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Identified and articulated object detection applications

Author: LiZuoZuo
Tutor: ChenYanQiu
School: Fudan University
Course: Applied Computer Technology
Keywords: Object Recognition Iteration Shape Feature Variable Models Articulated objects Background debris Feature Selection Personalized custom clothing
CLC: TP391.41
Type: Master's thesis
Year: 2011
Downloads: 42
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Abstract


Computer Vision (Computer Vision) is the study of how to make the machine for multi-dimensional image data generated IntelliSense science. Target detection and recognition is based on research in the field of image understanding is essential. Efficient object recognition algorithm for image retrieval framework, medical image processing, video surveillance, human computer interface systems in areas such premise protection. However, the target object detection and recognition technology is still in its early stages, prospects, articulated object detection and recognition of universal theories and algorithms robust framework still does not appear. This paper presents a simple model based on iterative deformable articulated framework for object detection and recognition algorithms. The algorithm of the traditional model of articulated objects make corresponding improvements, based on the improved characteristics of the underlying image shape matching looking for local articulated objects, and organize together to produce the target object detection and recognition of assumptions result. Background debris interference signal affecting the performance of the target object detection and recognition of the key factors is the characteristic improvement is mainly to avoid the background signal characteristics of the target object's shape and make the shape of the interference characteristics of the articulation body has a better local deformation tolerance. The use of a priori probability model to find the image assumption articulated object detection result is a top-down search for the identification process, the target object detection and recognition accuracy rate is higher, but not ideal. In order to improve the detection target recognition accuracy, using classifier articulation object detection and recognition results for the true and false assumptions to identify and combine the bottom-up divided regions for articulation of local objects in the foreground of the image estimation information. The final result can be seen from the use of this method the underlying shape of the extracted image features can be better to remove debris interfering background signals, the target object detection and recognition, detection of a high recognition accuracy. Matching the target object detection and recognition process to ensure results articulated object underlying local shape information is true and complete. In the field of digital fashion, personalized custom clothing (P.MTM) is an important application of human recognition algorithm scenes. Based on customer size mannequins can provide fast channel apparel manufacturing batch. The proposed object recognition method can provide information about the user's body parameters used to establish personalized mannequin.

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CLC: > Industrial Technology > Automation technology,computer technology > Computing technology,computer technology > Computer applications > Information processing (information processing) > Pattern Recognition and devices > Image recognition device
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