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By Zhihui X. (ed.)

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Lowe uses a histogram binning of 8 different gradient directions (covering 360◦ ). For a 16 × 16 patch, this leads to 16 sample regions, and a SIFT descriptor vector of dimension 128 (16 regions × 8 orientations). For the most up-to-date and complete presentation of SIFT descriptors see [123]. The SIFT representation has been further improved by applying principal component analysis to the gradient patches, resulting in PCA-SIFT descriptors [91]. 3 below. g. parametric eigenspace) work directly on the grayvalues of the pixels of an input image, but are more suited for specific OR than for categorization.

But I include a very brief discussion of potential 50 Building Blocks for Categorization benefits that can be expected from 3D reconstructionist approaches at the end of this section. 1 Bags of keypoints The simplest possible object model is to use no model at all. This approach has also been termed ‘model-free’ or ‘geometry-free’. The basic idea is to extract salient points (keypoints) from images, and to represent an image as a set of such keypoints including some descriptor. 2. In the first step, a set of keypoints and their descriptors (feature vectors) can be extracted from all images that are presented to a ‘bags of keypoints’ categorization system.

What is the role of visual episodes? Is there a kind of episodic memory? These and similar questions have been researched under the term ‘Visual Active Memory Processes’ within the European Cognitive Vision Project ‘Vampire’17 , and a prototype of visual active memory that can hold information about visual events and episodes at different levels of abstraction has been built [77]. As a final remark, analysis of visual structure is related to analysis of motion. g. in functional MRI of monkeys [119].

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