2 resultados para visual object categorization

em Digital Commons at Florida International University


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Today, most conventional surveillance networks are based on analog system, which has a lot of constraints like manpower and high-bandwidth requirements. It becomes the barrier for today's surveillance network development. This dissertation describes a digital surveillance network architecture based on the H.264 coding/decoding (CODEC) System-on-a-Chip (SoC) platform. The proposed digital surveillance network architecture includes three major layers: software layer, hardware layer, and the network layer. The following outlines the contributions to the proposed digital surveillance network architecture. (1) We implement an object recognition system and an object categorization system on the software layer by applying several Digital Image Processing (DIP) algorithms. (2) For better compression ratio and higher video quality transfer, we implement two new modules on the hardware layer of the H.264 CODEC core, i.e., the background elimination module and the Directional Discrete Cosine Transform (DDCT) module. (3) Furthermore, we introduce a Digital Signal Processor (DSP) sub-system on the main bus of H.264 SoC platforms as the major hardware support system for our software architecture. Thus we combine the software and hardware platforms to be an intelligent surveillance node. Lab results show that the proposed surveillance node can dramatically save the network resources like bandwidth and storage capacity.

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To navigate effectively in three-dimensional space, flying insects must approximate distances to nearby objects. Humans are able to use an array of cues to guide depth perception in the visual world. However, some of these cues are not available to insects that are constrained by their rigid eyes and relatively small body size. Flying fruit flies can use motion parallax to gauge the distance of nearby objects, but using this cue becomes a less effective strategy as objects become more remote. Humans are able to infer depth across far distances by comparing the angular distance of an object to the horizon. This study tested if flying fruit flies, like humans, use the relative position of the horizon as a depth cue. Fruit flies in tethered flight were stimulated with a virtual environment that displayed vertical bars of varying elevation relative to a horizon, and their tracking responses were recorded. This study showed that tracking responses of the flies were strongly increased by reducing the apparent elevation of the bar against the horizon, indicating that fruit flies may be able to assess the distance of far off objects in the natural world by comparing them against a visual horizon.