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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...
As local correspondence methods alone often do not achieve a sufficient robustness and accuracy, a subsequent refinement stage is commonly employed to improve upon the initial disparity estimates. In recent years, cross bilateral filters have already been successfully applied for this purpose. They do not only smooth the disparity maps but also help to better align the disparity values with the object...
This paper presents a novel non-rigid object localization and segmentation algorithm using an eigenspace representation. Previous approaches to eigenspace methods for object tracking use vectorized image regions as observations, whereas the proposed method uses each individual pixel as an observation. Localization using the pixel-wise eigenspace representation is robust to noise and occlusions. A...
We present a novel approach to change detection between two brain MRI scans (reference and target.) The proposed method uses a single modality to find subtle changes; and does not require prior knowledge (learning) of the type of changes to be sought. The method is based on the computation of a local kernel from the reference image, which measures the likeness of a pixel to its surroundings. This...
We present an efficient numerical solution of a PDE-driven model for color image segmentation and give numerical examples of the results. The method combines the vector-valued Allen-Cahn phase field equation with initial data fitting terms with prescribed interface width and fidelity constants. Efficient numerical solution is achieved using a multigrid splitting of a finite element space, thereby...
Computer assisted or automated histological grading of tissue biopsies for clinical cancer care is a long-studied but challenging problem. It requires sophisticated algorithms for image segmentation, tissue architecture characterization, global texture feature extraction, and high-dimensional clustering and classification algorithms. Currently there are no automatic image-based grading systems for...
Robust detection of moving objects in complex and dynamic scenes is one of the most challenging issues in computer vision. In this paper, we present an approach to segmenting moving objects with nonparametric estimated local kernel histogram (ELKH) in dynamic scenes. By using the correlation and texture of spatially proximal pixels, local kernel histogram background model is constructed. Then probability...
Region based features are getting popular due to their higher descriptive power relative to other features. However, real world images exhibit changes in image segments capturing the same scene part taken at different time, under different lighting conditions, from different viewpoints, etc. Segmentation algorithms reflect these changes, and thus segmentations exhibit poor repeatability. In this paper...
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