I am a postdoc at PAVIS, Istituto Italiano di Tecnologia (IIT), Genova, Italy, working with Dr. Alessio Del Bue (previously with Prof. Vittorio Murino). My current work is broadly on defect detection in infrastructure elements using computer vision and machine learning. I am also interested in the topics of multi-exposure and multi-illumination fusion. Earlier I have worked on industrial projects focused on defect enhancement and detection mainly for combustion chamber tiles and textile yarn.
Prior to joining IIT, I worked as a senior research engineer at Vehant Technologies (Mar. 2017 - Dec. 2018).
A novel setup for automatic visual inspection of cracks in ceramic tile as well as studies the effect of various classifiers and height-varying illumination conditions for this task.
Our book presents image super-resolution methods and techniques for automatically detecting and inpainting damaged regions in heritage monuments, in order to provide an enhanced visual experience.
In this chapter, we discuss techniques for automatically detecting the damaged facial regions and cracks in photographs of monuments. Unlike the usual practice of manually selecting the mask for inpainting, the regions to be inpainted are automatically selected and inpainting is done using the existing algorithm.
We construct dictionaries of image-representative low and high resolution patch pairs from the known regions in the test image and its coarser resolution. Inpainting of the missing pixels is performed using exemplars found by comparing patch details at a finer resolution by making use of the constructed dictionaries.
A technique for automatically detecting the cracked regions in photographs of monuments, based on comparison of patches using a measure derived from the edit distance. This is extended to perform inpainting of video frames using SIFT and homography, which is quantified with our temporal consistency measure.
Automates the process of identifying the damage to visually dominant regions viz. eyes, nose and lips in facial image of statues, for the purpose of inpainting.
We present a Singular Value Decomposition (SVD) based technique for automatic detection of the damaged regions in the photographed object/scene, for digitally restoring the entirety using inpainting.
Unlike simply copying pixels from an exemplar into the target or damaged pixels (i.e. pixels to be inpainted), we estimate and use autoregressive parameters along with the best matching exemplar to modify the damaged pixel.
This is an old code that I created sometime in 2010. While working on video shot detection, I needed a tool to perform frame-by-frame visual analysis. However, I wasn't aware of any that existed at that time and created one with Matlab. To sharpen my skills with C and OpenCV, I re-created the same.