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Algorithms

 Re: state-of-the art computer/machine vision system Bernhard Hiller10-Oct-11 23:17 Bernhard Hiller 10-Oct-11 23:17
 Re: state-of-the art computer/machine vision system Richard MacCutchan11-Oct-11 0:12 Richard MacCutchan 11-Oct-11 0:12
 Re: state-of-the art computer/machine vision system killabyte11-Oct-11 1:10 killabyte 11-Oct-11 1:10
 Re: state-of-the art computer/machine vision system mikemar18-Oct-11 13:35 mikemar 18-Oct-11 13:35
 mikemarquard wrote:1) Defining the edge of objects: Most objects in the real world will have areas where the edges are blurred rather than sharp color changes. Look up canny edge detection and it will explain some of this stuff. 2) Recognizing 2 areas are part of the same object: Consider a cat with black and white patches. How is a vision system supposed to know that 2 areas with radically different colors are part of the same object. 3) Depth Perception: If you use 2 cameras similar to our 2 eyes you can match 2 objects and then compare the parallax shift. However, this only works at certain distances. Our brains probably only use this at short distances, several other methods are used at long distances where the parallax shift isn't large enough to judge.   1) I would agree that my ideas will change in time because they already have, but for the better, at first i started off trying edge detection methods but later on realised that edge detection is not necessary, descriptors such as SIFT,SURF,DOT,HOG and many more use orientation and not contours. This is supported by biological vision in simple and complex cells, my system follows this trend. orientation is not affected by blurring thus more robust and descriptive. 2) My system uses local image patches and a part based recognition infrastructure without segmentation since segmentation is a by-product of recognition then the vision system is not supposed to segment out scenes or potential objects before recognizing them. 3) My system is not currently designed to use stereo cameras it uses a single camera and does not need depth or capturing a 3D representation to aid recognition. my project as evolved in actual sense and i'am using my on vision library to implement the system and i have figured out how to encode image data in an efficient and robust manner for building a generic object recognition system. How do i know that it will work?well i have been progressively testing simple building blocks of the system and now i'am certain that this will work when the whole system is put together. i am optimizing my vision library for the final implementation and probably months remaining before completion.
 Re: state-of-the art computer/machine vision system mikemar18-Oct-11 16:43 mikemar 18-Oct-11 16:43
 Re: state-of-the art computer/machine vision system mikemar19-Oct-11 18:49 mikemar 19-Oct-11 18:49
 Re: state-of-the art computer/machine vision system mikemar20-Oct-11 16:58 mikemar 20-Oct-11 16:58
 Re: state-of-the art computer/machine vision system YvesDaoust19-Oct-11 3:37 YvesDaoust 19-Oct-11 3:37