Object Recognition and Categorization
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Home > Art, Film & Photography > Object Recognition and Categorization: Scale-Invariant Feature Transform, Bag of Words Model in Computer Vision, Microsoft Surface
Object Recognition and Categorization: Scale-Invariant Feature Transform, Bag of Words Model in Computer Vision, Microsoft Surface

Object Recognition and Categorization: Scale-Invariant Feature Transform, Bag of Words Model in Computer Vision, Microsoft Surface


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About the Book

Please note that the content of this book primarily consists of articles available from Wikipedia or other free sources online. Pages: 33. Chapters: Scale-invariant feature transform, Bag of words model in computer vision, Microsoft Surface, Gesture recognition, Histogram of oriented gradients, Object categorization from image search, Object recognition, LabelMe, Segmentation-based object categorization, 3D single object recognition, Face detection, Boosting methods for object categorization, Viola-Jones object detection framework, 3D computer vision, Google Goggles, Part-based models. Excerpt: Scale-invariant feature transform (or SIFT) is an algorithm in computer vision to detect and describe local features in images. The algorithm was published by David Lowe in 1999. Applications include object recognition, robotic mapping and navigation, image stitching, 3D modeling, gesture recognition, video tracking, and match moving. The algorithm is patented in the US; the owner is the University of British Columbia. For any object in an image, interesting points on the object can be extracted to provide a "feature description" of the object. This description, extracted from a training image, can then be used to identify the object when attempting to locate the object in a test image containing many other objects. To perform reliable recognition, it is important that the features extracted from the training image be detectable even under changes in image scale, noise and illumination. Such points usually lie on high-contrast regions of the image, such as object edges. Another important characteristic of these features is that the relative positions between them in the original scene shouldn't change from one image to another. For example, if only the four corners of a door were used as features, they would work regardless of the door's position; but if points in the frame were also used, the recognition would fail if the door is opened or closed. Similarly, features loc...


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Product Details
  • ISBN-13: 9781233181360
  • Publisher: Books LLC, Wiki Series
  • Publisher Imprint: Books LLC, Wiki Series
  • Height: 246 mm
  • No of Pages: 34
  • Spine Width: 2 mm
  • Weight: 82 gr
  • ISBN-10: 123318136X
  • Publisher Date: 24 Aug 2011
  • Binding: Paperback
  • Language: English
  • Returnable: N
  • Sub Title: Scale-Invariant Feature Transform, Bag of Words Model in Computer Vision, Microsoft Surface
  • Width: 189 mm


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Object Recognition and Categorization: Scale-Invariant Feature Transform, Bag of Words Model in Computer Vision, Microsoft Surface
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