NOTES : [ Object Detection and Tracking, Image Segmentation, Optical Flow | Structure from Motion (SfM), Machine Vision - Motion Models [CV] ] and some of my works in Computer Vision:
I wrote articles on CNN architectures - GoogLeNet, ResNet etc few year back while researching about them and published this paper at Elsevier: doi.org/10.1016/j.tice.2019.02.001 in Computer Vision. I also worked on “6D pose tracking of objects in occlusion or cluttered environments” for 6 DoF robotic arm manipulation
planning at the Robotics Laboratory, School of Computing, University of Leeds. I have implemented CV solutions in different projects and graduated from the following ML schools : IEEE RAS Winter School on SLAM in Deformable Environments, CVIT Computer Vision and Machine Learning Summer School.
I also developed a control station for drone autonomy in lab and presented my hand gesture recognition work to Senator, Dr. Anjes Tjarks ( Behörde für Verkehr und Mobilitätswende ), Hamburg and LSBG CEO, Dr Clotz at DigiLab, LSBG [post].
Some good Computer Vision classes:
- MIT 6.801 Machine Vision, Fall 2020, Stanford CS231n: Deep Learning for Computer Vision : (web).
- Computer Vision — Andreas Geiger, First Principles of Computer Vision, Michigan Online : Deep Learning for CV, UC Berkeley CS 198-126: Modern CV.
resources: OpenCV, intro-blog, Deep Learning for Computer Vision with Python and TensorFlow, CV3DST : Dynamic Vision and Learning Group, @CyrillStachniss, @github/google-mediapipe, Kalman Filter for Robot Vision: A Survey, Topological simultaneous localization and mapping: a survey.