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)
|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|
|Note||Doctoral Dissertation Computer Science 2018|
|Collaborating Institutions||Graduate College / ASU Library|
|Additional Formats||MODS / OAI Dublin Core / RIS|