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This paper presents a novel approach to detecting the presence of objects in a scene from the 3D sparse disparity space obtained by stereo matching. The use of stereo imaging makes the proposed method particularly useful for detecting stationary objects without the need of learning the appearance patterns on an object or the background. Our approach is based on the fact that sparse image features...
To date, various fields of applications have utilized spatio-temporal databases not only to store data, but to support decision making. For example, in traffic accident analysis; it is required to have knowledge on the pattern of accidents resulting in death. Thus, in such analysis, clustering technique is desired to implement pattern extraction. This paper presents clustering of spatio-temporal database...
The isometric feature mapping (Isomap) method has demonstrated promising results in finding low-dimensional manifolds from data points in high-dimensional input space. Isomap has one free parameter (number of nearest neighbours K or neighbourhood radius ε), which has to be specified manually. This paper presents a novel method called Hierarchical Neighbourhood Technique (HNT), in order to obtain a...
In this study, a multi-level medical image semantic modeling approach based on fuzzy Bayesian networks is proposed. Its two forms are built. The one is a Bayesian network embedding Conditional Gaussian (CG) models, called BN-CG, and another is a Bayesian network embedding Gaussian mixture model (GMM), called BN-GMM. CG and GMM are employed to implement a fuzzy procedure to perform the soft quantification...
This paper presents a new algorithm for hematoxylin and eosin (H&E) stained histology image segmentation. With both local and global clustering, Gaussian mixture models (GMMs) are applied sequentially to extract tissue constituents such as nuclei, stroma, and connecting contents from background. Specifically, local GMM is firstly applied to detect nuclei by scanning the input image, which is followed...
This paper presents an apple recognition method based on texture features and Maximum Expectation (EM) algorithm for Gaussian Mixture Model (GMM). The images were converted to HSV space from RGB space and the H channel images were selected as interested images to be processed. The images of H channel were divided into blocks of 8*8 pixels and the texture features of the blocks were calculated. Angular...
This paper presents a set of algorithms for vehicle detection in large scale aerial images. Vehicles are detected based on geometric and radiometric features, extracted within a multiresolution linear Gaussian scale-space. The image features, described by their local structures, are classified using support vector machines. Classified features are then clustered by an unsupervised affine propagation...
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...
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...
This paper marks the beginning of a new way of analyzing fMRI images. The idea is to model these images with a mixture of Gaussians, that allows to carry out some complex tasks more easily. One application of this approach is the artifacts subtraction. It consists of removing certain high-intensity voxels that are not relevant to the analysis of the images. On the other hand, this approach provides...
Common visual codebook generation methods used in a Bag of Visual words model, e.g. k-means or Gaussian Mixture Model, use the Euclidean distance to cluster features into visual code words. However, most popular visual descriptors are histograms of image measurements. It has been shown that the Histogram Intersection Kernel (HIK) is more effective than the Euclidean distance in supervised learning...
A novel remote-sensing image segmentation method is presented in the framework of Normalized Cuts to solve the perceptual grouping problem by means of graph partitioning. In this method, texton is applied to obtain color features and texture features of remote-sensing image. Clustering of the original color values and the filter responses of the images is performed to find texton. The filter bank...
In this paper, we propose an efficient speaker clustering approach based on a locality preserving linear projective mapping in the Gaussian mixture model (GMM) mean supervector space. While the GMM mean supervector has turned out to be an effective representation of speakers, its dimensionality is usually very high. The locality preserving projection (LPP) maps the high-dimensional GMM mean supervector...
The task of clustering multivariate trajectory data of varying length exists in various domains. Model-based methods are capable of handling varying length trajectories without changing the length or structure. Hidden Markov models (HMMs) are widely used for trajectory data modeling. However, HMMs are not suitable for trajectories of long duration. In this paper, we propose a similarity based representation...
In this paper, we propose a self-organized clustering method for feature mapping to compensate the channel variation in spoken language recognition. The self-organized clustering is realized by transforming the utterances into the Gaussian mixture model (GMM) supervectors and categorizing the supervectors through k-mean algorithm. Based on the language-dependent cluster-of-utterance information of...
Lip feature extraction is one of the most challenging tasks in the lip reading systems' performance. In this paper, a new approach for lip contour extraction based on fuzzy clustering is proposed. The algorithm employs a stochastic cost function to partition a color image into lip and non-lip regions such that the joint probability of the two regions is maximized. First, the mouth location is determined...
Typical unsupervised feature selection algorithms select a common feature subset for all the clusters. Consequently, clusters embedded in different feature subspaces are not discovered. In this paper, we propose a novel approach of simultaneous localized feature selection and model detection for unsupervised learning. In our approach, local feature saliency, together with other parameters of Gaussian...
Facial expression recognition is an active research area that finds a potential application in human emotion analysis. This work presents an efficient approach of facial expression features clustering based on Support Vector Clustering (SVC). Common approaches to facial expression features clustering are designed considering two main parts: (1) features extraction, and (2) features clustering. In...
This paper presents a work on accurate image segmentation utilizing local image characteristics. Image features are measured by employing an appropriate Gabor filter with adaptively chosen size, orientation, frequency and phase for each pixel. An image property called phase divergence is used for the selection of the appropriate filter size. Characteristic features related to the change in brightness,...
In this paper fuzzy clustering algorithms are utilized for the segmentation of hyperspectral images. For this purpose fuzzy c-means and an extended version of this algorithm, namely the fuzzy Gustafson-Kessel algorithms are used. Because of the high dimensionality in hyperspectral images, the data dimension is reduced using the Discrete Wavelet Transform. The advantage of using fuzzy approaches for...
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