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Simultaneous object detection and segmentation using top-down and bottom-up processing

Sharma, Vinay
http://rave.ohiolink.edu/etdc/view?acc_num=osu1196372113

Abstract Details

2008, Doctor of Philosophy, Ohio State University, Computer and Information Science.
This thesis addresses the fundamental tasks of detecting objects in images, recovering their location, and determining their silhouette shape. We focus on object detection techniques that 1) enable simultaneous recovery of object location and object shape, 2) require minimal manual supervision during training, and 3) are capable of consistent performance under varying imaging conditions found in real-world scenarios. The work described here results in the development of a unified method for simultaneously acquiring both the location and the silhouette shape of specific object categories in outdoor scenes. The proposed algorithm integrates top-down and bottom-up processing, and combines cues from these processes in a balanced manner. The framework provides the capability to incorporate both appearance and motion information, making use of low-level contour-based features, mid-level perceptual cues, and higher-level statistical analysis. A novel Markov random field formulation is presented that effectively integrate the various cues from the top-down and bottom-up processes. The algorithm attempts to leverage the natural structure of the world, thereby requiring minimal user supervision during training. Extensive experimental evaluation shows that the approach is applicable to different object categories, and is robust to challenging conditions such as large occlusions and drastic changes in viewpoint. For static camera scenarios, we present a contour-based background-subtraction technique. Utilizing both intensity and gradient information, the algorithm constructs a fuzzy representation of foreground boundaries called a Contour Saliency Map. Combined with a low-level data-driven approach for contour completion and closure, the approach is able to accurately recover object shape. We also present object detection and segmentation approaches that combine information from visible and thermal imagery. For object detection, we present a contour-based fusion algorithm for background-subtraction. We also introduce a feature-selection approach for object segmentation from multiple imaging modalities. Starting from an incomplete segmentation from one sensor, the approach automatically extracts relevant information from other sensors to generate a complete segmentation of the object. The algorithm utilizes criteria based on Mutual Information for defining feature relevance, and does not rely on a training phase.
James Davis (Advisor)
226 p.
Computer Science
Object detection; Object segmentation; Simultaneous detection and segmentation; Thermal imagery; IR imagery; EO-IR fusion

Recommended Citations

Citations

  • Sharma, V. (2008). Simultaneous object detection and segmentation using top-down and bottom-up processing [Doctoral dissertation, Ohio State University]. OhioLINK Electronic Theses and Dissertations Center. http://rave.ohiolink.edu/etdc/view?acc_num=osu1196372113

    APA Style (7th edition)

  • Sharma, Vinay. Simultaneous object detection and segmentation using top-down and bottom-up processing. 2008. Ohio State University, Doctoral dissertation. OhioLINK Electronic Theses and Dissertations Center, http://rave.ohiolink.edu/etdc/view?acc_num=osu1196372113.

    MLA Style (8th edition)

  • Sharma, Vinay. "Simultaneous object detection and segmentation using top-down and bottom-up processing." Doctoral dissertation, Ohio State University, 2008. http://rave.ohiolink.edu/etdc/view?acc_num=osu1196372113

    Chicago Manual of Style (17th edition)

osu1196372113
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© 2007, all rights reserved.
This open access ETD is published by The Ohio State University and OhioLINK.