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This paper proposes a new mesh simplification algorithm which makes effort in reducing the approximation error and improving the mesh regularity of simplified mesh at the same time. In previous mesh simplification researches, algorithms generally focused on the appearance error between the simplified mesh and the original mesh. However, a so-called high quality simplified mesh must have low approximation...
In the fiber image analysis system, correctly segmenting fiber from fiber micrograph is critical for fiber feature extraction and further identification. In this paper, the GVF snake model with the initial contour obtained by contour tracking method based on K-means clustering segmentation is proposed for fiber segmentation. Firstly, the K-means clustering method is used to obtain the initial coarse...
In this paper, we propose a new segmentation algorithm that combines a graph-based shape model with image cues based on boosted features. The landmark-based shape model encodes prior constraints through the normalized Euclidean distances between pairs of control points, alleviating the need of a large database for the training. Moreover, the graph topology is deduced from the dataset using manifold...
This paper presents concepts, ecosystem, research challenges and directions of Social Services Computing. Social Services Computing is an emerging computing paradigm which sweeps through Social Computing, Internet of Things, Services Computing, and Cloud Computing. Physical things, computer systems and social individuals are connected together through dedicate and complex communication and control...
In order to improve detection efficiency of on-line web news stream, we propose a new method to accomplish detection task with window-adding, named entity recognition and suffix tree clustering. In our method, we make full use of informative elements of news stream(such as date, place, person and so on) to help detection process, and this method decreases text similarity computation greatly. Experimental...
Questioned document examination is extensively used by forensic specialists for criminal identification. This paper presents a writer recognition system based on allographic features operating in identification mode (one-to-many). It works at the level of isolated characters, considering that each writer uses a reduced number of shapes for each one. Individual characters of a writer are manually segmented...
The paper proposes a kind of visual speech feature for the speaking mouth images from the video combining clues of the shape and local teeth texture. The geometric feature we proposed based on the computing the Euclidian distant between each the feature point around the inner and outer lip. The local texture with G and B components as baseline is employed to calculate the color moment to describe...
Efficient data mining and indexing is important for multimedia analysis and retrieval. In the field of large-scale video analysis, effective genre categorization plays an important role and serves one of the fundamental steps for data mining. Existing works utilize domain-knowledge dependent feature extraction, which is limited from genre diversification as well as data volume scalability. In this...
Anomaly detection in crowd scene is very important because of more concern with people safety in public place. This paper presents an approach to automatically detect abnormal behavior in crowd scene. For this purpose, instead of tracking every person, KLT corners are extracted as feature points to represent moving objects and tracked by optical flow technique to generate motion vectors, which are...
Archetypal analysis (AA) proposed by Cutler and Breiman in estimates the principal convex hull of a data set. As such AA favors features that constitute representative 'corners' of the data, i.e. distinct aspects or archetypes. We will show that AA enjoys the interpretability of clustering - without being limited to hard assignment and the uniqueness of SVD - without being limited to orthogonal representations...
Semantic analysis of a document collection can be viewed as an unsupervised clustering of the constituent words and documents around hidden or latent concepts. This has shown to improve the performance of visual bag of words in image retrieval. However, the enhancement in performance depends heavily on the right choice of number of semantic concepts. Most of the semantic indexing schemes are also...
In this paper, we present a graph cut application dealing with MRI brain image segmentation. We here propose another emerging approach of region segmentation based on graph cut which supports on the eigenspace characteristics and the perceptual grouping properties to classify brain tumoral tissue. Image segmentation is considered as solving the partitioning clustering problem by extracting the global...
This paper present an efficient real time rectangle speed limit sign recognition system. The system design considers computation load and hardware resources for driver assistant system. First multi-scale overlapping LBP features are used to train AdaBoost cascade classifier for speed limit sign object detection. Then a simple linear prediction method is used to do tracking task. At the recognition...
Document clustering as an unsupervised approach extensively used to navigate, filter, summarize and manage large collection of document repositories like the World Wide Web (WWW). Recently, focuses in this domain shifted from traditional vector based document similarity for clustering to suffix tree based document similarity, as it offers more semantic representation of the text present in the document...
When objects in images are small or blurred enough, geometric features are inadequate for reliable pattern recognition. We introduce the Pattern Recognition by Cluster Accumulation (PRCA) method to show that pattern recognition performance can be improved in this situation by using radiometric features for object detection. In addition, PRCA uses clustering to provide feature selection and dimensionality...
This paper presents an innovative approach for localizing and segmenting duplicate objects for industrial applications. The working conditions are challenging, with complex heavily-occluded objects, arranged at random in the scene. To account for high flexibility and processing speed, this approach exploits SIFT keypoint extraction and mean shift clustering to efficiently partition the correspondences...
Moving objects detection is a fundamental step in many vision based applications. Background subtraction is the typical method. Many background models have been introduced to deal with different problems. The method based on mixture of Gaussians is a good balance between accuracy and complexity, and is used frequently by many researchers. But it still cannot provide satisfied results in some cases...
It is well known that information retrieval systems based entirely on syntactic contents have serious limitations. In order to achieve high precision and recall on IR systems, the incorporation of natural language processing techniques that provide semantic information is needed. For this reason, by determining the semantic for the constituents of documents, a clustering method is presented in this...
In the field of speaker recognition, the Gaussian mixture model with diagonal covariance matrices is a popular technique, in this way, it simplified model and reduced the amount of computation, but lost the correlation information between feature vectors, and then influenced the classification performance. In this paper, in order to compensate the correlation between feature elements, we proposed...
In this paper we present a new video object trajectory clustering algorithm, which allows us to model and analyse the patterns of object behaviors based on the extracted features using tensor analysis. The proposed algorithm consists of three steps as follows: extraction of trajectory features by tensor analysis, non-parametric probabilistic mean shift clustering and clustering correction. The performance...
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