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The paper is to propose a framework to qualitatively and quantitatively evaluate five of state-of-the-art over-segment approaches. Moreover upon over-segments evaluation, an efficient approach is developed for dense stereo matching through robust higher-order MRFs and graph cut based optimization, which combines the conventional data and smoothness terms with the robust higher-order potential term...
This paper presents a new method for robust color image segmentation based on tensor voting, a robust perceptual grouping technique used to extract salient information from noisy data. First, an adaptation of tensor voting to both image denoising and robust edge detection is applied. Second, pixels in the filtered image are classified into likely-homogeneous and likely-inhomogeneous by means of the...
In this paper we give a step-by-step detailed analysis on the performance of shortest spanning tree (SST) and its revised version, recursive SST (RSST). We further propose a novel segmentation scheme based on recursive SST in the warped domain produced by density estimation. The proposed method is robust for variant natural image input and is easy to implement. Experimental results and comparisons...
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 segmentation system for monocular video sequences with static camera that aims at foreground/background separation and tracking. We propose to combine a simple pixel-wise model for the background with a general purpose region based model for the foreground. The background is modeled using one Gaussian per pixel, thus achieving a precise and easy to update model. The foreground...
Current state-of-the-art image-based scene reconstruction techniques are capable of generating high-fidelity 3D models when used under controlled capture conditions. However, they are often inadequate when used in more challenging outdoor environments with moving cameras. In this case, algorithms must be able to cope with relatively large calibration and segmentation errors as well as input images...
In recent years, graph cut has been regarded as an effective discrete optimization method and received increasing attentions in vision community. However, many existing graph cut segmentation algorithms require interactive operations, which are not appropriate for automatic applications. In this paper, we propose an automatic segmentation algorithm via graph cut. Firstly, the data term in traditional...
In this paper, we propose a hierarchical approach to image segmentation based on the use of a graph regularisation algorithm. The initial segmentation map is obtained using the normalized cut segmentation algorithm. We then refine the segmentation results by iteratively propagating the class-labels from coarse-to-fine sampling levels. Image segmentation at each intermediate level is recast as a constrained...
This paper presents a new robust graph theoretic approach for image segmentation. The proposed method which is capable of accurately locating region boundaries has the following salient features. First, it is a non-supervised approach which reflects the non-local properties of the image. Second, it guarantees that the regions are connected. Finally, it produces robust results which is almost unaffected...
This work proposes a new MAP-based segmentation framework of multimodal images. In this work a joint MGRF model is used to describe the image. The main focus here is a more accurate model identification. For a known number of classes in the given image, the empirical distributions of this image signals are precisely approximated by a LCG distributions with positive and negative components. Gibbs potential,...
This paper proposes a video scene search system with a sketch query interface, whose search algorithm is based on a stochastic ARG (Attributed Relational Graph) matching. In this system, using the sketch query interface, the user can enter a sketch image as his/her query intuitively for searching scenes of a video. As its preprocessing, the system divides a video into several different scenes by extracting...
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