Implementing algorithms on topics such scene understanding, object recognition or cognitive neuroscience of visual object recognition.
Implementing algorithms on topics such machine learning, deep learning or reinforcement learning.
Achieved using CUDA parallelization and highly optimized data structures and algorithms.
Using C++, GPU, CUDA, Python, OpenCV, Robot Operating System (ROS), Linux, Eclipse, CMake, Git, QT, Design Patterns, OOP, UML, TDD.
Robotic automation has transformed the manufacturing industry and has the potential to change many other aspects of our lives. However robotics has made relatively less progress in other important industries which have a complex and time variant landscape. Vision is the missing capability that currently prevents robots from performing useful tasks in the complex, unstructured and dynamically changing environments in which we live and work.
Seeing is a lot more than just processing images. It is a complex process tightly coupled with memory - which enables an understanding of the scenario required to robustly perform tasks that involve objects and places. These are tightly coupled with action, thereby providing rapid and continuous feedback for control.
For these reasons combining Image Processing with Machine Learning is highly advisable. All algorithms are to be developed in a manner that enables real-time execution on commodity embedded systems.
#1 Software Development
Java, C++, CUDA, Linux, Eclipse, QT, Git, Agile
#2 Robotic Vision
GPU, CUDA, OpenCV
#3 Machine Learning
Object Recognition, Deep Learning, Reinforcement Learning
#4 Embedded Systems
ROS, NVIDIA Jetson
In this 5’th project from the Self-Driving Car engineer program designed by Udacity, our goals are the following: Perform a Histogram of Oriented Gradients (HOG) feature extraction on a labeled training set of images and train a classifier Linear SVM classifier Optionally, you can also apply a color transform and append binned color features, as[…]
In this 4’th project from the Self-Driving Car engineer program designed by Udacity, our goal is to write a software pipeline to identify the lane boundaries in a video from a front-facing camera on a car. The camera calibration images, test road images, and project videos are available here repository. The goals / steps of[…]
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This prototype tests different implementations of the histogram calculation for images using C++, CUDA, OpenCV 3.X. An image histogram is a type of histogram that acts as a graphical representation of the tonal distribution in a digital image. It plots the number of pixels for each tonal value. By looking at the histogram for a specific[…]
This prototype tests different implementations of the bilateral filtering to smooth images using C++, CUDA, OpenCV 3.X. Several smoothing algorithms exist, but the most popular are: Normalized Box Filter: this filter is the simplest of all. Each output pixel is the mean of its kernel neighbors (all of them contribute with equal weights) Gaussian Filter: Probably the most useful[…]