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7 篇 会议

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检索条件"任意字段=Colloquium on Real Time Image Processor Architectures for Use in Computer Vision and Robotics."

共 7 条记录，以下是1-10 订阅

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colloquium on real time image processor architectures for use in computer vision and robotics.

COLLOQUIUM ON REAL TIME IMAGE PROCESSOR ARCHITECTURES FOR US...

引用

colloquium on real time image processor architectures for use in computer vision and robotics.

作者： Anon

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PROGRAMMABLE image processor.

PROGRAMMABLE IMAGE PROCESSOR.

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colloquium on real time image processor architectures for use in computer vision and robotics.

作者： Wavish, P.R.

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ON THE use OF BANDPASS image FILTERS.

ON THE USE OF BANDPASS IMAGE FILTERS.

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colloquium on real time image processor architectures for use in computer vision and robotics.

作者： Baker, K.D.

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PRACTICAL PARALLEL PROCESSING: A useR'S VIEW OF CLIP4.

PRACTICAL PARALLEL PROCESSING: A USER'S VIEW OF CLIP4.

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colloquium on real time image processor architectures for use in computer vision and robotics.

作者： Wood, A.M.

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computer vision SYSTEMS FOR INDUSTRY: WISARD AND THE LIKE.

COMPUTER VISION SYSTEMS FOR INDUSTRY: WISARD AND THE LIKE.

引用

colloquium on real time image processor architectures for use in computer vision and robotics.

作者： Aleksander, Igor Stonham, T.John Wilkie, Bruce V.

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On implementing motion-based Region of Interest detection on multi-core CELL

On implementing motion-based Region of Interest detection on...

引用

作者： Kumar, Avin Li, Baoxin Department of Computer Science and Engineering Arizona State University Tempe AZ 85281 United States

Region of Interest (ROI) detection is a well-studied problem in computer vision for applications such as video surveillance and vision-based robotics. ROI detection may be done using background subtraction schemes with change detection and background estimation. When the camera is not static, these schemes will be ineffective and hence there is a need for global motion estimation (GME) to compensate the camera motion. Robust GME algorithms often require high computation power, rendering them unsuitable for real-time, embedded vision applications. In this article, we use a multi-core processor platform - CELL, to meet the computational requirements of the ROI detection system and to explore the feasibility of potential usage of such heterogeneous processor architecture for vision applications. In particular, we analyze the algorithmic components of a typical GME-based ROI detection system and show how to make efficient use of the parallel and vector computation capabilities in the CELL cores for maximizing the gain on speed performance. We have also ported our system on a Sony PS3 system and promising results have been achieved. Based on the study, various design aspects and implementation challenges are discussed which are believed to be useful for future work in porting vision algorithms on multi-core architectures for real-time embedded applications. © 2010 Elsevier Inc. All rights reserved.

关键词： Object detection

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Versatile educational and research robotic platform based on reconfigurable hardware

Versatile educational and research robotic platform based on...

引用

International Conference on Reconfigurable Computing and FPGAs (ReConFig)

作者： Carlos Andres Lara-Niño Cesar Torres-Huitzil Jose Hugo Barron-Zambrano LTI CINVESTAV Tamaulipas Mexico Universidad Politecnica de Victoria Ciudad Victoria Tamaulipas Mexico

This paper presents the development of a field-programmable gate array (FPGA)-based platform. This platform is intended for the community as an educational and efficient prototyping tool, in digital signal processing, robotics. and control, but it can also be used as to develop custom robotic architectures. The proposed platform centered around an FPGA device is embedded on an off-the-shelf Phoenix hexapod robot. The platform is equipped with a camera module targeted to real time computer vision. From an academic point of view, the main use for the developed platform is to be employed as a didactic resource that facilitates the understanding of theoretical concepts commonly used in engineering courses and to speed up the development cycle in hands-on practice. A set of software tools and low-level drivers have been developed to configure the vision sensor, transfer images to/from a computer and to manage the testing of image and digital processing and control tasks in a transparent way. To validate the platform a hardware vision architecture and a locomotion control module have been created. The high level control is performed in a soft-processor that simulates the Arduino ONE micro controller. The soft-processor takes information from the vision sensor to generate controls signals for a generic module able to take a digital input and deliver a pulse-width modulation (PWM) output.

关键词： Robot sensing systems Field programmable gate arrays Servomotors computer architecture Process control image processing

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