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This paper addresses the problem of self-validated labeling of Markov random fields (MRFs), namely to optimize an MRF with unknown number of labels. We present graduated graph cuts (GGC), a new technique that extends the binary s-t graph cut for self-validated labeling. Specifically, we use the split-and-merge strategy to decompose the complex problem to a series of tractable subproblems. In terms...
A novel method based on mixed graph structure is proposed for image representation and matching. The mixed graph structure is constructed according to the spatial relation of the regions within an image. This structure does not require redundant information to describe images. An image matching process focuses on evaluating region attributes and relationships contained in the corresponding mixed graph...
Uncertainty is one of the major challenges related to the semantic gap in multimedia data description and retrieval. It is due not only to errors and imprecisions in content classification but also to the extended range of user queries. In this paper, an extension of fuzzy conceptual graphs, suitable for handling uncertainty in visual event description and retrieval, is presented. We deal with two...
Reliable tracking of objects is an inevitable prerequisite for automated video surveillance systems. As most object detection methods, which are based on machine learning, require adequate data for the application scenario, foreground segmentation is a popular method to find possible regions of interest. These usually require a specific learning phase and adaptation over time. In this work we will...
In this paper, we present a graph-based approach to automatically detect defective zebrafish embryos. Here, the zebrafish is segmented from the background using a texture descriptor and morphological operations. In this way, we can represent the embryo shape as a graph, for which we propose a vectorisation method to recover clique histogram vectors for classification. The clique histogram represents...
The success of visual tracking systems is highly dependent upon the effectiveness of the measurement function used to evaluate the likelihood of a hypothesized object state. Generative tracking algorithms attempt to find the global and other local maxima of these measurement functions. As such, designing measurement functions which have a small number of local maxima is highly desirable. Edge based...
This paper presents an algorithm for object localization and segmentation. The algorithm uses machine learning, and statistical and combinatorial optimization tools to build a tracker that is robust to noise and occlusions. The method is based on a novel energy formulation and its dual use for object localization and segmentation. The energy uses kernel principal component analysis to incorporate...
Characteristic marks on the cartridge can be viewed as a ??fingerprint?? for identification of a firearm. Sometimes, however, not all information can be obtained from just one image due to the limitations of microscope and the unsmoothed specimen surface in the cartridge case image detection. Image mosaic that refers to the combination of two or more images into a single composite image is precisely...
Extracting hepatic vasculature from three dimensional imagery is important for diagnosis of liver disease and planning of liver surgery. In this paper we propose a method for generation of 3D skeletal graph of liver vessels using thinning algorithm and graph theory. First of all, basic methodology in the proposed method is introduced. Secondly, the skeletonization method together with a pre-processing...
Fully automatic segmentation of natural image is still a tough task. In this paper, we investigate a special class of natural images, whose foreground objects appear sharp while background are blurred due to out of depth of field, we call that Background Defocused Images. This kind of pictures is frequently seen on newspapers, advertisements and feature shoots. An algorithm is proposed to automatically...
This paper addresses gradual transition detection which is part of video segmentation problem, and consists in identifying the boundary between consecutive shots. In this work, we propose an approach to cope with gradual transition detection in which we define and use a new dissimilarity measure based on the size of the maximum cardinality matching calculated using a bipartite graph with respect to...
In this paper the human object present in images are identified. K-means clustering algorithm is used for segmenting the images and Connected Component Analysis is used as post-processing step. Initially, templates are created from human posture in idle and various angles. The adjacency matrix is constructed from objects and it is matched with templates. A similarity measure has been proposed for...
This paper presents a new algorithm for segmentation of overlapped cursive handwritten digits. Segmentation is a common issue for automatic character recognition. The work in digits is common in applications as bank check or postal code processing. Our method is based on the segmentation achieved by the path traversed by a hypothetical ball which rolls over the digits under the influence of inertial...
In this paper, fifteen watershed algorithms are reviewed. For clarity, first we expose two graph exploring methods modified to be guidelines for understanding the approaches taken by these algorithms: the breadth-first watershed and the depth-first watershed. Both paradigms rely on the visiting order applied by the algorithms. The breadth-first is more recognizable as a seed region growing or marker...
As an active topic in pattern recognition, the graph spectral is applied in clustering and segmentation. But issues in the analysis to image, especially the texture image, could not been retrieved till now. In this paper, we present a novel texture analysis method, which introduces graph spectral theory into the field of texture image analysis. At first, the image is partitioned into several sub images...
This paper presents a new concept on characterizing the similarity between nodes of a weighted undirected graph with application to multiscale spectral clustering. The contribution may be divided into three parts. First, the generalized mean first-passage time (GMFPT) and the generalized mean recurrence time (GMRT) are proposed based on the multi-step transition probability of the random walk on graph...
The methods presented in this paper aim at detecting and recognizing players on a sport-field, based on a distributed set of loosely synchronized cameras. Detection assumes player verticality, and sums the cumulative projection of the multiple views' foreground activity masks on a set of planes that are parallel to the ground plane. After summation, large projection values indicate the position of...
The development of 3D ultrasonic technology and its extensive applications in industrial and medical fields are introduced firstly. At the following time, the principle of ultrasonic testing, the ultrasonic signal processing and the mathematical modeling are put forward. Next, a data processing method of defect positioning to reconstruct a 3D graph based on splicing technology and segmentation method...
This paper highlights 3D building rooftop detection procedure based on 3D line data. Our approach begins with area-based stereo matching method to generate digital elevation map (DEM). Subpixel interpolation, normalized cross correlation and multi-resolution scheme are employed to generate an elaborate DEM. 3D lines are evaluated by using line fitting of DEM on 2D line coordinates of ortho-image....
In this paper we propose a multistage computational procedure for segmentation of images that can also be used for partitioning of large process data sets. In the first step the original "raw" data set (e.g. the set of pixels from a given image) is compressed by use of the neural-gas unsupervised learning algorithm into compressed information model (CIM) that contains small predefined number...
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