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Author

Malay K. Kundu

Other affiliations: Intel
Bio: Malay K. Kundu is an academic researcher from Indian Statistical Institute. The author has contributed to research in topics: Image retrieval & Digital watermarking. The author has an hindex of 33, co-authored 151 publications receiving 3283 citations. Previous affiliations of Malay K. Kundu include Intel.


Papers
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Book ChapterDOI
01 Jan 2000
TL;DR: The relevance of integrating the merits of different soft computing tools for designing efficient image processing and analysis systems and the feasibility of such systems and different ways of integration are described.
Abstract: The relevance of integrating the merits of different soft computing tools for designing efficient image processing and analysis systems is explained. The feasibility of such systems and different ways of integration, so far made, are described. Scope for further research and development is outlined. An extensive bibliography is also provided.

5 citations

Proceedings ArticleDOI
01 Dec 2012
TL;DR: A novel automatic thresholding method for the detection of abrupt shot transition (AST), based on statistics of the pixel difference value of the consecutive frames, from a given video sequence is proposed.
Abstract: In this article, we propose a novel automatic thresholding method for the detection of abrupt shot transition (AST), based on statistics of the pixel difference value of the consecutive frames, from a given video sequence. An outlier removal and a false alarm elimination scheme are also introduced to counteract the disturbances from illumination variations, object and camera movements. Experimental results and comparisons with state-of-the-art SBD schemes show the effectiveness of the proposed method and having average superior accuracy.

5 citations

Book ChapterDOI
27 Jun 2011
TL;DR: The purpose of this paper is to detect the reference points considering the uncertainty for imperfection of fingerprint reference point position.
Abstract: Reference points play important role in the field of fingerprint recognition. It is mainly used for fingerprint classification and fingerprint matching. There are many methods proposed for fingerprint reference point detection like Poincare Index technique, Direction curvature technique etc. The purpose of this paper is to detect the reference points considering the uncertainty for imperfection of fingerprint reference point position.

5 citations

Journal ArticleDOI
TL;DR: A novel method for texture-based text-graphic segmentation in a text embedded image using Nonsubsampled contourlet transform and interval type-2 fuzzy membership functions (IT2FMF).
Abstract: This paper presents a novel method for texture-based text-graphic segmentation in a text embedded image. In the method, features are computed applying Multi-scale Geometric Analysis(MGA). The MGA of the image is done by Nonsubsampled contourlet transform(NSCT). The NSCT sub-bands help to generate the features which represent textures of the text portions and graphics portions of the image. In a segmentation process, the uncertainties arise mainly for two reasons: one is the ambiguity in gray level and other is the spatial ambiguity. Here the uncertainties are managed by interval type2 fuzzy set (IT2FS). The human vision model called human psychovisual phenomenon (HVS) is incorporated in the process for generating the interval type-2 fuzzy membership functions (IT2FMF). The efficiency of the proposed scheme is measured on the benchmark dataset. The robustness and performance bound of the proposed technique under noise corruption are measured statistically using modified Cramer-Rao bound. We found that effectiveness of the features by NSCT in combination with the IT2FS are quite promising in comparison to the state-of-the-arts methods.

5 citations

Patent
29 Aug 2002
TL;DR: In this article, an architecture for processing grey level images, comprising comparators for detecting gradients between pairs of pixels surrounding a selected pixel and subsequent combinatorial circuits for classifying the selected pixel into one of a number of categories (crest, valley, plateau, undecided).
Abstract: An architecture for processing grey level images, comprising comparators for detecting gradients between pairs of pixels surrounding a selected pixel and subsequent combinatorial circuits for classifying the selected pixel into one of a number of categories (crest, valley, plateau, undecided). Suitable for the preprocessing of fingerprint images.

4 citations


Cited by
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Journal ArticleDOI

[...]

08 Dec 2001-BMJ
TL;DR: There is, I think, something ethereal about i —the square root of minus one, which seems an odd beast at that time—an intruder hovering on the edge of reality.
Abstract: There is, I think, something ethereal about i —the square root of minus one. I remember first hearing about it at school. It seemed an odd beast at that time—an intruder hovering on the edge of reality. Usually familiarity dulls this sense of the bizarre, but in the case of i it was the reverse: over the years the sense of its surreal nature intensified. It seemed that it was impossible to write mathematics that described the real world in …

33,785 citations

Journal ArticleDOI
TL;DR: Attempts have been made to cover both fuzzy and non-fuzzy techniques including color image segmentation and neural network based approaches, which addresses the issue of quantitative evaluation of segmentation results.

3,527 citations

01 Jan 1979
TL;DR: This special issue aims at gathering the recent advances in learning with shared information methods and their applications in computer vision and multimedia analysis and addressing interesting real-world computer Vision and multimedia applications.
Abstract: In the real world, a realistic setting for computer vision or multimedia recognition problems is that we have some classes containing lots of training data and many classes contain a small amount of training data. Therefore, how to use frequent classes to help learning rare classes for which it is harder to collect the training data is an open question. Learning with Shared Information is an emerging topic in machine learning, computer vision and multimedia analysis. There are different level of components that can be shared during concept modeling and machine learning stages, such as sharing generic object parts, sharing attributes, sharing transformations, sharing regularization parameters and sharing training examples, etc. Regarding the specific methods, multi-task learning, transfer learning and deep learning can be seen as using different strategies to share information. These learning with shared information methods are very effective in solving real-world large-scale problems. This special issue aims at gathering the recent advances in learning with shared information methods and their applications in computer vision and multimedia analysis. Both state-of-the-art works, as well as literature reviews, are welcome for submission. Papers addressing interesting real-world computer vision and multimedia applications are especially encouraged. Topics of interest include, but are not limited to: • Multi-task learning or transfer learning for large-scale computer vision and multimedia analysis • Deep learning for large-scale computer vision and multimedia analysis • Multi-modal approach for large-scale computer vision and multimedia analysis • Different sharing strategies, e.g., sharing generic object parts, sharing attributes, sharing transformations, sharing regularization parameters and sharing training examples, • Real-world computer vision and multimedia applications based on learning with shared information, e.g., event detection, object recognition, object detection, action recognition, human head pose estimation, object tracking, location-based services, semantic indexing. • New datasets and metrics to evaluate the benefit of the proposed sharing ability for the specific computer vision or multimedia problem. • Survey papers regarding the topic of learning with shared information. Authors who are unsure whether their planned submission is in scope may contact the guest editors prior to the submission deadline with an abstract, in order to receive feedback.

1,758 citations

Journal ArticleDOI
TL;DR: This paper provides a state-of-the-art review and analysis of the different existing methods of steganography along with some common standards and guidelines drawn from the literature and some recommendations and advocates for the object-oriented embedding mechanism.

1,572 citations

Journal ArticleDOI
TL;DR: The superiority of the GA-clustering algorithm over the commonly used K-means algorithm is extensively demonstrated for four artificial and three real-life data sets.

1,337 citations