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This paper presents a comparative study of two recent word spotting techniques ([1] and [2]) directly in the run-length compressed domain. The first technique is based on partial decompression and limited usage of OCR, and the second technique is completely decompression-less and OCR-less. Both the word spotting techniques use word bounding box ratio feature initially for matching words in the database...
Expression image is depended on analyzing and studying about emotions based on expression recognition technology. However, in-depth research of emotional analysis cannot be supported because of the limited sample size, the shot scene set in constrained environment like laboratory and image with simple labeling expression information. In order to solve these issues, LDA learning emotion database is...
This paper presents a model of Pulse-Coupled Neural Network (PCNN) for multispectral image segmentation. Its application for license plate recognition (LPR) is considered; this consists of three processing steps. First step extracts the license plate coordinates from the original image; second step is the PCNN-based segmentation method to obtain a binary image containing only the characters of the...
In this paper we propose a fast method for single image super resolution using self-example learning method. We first divide input image into a number of blocks. For each block a dictionary, is learnt using image patches in the block and its eight neighborhood block around it. In this learning we only use the image patches with considerable details. Each low resolution patch in image is presented...
This paper is intended to support the preservation of national cultural asset, particularly for ancient symbols. By using image processing principle, an automatic system that can be designed and implemented to translate ancient manuscript documents. The system is composed of several phases, from scanning, preprocessing, segmentation, feature extraction and classification. Sample images of the document...
Segmentation is considered as a core step for any recognition or classification method and for the text within any document to be effectively recognized it must be segmented accurately. In this paper a text and writer independent algorithm for the segmentation of sub-words in Arabic words has been presented. The concept is based around the global binarization of an image at various thresholding levels...
In this paper we present a handwritten line segmentation method devised to work on documents composed of several paragraphs with multiple line orientations. The method is based on a variation of the EM algorithm for the estimation of a set of regression lines between the connected components that compose the image. We evaluated our method on the ICDAR2009 handwriting segmentation contest dataset with...
In this paper, we present three versions of an open source software for biometric iris recognition called OSIRIS_V2, OSIRIS_V3, OSIRIS_V4 which correspond to different implementations of J. Daugman's approach. The experimental results on the database ICE2005 show that OSIRIS_V4 is the most reliable on difficult images while OSIRIS_V2 is the fastest. So, we propose a novel strategy for iris recognition...
A statistical methodology is proposed to rank several estimation methods of a relevant clinical parameter when no gold standard is available. Based on a regression without truth method, the proposed approach was applied to rank eight methods without using any a priori information regarding the reliability of each method and its degree of automation. It was only based on a prior concerning the statistical...
Automatic color balancing approaches for different applications have been studied by different research communities in the past decade. However, in this paper we address color balancing for the purpose of change detection. Images of a scene taken at different times may have variations in lighting and structural content. For such multitemporal images, an ideal color correction approach should be effective...
Image-based building reconstruction is a hot point in computer vision and computer graphics, but few works have done on reconstruction from a single image because it is an ill-conditional problem and is very difficult to resolve. In this paper, we present an efficient method by matching contours between image and projection of 3D models. Our method simplifies the reconstruction process and avoids...
This paper presents a novel method for natural image understanding. We improved the effect of saliency detection for the purpose of image segmentation at first. Then Graph cuts are used to find global optimal segmentation of N-dimensional image. After that, we adopt the scheme of supervised learning to classify the scene type of the image. The main advantages of our method are that: Firstly we revised...
Segmentation of deep brain structures is a challenging task for MRI images due to blurry structure boundaries, small object size and irregular shapes. In this paper, we present a new atlas-based segmentation method. It first uses a prior spatial dependency tree to constrain the relative positions between different deep brain structures and determine an optimal sequence for the structureby-structure...
Applying biometrics to daily scenarios involves demanding requirements in terms of software and hardware. On the contrary, current biometric techniques are also being adapted to present-day devices, like mobile phones, laptops and the like, which are far from meeting the previous stated requirements. In fact, achieving a combination of both necessities is one of the most difficult problems at present...
Clustering is a useful approach in data mining, image segmentation, and other problems of pattern recognition. Fuzzy clustering process can be quite slow when there are many objects or pattern to be clustered. This article discusses about an algorithm, ckMeans, which is able to reduce the number of distinct patterns which must be clustered without adversely affecting partition quality. The reduction...
This work aims at investigating the influence of luminance information and environment illumination on skin classification. We explore Bayesian approaches to perform automatic classification of human skin pixels on digital images, using color features as input. Two probabilistic skin color models were built on different color spaces (RGB, normalized RG, HSI, HS, YCbCr and CbCr) and tested in a task...
This paper introduces an unsupervised color segmentation method. The underlying idea is to segment the input image several times, each time focussing on a different salient part of the image and to subsequently merge all obtained results into one composite segmentation. We identify salient parts of the image by applying affinity propagation clustering to efficiently calculated local color and texture...
After the handwritten segmentation process, it is common to have connected digits. This is due to the great size and shape digit variations. In addition, the acquisition and the binarization processes can add noise to the images. These under segmented images, when given as input to classifiers which are specialists to deal with digits separately, should lead to errors. Aiming to detect the handwritten...
Empirical mode decomposition (EMD) developed by Huang et al. is a nonlinear data analysis method for nonstationary real-valued time series. It has been applied extensively in many research areas. Recently, several generalized EMD methods for complex-valued data analysis was proposed. Since a plane closed curve comprises many two-dimensional (2D) space data points, one can imagine that the boundary...
Skin color is an important feature for face detection and recognition in color images. In order to obtain the possible face regions in color images, the skin color models are always constructed by statistical analysis. Owing to low accuracy of the static models, researches have discussed several dynamic models to correct input image such as illumination compensation, white balance, edge points addition,...
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