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Home > Authors > P > Jean-Marc Pelletier : 67 objects/ 1 library

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Objects
cv.jit.elongationAbstraction Estimates how thin a shape is.
cv.jit.erodeExternal This operation does somewhat the opposite of "dilate".
cv.jit.facesExternal Finds human faces in an image.
cv.jit.faces.drawAbstraction Visualize output of cv.jit.faces.
cv.jit.featuresExternal Finds areas of high contrast, pixels that are easy to track.
cv.jit.features.drawAbstraction Visualize output of cv.jit.features.
cv.jit.features2trackExternal Initialize cv.jit.track to easiest pixels to track.
cv.jit.floodfillExternal The floodfill algorithm takes a pixel coordinate specified by the "seed" attributes and checks the value of that pixel.
cv.jit.flow.drawAbstraction Visualize optical flow.
cv.jit.framesubAbstraction Difference between consecutive frames.
cv.jit.grabAbstraction Wrapper for jit.qt.grab/jit.dx.grab.
cv.jit.hough2linesAbstraction Find straight lines in Hough space.
cv.jit.HSflowExternal Estimates the optical flow using the Horn-Schunk method.
cv.jit.labelExternal This algorithm scans through the image and gives each connected component an individual value.
cv.jit.learnExternal Performs pattern analyis and recognition on an incoming list.
cv.jit.linesExternal Finds straight lines in a greyscale image.
cv.jit.lines.drawAbstraction Visualize output of cv.jit.lines.
cv.jit.LKflowExternal Estimates the optical flow using the Lucas-Kanade method.
cv.jit.massExternal This simple external returns the number of ON pixels in a binary image.
cv.jit.meanExternal Computes the mean value of each pixel over time.
cv.jit.momentsExternal Apart from returning centroids and mass, it outputs two lists of shape descriptors.
cv.jit.openExternal An "open" operation is simply an "erode" followed by a "dilate".
cv.jit.opticalflowExternal Calculate optical flow using a variety of techniques.
cv.jit.orientationAbstraction Calculates a shape's main axis.
cv.jit.perimeterAbstraction Counts the number of edge pixels.

Library
cv.jit cv.jit is a collection of max/msp/jitter tools for computer vision applications. The goals of this project are to provide externals and abstractions to assist users in tasks such as image segmentation, shape and gesture recognition, motion tracking, etc. as well as to provide educational tools that outline the basics of computer vision techniques.

4853 objects and 135 libraries within the database Last entries : January 20th, 2022 Last comments : 0 0 visitory and 13648586 members connected RSS
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