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Fuzzy logic based selera recognition

Fuzzy logic based selera recognition

引用

2014 IEEE International Conference on Fuzzy Systems, FUZZ-IEEE 2014

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

ISBN: (纸本)9781479920723

In this paper a selera recognition and validation system is proposed. Here selera segmentation was performed by Fuzzy logic-based clustering. Since the selera vessels are not prominent, image enhancement was required. A Fuzzy logic-based Brightness Preserving Dynamic Fuzzy Histogram Equalization and discrete Meyer wavelet was used to enhance the vessel patterns. For feature extraction, the Dense Local Binary pattern (D-LBP) was used. D-LBP patch descriptors of each training image are used to form a bag of features, which is used to produce the training model. Support Vector Machines (SVMs) are used for classification. The UBIRIS version 1 dataset is used here for experimentation. An encouraging Equal Error Rate (EER) of 4.31% was achieved in our experiments. © 2014 IEEE.

关键词： Support vector machines

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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...

引用

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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Multi-angle based lively sclera biometrics at a distance

Multi-angle based lively sclera biometrics at a distance

引用

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 Las Palmas Spain

ISBN: (纸本)9781479945344

This piece of work proposes a liveliness based sclera eye biometric, validation and recognition technique at a distance. The images in this work are acquired by a digital camera in the visible spectrum at varying distance of about 1 meter from the individual. Each individual during registration as well as validation is asked to look straight and move their eye ball up, left and right keeping their face straight to incorporate liveliness of the data. At first the image is divided vertically into two halves and the eyes are detected in each half of the face image that is captured, by locating the eye ball by a Circular Hough Transform. Then the eye image is cropped out automatically using the radius of the iris. Next a C-means-based segmentation is used for sclera segmentation followed by vessel enhancement by the adaptive histogram equalization and Haar filtering. The feature extraction was performed by patch-based Dense-LDP (Linear Directive pattern). Furthermore each training image is used to form a bag of features, which is used to produce the training model. Each of the images of the different poses is combined at the feature level and the image level to obtain higher accuracy and to incorporate liveliness. The fusion that produces the best result is considered. Support Vector Machines (SVMs) are used for classification. Here images from 82 individuals (both left and right eye i.e. 164 different eyes) are used and an appreciable Equal Error Rate of 0.52% is achieved in this work. © 2014 IEEE.

关键词： Support vector machines

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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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Off-line Handwritten Thai Name recognition for Student Identification in an Automated Assessment System

Off-line Handwritten Thai Name Recognition for Student Ident...

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International Joint Conference on Neural Networks

作者： Hemmaphan Suwanwiwat Vu Nguyen Michael Blumenstein Umapada Pal School of Information and Communication Technology Griffith University Computer Vision and Pattern Recognition Unit Indian Statistical Institute

ISBN: (纸本)9781479914821

In the field of pattern recognition, off-line handwriting recognition is one of the most intensive areas of study. This paper proposes an automatic off-line Thai language student name identification system which was built as a part of a completed off-line automated assessment system. There is limited work undertaken in developing off-line automatic assessment systems using handwriting recognition. To the authors' knowledge, none of the work on the proposed system has been performed on the Thai language. In addition the proposed system recognises each Thai name by using an approach for whole word recognition, which is different from the work found in the literature as most perform character-based recognition. In this proposed system, the Gaussian Grid Feature (GGF) and the Modified Direction Feature (MDF) extraction techniques are investigated on upper and lower contours, loops from full word contour images of each name sample, and artificial neural networks and support vector machine are used as classifiers. The encouraging recognition rates for both feature extraction techniques were achieved when applied on loop, upper and lower contour images (99.27% accuracy rate was achieved using MDF on artificial neural networks and 99.27% using GGF with a support vector machine classifier).

关键词： Off-line handwriting recognition Automated assessment system Student identification system Modified directional feature Gaussian grid feature

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A Study on Word-Level Multi-script Identification from Video Frames

A Study on Word-Level Multi-script Identification from Video...

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International Joint Conference on Neural Networks

作者： Nabin Sharma Umapada Pal Michael Blumenstein School of Information and Communication Technology Griffith University Computer Vision and Pattern Recognition Unit Indian Statistical Institute

ISBN: (纸本)9781479914821

The presence of multiple scripts in multi-lingual document images makes Optical Character recognition (OCR) of such documents a challenging task. Due to the unavailability of a single OCR system which can handle multiple scripts, script identification becomes an essential step for choosing the appropriate OCR. Although, there are various techniques available for script identification from handwritten and printed documents having simple backgrounds, however script identification from video frames has been seldom explored. Video frames are coloured and suffer from low resolution, blur, complex background and noise to mention a few, which makes the script identification process a challenging task. This paper presents a study of various combinations of features and classifiers to explore whether the traditional script identification techniques can be applied to video frames. A texture based feature namely, Local Binary pattern (LBP), Gradient based features namely, Histogram of Oriented Gradient (HoG) and Gradient Local Auto-Correlation (GLAC) were used in the study. Combination of the features with SVMs and ANNs where used for classification. Three popular scripts, namely English, Bengali and Hindi were considered in the present study. Due to the inherent problems with the video, a super resolution technique was applied as a pre-processing step. Experiments show that the GLAC feature has performed better than the other features, and an accuracy of 94.25% was achieved when testing on 1271 words from three different scripts. The study also reveals that gradient features are more suitable for script identification than the texture features when using traditional script identification techniques on video frames.

关键词： Video document analysis Script identification Word segmentation OCR

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Segmentation-Free Keyword Spotting for Bangla Handwritten Documents

Segmentation-Free Keyword Spotting for Bangla Handwritten Do...

引用

International Workshop on Frontiers in Handwriting recognition

作者： Xi Zhang Umapada Pal Chew Lim Tan School of Computing National University of Singapore Singapore Computer Vision And Pattern Recognition Unit Indian Statistical Institute Kolkata India

In this paper, a segmentation-free keyword spotting method is proposed for Bangla handwritten documents. In order to tolerate large variations in handwritten scenarios, we extracted key points based on SIFT key point detector, and the end and intersection points found by morphological operations. Heat Kernel signature (HKS) is used to present the local characteristics of detected key points. Instead of using the same size of patch for all the key points, we apply a method dynamically deciding the patch size. Furthermore, our spotting method reduces the scope of searching on the document by only considering the candidate local zones with similar candidate key points, and does not need pre-processing steps. From the experiment on Bangla handwritten text we obtained encouraging results.

关键词： Feature extraction Entropy Hidden Markov models Detectors Heating Kernel Indexes

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Entropy Computations of Document Images in Run-Length Compressed Domain

Entropy Computations of Document Images in Run-Length Compre...

引用

International Conference on Signal and Image Processing (ICSIP)

作者： P. Nagabhushan Mohammed Javed B.B. Chaudhuri Department of Studies in Computer Science University of Mysore Mysore India Computer Vision and Pattern Recognition Unit Indian Statistical Institute Kolkata India

Compression of documents, images, audios and videos have been traditionally practiced to increase the efficiency of data storage and transfer. However, in order to process or carry out any analytical computations, decompression has become an unavoidable pre-requisite. In this research work, we have attempted to compute the entropy, which is an important document analytic directly from the compressed documents. We use Conventional Entropy Quantifier (CEQ) and Spatial Entropy Quantifiers (SEQ) for entropy computations [1]. The entropies obtained are useful in applications like establishing equivalence, word spotting and document retrieval. Experiments have been performed with all the data sets of [1], at character, word and line levels taking compressed documents in run-length compressed domain. The algorithms developed are computational and space efficient, and results obtained match 100% with the results reported in [1].

关键词： Entropy Image coding Feature extraction Energy measurement Data mining Position measurement

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Off-Line Handwritten Bilingual Name recognition for Student Identification in an Automated Assessment System

Off-Line Handwritten Bilingual Name Recognition for Student ...

引用

International Workshop on Frontiers in Handwriting recognition

作者： Hemmaphan Suwanwiwat Vu Nguyen Michael Blumenstein Umapada Pal School of Information and Communication Technology Griffith University Australia Computer Vision and Pattern Recognition Unit Indian Statistical Institute India

The Student name Identification System (SIS) proposed here was investigated for English and Thai languages combined. The proposed system recognises each name by using an approach for whole word recognition. In the proposed system, the Gaussian Grid Feature (GGF), and Modified Direction Feature (MDF), together with a proposed hybrid feature extraction technique called Water Reservoir, Loop and Gaussian Grid Feature (WRLGGF) were investigated on full word contour images of each name sample. Artificial neural networks and support vector machines were used as classifiers. An encouraging recognition accuracy of 99.25% was achieved employing the proposed technique compared to 98.59% for GGF, and 96.63% using MDF.

关键词： Feature extraction Reservoirs Support vector machines Vectors Image recognition Artificial neural networks

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Automatic Detection of Handwritten Texts from Video Frames of Lectures

Automatic Detection of Handwritten Texts from Video Frames o...

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International Workshop on Frontiers in Handwriting recognition

作者： Purnendu Banerjee Ujjwal Bhattacharya Bidyut B. Chaudhuri Society for Natural Language Technology Research Kolkata India Computer Vision and Pattern Recognition Unit Indian Statistical Institute Kolkata India

Automatic recognition of handwritten texts in video lectures has important applications. In video lectures, the presenter usually writes on white / colored board. The video camera often captures the writing board along with certain other objects possibly including the presenter itself. recognition of handwritten texts from such a video frame requires prior detection of the region of texts in the frame. In this article, we present our recent study of text localization in such video lecture frames. Here, we use Scale Invariant Feature Transform (SIFT) descriptors densely over the entire region of the frame. The descriptors are located on a regular grid of 5 pixels following the usual practice and considered a uniform patch size of 60 × 60 pixels as its support on the basis of an empirical study. This SIFT descriptor at each location (grid point) is fed as a 128-dimensional input feature vector to a Multilayer Perceptron (MLP) network which gives response for each grid point as either text or non-text. Depending on certain aggregate response at each pixel we localize text regions in the input video frame. Next, we employ K-means clustering to detect the text components present in the localized region of the video frame. Finally, two simple rules are applied to decide certain possible detected text components as noise. We obtained encouraging simulation results of this approach on a variety of video lecture frames.

关键词： Handwriting recognition Text recognition Training Databases Image color analysis Cameras Noise

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