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In this paper we propose a novel OCR system which can recognize and calculate handwritten Persian arithmetic expressions without using a keyboard or a memory to store the intermediate results. Our research is composed of two major phases: character recognition and calculation. The recognition phase is based on a new approach for feature extraction. Fuzzy Support Vector Machines (FSVMs) are employed...
The paper describes a gesture recognition system which can effectively recognize static single-hand gestures and be applied in complex environments. The system involves a vocabulary of 20 gestures consisting of Chinese sign language for certain letters and digits. Segmentation based on color learning and normalization based on image moment invariants are used to extract candidate hand regions. Its...
Video data is becoming increasingly important in many commercial and scientific areas with the advent of applications such as digital broadcasting, video-conferencing and multimedia processing tools, and with the development of the hardware and communications infrastructure necessary to support visual applications. The objective of this work is to propose a method for event detection in a video stream...
We focused on the body three-dimensional medical image data prior to cutting effective training method. We focus on the methods which is the body three-dimensional medical image data prior to cutting effective training. Digital images based on various methods of training, the support vector machine based on image data before cutting training algorithms. Though the various methods of the training,...
In this paper, we propose a novel OCR system which can recognize and calculate handwritten Persian arithmetic expressions without using a keyboard or a memory to store the intermediate results. Our system is composed of two major phases: character recognition and calculation. The recognition phase is based on a new approach for feature extraction followed by a Fuzzy Support Vector Machines (FSVMs)...
To settle the problem of blurring and visible artifacts around the edge regions of color filter array (CFA) interpolation images, a support vector machines (SVMs) interpolation based edge correction scheme is proposed. In this scheme, a simple CFA interpolation method is used, and the support vector regression (SVR) is trained to rectify the color values at the edge of the result image. This scheme...
Relevance feedback is a good method for semantic gap in image retrieval. In this paper we propose a method which uses support vector machines for conducting effective relevance feedback for trademark retrieval. The algorithm takes the test results to adjust the already trained support vector machines. We select the Tamura textures features which consistent with human vision perception and the low-level...
We present a two-layer night time vehicle detector in this work. At the first layer, vehicle headlight detection is applied to find areas (bounding boxes) where the possible pairs of headlights locate in the image, the Haar feature based AdaBoost framework is then applied to detect the vehicle front. This approach has achieved a very promising performance for vehicle detection at night time. Our results...
In human's expression recognition, the representation of expression features is essential for the recognition accuracy. In this work we propose a novel approach for extracting expression dynamic features from facial expression videos. Rather than utilising statistical models e.g. Hidden Markov Model (HMM), our approach integrates expression dynamic features into a static image, the Histogram Variances...
The authors proposed a novel approach for the acetowhite region segmentation in order to improve the segmentation accuracy. The proposed algorithm took two steps to finish the segmentation work, the first step: applied the watershed algorithm to finish the coarse segmentation and locate the acetowhite region, the second step: adjusted the acetowhite region boundary to plot precise acetowhite region...
A main challenge for texture analysis is to construct a compact texture descriptor which is not only highly discriminative to intra-class textures, but also robust to inter-class variations, geometric and photometric changes. In this paper, a new texture descriptor is developed by integrating the local affine-invariant texture features and the global viewpoint-invariant statistics. Based on the pixel...
Multi-category classification is an ongoing research topic with numerous applications. In this paper, a novel approach called margin and domain integrated classifier (MDIC) is addressed. It handles multi-class problems as a combination of several target classes plus outliers. The basic idea behind the proposed approach is that target classes possess structured characteristics while outliers scatter...
Decomposition of mixture pixels was the keystone and nodus for processing of remote sensing image data. This article compared four kinds of unmixing approaches to surface cover image: linear spectral mixed model (LSMM), fuzzy C-means (FCM) approach, neural network (NN) approach and support vector machines (SVMs) approach. All approaches were applied to Moderate Resolution Imaging Spectroradiometer...
In this paper, we propose a new method to detect least significant bit (LSB) matching steganography which is based on neighbourhood node degree histogram characteristic function (NDHCF). First we calculate the center of mass (COM) of the NDHCF then embed another random secret message to compute the alteration rate of the NDHCF COM. We select NDHCF COM and the alteration rate as features and use support...
The error concealment (EC) technology attempt to recover the lost blocks by utilizing correlation block information (spatial or temporal). This paper introduces an adaptive spatial EC algorithm selection framework for block-based image/video coding systems. By using surrounding blocks information, fuzzy classifier can adaptively select the suitable EC method for each damaged block, and fuzzy rule...
Quantitative techniques for spatial prediction and classification in geological survey are developing rapidly. The recent applications of machine learning techniques confirm possibilities of their application in this field of research. The paper introduces Support Vector Machines, a method derived from recent achievements in the statistical learning theory, in classification of geological units based...
In this paper, classification of pavement surface distress and the statistics of the distress data are discussed. In order to improve the accuracy and efficiency to identify the pavement surface distress by the image information, a new algorithm based on SVM is discussed. In this study, support vector classification (SVC), which is a novel and effective classification algorithm, is applied to crack...
Change detection based on oriented-object employs objects to show real world. It can reflect visually change of real objects. Result of the method is easier to be understood and re-used. Meanwhile, applying support vector machine (SVM) to change detection can avoid requiring for samples distributing like traditional methods and the questions resulted from over learning like other machine learning...
The paper proposed a novel algorithm for texture classification system. This texture classification system is based on the extracted features on the performance of texture images' nonsubsampled contourlet transform (NSCT). To decrease the dimension of feature vector, we achieve the mean and standard deviation of NSCT coefficients matrix in different subbands and various directions. To compare the...
As traditional Chinese calligraphic (TCC) occupies an important place in the life of modern Chinese, there are a lot of TCC images digitalized and exhibited on the Internet. However, effective classification in them is an imperative problem need to be addressed. The paper proposes a content-based classification system that represents the visual content of TCC images by a textural feature set. Four...
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