Author
P. Nagabhushan
Other affiliations: Indian Institutes of Information Technology, Indian Institute of Information Technology, Allahabad, Indian Statistical Institute ...read more
Bio: P. Nagabhushan is an academic researcher from University of Mysore. The author has contributed to research in topics: Image segmentation & Feature extraction. The author has an hindex of 24, co-authored 171 publications receiving 1862 citations. Previous affiliations of P. Nagabhushan include Indian Institutes of Information Technology & Indian Institute of Information Technology, Allahabad.
Papers published on a yearly basis
Papers
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TL;DR: The results of the experiments emphasize that the proposed model outperforms other models specifically the Hough transform and its variants in addition to being robust to image transformations such as rotation, scaling and translation.
137 citations
TL;DR: A new dissimilarity index is proposed, based on an automatic adaptive tuning function, to include both proximity measures w.r.t. values and the growth behavior of the time series.
Abstract: The most widely used measures of time series proximity are the Euclidean distance and dynamic time warping. The latter can be derived from the distance introduced by Maurice Frechet in 1906 to account for the proximity between curves. The major limitation of these proximity measures is that they are based on the closeness of the values regardless of the similarity w.r.t. the growth behavior of the time series. To alleviate this drawback we propose a new dissimilarity index, based on an automatic adaptive tuning function, to include both proximity measures w.r.t. values and w.r.t. behavior. A comparative numerical analysis between the proposed index and the classical distance measures is performed on the basis of two datasets: a synthetic dataset and a dataset from a public health study.
133 citations
TL;DR: In this paper, a novel similarity measure for estimating the degree of similarity between two patterns (described by interval type data) is proposed, which is based on a modified agglomerative method by introducing the concept of mutual similarity value.
111 citations
TL;DR: A novel approach for unconstrained handwritten text-line segmentation is proposed using a new painting technique that enhances the separability between the foreground and background portions enabling easy detection of text-lines.
105 citations
TL;DR: The introduced (2D)^2 FLD method has the advantage of higher recognition rate, lesser memory requirements and better computing performance than the standard PCA/2D-PCA/ 2D-FLD method.
57 citations
Cited by
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01 Jan 1990
TL;DR: An overview of the self-organizing map algorithm, on which the papers in this issue are based, is presented in this article, where the authors present an overview of their work.
Abstract: An overview of the self-organizing map algorithm, on which the papers in this issue are based, is presented in this article.
2,933 citations
Proceedings Article•
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TL;DR: The main focus in MUCKE is on cleaning large scale Web image corpora and on proposing image representations which are closer to the human interpretation of images.
Abstract: MUCKE aims to mine a large volume of images, to structure them conceptually and to use this conceptual structuring in order to improve large-scale image retrieval. The last decade witnessed important progress concerning low-level image representations. However, there are a number problems which need to be solved in order to unleash the full potential of image mining in applications. The central problem with low-level representations is the mismatch between them and the human interpretation of image content. This problem can be instantiated, for instance, by the incapability of existing descriptors to capture spatial relationships between the concepts represented or by their incapability to convey an explanation of why two images are similar in a content-based image retrieval framework. We start by assessing existing local descriptors for image classification and by proposing to use co-occurrence matrices to better capture spatial relationships in images. The main focus in MUCKE is on cleaning large scale Web image corpora and on proposing image representations which are closer to the human interpretation of images. Consequently, we introduce methods which tackle these two problems and compare results to state of the art methods. Note: some aspects of this deliverable are withheld at this time as they are pending review. Please contact the authors for a preview.
2,134 citations
15 Oct 2004
2,118 citations