Knowledge and model based adaptive signal processor

• US 5,018,215 A
• Filed: 03/23/1990
• Issued: 05/21/1991
• Est. Priority Date: 03/23/1990
• Status: Expired due to Fees

• Current Assignee: Honeywell Incorporated (Honeywell International Inc.)
• Original Assignee: Honeywell Incorporated (Honeywell International Inc.)
• Inventors: Bazakos, Michael E., Nasr, Hatem N., Amehdi, Hossien, Sadjadi, Firooz A.
• Primary Examiner(s): Boudreau, Leo H.
• Assistant Examiner(s): Fallon, Steven P.
• Application Number:
  US07/498,196
• Time in Patent Office: 424 Days
• Field of Search: 382/1, 382/14, 382/15, 355/125, 355/126, 364/516, 342/64
• US Class Current: 382/155
• CPC Class Codes: G06F 18/00 Pattern recognition
• CPC Class Codes: Y10S 706/902 Application using ai with detail of the ai system

Abstract

A knowledge and model based adaptive signal processor for ensuring optimum performance in automated object recognition (AOR) systems. The invention does not require ground-truth information but uses predictive performance models for parameter adaption and updates for the AOR. The performance models are representations of continuous surfaces that interpolate and extrapolate for data and scenarios not previously tested. The present invention increases probability of detection and decreases false detection alarm rates of AOR systems.

Claims

1. A knowledge and model based adaptive signal processor comprising:

model means for providing a plurality of performance models having parameters;

differential means, connected to said model means, for differentiating each of the performance models;

change determining means for computing changes in metrics from a previous imagery frame to a current imagery frame;

optimizing means, connected to said differential means and to said change determining means, for determining and/or adjusting parameters that optimize the performance models; and

averaging means, connected to said optimizing means, for computing a weighted average of the parameters.

2. Apparatus of claim 1 wherein said model means has performance models which are a function of image metrics wherein the metrics are used by said optimizing means to adjust parameters that optimize the performance models.

3. Apparatus of claim 2 wherein:

said change determining means is connected to an automated instrumentation and evaluation means;

said optimizing means is connected to the automated instrumentation and evaluation means;

said averaging means is connected to the automated instrumentation and evaluation means; and

the automated instrumentation and evaluation means is connected to an automatic object recognition means.

4. Apparatus of claim 3 wherein:

each of the performance models has at least one axis representing metric values, an axis representing parameter values and an axis representing performance values; and

each performance model reveals a parameter value for a given metric value to obtain optimal performance of the automatic object recognition means.

5. Apparatus of claim 4 wherein each performance model indicates a change in the parameter value needed for a next imagery frame of the automatic recognition means, for a given change in the metric value from a current imagery frame to the next frame, to obtain an optimal performance value for obtaining optimal performance of the automatic object recognition means during the next frame.

6. Apparatus of claim 5 wherein said knowledge and model based adaptive signal processor is a Symbolics 3670 computer.

7. Apparatus of claim 5 wherein said knowledge and model based adaptive signal processor is a Sun 3/60 computer.

8. A knowledge and model based adaptive signal processor comprising:

model means for providing performance models having parameters;

knowledge means for providing a knowledge base;

optimizing means, connected to said model means and to said knowledge means, for optimizing the parameters of the performance models;

inference means, connected to said knowledge means, for inferring a set of parameter adaption rules from said knowledge means; and

controller means, connected to said inference means and to said optimization means, for activating the set of parameter adaption rules, receiving metrics of images, processing optimized parameters, and providing updated parameters.

9. A knowledge and model based adaptive signal processor comprising:

algorithm execution means, connected to a scenario and segmentation unit, for executing algorithms, processing images from the scenario and instrumentation unit, and outputting regions of interest to the scenario and segmentation unit;

computing means, connected to the scenario and segmentation unit, for computing changes in image and object metrics from a previous image to a current image;

a performance model base;

a heuristic knowledge base;

optimizing means, connected to said algorithm execution means, said computing means, said performance model base and said heuristic knowledge base, for differentiating performance models from said from said performance model base, obtaining heuristic search knowledge for reducing search space and achieving rapid convergences toward optimal parameters based on differential performance models, on prior parameters from said algorithm execution means, and on changes in image and object metrics from previous to current images;

update means, connected to said optimizing means, for adding or subtracting a optimized parameter change to or from a previous image parameter setting; and

summing means, connected to said algorithm execution means and said heuristic knowledge base, for summing outputs from said heuristic knowledge means and said update means and for outputting updated parameters to said algorithm execution means.

10. Method for knowledge and model based adaptive signal processing, for optimizing performance of an automatic object recognition system, comprising providing a plurality of performance models wherein:

each performance model has at least one axis representing a metric value, an axis representing a parameter value and an axis representing a corresponding performance value;

the performance model reveals the parameter value for a given metric value to obtain optimal performance of the automatic object recognition system; and

each performance model indicates a change in parameter value needed for a next imagery frame of the automatic object recognition system, for a given change in metric value between a current imagery frame and a previous imagery frame, to obtain an optimal performance value in order to obtain optimal performance of the automatic object recognition system during the next frame.

11. Method of claim 10 further comprising:

differentiating the plurality of performance models into differential values;

determining metric values from the current frame;

determining metric values from a previous frame;

computing the differences of metric values from the previous frame to the current frame;

obtaining parameter values from the current frame; and

optimizing parameter values for the next frame for each of the plurality of performance models, in order to obtain optimal performance values.

12. Method claim 11 further comprising computing a weighted average of parameter values optimized for the next frame, to obtain new parameters for a parameter update to the automatic object recognition system.