Skills

Computer Vision

• Solving Computer Vision tasks using Deep Learning
• Object Detection, Image Classification, Video Recognition
• Simultaneous Localization and Mapping (SLAM), including ORB-SLAM, Underwater SLAM, Opti-Acoustic SLAM
• Image segmentation, Detection and Tracking of Pedestrians in single (multiple) video(s)
• Background Subtraction
• Camera Calibration, Sonar Calibration, Opti-Acoustic Stereo Calibration, Multi-view Camera Calibration, Camera-Lidar Calibration
• Multiple View Geometry, Motion Estimation, 3-D Reconstruction in Multimodal Imaging Systems
• Multimodal Calibration, Opti-acoustic
• (Non-) Linear Optimization in Matlab and Python

Machine Learning

• Regression: (Non-) Linear Regression, Penalized Linear Regression (Ridge, Lasso), Polynomial Regression
• Classification: Support Vector Machines (SVM), Logistic Regression, Boosted Decision Trees, Random Forests, Bagged Decision Trees, and Logistic Regression
• Dimensionality Reduction: Non-negative Matrix Factorization, PCA, LLE, LE, SNE
• Clustering: K-means, GMM, DBSCAN
• Probabilistic Graphical Model: Learning and inference in Markov Random Fields (MRF) and Conditional Random Fields (CRF)

Tools

• Programming languages: C++, Matlab, Python
• Machine learning tools: Pytorch, Tensorflow, Keras, Scikit-learn, NumPy, Pandas, SciPy
• Computer Vision: OpenCV, PCL, OpenMVG, ITK, VTK, OpenCL, Dlib, Boost, Google Ceres Solver, g2o
• Development tools: CMake, Docker, Google gtest, Boost, Git, Github, Bitbucket, Unit Testing

Projects

Please contact me to get the list of projects