Key ingredients for embedded computer vision apps

Embedded computer vision applications are becoming more popular due to their ability to accurately detect and classify objects in real-time. To achieve this, there are three main components used: a camera, an embedded processor, and machine learning algorithms.

The camera is the key component of any embedded computer vision system. It captures the images that will be analyzed and is usually connected to the processor via a serial cable. It must be chosen carefully to ensure that it can capture the right image data for the task at hand.

The embedded processor is responsible for analyzing the image data and making decisions about what objects the system has seen. It can be a microcontroller, FPGA (Field Programmable Gate Array) or an ASIC (Application Specific Integrated Circuit). The processor should have enough power to run the algorithms quickly, as well as plenty of RAM for storing large images.

Finally, machine learning algorithms are used to make predictions about the objects detected by the camera. Common algorithms used for object recognition include convolutional neural networks and support vector machines. These algorithms require training data to be able to learn how to recognize new objects.

In conclusion, embedded computer vision applications rely on three key components: a camera, an embedded processor, and machine learning algorithms. Each component is vital to the success of the system and must be chosen carefully. With the right combination of components, embedded computer vision systems can accurately detect and classify objects in real-time.

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