Thesis (Ph.D )--University of Texas at Arlington, 2009.

Thesis (Ph. D.)--University of Texas at Austin, 2002. / Vita. Includes bibliographical references. Available also from UMI Company.

published_or_final_version / Electrical and Electronic Engineering / Doctoral / Doctor of Philosophy

This thesis proposes to learn the absent cognitive element in conventional scene categorization methods: sub-scenes, and use them to better categorize and understand scenes. In scene categorization, it has been observed that the problem of ambiguity occurs when treating the scene as a whole. Scene ambiguity arises from when a similar set of sub-scenes are arranged differently to compose different scenes, or when a scene literally contains several categories. However, these ambiguities can be discerned by the knowledge of sub-scenes. Thus, it is worthy to study sub-scenes and use them to better understand a scene. The proposed research firstly considers an unsupervised method to segment sub-scenes. It emphasizes on generating more integral regions instead of over-segmented regions usually produced by conventional segmentation methods. Several properties of sub-scenes are explored such as proximity grouping, area of influence, similarity and harmony based on psychological principles. These properties are formulated into constraints that are used directly in the proposed framework. A self-determined approach is employed to produce a final segmentation result based on the characteristics of each image in an unsupervised manner. The proposed method performs competitively against other state-of-the-art unsupervised segmentation methods with F-measure of 0.55, Covering of 0.51 and VoI of 1.93 in the Berkeley segmentation dataset. In the Stanford background dataset, it achieves the overlapping score of 0.566 which is higher than the score of 0.499 of the comparison method. To segment and label sub-scenes simultaneously, a supervised approach of semantic segmentation is proposed. It is developed based on a Hierarchical Conditional Random Field classification framework. The proposed method integrates contextual information into the model to improve classification performance. Contextual information including global consistency and spatial context are considered in the proposed method. Global consistency is developed based on generalizing the scene by scene types and spatial context takes the spatial relationship into account. The proposed method improves semantic segmentation by boosting more logical class combinations. It achieves the best score in the MSRC-21 dataset with global accuracy at 87% and the average accuracy at 81%, which out-performs all other state-of-the-art methods by 4% individually. In the Stanford background dataset, it achieves global accuracy at 80.5% and average accuracy at 71.8%, also out-performs other methods by 2%. Finally, the proposed research incorporates sub-scenes into the scene categorization framework to improve categorization performance, especially in ambiguity cases. The proposed method encodes the sub-scene in the way that their spatial information is also considered. Sub-scene descriptor compensates the global descriptor of a scene by evaluating local features with specific geometric attributes. The proposed method obtains an average categorization accuracy of 92.26% in the 8 Scene Category dataset, which outperforms all other published methods by over 2% of improvement. It evaluates ambiguity cases more accurately by discerning which part exemplifies a scene category and how those categories are organized. / published_or_final_version / Electrical and Electronic Engineering / Doctoral / Doctor of Philosophy

University of Macau / Faculty of Science and Technology / Department of Computer and Information Science

University of Macau / Faculty of Science and Technology / Department of Computer and Information Science

University of Macau / Faculty of Science and Technology / Department of Computer and Information Science

University of Macau / Faculty of Science and Technology / Department of Computer and Information Science

University of Macau / Faculty of Science and Technology / Department of Computer and Information Science

This dissertation examines the suitability of Display-Processor (DP) image computers for image enhancement and restoration tasks. Because the major architectural feature of the DP devices is their ability to rapidly evaluate finite impulse response (FIR) convolutions, much of the study focusses on the use of spatial-domain FIR convolutions to approximate Fourier-domain filtering. When the enhancement task requires the evaluation of only a single convolution, it is important that the FIR kernel used to implement the convolution is designed so that the resulting output is a good approximation of the desired output. A Minimum-Mean-Squared-Error design criterion is introduced for the purpose of FIR kernel design and its usefulness is demonstrated by showing some results of its use. If the restoration or enhancement task requires multiple convolutions in an iterative algorithm, it is important to understand how the truncation of the kernel to a finite region of support will affect the convergence properties of an algorithm and the output of the iterative sequence. These questions are examined for a limited class of nonlinear restoration algorithms. Because FIR convolutions are most efficiently performed on computing machines that have limited precision and are usually limited to performing fixed-point arithmetic, the dissertation also examines the effects of roundoff error on output images that have been computed using fixed point math. The number of bits that are needed to represent the data during a computation is algorithm dependent, but for a limited class of algorithms, it is shown that 12 bits are sufficient. Finally, those architectural features in a DP that are necessary for useful enhancement and restoration operations are identified.