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Discriminatory Projection of Camouflaged Texture Through Line Masks
( Nagappa Bhajantri ),( Pradeep Kumar R ),( Nagabhushan P ) 한국정보처리학회 2013 Journal of information processing systems Vol.9 No.4
The blending of defective texture with the ambience texture results in camouflage. The gray value or color distribution pattern of the camouflaged images fails to reflect considerable deviations between the camouflaged object and the sublimating background demands improved strategies for texture analysis. In this research, we propose the implementation of an initial enhancement of the image that employs line masks, which could result in a better discrimination of the camouflaged portion. Finally, the gray value distribution patterns are analyzed in the enhanced image, to fix the camouflaged portions.
Automatic Detection of Texture-defects using Texture-periodicity and Jensen-Shannon Divergence
Asha, V.,Bhajantri, N.U.,Nagabhushan, P. Korea Information Processing Society 2012 Journal of information processing systems Vol.8 No.2
In this paper, we propose a new machine vision algorithm for automatic defect detection on patterned textures with the help of texture-periodicity and the Jensen-Shannon Divergence, which is a symmetrized and smoothed version of the Kullback-Leibler Divergence. Input defective images are split into several blocks of the same size as the size of the periodic unit of the image. Based on histograms of the periodic blocks, Jensen-Shannon Divergence measures are calculated for each periodic block with respect to itself and all other periodic blocks and a dissimilarity matrix is obtained. This dissimilarity matrix is utilized to get a matrix of true-metrics, which is later subjected to Ward's hierarchical clustering to automatically identify defective and defect-free blocks. Results from experiments on real fabric images belonging to 3 major wallpaper groups, namely, pmm, p2, and p4m with defects, show that the proposed method is robust in finding fabric defects with a very high success rates without any human intervention.
Automatic Detection of Texture-defects using Texture-periodicity and Jensen-Shannon Divergence
( V Aha ),( N U Bhajantri ),( P Nagabhushan ) 한국정보처리학회 2012 Journal of information processing systems Vol.8 No.2
In this paper, we propose a new machine vision algorithm for automatic defect detection on patterned textures with the help of texture-periodicity and the Jensen-Shannon Divergence, which is a symmetrized and smoothed version of the Kullback-Leibler Divergence. Input defective images are split into several blocks of the same size as the size of the periodic unit of the image. Based on histograms of the periodic blocks, Jensen-Shannon Divergence measures are calculated for each periodic block with respect to itself and all other periodic blocks and a dissimilarity matrix is obtained. This dissimilarity matrix is utilized to get a matrix of true-metrics, which is later subjected to Ward`s hierarchical clustering to automatically identify defective and defect-free blocks. Results from experiments on real fabric images belonging to 3 major wallpaper groups, namely, pmm, p2, and p4m with defects, show that the proposed method is robust in finding fabric defects with a very high success rates without any human intervention.
An Experimental Investigation on Geopolymer Concrete Utilising Micronized Biomass Silica and GGBS
Srinivasan Vediyappan,Pazhani Kandukalpatti Chinnaraj,Bharatkumar Bhajantri Hanumantraya,Sundar Kumar Subramanian 대한토목학회 2021 KSCE JOURNAL OF CIVIL ENGINEERING Vol.25 No.6
In the atmosphere greenhouse gases concentration has rapidly increased due to anthropogenic activities which cause global warming. Portland cement manufacturing process requires enormous amount of energy and also consumes huge quantity of natural resources. To overcome this issue, Portland cement free geopolymer concrete is produced with ground granulated blast furnace slag (GGBS) as main binder and micronized biomass silica (MBS) is substituted in place of GGBS at different quantities. Experimental investigation highlighting mechanical properties and durability performance of geopolymer concrete mixes produced with GGBS and MBS are presented here. Rice husk is used in the manufacture of MBS. Compressive, flexural, split tensile strength, elastic modulus and durability parameters like water absorption, sorptivity, rapid chloride permeability test were conducted. Geopolymer concrete mix with 20 percent MBS and remaining GGBS as binder was found to have optimal strength and durability performance. However, the compressive strength was above the target design strength for all the geopolymer concrete mixes. This experimental investigation vindicates the feasibility of utilizing MBS as a binder raw material in geopolymer concrete production.