CEC Semenster Undefined
Implementing Embedded Vision: Designing Systems That See & Understand Their Environments
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March 18,
2013
What Can You Do With Embedded Vision?
Embedded vision is the incorporation of computer vision techniques into embedded systems, mobile devices, PCs, and the cloud. In this session, we'll look at some of the coolest new applications of embedded vision, such as systems that read a person's emotional state from facial images and systems that help prevent driving accidents by monitoring the road. We'll touch on the algorithms that enable these capabilities and the types of processors used to run those algorithms.
Course Resources
Special Educational Materials
March 19,
2013
Interfacing to and Processing Data From Image Sensors
Image sensors use varied hardware interfaces and output data formats, which can complicate system design and make it difficult to switch sensors. Their high output rate can overwhelm data connections and processors. Programmable logic devices can solve both problems: Their flexibility can comprehend normally incompatible interfaces, and they can accelerate common functions like color space conversion, image resizing, frame rate transformation, aspect ratio alteration, and edge detection.
Course Resources
Special Educational Materials
March 20,
2013
Improving Image Understanding by Improving Image Quality
Cameras typically apply preprocessing algorithms to raw pixel data to generate pleasant images by compressing dynamic range. We'll discuss how appropriate image preprocessing can ease the work of image-understanding algorithms, and how these algorithms can assist in preprocessing.
Course Resources
Special Educational Materials
March 21,
2013
When to Use FPGAs to Accelerate Embedded Vision Applications
FPGAs can accelerate some image processing algorithms, while reducing latency and jitter compared to using CPUs. We'll compare CPUs and FPGAs as embedded vision processing engines, exploring which types of vision algorithms and applications can benefit from implementation on an FPGA, and which are better suited for a CPU or other type of processor. We'll share benchmark results comparing FPGA and CPU implementations of vision applications, and introduce high-level programming of FPGAs.
Course Resources
Special Educational Materials
March 22,
2013
Developing Low-Cost, Low-Power, Small Vision Systems
We’ll present a detailed case study of the development of a smart, automotive, rear-view camera system incorporating vision-based object detection and distance estimation. We’ll discuss the challenges associated with creating an embedded vision system that meets very demanding cost, size, power, and performance requirements. We’ll present the lessons learned during algorithm, software, and system development, and how those lessons apply to other embedded vision applications.
Course Resources
Special Educational Materials
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