropped)
- Processing of wide-field-of-view images: “Fisheye images of Iran-Canon EF 8-15mm lens-HDR photography technique-2014-Photo by Mostafa Meraji-Free Pictures-Wikipedia commons 19” Mostafameraji / CC0 (cropped)
- Related lab (Computer Vision Lab (Kenji Hara)): “360 Degree of Shah Makhdum College 04” This work was made by Nahid Hossain and released under the license(s) stated below. Please feel free to use it for any purpose as long as you credit Nahid Hossain as author and follow the terms of the chosen license. If you use this work outside of the Wikimedia projects, I would very much like to get a note from you. Thanks! / CC BY-SA 4.0 (cropped)
- Related lab (Pattern Recognition Lab): “Flying Ladybug pixel art mosaic” Copyleft / CC BY-SA 4.0 (cropped)
- Related lab (Pattern Recognition Lab (Kohei Inoe)): “PatternRecognition1” Jean Constant / CC BY-SA 4.0 (cropped)
- Related lab (Computer Graphics Lab): “Danjogaran Koyasan Daito Projection Mapping01” Naokijp / CC BY-SA 4.0 (cropped)
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