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A hidden Markov model (HMM) based method for Chinese legal amount recognition is presented in this paper. In the training phase, gradient feature is extracted from sliding windows and character HMMs are trained with single character images. In the recognition phase, the text line image is segmented using sentence HMM, which is constructed by character HMMs according to a strict language model. The...
This paper presents a new method for writer identification, which emulates the approach taken by forensic document examiners. It combines a novel feature, which uses contour gradients to capture local shape and curvature, with character segmentation to create a pseudo-alphabet for a given handwriting sample. A distance metric is then defined between elements of these alphabets that captures character...
In this paper, two techniques are presented, appropriate to detect the text main body size in a document image. One measures it directly, while the other estimates the baselines first. Both are segmentation free. Experimental results are presented over a collection of handwritten text, as well as for a small collection of 10 printed document images, in order to give more objective results.
We propose a new segmentation-free method for keyword spotting in handwritten documents based on Heat Kernel Signature (HKS). After key points are located by the key point detector for SIFT on the document pages and the query image, HKS descriptors are extracted from a local patch centered at each key point. In order to locate the positions where the query image appears in document pages, we present...
In this paper, we present a solution towards building a retrieval system over handwritten document images that i) is recognition-free, ii) allows text-querying, iii) can retrieve at sub-word level, iv) can search for out-of-vocabulary words. Unlike previous approaches that operate at either character or word levels, we use character n-gram images (CNG-img) as the retrieval primitive. CNG-img are sequences...
In this paper, we show that learning features with convolutional neural networks is better than using hand-crafted features for handwritten word recognition. We consider two kinds of systems: a grapheme based segmentation and a sliding window segmentation. In both cases, the combination of a convolutional neural network with a HMM outperform a state-of-the art HMM system based on explicit feature...
This paper presents a new method for object detection by edge grouping. This method can detect the boundaries of objects under complex background where the object contours are partly occluded or missing during contour extraction. Our method is adapted to detect the objects with not only closed boundaries but also open-boundaries. There are three contributions in this work. First, the shape of an object...
We proposed a motion segmentation method a few months ago. The problem of it is that the contour of the object segmented from the image sequence is always unclosed. This paper presents a method of the Gradient Vector Flow (GVF) field snake to close the contour. The initial curve of the GVF snake is rectangle, so that the curve can be used in the tracking and recognition applications. However, corners...
In this work, a new framework for accurate classification of hyperspectral images is proposed. The new method is based on Hidden Markov Random Field and its Expectation Maximization (HMRF-EM) and Support Vector Machine (SVM) classifier. In order to preserve edges in final map, the Sobel edge detector is used. Result confirms that the combination of the spectral and spatial information can significantly...
This paper addresses the problem of change detection in high-resolution multi-temporal synthetic aperture radar (SAR) images (e.g. TerraSAR-X SAR images). Given two images, the proposed method first computes a difference map between them, by taking into account both the spatial and temporal correlations. Change detection is then formulated as a binary (changed/unchanged) segmentation problem of the...
Efficient segmentation of foreground moving objects is an important procedure to achieve stable object tracking and recognition. In this paper, we have developed a novel algorithm for foreground segmentation that can extract moving objects from background accurately and efficiently. In our proposed algorithm, Gaussian Mixture Model is first used to model the static background regions. The boundary...
Determining the extent of pulmonary emphysema with quantitative computed tomography commonly relies on fixed intensity threshold values. However, the reliability of such measures is limited due to variability in parenchymal intensities and noise levels in CT images. In this work, we present a novel method for emphysema quantification, based on a lung tissue segmentation with a Hidden Markov Measure...
In this paper, gesture recognition algorithm with kinect sensor is proposed. the depth cue is used to locate the hand area. Based on the histograms of oriented gradient (HOG) and adaboost learning methods, the static hand algorithm is designed to recognize the predefine gesture in the hand Area. by tracking the hand trajectory by kinect, hmms is used to train and classify dynamic gesture. an intelligent...
This paper is concerned with the unsupervised learning of object representations by fusing visual and motor information. The problem is posed for a mobile robot that develops its representations as it incrementally gathers data. The scenario is problematic as the robot only has limited information at each time step with which it must generate and update its representations. Object representations...
Hidden Markov Random Field (HMRF) model and Finite Mixture Model (FMM) parameter estimation algorithm provides an interesting framework for image segmentation task, hence a technique that capitalizes on the benefits of both algorithms would achieve better performance. In this regard, we propose a new segmentation algorithm which combines with HMRF model and FMM parameter estimation algorithm. Firstly,...
Hand gesture has become a powerful means for human-computer interaction. Traditional gesture recognition just consider hand trajectory. For some specific applications, such as virtual reality, more natural gestures are needed, which are complex and contain movement in 3-D space. In this paper, we introduce an HMM-based method to recognize complex singlehand gestures. Gesture images are gained by a...
Automatic image annotation is a promising key to semantic-based image retrieval by keywords. Most existing automatic image annotation approaches focused on exploring the relationship between images and annotation words and neglected the semantic information of the annotated keywords. In this paper we propose a semi-automatic image annotation framework. First we annotate the training images with our...
Object detectors are typically trained on a large set of still images annotated by bounding-boxes. This paper introduces an approach for learning object detectors from real-world web videos known only to contain objects of a target class. We propose a fully automatic pipeline that localizes objects in a set of videos of the class and learns a detector for it. The approach extracts candidate spatio-temporal...
Segmenting regions of high angiogenic activity corresponding to malignant tumors from DCE-MRI is a time-consuming task requiring processing of data in 4 dimensions. Quantitative analyses developed thus far are highly sensitive to external factors and are valid only under certain operating assumptions, which need not be valid for breast carcinomas. In this paper, we have developed a novel Statistical...
Arabic Pattern recognition can be regarded as a problem of classification, where different patterns are presented and be needed to classify into specified classes. One way to improve the recognition rates of pattern recognition tasks is to improve the accuracy of individual classifiers, and another is to apply ensemble of classifiers methods. The advantage of dynamic ensemble selection vs dynamic...
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