PIPE: A Parallel Processor For Dynamic Image Processing

Luck, Randall L.

doi:10.1117/12.940075

SPIE Proceedings

1987

DOI: 10.1117/12.940075

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PIPE: A Parallel Processor For Dynamic Image Processing

Randall L. Luck

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1991

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Cited by 3 publications

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“…We define Equation (8) 4 and ly, respectively, and are used to determine M and <1> using equations (9) and (10 Figure l{b). The slope due to the tree (closer object) is smaller than the slope due to the railroad car when the slope is measured from the time (vertical) axis.…”

mentioning

confidence: 99%

Analysis of optical flow estimation using epipolar plane images

Rangachar¹,

Hong²,

Herman³

et al. 1991

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mentioning

confidence: 99%

Analysis of optical flow estimation using epipolar plane images

Rangachar¹,

Hong²,

Herman³

et al. 1991

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Real-time echocardiographic noise reduction, border extraction, and velocity derivation

Thomas

Higginbotham

Waxman

et al.

Proceedings. Computers in Cardiology 1988

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We describe initial experience in echocardiographic image processing utilizing a high-speed eight-stage array processor capable of performing multiple sequential algorithms at the real-time video field rate. Algorithms implemented include noise reduction with recursive temporal filtering, Gaussian spatial filtering and edge-preserving smoothing; edge enhancement with the Sobel magnitude and angle operators and difference of Gaussian convolutions; and velocity quantification with convected activation, a recently described method to derive normal motion from a sequence of binary edge maps.

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<title>Three-dimensional reconstruction from optical flow using temporal integration</title>

et al. 1991

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Image flow, the apparent motion of brightness patterns on the image plane, can provide important visual information such as distance, shape, surface orientation, and boundaries. It can be determined by either feature tracking or spatio-temporal analysis. The optical flow thus determined can be used to reconstruct the 3-D scene by determining the depth from camera of every point in the scene. However, the optical flow determined by either of the methods mentioned above will be noisy. As a result, the depth information obtained from optical flow can not be successfully used in practical applications such as image segmentation, 3-D reconsiruction, path planning, etc. By using temporal integration, we can increase the accuracy of both the optical flow and the depth determined from optical flow.In this work, we describe an incremental integration scheme called the running average method to temporally integrate the image flow. We integrate the depth from camera obtained using optical flow determined from gradient based methods, and show that the results of temporal integration are much more useful in practical applications than the results from local edge operators. Finally, we consider an image segmentation example and show the advantages of temporal integration.

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PIPE: A Parallel Processor For Dynamic Image Processing

Cited by 3 publications

References 0 publications

Analysis of optical flow estimation using epipolar plane images

Analysis of optical flow estimation using epipolar plane images

Real-time echocardiographic noise reduction, border extraction, and velocity derivation

<title>Three-dimensional reconstruction from optical flow using temporal integration</title>

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