Image detection and compression for memory efficient system analysis

Mustafa Bayraktar

doi:10.1117/12.2181359

14 February 2015 Image detection and compression for memory efficient system analysis

Mustafa Bayraktar

Proceedings Volume 9445, Seventh International Conference on Machine Vision (ICMV 2014); 94450O (2015) https://doi.org/10.1117/12.2181359
Event: Seventh International Conference on Machine Vision (ICMV 2014), 2014, Milan, Italy

Abstract

The advances in digital signal processing have been progressing towards efficient use of memory and processing. Both of these factors can be utilized efficiently by using feasible techniques of image storage by computing the minimum information of image which will enhance computation in later processes. Scale Invariant Feature Transform (SIFT) can be utilized to estimate and retrieve of an image. In computer vision, SIFT can be implemented to recognize the image by comparing its key features from SIFT saved key point descriptors. The main advantage of SIFT is that it doesn't only remove the redundant information from an image but also reduces the key points by matching their orientation and adding them together in different windows of image [1]. Another key property of this approach is that it works on highly contrasted images more efficiently because it`s design is based on collecting key points from the contrast shades of image.

Citation Download Citation

Mustafa Bayraktar "Image detection and compression for memory efficient system analysis", Proc. SPIE 9445, Seventh International Conference on Machine Vision (ICMV 2014), 94450O (14 February 2015); https://doi.org/10.1117/12.2181359

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