Microsoft Vision AI Developer Kit Simplifies Building Vision-Based Deep Learning Projects

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For the Vision AI Developer Kit, Microsoft and Qualcomm have partnered to simplify training and deploying computer vision-based AI models. Developers can use Microsoft's cloud-based AI and IoT services on Azure to train models while deploying them on the smart camera edge device powered by a Qualcomm's AI accelerator. Let's take a close look at Vision AI Developer Kit. The Vision AI Developer Kit not only looks stylish and sophisticated, but also boasts of an impressive configuration. The kit is powered by a Qualcomm Snapdragon 603 processor, 4GB of LDDR4X memory and 16GB of eMMC storage.



Low Computation Vision-Based Navigation For a Martian Rover

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In the design and construction of mobile robots vision has always been one of the most potentially useful sensory systems. In practice however, it has also become the most difficult to successfully implement. At the MIT Mobile Robotics (Mobot) Lab we have designed a small, light, cheap, and low power Mobot Vision System that can be used to guide a mobile robot in a constrained environment. The target environment is the surface of Mars, although we believe the system should be applicable to other conditions as well. It is our belief that the constraints of the Martian environment will allow the implementation of a system that provides vision based guidance to a small mobile rover.


Revolutionizing Brick And Mortar Retail With Computer Vision

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Customers may sometimes face difficulties in understanding how a product is supposed to be used. In such situations, customers may avoid trying out new products. Retailers can address this issue with a computer vision-based mobile app. Computer vision-based mobile apps can recognize different products and provide contextual information. For example, a customer can scan a newly-launched shampoo.


Attacking Vision-based Perception in End-to-End Autonomous Driving Models

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Recent advances in machine learning, especially techniques such as deep neural networks, are enabling a range of emerging applications. One such example is autonomous driving, which often relies on deep learning for perception. However, deep learning-based perception has been shown to be vulnerable to a host of subtle adversarial manipulations of images. Nevertheless, the vast majority of such demonstrations focus on perception that is disembodied from end-to-end control. These attacks target deep neural network models for end-to-end autonomous driving control.