My thesis topic is Hand Tracking for Virtual Reality and my advisor is Prof. Parag Chaudhuri.
The research involves Computer Graphics, Computer Vision, Optimization and Machine Learning, and heavily uses
C++ (Eigen, Ceres-solver, Opencv, Open3d, pybind11) and Python (Tensorflow, Scipy).
- Kalshetti P, Chaudhuri P. "Unsupervised incremental learning for hand shape and pose estimation". In ACM SIGGRAPH 2019 Posters 2019 Jul 28 (pp. 1-2). https://doi.org/10.1145/3306214.3338553
- Kalshetti P, Bundele M, Rahangdale P, Jangra D, Chattopadhyay C, Harit G, Elhence A. "An interactive medical image segmentation framework using iterative refinement". Computers in Biology and Medicine. 2017 Apr 1;83:22-33. https://doi.org/10.1016/j.compbiomed.2017.02.002 (Accorded Honors status!)
Some of my projects are listed below.
Point Cloud Sampling using Graph Signal Processing
This project uses graph signal processing to sample a point cloud such that application-dependent features are preserved. Initially a graph is constructed from the point cloud. A sampling distribution is then computed using this graph and the desired application. More precisely, the application decides the type of graph filter to be used for computing the distribution. Finally this distribution is used to sample the point cloud.
Fit Mesh to PointCloud
The algorithm is expressed as energy minimization. The energy is written as a sum of squares that is then optimized using Levenberg-Marquardt. For this project, the initialization is provided manually, however this can be provided by a discriminative model. The key novelty is to jointly optimize over both model parameters and correspondences between observed data points and the model surface.
Single Shot MultiBox architecture was used to solve this classification + regression problem and the variable dimensional output was handled using anchor boxes, resulting in real-time accurate detection.
Medical Image Segmentation Tool
In order to obtain the most suitable method for medical image segmentation, we propose MIST (Medical Image Segmentation Tool), a two stage algorithm. The first stage automatically generates a binary marker image of the region of interest using mathematical morphology. This marker serves as the mask image for the second stage which uses GrabCut to yield an efficient segmented result. The obtained result can be further refined by user interaction.
Paper Link: https://doi.org/10.1016/j.compbiomed.2017.02.002