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Subspace segmentation is one of the hottest issues in computer vision and machine learning fields. Generally, data (e.g. face images) are lying in a union of multiple linear subspaces, therefore, it is the key to find a block diagonal affinity matrix, which would result in segmenting data into different clusters correctly. Recently, graph construction based segmentation methods attract lots of attention...
Robots are often equipped with 2D laser-rangefinders (LRFs) and cameras since they complement well to each other. In order to correctly combine measurements from both sensors, it is required to know their relative pose, that is, to solve their extrinsic calibration. In this paper we present a new approach to such problem which relies on the observations of orthogonal trihedrons which are profusely...
An automatic segmentation of leukocytes can assist pharmaceutical companies to take decisions in the discovery of drug and encourages for development of automated leukocytes recognition system. Segmentation of leukocytes in tissue images is a complex process due to the presence of various noise effects, large variability in the images, and shape of the nuclei. Surprisingly, rare efforts have been...
In allusion to the problem of low accuracy rate recognition in Chinese/English mixed characters, the paper researches on optimization algorithm for segmentation in Chinese/English mixed characters based on OCR system. Rough segment for text images is based on vertical projection method, which follows to characters segmentation theory, extraction of Chinese character, Chinese character component and...
A novel spectral-spatial hyperspectral image classification is proposed based on extended random walkers. First, a widely used pixel-wise classifier, i.e., the support vector machine (SVM), is adopted to obtain probability maps for a hyper-psectral image, which measure the probabilities that a pixel belongs to different classes. Then, the initial probabilities are optimized with the extended random...
This paper proposes a novel unsupervised cosegmentation method which automatically segments the common objects in multiple images. It designs a simple superpixel matching algorithm to explore the inter-image similarity. It then constructs the object mask for each image using the matched superpixels. This object mask is a convex hull potentially containing the common objects and some backgrounds. Finally,...
Cell detection plays a significant role in automated biomedical image analysis. However, it is challenging to achieve accurate detection due to dense crowding/touching of cells. In this paper, we propose a robust decomposition algorithm for cell detection on adipocyte images. It formulates the decomposition into a cut selection problem using the polygon triangulation approximation and a specific-defined...
In this paper, we introduce the concept of proximity priors into semantic segmentation in order to discourage the presence of certain object classes (such as 'sheep' and 'wolf') 'in the vicinity' of each other. 'Vicinity' encompasses spatial distance as well as specific spatial directions simultaneously, e.g. 'plates' are found directly above 'tables', but do not fly over them. In this sense, our...
In the spectral-type subspace segmentation models, the rank minimization problem was relaxed as Nuclear Norm Minimization(NNM) problem. However, to guarantee the success of NNM, one needs some strict conditions, and NNM may yield the matrix with much higher rank than the real one. In this paper, the L1/2 regularization is introduced into the low-rank spectral-type subspace segmentation model, combining...
We present a novel and fast interactive approach to multi-modality cardiac image segmentation, which employs the linearly ordered surfaces as an additional constraint. We show using such a geometrical constraint helps to significantly reduce user interaction and improve the accuracy of segmentation results at the same time. We solve the proposed multiregion segmentation problem with the order constraints...
In this paper, the importance of cost aggregation for belief propagation (BP) and the interaction of them are discussed. A global stereo matching algorithm based on BP with local edge detection-based cost aggregation is proposed. Firstly, a virtual closed edge is formed surrounding each pixel via second derivative operator in order to construct the adaptive window. Then, for centered pixel, local...
This paper researches image segmentation as a global optimization problem and proposes a new way, which is called super pixel status model, to integrate boundary and region cue. Super pixel status model is a label model which describes the joint distribution of boundary and region classification in a bayesian framework. For organizing a boundary classifier, the contour of super pixel is decomposed...
The topic of registering multi- and hyperspectral imagery is well studied in literature. However when the registration must be done between multispectral images and vector data, the literature is more limited. In this paper we focus on registering aerial images in the thermal (IR) band, and vector data delineating houses and other man-made structures in the same region. This differs from classical...
Fisher's linear discriminant analysis (FLDA) is one of the most well-known linear subspace selection methods. However, FLDA suffers from the class separation problem. The projection to a subspace tends to merge close class pairs. Recent results show that maximizing the geometric mean or harmonic mean of Kullback-Leibler (KL) divergences of class pairs can significantly reduce this problem. In this...
Spinal cord analysis is an important problem in the study of various neurological diseases. Current segmentation and analysis methods in clinical use are slow and labor-intensive, especially for pathological data. ``Spinal Crawlers'' are a recently developed technique based on an artificial life framework for medical image analysis that complements classical deformable models (snakes and deformable...
In this paper, we propose a region-based MRF model with optimized initial regions (RMRF-OIR) for image segmentation. In the RMRF-OIR, a modified mean shift is introduced to get the optimized initial over segmented regions. Then, a region-based MRF is used to model these initial regions on the region adjacency graph. Finally, the segmentation results will be obtained by using a region merging scheme...
A new spectral-spatial method for hyperspectral data classification is proposed. For a given hyperspectral image, probabilistic pixelwise classification is first applied. Then, hierarchical step-wise optimization algorithm is performed, by iteratively merging neighboring regions with the smallest Dissimilarity Criterion (DC) and recomputing class labels for new regions. The DC is computed by comparing...
In this paper we propose temporal and optimized video scene segmentation techniques. Temporal video scene segmentation methods aims to partition the video to elementary image sequences termed scenes. The main purpose of video scene segmentation is to extract objects from series of consecutive video frames. One important goal in video analysis is to group the shots, such that all shots in a single...
Prostate contour segmented from Trans Rectal Ultra Sound (TRUS) and Magnetic Resonance (MR) images could improve inter-modality registration accuracy and reduce computational complexity of the procedure. However, prostate segmentation in each of these modalities is a challenging task in presence of imaging artifacts, intensity heterogeneities, and large inter patient shape variabilities of the prostate...
Support Vector Machine (SVM) is a useful technique for data classification with successful applications in different fields of bioinformatics, image segmentation, data mining, etc. A key problem of these methods is how to choose an optimal kernel and how to optimize its parameters in the learning process of SVM. The objective of this study is to propose a Genetic Algorithm approach for parameter optimization...
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