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In this paper, we propose an efficient approach to identify the opinion leader from group discussion. This approach is able to recognize the opinion leader without analyzing semantic and syntactic features, which may cost a lot more computing effort. We firstly propose algorithms to evaluate the degree of participation and the emotion expression from the speaking of each member during group discussion...
In this paper we propose a novel bottom-up visual saliency detection model by analysis of image complexity. Compared with existing works, we emphasize the important impact of image complexity on saliency detection. Inspired by the free energy theory, a hybrid parametric and non-parametric model is used to estimate the complexity of a visual signal. Taking the image complexity as a new feature, this...
Chinese character component recognition plays an important role in Chinese character studying process. In this digital age, using the technology of computer science to analyzing Chinese character component, has a very important significance for the development of traditional culture. In this paper, we propose an optimal combined strategy based method for component recognition. First, we execute preprocessing...
Carotid stenosis is an important indicator and one of the main causes of ischemic stroke. Since the stenosis transformation is gradual, the patients usually can't be aware of abnormality immediately. Therefore a convenient non-invasive examination method is needed to help the general public monitor the status of carotid artery stenosis. In this work we used electronic stethoscope to record the sound...
In this paper, we present a new algorithm for blind/no-reference image quality assessment (BIQA/NR-IQA). Most existing measures are “opinion-aware”, demanding human opinion scored images to map image features to them. The task of obtaining human scores of images is, however, commonly thought to be uneconomical, and thus we focus on “opinion free” (OF) quality metrics in this research. By integrating...
Image segmentation in the big data context is a hot topic in the field of image understanding. Contrary to traditional computing paradigm with precise description of problems, Granular Computing (GrC) is studied by utilizing the toleration of imprecise, incomplete, uncertain and mass information to make systems manageable, robust, low-cost and harmonious. Thus it is an efficient measure to simplify...
Given a set of images containing the instances of the same object class, the proposed method not only partitions every image into object and background, but also parses every object into several visual segments. Unlike the semantic parts based on high-level concepts, the visual segments prefer focusing on the low-level visual features which are easier to find and match from one image to other image...
This paper proposes a novel stroke extraction method for the Chinese character. In our method, a Chinese character is represented as a set of triangular mesh that is generated by using the canny contour detector and the constraint Delaunay triangulation (CDT). Based on the representation, the singular regions and the sub-strokes are firstly determined by the properties of triangular mesh. The point-to-boundary...
This paper presents a method for the square detection based on distance distribution of the edge points of the image. The orientation line of the edge point is defined first, then for each pixel of the image, the distances between the pixel and the orientation lines of the edge points in the defined neighborhood of the pixel are computed to obtain the feature length and feature energy of the pixel,...
In this paper, we present a 3D X-Ray Transform based feature extraction and classification method for Digital Multi-focal Images (DMI). In such images, morphological information for a transparent specimen can be captured in the form of a stack of high-quality images, representing individual focal planes through the specimen's body. We present a method that can effectively exploit the entire information...
In this paper, we present a 3D X-Ray Transform based multilinear feature extraction and classification method for Digital Multi-focal Images (DMI). In such images, morphological information for a transparent specimen can be captured in the form of a stack of high-quality images, representing individual focal planes through the specimen's body. We present a method that can effectively exploit the entire...
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