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Manesh Kokare

Researcher at Shri Guru Gobind Singhji Institute of Engineering and Technology

Publications -  99
Citations -  2516

Manesh Kokare is an academic researcher from Shri Guru Gobind Singhji Institute of Engineering and Technology. The author has contributed to research in topics: Image retrieval & Wavelet transform. The author has an hindex of 18, co-authored 94 publications receiving 1918 citations. Previous affiliations of Manesh Kokare include Indian Institutes of Technology & Government College.

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Indian Diabetic Retinopathy Image Dataset (IDRiD): A Database for Diabetic Retinopathy Screening Research

TL;DR: The IDRiD (Indian Diabetic Retinopathy Image Dataset), is the first database representative of an Indian population and makes it perfect for development and evaluation of image analysis algorithms for early detection of diabetic retinopathy.
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Texture image retrieval using new rotated complex wavelet filters

TL;DR: A novel approach for texture image retrieval is proposed by using a set of dual-tree rotated complex wavelet filter (DT-RCWF) andDual-tree-complex wavelet transform ( DT-CWT) jointly, which obtains texture features in 12 different directions.
Proceedings ArticleDOI

Comparison of similarity metrics for texture image retrieval

TL;DR: Experimental results on the Brodatz texture database indicate that the retrieval performance can be improved significantly by using the Canberra and Bray-Curtis distance metrics as compare to traditional Euclidean and Mahalanobis distance based approaches.
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IDRiD: Diabetic Retinopathy – Segmentation and Grading Challenge

TL;DR: The set-up and results of this challenge that is primarily based on Indian Diabetic Retinopathy Image Dataset (IDRiD), which received a positive response from the scientific community, have the potential to enable new developments in retinal image analysis and image-based DR screening in particular.
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Texture image retrieval using rotated wavelet filters

TL;DR: A novel approach for texture image retrieval is proposed by using a new set of two-dimensional rotated wavelet filters (RWF) and discrete wavelet transform (DWT) jointly, which improves retrieval rate and retains comparable levels of computational complexity.