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In wavelet-based Bayesian denoising, the performance of several methods strongly depends on the correctness of the distribution that is used to describe the data. Therefore, the selection of a proper model for distribution is thus an important issue in the denoising process. This paper presents a new image denoising algorithm based on bivariate Pearson type VII distribution with approximated MAP estimation...
The problem of automatic object categorization is investigated under the proposed bag of feature object categorization framework. The framework consists of feature detection and representation which uses the scale invariant feature transform (SIFT) as local feature and bag of feature model to represent the image. Learning process utilizes k-NN (k-nearest neighbour). In this paper, we propose the dimensionality...
This paper proposes a novel approach called noise-cluster HMM interpolation for robust speech recognition. The approach helps alleviating the problem of speech recognition under noisy environments not trained in the system. In this method, a new HMM is interpolated from existing noisy-speech HMMs that are best matched to the input speech. This process is performed on-the-fly with an acceptable delay...
An approach to image annotation is proposed. Generally, the relation between visual characteristics and the annotation label is estimated from the annotated corpus and is used to predict label for new test image. Unfortunately, when limited number of images are annotated, with possible multiple labels per image, this relation cannot be reliably estimated. Moreover, the common approach cannot take...
Nowadays, Internet is widely used almost all over the world including Thailand. People can find Web site or information that they need by using search engines like Sansarn or Google. When users type words or phrases into the search box, sometimes they are not satisfied with the returned results. One of the most important problems is misspelled query due to typographical and cognitive errors. To address...
This paper proposes the use of tree-structured model selection and simulated-data in maximum likelihood linear regression (MLLR) adaptation for environment and speaker robust speech recognition. The objective of this work is to solve major problems in robust speech recognition system, namely unknown speaker and unknown environmental noise. The proposed solution is composed of two components. The first...
In this paper, we proposed a novel technique for face recognition using Two-Dimensional Random Subspace Analysis (2DRSA), based on the Two-Dimensional Principal Component Analysis (2DPCA) technique and Random Subspace Method (RSM). In conventional 2DPCA, the image covariance matrix is directly calculated via 2D images in matrix form, by concept of the covariance of a random variable. However, 2DPCA...
The problem of automating sports video classification is investigated by analyzing the low-level visual signal patterns using autocorrelogram. In this paper, two discriminant techniques are tested, namely, neural network with PCA and support vector machine (SVM), when testing data set is larger size than training data set. Seven different kinds of popularly televised sports are studied, namely basketball,...
In this paper, we proposed a new two-dimensional linear discriminant analysis (2DLDA) method. Based on two-dimensional principle component analysis (2DPCA), face image matrices do not need to be previously transformed into a vector. In this way, the spatial information can be preserved. Moreover, the 2DLDA also allows avoiding the small sample size (SSS) problem, thus overcoming the traditional LDA...
In this paper, we proposed a class-specific subspace-based two-dimensional principal component analysis (2DPCA) for face recognition. In 2DPCA, 2D face image matrices do not need to be previously transformed into a vector. In this way, the spatial information can be preserved. Moreover, 2DPCA can achieve higher performance than PCA both in face recognition and face representation task. However, both...
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