Huanzhang Fu

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Feature subset selection is an important subject when training classifiers in Machine Learning (ML) problems. Too many input features in a ML problem may lead to the so-called “curse of dimensionality”, which describes the fact that the complexity of the classifier parameters adjustment during training increases exponentially with the number of features.(More)
Due to the flexibility and adaptability of human, manual handling work is still very important in industry, especially for assembly and maintenance work. Well-designed work operation can improve work efficiency and quality; enhance safety, and lower cost. Most traditional methods for work system analysis need physical mock-up and are time consuming. Digital(More)
This paper presents a novel approach for visual object categorization using region based features and statistical measures based image modeling. Our region-based features are extracted from coarse regions obtained by the Gestalt theory inspired region segmentation algorithm and they capture visually significant information such as segments and colors. The(More)
In this paper, we consider the problem of classifying a real world image to the corresponding object class based on its visual content via sparse representation, which is originally used as a powerful tool for acquiring, representing and compressing high-dimensional signals. Assuming the intuitive hypothesis that an image could be represented by a linear(More)
Visual object recognition is one of the most challenging problems in computer vision, due to both inter-class and intra-class variations. The local appearance-based features, especially SIFT, have gained a big success in such a task because of their great discriminative power. In this paper, we propose to adopt two different kinds of feature to characterize(More)
Generic Visual Object Categorization (VOC) aims at predicting whether at least one or several objects of some given categories are present in an image. In fact, VOC is a fundamental problem in computer vision and pattern recognition, and has become an important research topic due to the wide range of possible applications such as video monitoring, video(More)
This paper presents a novel approach for visual object classification. Based on Gestalt theory, we propose to extract features from coarse regions carrying visually significant information such as line segments and/or color and to include neighborhood information in them. We also introduce a new classification method based on the polynomial modeling of(More)
Since the challenging visual object categorization has attracted more and more attention in recent years, we present in this paper a novel approach called statistical measures based image modeling for this problem, thus avoiding the major difficulty of the popular “bag-of-visual words” approach which needs to fix a visual vocabulary size. We(More)