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Australian National University

1. Zhang, Haoyang. Learning to Generate and Refine Object Proposals .

Degree: 2018, Australian National University

Visual object recognition is a fundamental and challenging problem in computer vision. To build a practical recognition system, one is first confronted with high computation complexity due to an enormous search space from an image, which is caused by large variations in object appearance, pose and mutual occlusion, as well as other environmental factors. To reduce the search complexity, a moderate set of image regions that are likely to contain an object, regardless of its category, are usually first generated in modern object recognition subsystems. These possible object regions are called object proposals, object hypotheses or object candidates, which can be used for down-stream classification or global reasoning in many different vision tasks like object detection, segmentation and tracking, etc. This thesis addresses the problem of object proposal generation, including bounding box and segment proposal generation, in real-world scenarios. In particular, we investigate the representation learning in object proposal generation with 3D cues and contextual information, aiming to propose higher-quality object candidates which have higher object recall, better boundary coverage and lower number. We focus on three main issues: 1) how can we incorporate additional geometric and high-level semantic context information into the proposal generation for stereo images? 2) how do we generate object segment proposals for stereo images with learning representations and learning grouping process? and 3) how can we learn a context-driven representation to refine segment proposals efficiently? In this thesis, we propose a series of solutions to address each of the raised problems. We first propose a semantic context and depth-aware object proposal generation method. We design a set of new cues to encode the objectness, and then train an efficient random forest classifier to re-rank the initial proposals and linear regressors to fine-tune their locations. Next, we extend the task to the segment proposal generation in the same setting and develop a learning-based segment proposal generation method for stereo images. Our method makes use of learned deep features and designed geometric features to represent a region and learns a similarity network to guide the superpixel grouping process. We also learn a ranking network to predict the objectness score for each segment proposal. To address the third problem, we take a transformation-based approach to improve the quality of a given segment candidate pool based on context information. We propose an efficient deep network that learns affine transformations to warp an initial object mask towards nearby object region, based on a novel feature pooling strategy. Finally, we extend our affine warping approach to address the object-mask alignment problem…

Subjects/Keywords: object proposal; object candidate; object detection; object instance segmentation; convolutional neural network (CNN)

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APA (6th Edition):

Zhang, H. (2018). Learning to Generate and Refine Object Proposals . (Thesis). Australian National University. Retrieved from http://hdl.handle.net/1885/143520

Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation

Chicago Manual of Style (16th Edition):

Zhang, Haoyang. “Learning to Generate and Refine Object Proposals .” 2018. Thesis, Australian National University. Accessed September 19, 2019. http://hdl.handle.net/1885/143520.

Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation

MLA Handbook (7th Edition):

Zhang, Haoyang. “Learning to Generate and Refine Object Proposals .” 2018. Web. 19 Sep 2019.

Vancouver:

Zhang H. Learning to Generate and Refine Object Proposals . [Internet] [Thesis]. Australian National University; 2018. [cited 2019 Sep 19]. Available from: http://hdl.handle.net/1885/143520.

Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation

Council of Science Editors:

Zhang H. Learning to Generate and Refine Object Proposals . [Thesis]. Australian National University; 2018. Available from: http://hdl.handle.net/1885/143520

Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation

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