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Satyanarayana, P.
- S. Subba Rao 1936-1978
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Journal of Geological Society of India (Online archive from Vol 1 to Vol 78), Vol 19, No 11 (1978), Pagination: 530-530Abstract
No Abstract.- A New Species of Dunbaria Wight & Arnott (Fabaceae) from Burma
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Affiliations
1 Botanical Survey of India, Howrah, IN
1 Botanical Survey of India, Howrah, IN
Source
Nelumbo - The Bulletin of the Botanical Survey of India, Vol 27, No 1-4 (1985), Pagination: 152-153Abstract
No Abstract.- Three New Species of Rhynchosia Lour. (Fabaceae) from India
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1 Botanical Survey of India, Howrah, IN
1 Botanical Survey of India, Howrah, IN
Source
Nelumbo - The Bulletin of the Botanical Survey of India, Vol 28, No 1-4 (1986), Pagination: 241-246Abstract
No Abstract.- Critical Notes on Rhynchosia Lour. and Dunbaria Wight & Arn. (Fabaceae)
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1 Botanical Survey of India, Calcutta, IN
1 Botanical Survey of India, Calcutta, IN
Source
Nelumbo - The Bulletin of the Botanical Survey of India, Vol 36, No 1-4 (1994), Pagination: 235-237Abstract
Notes on a few endemic species of Rhynchosia Lour., and nomenclature of Dunbaria fusca Kurz are discussed.- Notes on Lectotypification of Genus Dunbaria Wight & Arn. (Leguminosae)
Abstract Views :180 |
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Affiliations
1 Botanical Survey of India, Howrah, IN
1 Botanical Survey of India, Howrah, IN
Source
Nelumbo - The Bulletin of the Botanical Survey of India, Vol 42, No 1-4 (2000), Pagination: 143-145Abstract
DUNBARIA Wight & Arn. (Leguminosae) was established in 1834. Since then a number of species have been added to this genus from different regions of the Eastern and South Eastern Asia. This genus has been treated in earlier works for over a century without mention of type species. However, Hutchinson (1964) mentioned Dunbaria heynei Wight & Arn. as its lectotype species. Due to the nomenclatural changes the above species has since been transferred to the nearest related genus CAJANUS DC. Hence, a correct and proper selection of lectotype species has been necessitated for the generic name DUNBARIA Wight & Arn.- Wavelet Transforms, Contourlet Transforms and Block Matching Transforms for Denoising of Corrupted Images via Bi-shrink Filter
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1 Department of E.C.E., Sri Venkateswara University College of Engineering, Tirupati - 517502, Andhra Pradesh, IN
2 Department of E.C.E., Annamacharya Institute of Technology and Sciences, Tirupati - 517520, Andhra Pradesh, IN
3 Department of E.C.E., Sri Venkateswara College of Engineering and Technology, Chittoor - 5170127, Andhra Pradesh, IN
1 Department of E.C.E., Sri Venkateswara University College of Engineering, Tirupati - 517502, Andhra Pradesh, IN
2 Department of E.C.E., Annamacharya Institute of Technology and Sciences, Tirupati - 517520, Andhra Pradesh, IN
3 Department of E.C.E., Sri Venkateswara College of Engineering and Technology, Chittoor - 5170127, Andhra Pradesh, IN
Source
Indian Journal of Science and Technology, Vol 9, No 44 (2016), Pagination:Abstract
Objectives: Image Denoising refers to the recovery of an image that has been corrupted by noise due to poor quality of image acquisition and transmission. Accordingly, there is a need to reduce the noise present in the image as a consequence of the denoised image formed. Methods/Statistical Analysis: This paper presents Image denoising using Wavelet transforms, Contourlet transforms and Block Matching Transforms governed by bivariate shrinkage (Bi-shrink) filter techniques. The Wavelet transform uses up-sampling, down-sampling, low pass filter and high pass filter to perform denoising operation, the Contourlet transform uses up-sampling, down-sampling, low pass filter and high pass filter and directional filter banks to perform denoising operation, the Block Matching Transform uses Haar Transforms, Discrete cosine transforms and Karhunen Loeve transform to perform denoising operation. Findings: The performance of wavelet transforms, Contourlet transforms and Block Matching Transforms are evaluated for Reference images (such as towers, shades and ruler images) corrupted by gaussian noise and salt and pepper noise, by computing two error metrics Peak Signal to Noise Ratio (PSNR) and Execution Time (ET) with help of shrinkage function. Programming these using MATLAB R2014a by exploring its wavelet transform, Contourlet transform, image processing and signal processing toolboxes and the values are presented in tabular forms and discussed in the section 6. In this paper the block matching haar discrete cosine transform is proposed for denoising of images (especially for those images possessing detailed textures) that works through haar transform and discrete cosine transform outstrips the basic transform discrete wavelet transform and semi translation invariant contourlet transform. For the images corrupted by Gaussian noise and denoised by the proposed transform outstrips the basic transform “Discrete Wavelet Transform by PSNR = 6.71 dB, ET = 25.89 sec” and “Semi Translation Invariant Contourlet Transforms by PSNR = 5.49 dB, ET = 5.89 sec”. For the images corrupted by Salt and Pepper noise and denoised by the proposed transform outstrips the basic transform “Discrete Wavelet Transform by PSNR = 21.15 dB, ET = 0.27 sec” and “Semi Translation Invariant Contourlet Transforms by PSNR = 20.05 dB, ET = 5.80 sec”. Application/Improvements: In this paper Block Matching Haar Discrete cosine transform is proposed to overcome the limitations of wavelet transforms and Contourlet transforms, hence to attain the trade-off between high peak signal to noise ratio and less execution time. Results and Discussion section illustrates the efficacy of the proposed transform in terms of peak signal to noise ratio, execution time and visual quality of images.Keywords
Bi-variate Shrinkage and Image Denoising, Block Matching Transforms, Contourlet Transforms, Wavelet Transforms.- A Study on Bio-Medical Waste Segregation Monitoring in a Tertiary Care Hospital at Andhra Pradesh
Abstract Views :307 |
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Authors
Affiliations
1 AMRI Hospital Dhakuria, Gariahat Road, Kolkata, West Bengal, IN
2 Dept of Medial Administration, Yashoda Hospital, Secunderabad, Telengana, IN
1 AMRI Hospital Dhakuria, Gariahat Road, Kolkata, West Bengal, IN
2 Dept of Medial Administration, Yashoda Hospital, Secunderabad, Telengana, IN