Object detection

Inactive Publication Date: 2005-06-16
SONY UK LTD
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  • Summary
  • Abstract
  • Description
  • Claims
  • Application Information

AI Technical Summary

Benefits of technology

[0012] The invention allows the use of multiple scales of object p

Problems solved by technology

In any sort of block-based analysis of a possible face, or an analysis involving a comparison between the possible face and some pre-derived data indicative of the presence of a face, there is the difficulty that the blocks have been “trained” on faces of a particular size.
This can make a simple block-based system unsuitable for detection of a fac

Method used

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Embodiment Construction

[0042] The embodiments will be described with respect to face detection, but are equally applicable to a detection of other objects such as cars, by training with training images representing the required objects.

[0043]FIG. 1 is a schematic diagram of a general purpose computer system for use as a face detection system and / or a non-linear editing system. The computer system comprises a processing unit 10 having (amongst other conventional components) a central processing unit (CPU) 20, memory such as a random access memory (RAM) 30 and non-volatile storage such as a disc drive 40. The computer system may be connected to a network 50 such as a local area network or the Internet (or both). A keyboard 60, mouse or other user input device 70 and display screen 80 are also provided. The skilled man will appreciate that a general purpose computer system may include many other conventional parts which need not be described here.

[0044]FIG. 2 is a schematic diagram of a video camera-recorde

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Abstract

A method of detecting objects using multiple sets of object probability data relating to different respective expected object sizes relative to the image size comprises the steps of: (a) storing a list of locations in an image where an object has been detected, the list defining a probability data set and a probability value for each detected object; (b) detecting object locations in a set of object probability data as locations at which a local maximum probability value exceeds a threshold probability value and, if that location is substantially similar to a location previously stored in the list, where the local maximum probability value exceeds that relating to the previously stored location; and (c) repeating step (b) successively in respect of each set of probability data.

Description

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Claims

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Application Information

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Owner SONY UK LTD
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