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Computer vision method and system for blob-based analysis using a probabilistic framework

A computerized, unassigned technology used in computer vision and analytics

Inactive Publication Date: 2006-07-05
KONINKLIJKE PHILIPS ELECTRONICS NV
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  • Summary
  • Abstract
  • Description
  • Claims
  • Application Information

AI Technical Summary

Problems solved by technology

The method allows segmenting an image from the foreground to determine clusters

Method used

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  • Computer vision method and system for blob-based analysis using a probabilistic framework
  • Computer vision method and system for blob-based analysis using a probabilistic framework
  • Computer vision method and system for blob-based analysis using a probabilistic framework

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Embodiment Construction

[0016] The invention discloses a block-based analysis method and system. The method disclosed here uses a probabilistic framework and an iterative process to determine the number, location and size of blocks in an image. A block is a number of pixels that stand out in an image. In general, salientation is produced by background-foreground segmentation, referred to herein as "foreground segmentation." Groups are pixels grouped together, where a group is defined by a shape determined to fit a particular group of pixels. Here, the term "cluster" is used to refer to both the shape determined to fit a particular group of pixels and the pixels themselves. It should be noted that, as regards figure 2 As shown in more detail, one block can be assigned to multiple clusters, and multiple blocks can be assigned to one cluster.

[0017] The invention can also add, remove and delete clusters. Additionally, clusters can be tracked independently, and trace information can be output.

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Abstract

Generally, techniques for analyzing foreground-segmented images are disclosed. The techniques allow clusters to be determined from the foreground-segmented images. New clusters may be added, old clusters removed, and current clusters tracked. A probabilistic framework is used for the analysis of the present invention. A method is disclosed that estimates cluster parameters for one or more clusters determined from an image comprising segmented areas, and evaluates the cluster or clusters in order to determine whether to modify the cluster or clusters. These steps are generally performed until one or more convergence criteria are met. Additionally, clusters can be added, removed, or split during this process. In another aspect of the invention, clusters are tracked during a series of images, and predictions of cluster movements are made.

Description

technical field [0001] The present invention relates to computer vision and analysis, and more particularly to computer vision methods and systems for block-based analysis using a probabilistic framework. Background technique [0002] A common computer vision approach is called "background-foreground segmentation", or more simply "foreground segmentation". In foreground segmentation, foreground objects are identified and highlighted in a certain way. One method for performing foreground segmentation is "background subtraction". In this scheme, the camera looks at the background for a predetermined number of images so that the computer vision system can "learn" the background. Once the background is learned, computer vision systems can determine changes in the scene by comparing new images with representations of background images. The difference between the two images represents foreground objects. Methods for background subtraction are included in A. Elgammal, D. Harwoo...

Claims

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Application Information

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IPC IPC(8): G06T5/00G06T7/20G06T7/00G06K9/46
CPCG06T7/2033G06K9/4638G06T7/246G06V10/457G06V10/26
Inventor A·科梅纳雷滋S·V·R·古特塔T·布罗德斯基
Owner KONINKLIJKE PHILIPS ELECTRONICS NV
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