Easy-to-Use C++ Computer Vision Library
C++ implementation pool for computer vision R&D projects.
Open Source Computer Vision Library
A machine vision library written in SYCL and C++ that shows performance-portable implementation of graph algorithms
C++ Image Processing and Computer Vision OpenCV Examples used for Teaching
The ARM Computer Vision and Machine Learning library is a set of functions optimised for both ARM CPUs and GPUs using SIMD technologies.
An introductory course on machine vision and related machine learning used in automation, autopilots, security and inspection systems. Topics covered include theory of computer and machine vision and related algorithms for image capture and processing, filtering, thresholds, edge detection, shape analysis, shape detection, salient object detection, pattern matching, digital image stabilization, stereo ranging, and methods of sensor and information fusion. Machine vision sensors covered include visible to long-wave infrared including passive EO/IR (Electro-Optical/Infrared) as well as active methods such as RGB depth mapping and LIDAR. Embedded and automation topics covered include implementation of these algorithms with FPGA or GP-GPU embedded real-time vision systems for autopilots (intelligent transportation), general machine vision automation and security including methods for detection, classification, recognition of targets, and applications including inspection, surveillance, search and rescue, and machine vision navigation.
Here, we will be showcasing our seminar series “CPP for Image Processing and Machine Learning” including presentations and code examples. There are image processing and machine learning libraries out there which use C++ as a base and have become industry standards (ITK for medical imaging, OpenCV for computer vision and machine learning, Eigen for linear algebra, Shogun for machine learning). The documentation provided with these packages, though extensive, assume a certain level of experience with C++. Our tutorials are intended for those people who have basic understanding of medical image processing and machine learning but who are just starting to get their toes wet with C++ (and possibly have prior experience with Python or MATLAB). Here we will be focusing on how someone with a good theoretical background in image processing and machine learning can quickly prototype algorithms using CPP and extend them to create meaningful software packages.