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Towards Developing Computer Vision Algorithms and Architectures for Real-world Applications


Abstract Computer vision technology automatically extracts high level, meaningful information from visual data such as images or videos, and the object recognition and detection algorithms are essential in most computer vision applications. In this dissertation, we focus on developing algorithms used for real life computer vision applications, presenting innovative algorithms for object segmentation and feature extraction for objects and actions recognition in video data, and sparse feature selection algorithms for medical image analysis, as well as automated feature extraction using convolutional neural network for blood cancer grading.

To detect and classify objects in video, the objects have to be separated from the background, and then the disc... (more)
Created Date 2018
Contributor Cao, Jun (Author) / Li, Baoxin (Advisor) / Liu, Huan (Committee member) / Zhang, Yu (Committee member) / Zhang, Junshan (Committee member) / Arizona State University (Publisher)
Subject Computer science / Computer Vision / Deep Learning / Feature Extraction / Machine Learning / Sparse Learning / Template Matching
Type Doctoral Dissertation
Extent 167 pages
Language English
Copyright
Note Doctoral Dissertation Computer Science 2018
Collaborating Institutions Graduate College / ASU Library
Additional Formats MODS / OAI Dublin Core / RIS


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Description Dissertation/Thesis