Feature Extraction and Image Analysis with the Applications to Print Quality Assessment, Streak Detection, and Pedestrian Detection

Xing Liu, Purdue University

Abstract

Feature extraction is the main driving force behind the advancement of the image processing techniques in fields such as image quality assessment, object detection, and object recognition. In this work, we perform a comprehensive and in-depth study on feature extraction for the following applications: image macro-uniformity assessment, 2.5D printing quality assessment, streak defect detection, and pedestrian detection. Firstly, a set of multi-scale wavelet-based features is proposed, and a quality predictor is trained to predict the perceived macro-uniformity. Secondly, the 2.5D printing quality is characterized by a set of merits that focus on the surface structure. Thirdly, a set of features is proposed to describe the streaks, based on which two detectors are developed: the first one uses Support Vector Machine (SVM) to train a binary classifier to detect the streak; the second one adopts Hidden Markov Model (HMM) to incorporates the row dependency information within a single streak. Finally, a novel set of pixel-difference features is proposed to develop a computationally efficient feature extraction method for pedestrian detection.

Degree

Ph.D.

Advisors

Allebach, Purdue University.

Subject Area

Artificial intelligence|Computer science|Mathematics

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