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In this paper we propose a novel prior-based variational object segmentation method in a global minimization framework which unifies image segmentation and image denoising. The idea of the proposed method is to convexify the energy functional of the Chan-Vese method in order to find a global minimizer, so called continuous graph cuts. The method is extended by adding an additional shape constraint...
This paper proposes a technique for simplifying a given Gaussian mixture model, i.e. reformulating the density in a more parcimonious manner, if possible (less Gaussian components in the mixture). Numerous applications requiring aggregation of models from various sources, or index structures over sets of mixture models for fast access, may benefit from the technique. Variational Bayesian estimation...
This article presents a method aiming at quantifying the visual similarity between an image and a class model. This kind of problem is recurrent in many applications such as object recognition, image classification, etc. In this paper, we propose to label a self-organizing map (SOM) to measure image similarity. To manage this goal, we feed local signatures associated to the regions of interest into...
We present a new approach for the identification and segmentation of objects undergoing periodic motion. Our method uses a combination of maximum likelihood estimation of the period, and segments moving objects using correlation of image segments over an estimated period of interest. Correlation provides the best locations of the moving objects in each frame. Segmentation tree provides the image segments...
This paper presents a method for triangulation of 3D points given their projections in two images. Recent results show that the triangulation mapping can be represented as a linear operator K applied to the outer product of corresponding homogeneous image coordinates, leading to a triangulation of very low computational complexity. K can be determined from the camera matrices, together with a so-called...
Video footage of real crowded scenes still poses severe challenges for automated surveillance. This paper evaluates clustering methods for finding independent dominant motion fields for an observation period based on a recently published real-time optical flow algorithm. We focus on self-tuning spectral clustering and Isomap combined with k-means. Several combinations of feature vector normalizations...
In this paper, a novel method for simultaneously registering multiple images acquired from different imaging modalities is presented. The optimal alignment is computed as the set of transformations that minimize the dispersion of the multi-dimensional joint phase moment distribution. Dispersion is measured as the cumulative quadratic orthogonal distance between samples from the joint phase moment...
In this paper, we propose a novel method for creating a high-quality texture atlas from a 3D model and a set of calibrated images. Our method focuses on avoiding visual artifacts such as color discontinuities, ghosting or blurring, which typically arise from photometric and geometric inaccuracies. We first compute a partition of mesh faces which realizes a good trade-off between visual detail and...
In this paper, we revisit the formulation of minimum classification error (MCE) training and propose a sample separation margin (SSM) based misclassification measure for MCE training of multiple-prototype-based pattern classifiers. Comparative experiments are conducted on the task of the recognition of isolated online handwritten Japanese Kanji characters using Nakayosi and Kuchibue databases. Experimental...
This paper shows how to construct pattern vectors from the Ihara zeta function for the purposes of characterizing graph structures. To avoid the risk of sampling the meaningless infinities at the poles of the Ihara zeta function, we take use of the coefficients of the polynomial of the reciprocal zeta function. The proposed pattern vector is proved to be permutation invariant to the node order of...
The paper attempted the recognition of multiple driverspsila emotional state from physiological signals. The major challenge of the research is due to the severe inter-driver variation such that the features of different emotional state are high correlated, and it is found that simple decorrelation method cannot normalize the features well to achieve acceptable classification accuracy. Hence, in this...
Robust voice activity detection (VAD) is a very crucial step and a challenging problem in developing real-time and high-performance speech recognition systems used in noisy environments. In this paper, we present a novel and efficient VAD algorithm for robust and real-time speech activity detection. The key idea of the algorithm is considering speech energy and edge information simultaneously when...
Two dimensional linear discriminant analysis (2DLDA) has received much interest in recent years. However, 2DLDA could make pairwise distances between any two classes become significantly unbalanced, which may affect its performance. Moreover 2DLDA could also suffer from the small sample size problem. Based on these observations, we propose two novel algorithms called regularized 2DLDA and Ridge Regression...
This paper proposes a method for constructing a discriminative rotation invariant object recognition system from the set of complex moments by using a multi-class boosting algorithm. Experimental results show that a large of number images can be discriminated accurately with only a small number of features. This basically means economy of computational effort in feature acquisition and also possibility...
A novel group theoretical method is proposed for autonomous navigation based on a spherical image camera. The environment of a robot is captured on a sphere. The three dimensional scenes at two different points in the space are related by a transformation from the special Euclidean motion group which is the semi-direct product of the rotation and the translation groups. The motion of the robot is...
In this paper, we propose a non-parametric discriminant analysis method (no assumption on the distributions of classes), called Parzen discriminant analysis (PDA). Through a deep investigation on the non-parametric density estimation, we find that minimizing/maximizing the distances between each data sample and its nearby similar/dissimilar samples is equivalent to minimizing an upper bound of the...
The Mahalanobis metric was proposed by extending the Mahalanobis distance to provide a probabilistic distance for a non-normal distribution. The Mahalanobis metric equation is a nonlinear second order differential equation derived from the equation of geometrically local isotropic independence, which is proposed to define normal distributions in a manifold. In this paper we provide experimental results...
Conventional clustering techniques provide a static snapshot of each vectorpsilas commitment to every group. With additive datasets, however, existing methods may not be sufficient for adapting to the presence of new clusters or even the merging of existing data-dense regions. To overcome this deficit, we explore the use of growing neural gas for temporal clustering and provide evidence that this...
Visual dictionaries are widely employed in object recognition to map unordered bags of local region descriptors into feature vectors for image classification. Most visual dictionaries have been constructed by unsupervised clustering. This paper presents an efficient discriminative approach, called iterative discriminative clustering (IDC), for dictionary learning. In this approach, each dictionary...
In this paper, we present a fast incremental one-class classifier algorithm for large scale problems. The proposed method reduces space and time complexities by reducing training set size during the training procedure using a criterion based on sample margin. After introducing the sample margin concept, we present the proposed algorithm and apply it to face detection database to show its efficiency...
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