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检索条件"机构=Computer Vision and Pattern"

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Combination of Features for Efficient Recognition of Offline Handwritten Devanagari Words

Combination of Features for Efficient Recognition of Offline...

引用

International Workshop on Frontiers in Handwriting Recognition

作者： Bikash Shaw Ujjwal Bhattacharya Swapan K. Parui Computer Vision and Pattern Recognition Unit Indian Statistical Institute Kolkata India

In this article, we describe our recent study of a novel combination of two feature vectors for holistic recognition of offline handwritten word images. In the literature, both contour and skeleton based feature representations have been studied for offline handwriting recognition purpose. However, to the best of our knowledge, there is no such study in which combination of the two feature representations have been considered for the purpose. In the proposed recognition scheme, we use multiclass SVM as the classifier. We have implemented the proposed approach for holistic recognition of Devanagari handwritten town names and tested its performance on a large handwritten word sample database of 100 Indian town names written in Devanagari. Experimental results show sharp improvement in recognition accuracy over the use of any of the individual feature representation schemes. The proposed approach is script independent and can be used for development of a holistic handwritten word image recognition of any script.

关键词： Feature extraction Handwriting recognition Histograms Vectors Shape Support vector machines Skeleton

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A Machine Learning Approach to Detection of Core Region of Online Handwritten Bangla Word Samples

A Machine Learning Approach to Detection of Core Region of O...

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International Workshop on Frontiers in Handwriting Recognition

作者： S. Baral S. Bhattacharya A. Chakraborty U. Bhattacharya S.K. Parui Computer Vision and Pattern Recognition Unit Indian Statistical Institute Kolkata India

Core region detection of handwritten cursive words is an important step towards their automatic recognition. Several preprocessing operations such as height normalization, slant estimation etc. Are often based on this core region. This is particularly useful for word recognition of major Indian scripts, which have large character sets. The main parts of majority of these characters belong to the core region that is bounded above by a headline and bounded below by an imaginary base line. Only a few such characters or their parts appear either above or below the core region. A few approaches are available in the literature for detection of such a core region of offline handwritten word samples of Latin script. Also, a similar region is often determined for recognition of images of printed Indian scripts. However, none of these approaches have studied detection of core region of an unconstrained online handwritten word. In this article, we propose a novel method for detection of the core region of online handwritten word samples of Bangla, a major Indian script. For this we first perform smoothing on the samples and then segment a stroke into sub strokes. We compute certain novel positional features from each such sub stroke. Using these features, a multilayer perceptron (MLP) is trained by back propagation (BP) algorithm. On the basis of the output of the MLP, we determine the position of both the headline and the baseline. We have tested this approach on a recently developed large database of online unconstrained handwriting Bangla word samples. The proposed approach would also work on similar samples of Devanagari, another major Indian script. Experimental results are encouraging.

关键词： Vectors Training Feature extraction Shape Databases Compounds Histograms

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Wavelet-based approach for writer identification in music score

Wavelet-based approach for writer identification in music sc...

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IEEE International Advance Computing Conference, IACC

作者： Debleena Das Sukalpa Chanda Partha Pratim Roy Umapada Pal Computer Vision and Pattern Recognition Unit Indian Statistical Institute Kolkata India

ISBN: (纸本)9781479925735

Writer identification is a vibrant research field, though a lot of work has been done on writer identification on normal text, writer identification in music score sheet has not been addressed in that large scale. Here we propose a method to identify writers of music score sheets using Daubchies wavelet features along with SVM classifier. We have evaluated our proposed approach in a sub-set of CVC-MUSCIMA dataset. From the experiment on 140 score sheet images from 7 writers we obtained encouraging results.

关键词： Support vector machines Feature extraction Accuracy Conferences Training Wavelet transforms

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TOWARDS FULLY UNCALIBRATED ROOM RECONSTRUCTION WITH SOUND

TOWARDS FULLY UNCALIBRATED ROOM RECONSTRUCTION WITH SOUND

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European Signal Processing Conference

作者： Marco Crocco Andrea Trucco Vittorio Murino Alessio Del Bue Pattern Analysis and Computer Vision (PAVIS) Istituto Italiano di Tecnologia (IIT)

ISBN: (纸本)9781479946037

This paper presents a novel approach for room reconstruction using unknown sound signals generated in different locations of the environment. The approach is very general, that is fully uncalibrated, i.e. the locations of microphones, sound events and room reflectors are not known a priori. We show that, even if this problem implies a highly non-linear cost function, it is still possible to provide a solution close to the global minimum. Synthetic experiments show the proposed optimization framework can achieve reasonable results even in the presence of signal noise.

关键词： Room reconstruction Microphone calibration Source localization Simulated annealing

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A Global-to-Local Approach to Binarization of Degraded Document Images

A Global-to-Local Approach to Binarization of Degraded Docum...

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International Conference on pattern Recognition

作者： Barun Biswas Ujjwal Bhattacharya Bidyut B. Chaudhuri Computer Vision and Pattern Recognition Unit Indian Statistical Institute Kolkata 108 India

ISBN: (纸本)9781479952106

This article deals with binarization of degraded document images. In the proposed approach, Canny edge image of the input degraded document image is obtained after blurring it with a Gaussian filter. Next, the gray values of the two pixels of the input image at the left and right of each edge pixel are noted to form a histogram of these gray values which possesses two distinct peaks and the lowest valley between them provides the global threshold value. Each pixel with gray value greater than the above threshold is turned as background pixel. A small square window is considered around each non-background pixel and certain simple statistics are computed on the gray values of the pixels of this small window based on which the said pixel is turned either background or foreground. Such a local thresholding method at the latter stage can efficiently handle various degradations in the document. The binarized image so obtained is finally subjected to certain common post-processing operations. The proposed method has been compared with a few existing binarization techniques.

关键词： Image edge detection Histograms Degradation Smoothing methods PSNR Image color analysis

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Road Segmentation via Iterative Deep Analysis

Road Segmentation via Iterative Deep Analysis

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IEEE International Conference on Robotics and Biomimetics

作者： Xiang Chen Yu Qiao Student at Shenzhen Key Laboratory of Computer Vision and Pattern Recognition Shenzhen Institutes of Advanced Technology Chinese Academy of Sciences Address: 1068 Xueyuan Avenue Shenzhen University Town Shenzhen P.R.China Researcher at Shenzhen Key Laboratory of Computer Vision and Pattern Recognition Shenzhen Institutes of Advanced Technology Chinese Academy of Sciences Address: 1068 Xueyuan Avenue Shenzhen University Town Shenzhen P.R.China

ISBN: (纸本)9781467396769

Nowadays, people are increasingly concerned about the safety of traffic systems. Road segmentation and recognition is a fundamental problem in perceiving traffic environments and serve as the basis for self-driving cars. In this paper, inspired by an iterative deep analysis thinking, we propose a novel method which is able to learning powerful features step by step, and solve the optimal precision by balancing local and global information to conduct pixel-level classification for road segmentation. Firstly, we introduce an iterative deep analysis thinking which shows that how to design a strong and robustness deep model from failure experience. Secondly, we choose a powerful global features learning network as basis to create a novel framework for our task. Meanwhile, we employ the patch and multi-scale pyramid as input to enhance local features learning. We conduct experiments on three datasets from KITTI vision Benchmark, namely UU, UM, UMM. The experimental results demonstrate that our proposed method obtains comparable performance with state-of-the-art methods on these datasets.

关键词： recognition fundamental datasets

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Weighted bag of visual words for object recognition

Weighted bag of visual words for object recognition

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作者： Biagio, Marco San Bazzani, Loris Cristani, Marco Murino, Vittorio Pattern Analysis and Computer Vision Istituto Italiano di Tecnologia Via Morego 30 Genova16163 Italy Departimento di Informatica Università di Verona Strada le Grazie 15 Verona37134 Italy

ISBN: (纸本)9781479957514

Bag of Visual words (BoV) is one of the most successful strategy for object recognition, used to represent an image as a vector of counts using a learned vocabulary. This strategy assumes that the representation is built using patches that are either densely extracted or sampled from the images using feature detectors. However, the dense strategy captures also the noisy background information, whereas the feature detection strategy can lose important parts of the objects. In this paper we propose a solution in-between these two strategies, by densely extracting patches from the image, and weighting them accordingly to their salience. Intuitively, highly salient patches have an important role in describing an object, while those with low saliency are still taken with low emphasis, instead of discarding them. We embed this idea in the word encoding mechanism adopted in the BoV approaches. The technique is successfully applied to vector quantization and Fisher vector, on Caltech-101 and Caltech-256. © 2014 IEEE.

关键词： Object recognition

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Automatic bridge crack detection - A texture analysis-based approach 6

引用

6th IAPR TC3 International Workshop on Artificial Neural Networks for pattern Recognition, ANNPR 2014

作者： Chanda, Sukalpa Bu, Guoping Guan, Hong Jo, Jun Pal, Umapada Loo, Yew-Chaye Blumenstein, Michael School of Information and Communication Technology Griffith University QLD4222 Australia School of Engineering Griffith University QLD4222 Australia Computer Vision and Pattern Recognition Unit Indian Statistical Institute Kolkata700108 India

ISBN: (纸本)9783319116556

To date, identifying cracks in bridges and determining bridge conditions primarily involve manual labour. Bridge inspection by human experts has some drawbacks such as the inability to physically examine all parts of the bridge, sole dependency on the expert knowledge of the bridge inspector. Moreover it requires proper training of the human resource and overall it is not cost effective. This article proposes an automatic bridge inspection approach exploiting wavelet-based image features along with Support Vector Machines for automatic detection of cracks in bridge images. A two-stage approach is followed, where in the first stage a decision is made as whether an image should undergo a pre-processing step (depending on image characteristics), and later in the second stage, wavelet features are extracted from the image using a sliding window-based technique. We obtained an overall accuracy of 92.11% while conducting experiments even on noisy and complex bridge images. © Springer International Publishing Switzerland 2014.

关键词： Crack detection

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A new efficient and adaptive sclera recognition system

A new efficient and adaptive sclera recognition system

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2014 IEEE Symposium Series on Computational Intelligence, IEEE SSCI 2014 - 2014 IEEE Symposium on Computational Intelligence in Biometrics and Identity Management, CIBIM 2014

作者： Das, Abhijit Pal, Umapada Ballester, Miguel Angel Ferrer Blumenstein, Michael Institute for Integrated and Intelligent Systems Griffith University GriffithQLD Australia Computer Vision and Pattern Recognition Unit Indian Statistical Institute Kolkata India IDeTIC University of Las Palmas de Gran Canaria Spain

ISBN: (纸本)9781479945344

In this paper an efficient and adaptive biometric sclera recognition and verification system is proposed. Sclera segmentation was performed by Fuzzy C-means clustering. Since the sclera vessels are not prominent, in order to make them clearly visible image enhancement was required. Adaptive histogram equalization, followed by a bank of Discrete Meyer Wavelet was used to enhance the sclera vessel patterns. Feature extraction was performed by, Dense Local Directional pattern (D-LDP). D-LDP patch descriptors of each training image are used to form a bag of features;further Spatial Pyramid Matching was used to produce the final training model. Support Vector Machines (SVMs) are used for classification. The UBIRIS version 1 dataset was used here for experimentation of the proposed system. To investigate regarding sclera patterns adaptively with respect to change in environmental condition, population, data accruing technique and time span two different session of the mention dataset are utilized. The images in two sessions are different in acquiring technique, representation, number of individual and they were captured in a gap of two weeks. An encouraging Equal Error Rate (EER) of 3.95% was achieved in the above mention investigation. © 2014 IEEE.

关键词： Support vector machines

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A new wrist vein biometric system

A new wrist vein biometric system

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2014 IEEE Symposium Series on Computational Intelligence, IEEE SSCI 2014 - 2014 IEEE Symposium on Computational Intelligence in Biometrics and Identity Management, CIBIM 2014

ISBN: (纸本)9781479945344

In this piece of work a wrist vein pattern recognition and verification system is proposed. Here the wrist vein images from the PUT database were used, which were acquired in visible spectrum. The vein image only highlights the vein pattern area so, segmentation was not required. Since the wrist's veins are not prominent, image enhancement was performed. An Adaptive Histogram Equalization and Discrete Meyer Wavelet were used to enhance the vessel patterns. For feature extraction, the vein pattern is characterized with Dense Local Binary pattern (D-LBP). D-LBP patch descriptors of each training image are used to form a bag of features, which was used to produce the training model. Support Vector Machines (SVMs) were used for classification. An encouraging Equal Error Rate (EER) of 0.79% was achieved in our experiments. © 2014 IEEE.

关键词： Support vector machines

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