A compressed-domain corner detection method for a DCT-based compressed image

Researchers have developed compressed-domain computer vision algorithms. Despite the recent progress, the demands for interesting point extraction in compressed-domain still exist. In this paper, we propose a compressed-domain corner detection for a DCT (Discrete Cosine Transform)-based compressed i...

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Bibliographic Details
Main Authors: Jongseok Lee, Hyunjae Lee, Dongkyu Lee, Seoung-Jun Oh
Format: Conference Proceeding
Language:English
Subjects:
Computer vision
Conferences
Detection algorithms
Discrete cosine transforms
Image coding
Image edge detection
Transform coding
Online Access:Request full text
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Summary:Researchers have developed compressed-domain computer vision algorithms. Despite the recent progress, the demands for interesting point extraction in compressed-domain still exist. In this paper, we propose a compressed-domain corner detection for a DCT (Discrete Cosine Transform)-based compressed image. It partially decodes the image to obtain DCT data and then split the data into the high precision data. The edge map is then estimated by gradient patterns and coefficients to detect corners. Experimental results show that the proposed method has about 6 pixels of average distance and about 2% miss rate compared to a pixel-domain method with low computational complexity.
ISSN:2158-4001
DOI:10.1109/ICCE.2017.7889330