Title page for etd-0809115-033644


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URN etd-0809115-033644
Author Jun-Mao Chan
Author's Email Address No Public.
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Department Computer Science and Engineering
Year 2014
Semester 2
Degree Master
Type of Document
Language zh-TW.Big5 Chinese
Title Hardware Acceleration of Feature Extraction Algorithms
Date of Defense 2015-07-29
Page Count 83
Keyword
  • image feature extraction
  • pedestrian detection
  • SIFT (Scale-Invariant Feature Transform)
  • SURF (Speeded-Up Robust Feature)
  • hardware acceleration
  • computer vision
  • LBP (Local Binary Pattern)
  • HOG (Histograms of Oriented Gradients)
  • FAST (Feature from Accelerated Segment Test)
  • Abstract The description of local features of images has been successfully applied to many areas, including object recognition, texture recognition, image retrieval, robot localization, video data mining, etc. However, pure software implementations usually cannot achieve the requirement of real-time processing. In this thesis, we present hardware acceleration for two popular image feature extraction/description algorithms, Features from Accelerated Segment Test (FAST) and Histograms of Oriented Gradients (HOG). FAST is recently adopted as OpenVX API for image and vision processing. Since there are few hardware implementation papers, this thesis will propose a new design and analyzes the tradeoff between execution time, quality, and hardware cost, in order to find a suitable hardware acceleration. HOG computation requires complicated arithmetic operations such as square root and trigonometric function, making it costly for hardware implementation. In this thesis, we modify the original HOG algorithm in order to significantly reduce the hardware complexity without sacrificing too much in speed and quality.
    Advisory Committee
  • Pei-Yung Hsiao - chair
  • Yun-Nan Chang - co-chair
  • Ming-Chih Chen - co-chair
  • Shen-Fu Hsiao. - advisor
  • Files
  • etd-0809115-033644.pdf
  • Indicate in-campus at 3 year and off-campus access at 3 year.
    Date of Submission 2015-09-09

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