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Temporal invariant shape moments intuitively seem to provide an important visual cue to human activity recognition in video sequences. In this paper, an SVM based method for human activity recognition is introduced. With this method, the feature extraction is carried out based on a small number of computationally-cheap invariant shape moments. When tested on the popular KTH action dataset, the obtained...
Real-time feature extraction is a key component for any action recognition system that claims to be truly real-time. In this paper we present a conceptually simple and computationally efficient method for real-time human activity recognition based on simple statistical features. Such features are very cheap to compute and form a relatively low dimensional feature space in which classification can...
Recently, the problem of automatic traffic accident recognition has appealed to the machine vision community due to its implications on the development of autonomous Intelligent Transportation Systems (ITS). In this paper, a new framework for real-time automated traffic accidents recognition using Histogram of Flow Gradient (HFG) is proposed. This framework performs two major steps. First, HFG-based...
A reliable model for human skin is a significant need for a wide range of computer vision applications ranging from face detection, gesture analysis, content-based image retrieval systems, searching and filtering image content on the web, and to various human computer interaction domains. In this paper, a robust neural model for human skin recognition is first presented. Then, a fully automated neural...
In this paper, a new wavelet-based copyright proving scheme is proposed. The original image is not required for watermark verification. Moreover, the proposed scheme does not introduce any visual quality degradation into the original image; however, a feature is extracted from the original image and mixed with the watermark to generate a secret key. This secret key is registered for verification....
Research in content-based image retrieval (CBIR) shows that high-level semantic concepts in image cannot be constantly depicted using low-level image features. So the process of designing a CBIR system should take into account diminishing the existing gap between low-level visual image features and the high-level semantic concepts. In this paper, we propose a new architecture for a CBIR system named...
Image classification problem is one of the most challenges of computer vision. In this paper, a robust image classification approach using multilevel neural networks is proposed. In this approach, each image is fixedly divided into five regions each equal to half of the original image. Then these regions are classified by the multilevel neural classifier into five categories, i.e., ??sky??, ??water??,...
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