Intention-Oriented Computational Visual Attention and Shape Recognition
Date Issued
2007
Date
2007
Author(s)
Fang, Chun-Hsiung
DOI
en-US
Abstract
This dissertation addresses the computational implementation of intentional attention and automated recognition in the human vision. An Intention-oriented Computational Visual Attention (ICVA) model is proposed to extract intentional features from images and eventually to find out attentive spots. An intention about favorite colors or target objects is expressed as a combination of preference vectors and topological relationships. Intentional features are extracted from the optical excitation data by matching the preference vectors. Attentive spots are found by fuzzy inference to fulfill the topological relationships. Synthesized Affine Invariant Function (SAIF) is derived to uniquely represent two-dimension shapes subject to affine transformations. SAIF is essentially a parameterized contour signal derived with the Synthesized Feature Signals (SFSs). It is shown Wavelet, Fourier, or Fourier cosine transforms can be used to deduce SFSs from a contour signal. Specifically, SAIF uses SFSs involving every component except translation of the contour signal. Hence, SAIF has minimum information loss in representing the contour signal for automated recognition. The invariance property, representative property, robustness against noise and correctness in the shape recognition of several significant SAIFs are compared experimentally. It is shown SAIFs outperform other affine invariant functions and the SAIF using four SFSs calculated with the dyadic wavelet transform is the most practical implementation.
Subjects
演算視覺
視注覺
意圖
仿射不變函數
形狀辨識
小波轉換
傅力葉轉換
餘弦轉換
computational vision
visual attention
intention
affine invariant function
shape recognition
wavelet transform
Fourier transform
cosine transform
Type
thesis
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