jack Fredo
Dr. Jac Fredo received his doctorate in Electrical Engineering from Anna University (India) and continued his research as a Research Associate at the Non-Invasive Imaging and Diagnostics Laboratory, Indian Institute of Technology Madras (India). He received a DST-MANF fellowship to pursue his Ph.D and a SERB-IUSSTF fellowship to pursue his post-doctoral research at Brain Development Imaging Lab (BDIL), San Diego State University (SDSU), USA. He continually seeks to broaden his scientific experiences, visiting labs in the USA, Germany, and The Netherlands through a series of travel awards. His main interests are neuroimage processing, computer vision techniques and machine learning.
less
Related Authors
InterestsView All (11)
Uploads
Papers by jack Fredo
better accuracy in both global and local damages compared to the geometrical features. The image based analysis carried out on this work is able to classify the impairment in composite materials; this framework can be used in the industrial applications for the quantification of damages.
material after applying 5 mm, 6 mm and 7 mm impingement. Initially, the images are filtered using median and anisotropic diffusion filter. The results are validated using peak signal to noise ratio and structural
similarity index measures. The damaged regions are segmented from the filtered images using threshold methods such as Otsu and Tsallis. The geometrical features such as area, perimeter, eccentricity and Major axis to Minor axis (MM) ratio are calculated from the delineated regions. The area calculated from the damaged region is correlated with the perimeter. The visual and validation results show that the median and anisotropic diffusion filter are able to remove the noise. The validation results suggest that anisotropic diffusion performs better than the median filter. The threshold methods are able to segment the damaged regions with the threshold level of four. Area and perimeter calculated from the delineated regions increases with increase in impingement. The damage dimension is seems to be high in the backside of the composite materials compared to the front side. The features calculated from the damaged region, extracted using Tsallis method is able to discriminate the damages better compared to the regions extracted using Otsu. The eccentricity of the damaged region increases and the MM ratio decreases with the increase in impingement. The shape of the damage extends from circle to ellipse and elevate towards the y-axis as the impingement increases. The area calculated from the damage region gives high correlation (R = 0.99) with the perimeter. This suggests that the damage spreads on the composite material as a ring. The image based analysis carried out on this work is able to characterise the impairment in composite materials; this framework can be used for the industrial applications for the quantification of damages.
better accuracy in both global and local damages compared to the geometrical features. The image based analysis carried out on this work is able to classify the impairment in composite materials; this framework can be used in the industrial applications for the quantification of damages.
material after applying 5 mm, 6 mm and 7 mm impingement. Initially, the images are filtered using median and anisotropic diffusion filter. The results are validated using peak signal to noise ratio and structural
similarity index measures. The damaged regions are segmented from the filtered images using threshold methods such as Otsu and Tsallis. The geometrical features such as area, perimeter, eccentricity and Major axis to Minor axis (MM) ratio are calculated from the delineated regions. The area calculated from the damaged region is correlated with the perimeter. The visual and validation results show that the median and anisotropic diffusion filter are able to remove the noise. The validation results suggest that anisotropic diffusion performs better than the median filter. The threshold methods are able to segment the damaged regions with the threshold level of four. Area and perimeter calculated from the delineated regions increases with increase in impingement. The damage dimension is seems to be high in the backside of the composite materials compared to the front side. The features calculated from the damaged region, extracted using Tsallis method is able to discriminate the damages better compared to the regions extracted using Otsu. The eccentricity of the damaged region increases and the MM ratio decreases with the increase in impingement. The shape of the damage extends from circle to ellipse and elevate towards the y-axis as the impingement increases. The area calculated from the damage region gives high correlation (R = 0.99) with the perimeter. This suggests that the damage spreads on the composite material as a ring. The image based analysis carried out on this work is able to characterise the impairment in composite materials; this framework can be used for the industrial applications for the quantification of damages.