Visual AI, computer vision, imaging and cameras, robotics & automation, depth sensing, forensic and expert image analysis, media cryptography
Systems design, architecture, HW/ SOC/ FPGA, software development, embedded systems, apps, libraries, optimizations for accuracy, power, performance
Competitive analysis, market research, IP portfolio analysis, patent generation and IP development
Krig Research is a pioneer in visual AI, computer vision, machine learning, advanced imaging, and robotics systems, providing complete solutions for clients ranging across a wide range of governments and industries, world-wide. Small and large projects are welcome.
An ongoing business relationship and continued business are mutually beneficial, so every effort is made to understand your requirements and provide the best possible solution within your project parameters. Find out if Krig Research has the expertise and experience you need.
A free confidential consultation is available on request. Krig Research provides complete engineering services to complete any visual computing project. In addition, specific projects can be executed to enhance your business via competitive market analysis, patent portfolio development, product positioning, technology briefings, and special reports.
The NeuroMatrix(tm) system contains a working model of the visual cortex of the human brain, including models of the eyes, early visual processing centers, and higher level reasoning centers. Capable of learning precise signatures of individual objects.
The NeuroTarget(tm) system locates targets in the visible light spectrum, therefore is capable of surviving common electronic warfare countermeasures which affect radar-based systems.
The massive project to learn visual DNA and visual genomes from hi-res images, and provide free visual AI products and services to the public. Sponsors and members are encouraged to apply - learn more below.
One of the most complete resource texts on computer vision. Download a complimentary copy of the 500 page classic from the Embedded Vision Alliance or APRESS.
The classic 653-page textbook edition in English from Springer-Verlag has also been translated into Chinese, updated with a comprehensive survey of deep learning, DNNs, RNNs, and historical computer vision background.
Ground-breaking text describing synthetic vision using Volume Learning (not deep learning) and Visual DNA, selected by the Visual Genomes Foundation, from DeGruyter Press.
The OpenVDNA initiative is the work of the Visual Genomes Foundation, providing public resources for visual AI solutions. Visual DNA are taken from thousands of small pieces of images, like puzzle pieces composing the fabric of the image. Visual DNA compose a rich feature space represented by over 16,000 individual metrics in four primary bases: Color, Shape, Texture and Glyphs. The VGF provides OpenVDNA resources:
A Synthetic Vision model is used to perform Volume Learning, to gather a large volume of multivariate and multidimensional visual DNA features from each image. Synthetic Vision is described in the De Gruyter book "Synthetic Vision Using Volume Learning And Visual DNA", informed by neuroscience models, deep learning models, and computer vision research. While deep learning models exist in a 1D array of feature weights, volume learning produces a multivariate and multidimensional features space supporting nearly 26,000 different feature metrics for each visual DNA feature.
The synthetic vision model includes a photographic visual memory for each visual cortex feature region V1..Vn. A neural feature space is organized using content addressable memory (CAM), and neural clusters, following neuroscience research such as the Hubel and Wiesel model of neural connectivity. Each visual DNA feature exists in a visual cortex model that includes feature-specific correspondence metrics.