A Second Generation Low Cost Embedded Color Vision System

Rowe, Anthony; Rosenberg, C.; Nourbakhsh, Illah

doi:10.1109/cvpr.2005.396

Cited by 22 publications

(15 citation statements)

References 4 publications

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“…Recently, it was decided to adopt a low cost camera to demonstrate the capabilities of computer vision system. The CMUcam is a low cost camera that was originally developed at Carnegie Mellon University [4]. It is a CMOS digital output camera that has an embedded microprocessor that does the image capture and some basic vision processing.…”

Section: Background On the Cameramentioning

confidence: 99%

Application of a low cost vision system to a mobile robot based mechatronics course

Surgenor

Miles

2008

2008 Canadian Conference on Electrical and Computer Engineering

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Experience with the application of a low cost vision system to an elective course in mechatronics is discussed. The course is structured around a series of laboratories involving a mobile robot. The course ends with a team based design project competition.A camera with embedded color tracking capabilities was added to the course to provide non-electrical engineering students with exposure to computer vision technology.

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Section: Background On the Cameramentioning

confidence: 99%

Application of a low cost vision system to a mobile robot based mechatronics course

Surgenor

Miles

2008

2008 Canadian Conference on Electrical and Computer Engineering

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“…If the object is known by another camera, it replies the label of the object; otherwise a new label is assigned which results in a consistent labelling over multiple cameras. Rowe et al (2005) promoted a low cost embedded vision system. The aim of this project was the development of a small camera with integrated image processing.…”

Section: Image Acquisition and Processing In Embedded Devicementioning

confidence: 99%

Embedded System for Biometric Identification

Rosli¹

2010

Robot Vision

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“…[17,7,14,6,9,5,10]. The MeshEye platform [7] uses an ARM7 microcontroller with 55MHz speed, and has 64KB RAM and 256KB flash memory.…”

Section: Introductionmentioning

confidence: 99%

“…It uses a higher resolution but has 128KB of memory. The CMUcam2 [17] is a low-cost embedded camera with 75MHz RISC processor and 384KB SRAM. Due to the limited memory and processing power, only low-level image processing can be performed.…”

Section: Introductionmentioning

confidence: 99%

Resource-Efficient Salient Foreground Detection for Embedded Smart Cameras br Tracking Feedback

Casares

Velipasalar

2010

2010 7th IEEE International Conference on Advanced Video and Signal Based Surveillance

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Battery-powered wireless embedded smart cameras have limited processing power, memory and energy. Since video processing tasks consume significant amount of power, the problem of limited resources becomes even more pronounced, and necessitates designing light-weight algorithms suitable for embedded platforms. In this paper, we present a resource-efficient salient foreground detection and tracking algorithm. Contrary to traditional methods that implement foreground object detection and tracking independently and in a sequential manner, the proposed method uses the feedback from the tracking stage in the foreground object detection. We compare the proposed method with a sequential method on the microprocessor of an embedded smart camera, and present the savings in the processing time and energy consumption and the gain in the lifetime of a battery-powered camera for different scenarios. The presented method provides significant savings in terms of the processing time of a frame. We take advantage of these savings by sending the microprocessor to idle state at the end of processing a frame, and when the scene is empty.

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A Second Generation Low Cost Embedded Color Vision System

Cited by 22 publications

References 4 publications

Application of a low cost vision system to a mobile robot based mechatronics course

Application of a low cost vision system to a mobile robot based mechatronics course

Embedded System for Biometric Identification

Resource-Efficient Salient Foreground Detection for Embedded Smart Cameras br Tracking Feedback

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