Sentient Vision Systems is an artificial intelligence (AI) company that uses advanced software to enhance the performance of sensors and mission systems. ViDAR (for Visual Detection and Ranging) can detect a target in the imagery feed, discriminate between possible alternatives, and draw the operator's eye to what he or she is looking for. The power of AI can differentiate, from a distance of five nautical miles, between an arctic ice floe, a breaking wave and an upturned boat. AI and mastery of traditional computer vision technology underpins everything that Sentient Vision Systems has done over the past 17 years, since it started working on target detection solutions over land and maritime environments. Sentient's ViDAR systems use the AI within its deep learning and computer vision algorithms to detect tiny targets that are almost invisible in the imagery feed from an EO/IR sensor, especially in very challenging conditions, and filter out irrelevant information.

This is the first in a multi-part series by guest blogger Adrian Rosebrock. Adrian writes at PyImageSearch.com about computer vision and deep learning using Python, and he recently finished authoring a new book on deep learning for computer vision and image recognition. I had two goals when I set out to write my new book, Deep Learning for Computer Vision with Python. The first was to create a book/self-study program that was accessible to both novices and experienced researchers and practitioners -- we start off with the fundamentals of neural networks and machine learning and by the end of the program you're training state-of-the-art networks on the ImageNet dataset from scratch. Along the way I quickly realized that a stumbling block for many readers is configuring their development environment -- especially true for those wanted to utilize their GPU(s) and train deep neural networks on massive image datasets (such as ImageNet).