Person: Mitra, Suman
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Suman Mitra
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Publication Metadata only Effectiveness analysis of fuzzy unsupervised clustering algorithms for brain tissue segmentation in single channel MR image(01-06-2011) Parmar, Ghanshyam; Mitra, Suman; DA-IICT, GandhinagarPublication Metadata only Object tracking in Curvelet Domain with dominant Curvelet Subbands(01-05-2012) Nayak, Rikin; Bhavsar, Jignesh; Chaudhari, Jitendra; Mitra, Suman; DA-IICT, GandhinagarPublication Metadata only On reconnection of broken ridges and binarization for fingerprint images(01-01-2014) Munshi, Paridhi; Mitra, Suman; DA-IICT, Gandhinagar; MunshI, Paridhi (201011042)Publication Metadata only A new probabilistic approach for fractal based image compression(ACM, 01-11-2008) Kundu, M K; Murthy, C A; Bhattacharya, B B; Acharya, T; Mitra, Suman; DA-IICT, GandhinagarPublication Metadata only A Study on image segmentation using moments(01-10-2012) Phophalia, Ashish; Mitra, Suman; Chawla, Charu; DA-IICT, Gandhinagar; Phophalia, Ashish (201021014)Publication Metadata only Segmentation of remotely sensed images using resampling based bayesian learning(01-10-2010) Singh, Abhishek; Jaikumar, Padmini; Mitra, Suman; DA-IICT, GandhinagarPublication Metadata only A bayesian network based sequential inference for diagnosis of diseases from retinal images(Elsevier, 01-03-2005) Lee, Te-Won; Goldbaum, Michael; Mitra, Suman; DA-IICT, GandhinagarWe propose a system that learns from the STARE (STructured Analysis of REtina) database and exploits the experience of ophthalmologists to assist in decision-making regarding the presence or absence of retinal diseases. The developed system automatically detects diseases given a description (a set of manifestations) of a retinal image. The manifestations in the retinal image are usually fed sequentially into the system where the manifestation dependences and order must be learned by the system. We apply naive Bayes classifier which is a simple case of Bayesian network to learn the conditional probabilities and to establish an approximate lookup table for sequential manifestation input. The system interacts with the ophthalmologist in determining the sequence of manifestations for inferring the correct disease. The overall performance of the system is found to be satisfactory and useful by ophthalmologists.Publication Metadata only Keypoint based comprehensive copy-move forgery detection(IET, 01-12-2021) Diwan, Anjali; Sharma, Rajat; Mitra, Suman; Mitra, Suman; Mitra, Suman; Roy, Anil; Roy, Anil; DA-IICT, Gandhinagar; Diwan, Anjali (201521013); Sharma, Rajat (201811045)Verifying the authenticity of a digital image has been challenging problem. The simplest of the image tampering tricks is the copy-move forgery. In copy-move forgery copied portion of the image is pasted on another part of the same image. Geometrical transformations are used on the copied portions of the image before pasting it for the tampered image to look realistic and visually convincing. To make it more complex, other processing approaches may also be applied in the forged region for hiding traces of forgery. These processings are the scale, rotation, JPEG compression, and AWGN. In this paper, an approach based on features of the CenSurE keypoint detector and FREAK descriptor is proposed. This combination has novelty in itself as it has never been used for this purpose before to the best of authors' literature studies. CenSurE detectors are fast and give stable and accurate output even in the case of rotated images, which we club with binary descriptor FREAK. Hierarchical clustering and Neighbourhood search is applied in such a way that it can locate and detect multiple copy-move forgeries. The authors are hopeful that the proposed approach may be used in real-time image authentication and copy-move forgery detection.Publication Metadata only Offline handwritten Gujarati numeral recognition using low-level strokes(InderScience, 01-10-2015) Goswami, Mukesh M; Mitra, Suman; DA-IICT, GandhinagarThis paper focuses on the development of offline handwritten Gujarati numeral database of reasonable size and its recognition using low-level stroke features. The database consists of 14,000 samples collected from 140 people with different age group, educational background, and work culture. A novel technique for the extraction of various low-level stroke features, like endpoints, junction points, line segments, and curve segments, is proposed, and the block-wise histogram of low-level stroke features is used for the recognition of offline handwritten numerals from two of the popular Indian scripts, namely Gujarati and Devanagari. The baseline experiments were performed using k-nearest neighbour (k-NN) classifier, and the results were further improved by using the statistically advance support vector machine (SVM) classifier with radial basis function (RBF) kernel. The average test accuracy obtained on Gujarati and Devanagari database were 98.46% and 98.65%, respectively, which is comparable to other existing work. The experiments were also performed on the mixed numerals recognition from Gujarati-Devanagari and Gujarati-English considering the multi-script scenarios in Indian documents.Publication Metadata only L1-norm orthogonal neighbourhood preserving projection and its applications(Springer, 01-11-2019) Koringa, Purvi A; Mitra, Suman; DA-IICT, Gandhinagar; Koringa, Purvi A (201321010)Dimensionality reduction techniques based on manifold learning are becoming very popular for computer vision tasks like image recognition and image classification. Generally, most of these techniques involve optimizing a cost function in L2-norm and thus they are susceptible to outliers. However, recently, due to capability of handling outliers, L1-norm optimization is drawing the attention of researchers. The work documented here is the first attempt towards the same goal where orthogonal neighbourhood preserving projection (ONPP) technique is performed using optimization in terms of L1-norm to handle data having outliers. In particular, the relationship between ONPP and PCA is established theoretically in the light of L2-norm and then ONPP is optimized using an already proposed mechanism of PCA-L1. Extensive experiments are performed on synthetic as well as real data for applications like classification and recognition. It has been observed that when larger number of training data is available L1-ONPP outperforms its counterpart L2-ONPP.