The goal of image understanding research is to develop techniques to automatically extract meaningful information from a population of images. This abstract goal manifests itself in a variety of application domains. Video understanding is a natural extension of image understanding. Many video understanding algorithms apply static-image algorithms to successive frames to identify patterns of consistency. This consumes a significant amount of irrelevant computation and may have erroneous results because static algorithms are not designed to indicate corresponding pixel locations between frames. Video is more than a collection of images, it is an ordered collection of images that exhibits temporal coherence, which is an additional feature like edges, colors, and textures. Motion information provides another level of visual information that can not be obtained from an isolated image. Leveraging motion cues prevents an algorithm from ?starting fresh? at each frame by focusing the region of attention. This approach is analogous to the attentional system of the human visual system. Relying on motion information alone is insufficient due to the aperture problem, where local motion information is ambiguous in at least one direction. Consequently, motion cues only provide leading and trailing motion edges and bottom-up approaches using gradient or region properties to complete moving regions are limited. Object recognition facilitates higher-level processing and is an integral component of image understanding. We present a components-based object detection and localization algorithm for static images. We show how this same system provides top-down segmentation for the detected object. We present a detailed analysis of the model dynamics during the localization process. This analysis shows consistent behavior in response to a variety of input, permitting model reduction and a substantial speed increase with little or no performance degradation. We present four specific enhancements to reduce false positives when instances of the target category are not present. First, a one-shot rule is used to discount coincident secondary hypotheses. Next, we demonstrate that the use of an entire shape model is inappropriate to localize any single instance and introduce the use of co-activation networks to represent the appropriate component relations for a particular recognition context. Next, we describe how the co-activation network can be combined with motion cues to overcome the aperture problem by providing context-specific, top-down shape information to achieve detection and segmentation in video. Finally, we present discriminating features arising from these enhancements and apply supervised learning techniques to embody the informational contribution of each approach to associate a confidence measure with each detection.
Library of Congress Subject Headings
Computer vision; Pattern recognition systems; Image processing--Digital techniques
Department, Program, or Center
Computer Science (GCCIS)
Lebo, Timothy, "Guiding object recognition: a shape model with co-activation networks" (2005). Thesis. Rochester Institute of Technology. Accessed from
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