Cited By
View all- Liu LNishikawa HZhou JTaniguchi IOnoye T(2024)Computer-Vision-Oriented Adaptive Sampling in Compressive SensingSensors10.3390/s2413434824:13(4348)Online publication date: 4-Jul-2024
Adaptive Sampling for Computer Vision-Oriented Compressive Sensing
Authors: 
Luyang Liu, Hiroki Nishikawa, Jinjia Zhou, Ittetsu Taniguchi, Takao OnoyeAuthors Info & Claims
Luyang Liu, Hiroki Nishikawa, Jinjia Zhou, Ittetsu Taniguchi, Takao OnoyeAuthors Info & ClaimsArticle No.: 98, Pages 1 - 5
Abstract
Compressive sensing (CS) is renowned for its efficient signal data compression. However, due to its compressive nature, the accuracy of downstream computer vision (CV) tasks by reconstruction inevitably degrades as sampling rate decreases. This limitation significantly hinders the application of existing CS techniques. To overcome the drawback, this paper presents a novel CS technique that employs adaptive sampling rates based on saliency distribution. The goal of this work is to enhance the preservation of information necessary for classification while reducing the weight of non-essential information. Experimental results show the effectiveness of the proposed adaptive sampling technique, which outperforms existing sampling CS techniques on STL10 and Imagenette datasets. The average classification accuracy is maximally improved by 26.23% and 18.25%, respectively.
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Index Terms
- Adaptive Sampling for Computer Vision-Oriented Compressive Sensing
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Published In
December 2023
745 pages
ISBN:9798400702051
DOI:10.1145/3595916
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Published: 01 January 2024
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View all- Liu LNishikawa HZhou JTaniguchi IOnoye T(2024)Computer-Vision-Oriented Adaptive Sampling in Compressive SensingSensors10.3390/s2413434824:13(4348)Online publication date: 4-Jul-2024
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