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Vision Product of the Year Awards: Categories


This hardware technology category includes any chip, integrated component assembly (SoC, SoM, etc.), or licensable IP that is used to implement and/or accelerate vision-based systems. Examples include CPU, GPU, DSP, FPGA, VPU/ASSP, licensable cores, etc.

Software or Algorithms

This category includes any software or firmware programs and/or algorithms used to implement and/or accelerate vision-based systems. Examples include optimized versions of computer vision software libraries and innovative algorithms for implementing visual intelligence.

Cameras and Sensors

The camera has become an intelligent subsystem for a vision system, leading to the emergence of “smart cameras” with integrated visual intelligence functionality. Examples of products in this category include image sensors, time-of-flight sensors and depth sensors, whether they are stand-alone, integrated with optics, and/or integrated with a processor.

Developer Tools

Developer tools are the programs and/or applications that software developers use to create, debug, maintain, or otherwise support vision-based systems. A software development kit (SDK) typically contains a set of software development tools that allow the creation of applications for vision-based systems. A hardware development kit (HDK) is designed for developers of hardware modules and systems and provides information to build custom hardware.

AI Technology

Artificial intelligence (AI) is a broad field of research and technology and is typically demonstrated when a machine mimics “cognitive” functions that humans associate with other human minds, such as “learning” and “problem solving.” Machine learning (ML) is a subset of AI and deep learning (DL) is a subset of ML. Traditionally, computer vision applications have relied on special-purpose algorithms that are painstakingly designed to recognize specific types of objects. Recently, however, CNNs (convolutional neural networks) and other deep learning approaches have been shown to be superior to traditional algorithms on a variety of image understanding tasks. This category includes any technology optimized for implementing machine learning-based vision algorithms. Examples include specialized device hardware/software for optimization of inference of a trained deep neural network model executing on a device.

Automotive Solutions

Vision is rapidly emerging in the automotive market and, fused with other sensor technologies, is increasing the safety and enhancing the driving experience. With new advances in deep learning techniques, vision systems are becoming more sophisticated in enabling vehicles to “see” their surroundings, thereby improving advanced driver assistance systems (ADAS) technology. These systems use cameras and computer vision techniques to understand the vehicle’s surroundings. This category includes hardware and/or software technologies developed specifically to improve the driving experience, whether by enhancing safety, providing navigational aid, improving the passenger experience, or providing/enabling autonomous driving functionality.

Cloud Solutions

Cloud-based technology plays a critical role in the development of the next generation of computer vision-based products and services. Virtually every “edge” application using deep learning includes a cloud-based component (e.g., training, analytics or storage) used to implement some or all of the visual intelligence. This category is for innovative visual intelligence solutions that include Cloud-based technology to develop and deploy vision-enabled products or services.

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