| Abstract: | Optical Character Recognition plays an important role in Digital Image Processing and Pattern Recognition. Even though ambient study had been performed on foreign languages like Chinese and Japanese, effort on Indian script is still immature. OCR in Malayalam language is more complex as it is enriched with largest number of characters among all Indian languages. The challenge of recognition of characters is even high in handwritten domain, due to the varying writing style of each individual. In this paper we propose a system for recognition of offline handwritten Malayalam vowels. The proposed method uses Chain code and Image Centroid for the purpose of extracting features and a two layer feed forward network with scaled conjugate gradient for classification |
| Description: | Emerging Trends in Electrical and Computer Technology (ICETECT), 2011 International Conference on |
| URI: | http://dyuthi.cusat.ac.in/purl/4196 |
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| Offline Handwri ... n Chain Code Histogram.pdf | (1.324Mb) |
| Abstract: | Malayalam is one of the 22 scheduled languages in India with more than 130 million speakers. This paper presents a report on the development of a speaker independent, continuous transcription system for Malayalam. The system employs Hidden Markov Model (HMM) for acoustic modeling and Mel Frequency Cepstral Coefficient (MFCC) for feature extraction. It is trained with 21 male and female speakers in the age group ranging from 20 to 40 years. The system obtained a word recognition accuracy of 87.4% and a sentence recognition accuracy of 84%, when tested with a set of continuous speech data. |
| Description: | International Journal of Computer Applications (0975 – 8887) Volume 19– No.5, April 2011 |
| URI: | http://dyuthi.cusat.ac.in/purl/4200 |
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| Automated Trans ... for Malayalam Language.pdf | (207.7Kb) |
| Abstract: | Learning disability (LD) is a neurological condition that affects a child’s brain and impairs his ability to carry out one or many specific tasks. LD affects about 10% of children enrolled in schools. There is no cure for learning disabilities and they are lifelong. The problems of children with specific learning disabilities have been a cause of concern to parents and teachers for some time. Just as there are many different types of LDs, there are a variety of tests that may be done to pinpoint the problem The information gained from an evaluation is crucial for finding out how the parents and the school authorities can provide the best possible learning environment for child. This paper proposes a new approach in artificial neural network (ANN) for identifying LD in children at early stages so as to solve the problems faced by them and to get the benefits to the students, their parents and school authorities. In this study, we propose a closest fit algorithm data preprocessing with ANN classification to handle missing attribute values. This algorithm imputes the missing values in the preprocessing stage. Ignoring of missing attribute values is a common trend in all classifying algorithms. But, in this paper, we use an algorithm in a systematic approach for classification, which gives a satisfactory result in the prediction of LD. It acts as a tool for predicting the LD accurately, and good information of the child is made available to the concerned |
| Description: | Neural Comput & Applic (2012) 21:1757–1763 DOI 10.1007/s00521-011-0619-1 |
| URI: | http://dyuthi.cusat.ac.in/purl/4206 |
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| Attribute reduc ... learning disabilities.pdf | (286.9Kb) |
| Abstract: | Handwritten character recognition is always a frontier area of research in the field of pattern recognition and image processing and there is a large demand for OCR on hand written documents. Even though, sufficient studies have performed in foreign scripts like Chinese, Japanese and Arabic characters, only a very few work can be traced for handwritten character recognition of Indian scripts especially for the South Indian scripts. This paper provides an overview of offline handwritten character recognition in South Indian Scripts, namely Malayalam, Tamil, Kannada and Telungu |
| Description: | National Conference on Indian Language Computing, Kochi, Feb 19-20, 2011 |
| URI: | http://dyuthi.cusat.ac.in/purl/4191 |
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| Handwritten Cha ... ndian Scripts A Review.pdf | (189.7Kb) |
| Abstract: | Development of Malayalam speech recognition system is in its infancy stage; although many works have been done in other Indian languages. In this paper we present the first work on speaker independent Malayalam isolated speech recognizer based on PLP (Perceptual Linear Predictive) Cepstral Coefficient and Hidden Markov Model (HMM). The performance of the developed system has been evaluated with different number of states of HMM (Hidden Markov Model). The system is trained with 21 male and female speakers in the age group ranging from 19 to 41 years. The system obtained an accuracy of 99.5% with the unseen data |
| Description: | International Journal of Advanced Information Technology (IJAIT) Vol. 1, No.5, October 2011 |
| URI: | http://dyuthi.cusat.ac.in/purl/4214 |
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| Malayalam Isola ... P cepstral coefficient.pdf | (172.8Kb) |
| Abstract: | Grey Level Co-occurrence Matrices (GLCM) are one of the earliest techniques used for image texture analysis. In this paper we defined a new feature called trace extracted from the GLCM and its implications in texture analysis are discussed in the context of Content Based Image Retrieval (CBIR). The theoretical extension of GLCM to n-dimensional gray scale images are also discussed. The results indicate that trace features outperform Haralick features when applied to CBIR. |
| Description: | International Journal of Computer Science, Engineering and Information Technology (IJCSEIT), Vol.2, No.2, April 2012 |
| URI: | http://dyuthi.cusat.ac.in/purl/4199 |
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| Grey Level Co-O ... And Some New Features.pdf | (379.3Kb) |
| Abstract: | The median problem is a classical problem in Location Theory: one searches for a location that minimizes the average distance to the sites of the clients. This is for desired facilities as a distribution center for a set of warehouses. More recently, for obnoxious facilities, the antimedian was studied. Here one maximizes the average distance to the clients. In this paper the mixed case is studied. Clients are represented by a profile, which is a sequence of vertices with repetitions allowed. In a signed profile each element is provided with a sign from f+; g. Thus one can take into account whether the client prefers the facility (with a + sign) or rejects it (with a sign). The graphs for which all median sets, or all antimedian sets, are connected are characterized. Various consensus strategies for signed profiles are studied, amongst which Majority, Plurality and Scarcity. Hypercubes are the only graphs on which Majority produces the median set for all signed profiles. Finally, the antimedian sets are found by the Scarcity Strategy on e.g. Hamming graphs, Johnson graphs and halfcubes |
| Description: | Ars Math. Contemp. 6 (2013) 127–145 |
| URI: | http://dyuthi.cusat.ac.in/purl/4234 |
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| Consensus strat ... ned profiles on graphs.pdf | (315.9Kb) |
| Abstract: | Almost self-centered graphs were recently introduced as the graphs with exactly two non-central vertices. In this paper we characterize almost selfcentered graphs among median graphs and among chordal graphs. In the first case P4 and the graphs obtained from hypercubes by attaching to them a single leaf are the only such graphs. Among chordal graph the variety of almost self-centered graph is much richer, despite the fact that their diameter is at most 3. We also discuss almost self-centered graphs among partial cubes and among k-chordal graphs, classes of graphs that generalize median and chordal graphs, respectively. Characterizations of almost self-centered graphs among these two classes seem elusive |
| Description: | TAIWANESE JOURNAL OF MATHEMATICS Vol. 16, No. 5, pp. 1911-1922, October 2012 |
| URI: | http://dyuthi.cusat.ac.in/purl/4213 |
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| Almost Self-Centered Median And Chordal Graphs.pdf | (225.6Kb) |
| Abstract: | A profile is a finite sequence of vertices of a graph. The set of all vertices of the graph which minimises the sum of the distances to the vertices of the profile is the median of the profile. Any subset of the vertex set such that it is the median of some profile is called a median set. The number of median sets of a graph is defined to be the median number of the graph. In this paper, we identify the median sets of various classes of graphs such as Kp − e, Kp,q forP > 2, and wheel graph and so forth. The median numbers of these graphs and hypercubes are found out, and an upper bound for the median number of even cycles is established.We also express the median number of a product graph in terms of the median number of their factors. |
| Description: | International Scholarly Research Network ISRN Discrete Mathematics Volume 2012, Article ID 583671, 8 pages doi:10.5402/2012/583671 |
| URI: | http://dyuthi.cusat.ac.in/purl/4212 |
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| Median Sets and Median Number of a Graph.pdf | (1.907Mb) |
| Abstract: | Axial brain slices containing similar anatomical structures are retrieved using features derived from the histogram of Local binary pattern (LBP). A rotation invariant description of texture in terms of texture patterns and their strength is obtained with the incorporation of local variance to the LBP, called Modified LBP (MOD-LBP). In this paper, we compare Histogram based Features of LBP (HF/LBP), against Histogram based Features of MOD-LBP (HF/MOD-LBP) in retrieving similar axial brain images. We show that replacing local histogram with a local distance transform based similarity metric further improves the performance of MOD-LBP based image retrieval |
| Description: | 2012 IEEE International Conference on Computational Intelligence and Computing Research |
| URI: | http://dyuthi.cusat.ac.in/purl/4205 |
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| Level Identific ... ogram of a LBP variant.pdf | (2.083Mb) |
| Abstract: | This paper presents the application of wavelet processing in the domain of handwritten character recognition. To attain high recognition rate, robust feature extractors and powerful classifiers that are invariant to degree of variability of human writing are needed. The proposed scheme consists of two stages: a feature extraction stage, which is based on Haar wavelet transform and a classification stage that uses support vector machine classifier. Experimental results show that the proposed method is effective |
| Description: | Procedia Engineering 30 (2012) 598 – 605 |
| URI: | http://dyuthi.cusat.ac.in/purl/4195 |
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| Unconstrained H ... tor Machine Classifier.pdf | (442.7Kb) |
| Abstract: | Magnetic Resonance Imaging play a vital role in the decision-diagnosis process of brain MR images. For an accurate diagnosis of brain related problems, the experts mostly compares both T1 and T2 weighted images as the information presented in these two images are complementary. In this paper, rotational and translational invariant form of Local binary Pattern (LBP) with additional gray scale information is used to retrieve similar slices of T1 weighted images from T2 weighted images or vice versa. The incorporation of additional gray scale information on LBP can extract more local texture information. The accuracy of retrieval can be improved by extracting moment features of LBP and reweighting the features based on users’ feedback. Here retrieval is done in a single subject scenario where similar images of a particular subject at a particular level are retrieved, and multiple subjects scenario where relevant images at a particular level across the subjects are retrieved |
| Description: | International Journal of Electrical, Robotics, Electronics and Communications Engineering Vol:7 No:8, 2013 |
| URI: | http://dyuthi.cusat.ac.in/purl/4233 |
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| Content Based I ... ross Different Classes.pdf | (1.187Mb) |
| Abstract: | In this paper, we propose a multispectral analysis system using wavelet based Principal Component Analysis (PCA), to improve the brain tissue classification from MRI images. Global transforms like PCA often neglects significant small abnormality details, while dealing with a massive amount of multispectral data. In order to resolve this issue, input dataset is expanded by detail coefficients from multisignal wavelet analysis. Then, PCA is applied on the new dataset to perform feature analysis. Finally, an unsupervised classification with Fuzzy C-Means clustering algorithm is used to measure the improvement in reproducibility and accuracy of the results. A detailed comparative analysis of classified tissues with those from conventional PCA is also carried out. Proposed method yielded good improvement in classification of small abnormalities with high sensitivity/accuracy values, 98.9/98.3, for clinical analysis. Experimental results from synthetic and clinical data recommend the new method as a promising approach in brain tissue analysis. |
| Description: | I.J. Image, Graphics and Signal Processing, 2013, 8, 29-36 |
| URI: | http://dyuthi.cusat.ac.in/purl/4229 |
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| Brain Tissue Cl ... pal Component Analysis.pdf | (491.3Kb) |
| Abstract: | A primary medium for the human beings to communicate through language is Speech. Automatic Speech Recognition is wide spread today. Recognizing single digits is vital to a number of applications such as voice dialling of telephone numbers, automatic data entry, credit card entry, PIN (personal identification number) entry, entry of access codes for transactions, etc. In this paper we present a comparative study of SVM (Support Vector Machine) and HMM (Hidden Markov Model) to recognize and identify the digits used in Malayalam speech. |
| URI: | http://dyuthi.cusat.ac.in/purl/4232 |
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| A Comparative s ... alam Digit Recognition.pdf | (200.2Kb) |
| Abstract: | In this paper, we propose a handwritten character recognition system for Malayalam language. The feature extraction phase consists of gradient and curvature calculation and dimensionality reduction using Principal Component Analysis. Directional information from the arc tangent of gradient is used as gradient feature. Strength of gradient in curvature direction is used as the curvature feature. The proposed system uses a combination of gradient and curvature feature in reduced dimension as the feature vector. For classification, discriminative power of Support Vector Machine (SVM) is evaluated. The results reveal that SVM with Radial Basis Function (RBF) kernel yield the best performance with 96.28% and 97.96% of accuracy in two different datasets. This is the highest accuracy ever reported on these datasets |
| Description: | I.J. Image, Graphics and Signal Processing, 2013, 4, 53-59 |
| URI: | http://dyuthi.cusat.ac.in/purl/4204 |
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| A System for Of ... rs in Malayalam Script.pdf | (535.2Kb) |
| Abstract: | Multispectral analysis is a promising approach in tissue classification and abnormality detection from Magnetic Resonance (MR) images. But instability in accuracy and reproducibility of the classification results from conventional techniques keeps it far from clinical applications. Recent studies proposed Independent Component Analysis (ICA) as an effective method for source signals separation from multispectral MR data. However, it often fails to extract the local features like small abnormalities, especially from dependent real data. A multisignal wavelet analysis prior to ICA is proposed in this work to resolve these issues. Best de-correlated detail coefficients are combined with input images to give better classification results. Performance improvement of the proposed method over conventional ICA is effectively demonstrated by segmentation and classification using k-means clustering. Experimental results from synthetic and real data strongly confirm the positive effect of the new method with an improved Tanimoto index/Sensitivity values, 0.884/93.605, for reproduced small white matter lesions |
| Description: | International conference on Communication and Signal Processing, April 3-5, 2013, India |
| URI: | http://dyuthi.cusat.ac.in/purl/4231 |
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| Wavelet based I ... ispectral Brain Tissue.pdf | (825.4Kb) |
| Abstract: | Given a non empty set S of vertices of a graph, the partiality of a vertex with respect to S is the di erence between maximum and minimum of the distances of the vertex to the vertices of S. The vertices with minimum partiality constitute the fair center of the set. Any vertex set which is the fair center of some set of vertices is called a fair set. In this paper we prove that the induced subgraph of any fair set is connected in the case of trees and characterise block graphs as the class of chordal graphs for which the induced subgraph of all fair sets are connected. The fair sets of Kn, Km;n, Kne, wheel graphs, odd cycles and symmetric even graphs are identi ed. The fair sets of the Cartesian product graphs are also discussed |
| Description: | arXiv preprint arXiv:1304.5378 |
| URI: | http://dyuthi.cusat.ac.in/purl/4230 |
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| Fair Sets of Some Classes of Graphs.pdf | (443.4Kb) |
| Abstract: | Speckle noise formed as a result of the coherent nature of ultrasound imaging affects the lesion detectability. We have proposed a new weighted linear filtering approach using Local Binary Patterns (LBP) for reducing the speckle noise in ultrasound images. The new filter achieves good results in reducing the noise without affecting the image content. The performance of the proposed filter has been compared with some of the commonly used denoising filters. The proposed filter outperforms the existing filters in terms of quantitative analysis and in edge preservation. The experimental analysis is done using various ultrasound images |
| Description: | I.J. Information Technology and Computer Science, 2013, 06, 1-9 |
| URI: | http://dyuthi.cusat.ac.in/purl/4210 |
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| Ultrasound Imag ... ghted Linear Filtering.pdf | (978.9Kb) |
| Abstract: | A spectral angle based feature extraction method, Spectral Clustering Independent Component Analysis (SC-ICA), is proposed in this work to improve the brain tissue classification from Magnetic Resonance Images (MRI). SC-ICA provides equal priority to global and local features; thereby it tries to resolve the inefficiency of conventional approaches in abnormal tissue extraction. First, input multispectral MRI is divided into different clusters by a spectral distance based clustering. Then, Independent Component Analysis (ICA) is applied on the clustered data, in conjunction with Support Vector Machines (SVM) for brain tissue analysis. Normal and abnormal datasets, consisting of real and synthetic T1-weighted, T2-weighted and proton density/fluid-attenuated inversion recovery images, were used to evaluate the performance of the new method. Comparative analysis with ICA based SVM and other conventional classifiers established the stability and efficiency of SC-ICA based classification, especially in reproduction of small abnormalities. Clinical abnormal case analysis demonstrated it through the highest Tanimoto Index/accuracy values, 0.75/98.8%, observed against ICA based SVM results, 0.17/96.1%, for reproduced lesions. Experimental results recommend the proposed method as a promising approach in clinical and pathological studies of brain diseases |
| Description: | Biomedical Signal Processing and Control 8 (2013) 667– 674 |
| URI: | http://dyuthi.cusat.ac.in/purl/4228 |
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| Spectral cluste ... ication from brain MRI.pdf | (1.561Mb) |
| Abstract: | Learning Disability (LD) is a neurological condition that affects a child’s brain and impairs his ability to carry out one or many specific tasks. LD affects about 15 % of children enrolled in schools. The prediction of LD is a vital and intricate job. The aim of this paper is to design an effective and powerful tool, using the two intelligent methods viz., Artificial Neural Network and Adaptive Neuro-Fuzzy Inference System, for measuring the percentage of LD that affected in school-age children. In this study, we are proposing some soft computing methods in data preprocessing for improving the accuracy of the tool as well as the classifier. The data preprocessing is performed through Principal Component Analysis for attribute reduction and closest fit algorithm is used for imputing missing values. The main idea in developing the LD prediction tool is not only to predict the LD present in children but also to measure its percentage along with its class like low or minor or major. The system is implemented in Mathworks Software MatLab 7.10. The results obtained from this study have illustrated that the designed prediction system or tool is capable of measuring the LD effectively |
| Description: | Soft Comput (2014) 18:1093–1112 DOI 10.1007/s00500-013-1129-0 |
| URI: | http://dyuthi.cusat.ac.in/purl/4219 |
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| Learning disabi ... ol using ANN and ANFIS.pdf | (2.950Mb) |
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