Showing papers in "Pattern Recognition in 2009"

TL;DR: A new texture feature called center-symmetric local binary pattern (CS-LBP) is introduced that is a modified version of the well-known localbinary pattern (LBP), and is computationally simpler than the SIFT.

TL;DR: An overview of current palmprint research is provided, describing in particular capture devices, preprocessing, verification algorithms, palmprint-related fusion, algorithms especially designed for real-time palmprint identification in large databases and measures for protecting palmprint systems and users' privacy.

TL;DR: A critical survey of researches on image-based face recognition across pose is provided, classified into different categories according to their methodologies in handling pose variations, and several promising directions for future research have been suggested.

TL;DR: A new model is proposed to assign the smallest number of boxes to cover the entire image surface at each selected scale as required, thereby yielding more accurate estimates of fractional dimension estimation accuracy.

TL;DR: Investigation of intensity-volume histogram metrics and shape and texture features extracted from PET images to predict patient's response to treatment suggests proposed approaches could potentially provide better tools and discriminant power for utilizing functional imaging in clinical prognosis.

TL;DR: A new feature selection algorithm based on dynamic mutual information, which is only estimated on unlabeled instances is proposed, which can bring most information measurements in previous algorithms together.

TL;DR: This paper proposes detecting tampered images by examining the double quantization effect hidden among the discrete cosine transform (DCT) coefficients, and is the only one to date that can automatically locate the tampered region.

TL;DR: This paper presents a fast two-scan algorithm for labeling of connected components in binary images, and proposes an efficient procedure for assigning provisional labels to object pixels and checking label equivalence.

TL;DR: This study compares some basic feature- selection methods in settings involving thousands of features, using both model-based synthetic data and real data and evaluates the performances of feature-selection algorithms for different distribution models and classifiers.

TL;DR: This paper investigates the problem of matching line segments automatically only from their neighborhood appearance, without resorting to any other constraints or priori knowledge, and proposes a novel line descriptor called mean-standard deviation line descriptor (MSLD).

TL;DR: The efficiency of the proposed methodology is demonstrated by experimentation conducted on two different datasets: (a) on the test set of the ICDAR2007 handwriting segmentation competition and (b) on a set of historical handwritten documents.

TL;DR: This work applies neutrosophic set, a formal framework that has been recently proposed, for image segmentation, and demonstrates that the proposed approach can segment the images automatically and effectively.

TL;DR: An image analysis system is proposed that operates on digitized H&E stained whole-slide NB tissue samples and classifies each slide as either stroma-rich orStroma-poor based on the degree of Schwannian stromal development and uses a multi-resolution approach that mimics the evaluation of a pathologist.

TL;DR: Calculated inter-cluster distances in the feature spaces can choose proper kernel parameters with which the testing accuracy of trained SVMs is competitive to the standard ones, and the training time can be significantly shortened.

TL;DR: A Nearest Spanning Chain (NSC) method is proposed to construct the image-based graph, whose edge-weights are derived from the chain-wise statistical information instead of the traditional pairwise similarities.

TL;DR: Experimental results support that the proposed reversible data hiding method provides high embedding capacity while keeping the distortions at a low level.

TL;DR: A new touching cells splitting algorithm based on concave points and ellipse fitting to split the binary contour of touching cells is proposed and experimental results show that the algorithm is efficient.

TL;DR: This work uses bounding techniques to find closest neighbors quickly and finds that LB_Improved-based search is faster than LB_Keogh over random-walk and shape time series.

TL;DR: A wavelet-based face recognition method that can be directly applied to single face image, without any prior information of 3D shape or light sources, nor many training samples, and has better edge-preserving ability in low frequency illumination fields.

TL;DR: This paper is the first attempt in the literature to develop new RG-based VSS schemes by means of extending the basic 2- out-of-2 scheme to the n-out- of-n as well as 2-out -of-n ones.

TL;DR: A trademark image retrieval (TIR) system to deal with the vast number of trademark images in the trademark registration system is proposed and a two-component feature matching strategy is used to measure the similarity between the query and database images.

TL;DR: A new method for multi-style LP recognition by representing the styles with quantitative parameters, i.e., plate rotation angle, plate line number, character type and format, which will result in a faster and more precise recognition.

TL;DR: A statistical framework for the word-spotting problem which employs hidden Markov models (HMMs) to model keywords and a Gaussian mixture model (GMM) for score normalization is introduced.

TL;DR: This work presents a Genetic Programming framework that allows nonlinear combination of image similarities and is validated through several experiments, where the images are retrieved based on the shape of their objects.

TL;DR: This paper performs breast cancer analysis based on thermography, using a series of statistical features extracted from the thermograms quantifying the bilateral differences between left and right breast areas, coupled with a fuzzy rule-based classification system for diagnosis.

TL;DR: This paper designs novel VCRG-n encryption schemes for binary, gray-level and color images, validate the correctness by formal proofs and demonstrate the feasibility by computer simulations.

TL;DR: This paper proposes to conduct model selection for the LS-SVM using an empirical error criterion and shows the usefulness of this classifier and demonstrates that model selection improves the generalization performance of the LS, the least squares SVM.

TL;DR: This paper proposes an on-line signature representation based on Sigma-Lognormal modeling and gives the key steps for the development of a completely automatic parameter extractor for complex human movements.

TL;DR: A simple method for detecting salient regions in images that requires only edge detection, threshold decomposition, the distance transform, and thresholding and is surprisingly effective, and has the benefit of easy implementation.

TL;DR: In this article, texture features derived from segmented components of tissues are extracted and processed by an automated classifier group trained with sample images with different grades of neuroblastic differentiation in a multi-resolution framework.