Image segmentation for distributed target tracking and scene analysis

Abstract

Pixels in a feature space can be divided using a hashing method. Random planes are selected within a feature space and a pixel's relationship to each plane determines a bit of a hash code. Clusters of pixels can be identified by local maxima in the hash cells in the feature space. Nearby pixels in the feature space can be further assigned to these local maxima based on hamming distance. An image can be segmented by observing adjacent pixels sharing a common hash code.

Description

CROSS REFERENCE TO RELATED APPLICATIONS[0001]The present application claims priority to provisional patent applications 61 / 418,789, 61 / 418,805, and 61 / 418,799 which are incorporated by reference in their entirety.[0002]The present application relates to co-pending patent applications entitled “Scene Analysis Using Image and Range Data” and “Distributed Target Tracking Using Self Localizing Smart Camera Networks Technology Field” both of which are incorporated by reference in their entirety and filed on the same day as the present application entitled “Image Segmentation for Distributed Target Tracking and Scene Analysis.”TECHNOLOGY FIELD[0003]The present invention relates generally to machine vision systems and methods and specifically to image segmentation to determine salient features in an image. The present invention relates generally to machine vision systems and methods and specifically to object tracking and scene analysis for distributed or mobile applications.BACKGROUND[000...

AI Technical Summary

Benefits of technology

[0013]Embodiments of the present invention address and overcome one or more of the above shortcomings and drawbacks, by providing devices, systems, and methods for segme

Problems solved by technology

Segmentation is a method of breaking an image into coherent regions and is a common problem in Computer Vision.

Many known methods of segmentation are computationally intensive, making them unsuitable for fast, low power, or low cost applications, such as for use with distributed or mobile devices.

One issue that one needs to be addressed in applying this algorithm to segmentation problems is the question of choosing an appropriate value for k, which is typically not known beforehand.

A second issue is the fact that the k-means scheme involves repeated rounds of distance computations.

This means that the computational complexity grows with the number of pixels, the dimension of the feature space and the number of clusters.

K-means clustering is typically considered an NP-hard problem.

While some approaches have been proposed to mitigate this problem including the method developed by Elkan, which seeks to accelerate the process by invoking the triangle inequality, and Locality Sensitive Hashing schemes that search for near neighbors in the feature space, these have been unable to fully mitigate the distance computations required.

This non-parameteric estimation scheme can be very time consuming which makes it less useful in situations where real time response is desired.

The Parzen Window density estimation scheme employed in this approach also limits the dimension of the feature spaces to which it can be applied effectively.

Similarly, it is desirable for a segmentation method to be operated without the need for structured training data, which may not be readily available for use with low-cost or large volume cameras and processors.

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