By Ramesh C. Jain, Anil K. Jain
Computer imaginative and prescient researchers were annoyed of their makes an attempt to immediately derive intensity details from traditional two-dimensional depth pictures. learn on "shape from texture", "shape from shading", and "shape from concentration" continues to be in a laboratory degree and had no longer noticeable a lot use in advertisement computing device imaginative and prescient platforms. a spread snapshot or a intensity map includes particular information regarding the space from the sensor to the thing surfaces in the box of view within the scene. information regarding "surface geometry" that's vital for, say, three-d item reputation is extra simply extracted from "2 half D" variety photographs than from "2D" depth pictures. therefore, either energetic sensors resembling laser diversity finders and passive ideas reminiscent of multi-camera stereo imaginative and prescient are being more and more used by imaginative and prescient researchers to resolve various difficulties. This ebook comprises chapters written via exceptional computing device imaginative and prescient researchers protecting the next parts: review of 3D imaginative and prescient variety Sensing Geometric Processing item attractiveness Navigation Inspection Multisensor Fusion A workshop file, written by way of the editors, additionally appears to be like within the e-book. It summarizes the state-of-the-art and proposes destiny learn instructions in diversity picture sensing, processing, interpretation, and functions. The e-book additionally comprises an intensive, up to date bibliography at the above issues. This ebook offers a distinct viewpoint at the challenge of three-d sensing and processing; it's the basically finished number of papers dedicated to variety pictures. either educational researchers attracted to study matters in 3D imaginative and prescient and business engineers looking for options to specific difficulties will locate this an invaluable reference book.
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Additional info for Analysis and Interpretation of Range Images
For example, range data obtained from a laser scanner, and intensity data obtained from a TV camera provide complementary information. Range data provides important clues on the geometry of an observed scene. However, it does not provide any information about the physical properties of the scene objects such as color or intensity. On the other hand, it is extremely difficult to extract geometrical information from TV data. Therefore, both types of data need to be analyzed. Doing this correctly involves understanding the physics of the problem, and thus how one sensor's output is related to another's.
9. Also assume that the camera positions are such that panel 2 is not visible in the left image. If we assume that the surface of the object is randomly textured, it is highly probable that the zero-crossings in the panel 2 portion in the right image will match with some zero-crossings from either panel 1 or 3 in the left image. 10 illustrates the calculated disparity values along line PQ shown in the previous figure. 9 presents no difficulties whatsoever when it comes to depth perception, even in the presence of occlusions, as shown there.
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Analysis and Interpretation of Range Images by Ramesh C. Jain, Anil K. Jain