Automatic target recognition apparatus and process

• US 6,597,800 B1
• Filed: 09/22/1999
• Issued: 07/22/2003
• Est. Priority Date: 11/05/1997
• Status: Expired due to Fees

• Current Assignee: Selex Galileo Limited (Leonardo S.p.A.)
• Original Assignee: BAE Systems Plc
• Inventors: Murray, Alan, Reavy, Richard
• Primary Examiner(s): Mehta, Bhavesh M.
• Assistant Examiner(s): AZARIAN, SEYED H
• Application Number:
  US09/401,412
• Time in Patent Office: 1,399 Days
• Field of Search: 382/103, 382/190, 382/260, 382/270, 382/173, 382/215, 382/151, 382/100, 382/171, 382/179, 382/177, 382/178, 382/175, 382/104, 382/165.198, 382/224-225, 345/157, 345/158, 345/167, 430/988, 342/66, 348/169, 348/170, 348/206, 348/207.9
• US Class Current: 382/103
• CPC Class Codes: F41G 7/2226 comparing the observed data with stored target data, e.g. target configuration data
• CPC Class Codes: F41G 7/2253 Passive homing systems, i.e. comprising a receiver and do not requiring an active illumination of the target
• CPC Class Codes: F41G 7/2293 using electromagnetic waves other than radio waves
• CPC Class Codes: G06T 7/00 Image analysis
• CPC Class Codes: G06V 10/255 Detecting or recognising potential candidate objects based on visual cues, e.g. shapes
• CPC Class Codes: G06V 10/40 Extraction of image or video features

Abstract

Apparatus for automatic recognition of a target object from an infrared or visible light image, includes primary segmentation and classification means (4,8,9,10,11,12) in which objects are either recognised or unrecognised and one or more secondary segmentation means (15,21,22,23,12) applicable to primary segmented image areas in which objects which are unrecognised by the primary segmentation means are further classified and either recognised or rejected.

Claims

1. Apparatus for automatic recognition of a target object from a multimetric wave infrared or visible light image, including an image producing device;

a primary separator for subjecting the image to primary segmentation in which the image is divided up into one or more primary homogeneous regions each approximating to an object of interest and data is extracted from the image about these primary regions,

a first feature extraction device for receiving the extracted primary region data from the primary separator and recognising and extracting features from the extracted primary region data, which features have been predetermined to separate objects in the primary regions into different classes,

a first classifying means for receiving the extracted features and classifying them thereby to recognise each object in the primary regions or to indicate that one or more of the objects is unclassified and therefore unrecognised,

a secondary segmentation unit for receiving from the primary separator data about the original segmented image primary region containing an unrecognised object and for submitting this data to secondary segmentation to provide sub regions of greater homogeneity, and for extracting data from the primary region about the sub regions,

a third feature extraction device for recognising and extracting classifying features from the extracted sub region data and,

a second classifying means for receiving the extracted sub-region classifying features and utilising them to classify and thereby recognise each object in the sub regions or to recognise that each object in the sub regions is not a target object.

2. Apparatus according to claim 1, including more than one secondary segmentation unit.

3. Apparatus according to claim 1, wherein the first classifying means includes a first classifier for receiving the extracted features from the first feature extraction device and sorting and classifying the extracted features, a first comparator for receiving, in parallel with the first classifier, the extracted features from the first feature extraction device, and generating a value of the probability that the classification by the first classifier is correct and first assessment means for receiving data from the first classifier and first comparator and determining recognition or non recognition of each object in the primary regions.

4. Apparatus according to claim 1 wherein the second classifying means includes a second classifier for receiving the extracted sub region classifying features from the third feature extraction device and sorting and classifying the extracted sub region features, a second comparator, in parallel with the second classifier, for receiving the extracted sub region classifying features from the third feature extraction device, and generating a value of the probability that the classification by the second classifier is correct, and second assessment means for receiving data from the second classifier and second comparator and determining recognition or non recognition of each object in the sub regions.

5. Apparatus according to claim 3 wherein the first and second assessment means are combined in a single assessor unit.

6. Apparatus according to claim 1, including a frame memory store for receiving and storing data from the image producing device for passage to the primary separator.

7. Apparatus according to claim 1, including a feature store for receiving data from the first feature extraction device and for passing the data to the first classifying means.

8. Apparatus according to claim 6, including a secondary store for receiving and storing image data from the frame memory store for passage to the secondary segmentation unit.

9. Apparatus according to claim 8, wherein the primary separator is operable to output bounding box data to the first feature extraction device, and including an image region extending device operable to receive the primary segmentation bounding box data outputted from the first feature extraction device extend the image region described by the bounding box data and pass the extended region data as a control signal to the secondary store and to the frame memory store.

10. Apparatus according to claim 1 including a second feature extraction device for receiving grey level data and binary mask data outputted from the primary separator and for extracting classifying features from the primary segmentation data for the previous frame in the sequence.

11. Apparatus according to claim 10, including a resegmentation prediction unit for receiving extracted primary segmentation classifying features from the second feature extraction device which estimates the optimum secondary segmentation process for the object of the classifying features and passes the process strategy as a control signal to the secondary segmentation unit.

12. A process for automatic recognition of a target object from an infrared or visible light image, including the steps of;

subjecting the image to primary segmentation in which the image is divided up into one or more primary homogeneous regions each approximating to an object of interest,

extracting data from the image about these primary regions,

recognising and extracting features from the extracted primary regions, which features have been predetermined to separate objects in the primary regions into different classes,

utilising the extracted features to classify and thereby recognise each object in the primary regions or to indicate that one or more of the objects is unclassified and therefore unrecognised,

subjecting the original segmented image primary region containing an unrecognised object to secondary segmentation to provide sub regions of greater homogeneity,

extracting data from the primary region about the sub regions,

recognising and extracting classifying features from the extracted sub region data and,

utilising the extracted classifying features to classify and thereby recognise each object in the sub regions or to recognise that each object in the sub regions is not a target object.

13. A process according to claim 12, including more than one secondary segmentation.

14. A process according to claim 12, in which data relating to the object recognised or unrecognised from the primary segmentation steps is compared with data relating to the object recognised or unrecognised from each further secondary segmentation steps, and assessed to provide object recognition or rejection.

15. A process according to claim 14, in which the assessment involves the production and consideration of a probability estimate value for each segmentation.

16. A process according to any claim 13, in which the primary segmentation is carried out by bounding the primary regions in dependence upon the intensity of illumination change at the boundary.

17. A process according to claim 16, in which each secondary segmentation is carried out by passing the data through a series of four modules each of which assesses the change of illumination intensity at the boundary or edge of the secondary region at different intensity change thresholds.

18. A process according to claim 16, in which each secondary segmentation is carried out by subjecting the data to a Fast Fourier Transform in two dimensions.

19. A process according to claim 13, in which primary segmentation is carried out to produce data comprising image region bounding box data, binary mask data and grey level data from which said features are extracted.

20. A process according to claim 19, in which the image region described by the primary bounding box data is extended to produce the sub regions of greater homogeneity to form extracted primary data, primary classifying features are extracted from the primary binary mask data and primary grey level data, the primary classifying features are submitted to an assessment and prediction of the optimum secondary segmentation route to form further extracted primary data, and the extracted primary data is submitted to secondary segmentation.

21. An automatic target recognition device when operation according to the process of claim 13.