INFRARED CAMERA DETECTION SYSTEMS AND METHODS

• US 20130147951A1
• Filed: 02/07/2013
• Published: 06/13/2013
• Est. Priority Date: 08/11/2010
• Status: Active Grant

• Current Assignee: Teledyne FLIR LLC (Teledyne Technologies Incorporated)
• Original Assignee: FLIR Systems, Inc. (Teledyne Technologies Incorporated)
• Inventors: Hgasten, Nicholas, Richards, Austin A., Brown, Andrew Phillip
• Granted Patent: US 9,247,159 B2
• Time in Patent Office: Days
• US Class Current: 348/143
• CPC Class Codes: G01N 25/18 by investigating thermal conductivity by calorimetry G01N25/20; by measuring change of resistance of an electrically-heated body G01N27/18
• CPC Class Codes: G01N 33/1833 Oil in water water in oil G01N33/2847
• CPC Class Codes: G06F 2218/08 Feature extraction
• CPC Class Codes: G06T 2207/10048 Infrared image
• CPC Class Codes: G06T 2207/20076 Probabilistic image processing
• CPC Class Codes: G06T 2207/30108 Industrial image inspection
• CPC Class Codes: G06T 2207/30181 Earth observation
• CPC Class Codes: G06T 7/0004 Industrial image inspection
• CPC Class Codes: H04N 5/33 Transforming infrared radiation cameras or camera modules for generating image signals from infrared radiation H04N23/20; circuitry of SSIS for transforming infrared radiation into image signals H04N25/20

Abstract

Infrared imaging systems and methods disclosed herein, in accordance with one or more embodiments, provide for detecting petroleum in an infrared image. The detection involves the evaluation of local background model likelihoods in light of extracted pixel features as well as the evaluation of a global outlier model in light of extracted pixel features. Those pixels having a low likelihood for both their local background model as well as the outlier model are identified as corresponding to oil-contaminated water within the infrared image.

Claims

1. An infrared camera system, comprising:

a sensor adapted to capture an image of a body of water, wherein the image comprises a plurality of digital pixels; and

a processor adapted to evaluate a first set of features from the digital pixels with regard to a local background model, the processor being further adapted to detect a petroleum-containing contaminant on the body of water responsive to the local background model evaluation

2. The infrared camera system of claim 1, wherein the processor is further adapted to evaluate a second set of features from the digital pixels with regard to an outlier model, the processor being further adapted to detect the petroleum-containing contaminant responsive to the outlier model evaluation.

3. The infrared camera system of claim 2, wherein the local background model comprises a plurality of local background models corresponding on a one-to-one basis to the plurality of digital pixels.

4. The infrared camera system of claim 3, wherein the plurality of digital pixels are downsampled pixels.

5. The infrared camera system of claim 2, wherein the outlier model comprises a single outlier model.

6. The infrared camera system of claim 3, wherein the first set of features comprises an intensity for each pixel, and wherein the evaluation with regard to the local background model comprises determining a likelihood of the local background model in light of the corresponding pixel'"'"'s intensity.

7. The infrared camera system of claim 6, wherein the processor is further adapted to determine a likelihood of the outlier model in light of the extracted features from each pixel.

8. The infrared camera system of claim 7, wherein the processor is further adapted to identify pixels whose extracted features are relatively unlikely under both the outlier model and the local background model, wherein the identified pixels correspond to the detected petroleum-containing contaminant.

9. A method of detecting a petroleum-containing contaminant within an infrared image comprised of a plurality of digital pixels, the method comprising:

extracting an at least one feature from each of the pixels, wherein for each pixel a corresponding local background model models the statistical behavior for the at least one feature;

determining a likelihood for each local background model in light of the at least one extracted feature; and

detecting contaminant-containing pixels within the infrared image responsive to the determined likelihoods.

10. The method of claim 9, wherein the at least one extracted feature comprises a pixel intensity.

11. The method of claim 10, wherein the at least one extracted feature further comprises a pixel contrast.

12. The method of claim 11, wherein the at least one extracted feature further comprises a pixel texture.

13. The method of claim 9, further comprising:

providing an outlier model, wherein the outlier model models the statistical behavior for the at least one extracted feature for pixels identified as corresponding to relatively unlikely local background models; and

determining a likelihood for the outlier model for each of the pixels, wherein the detection of the contaminant-containing pixels is further responsive to outlier model likelihoods.

14. The model of claim 13, wherein the statistical behavior modeled by each of the local background models and by the outlier model is in the absence of contaminant.

15. The model of claim 13, further comprising updating each of the local background models responsive to the extracted features.

16. The model of claim 15, further comprising weighting each pixel according to the likelihood of the corresponding local background model.

17. The model of claim 16, further comprising updating the outlier model responsive to pixel weights.

18. A method, comprising

sensing a first infrared image frame of an ocean surface;

wherein the infrared image frame comprises a plurality of digital pixels;

for each pixel, modeling a statistical behavior of at least one extracted image feature using a mixture of Gaussians model (MGM), each pixel thereby corresponding to a local background model that models the statistical behavior;

sensing a second infrared image frame of the ocean surface;

for each pixel in the second infrared image frame, extracting the at least one image feature;

for each pixel in the second infrared image frame, determining a likelihood of the corresponding local background model in light of the extracted image feature; and

detecting any oil-containing pixels in the second infrared image responsive to the determined likelihoods.

19. The method of claim 18, wherein the MGM comprise three Gaussians.

20. The method of claim 18, further comprising updating each local background model if no oil-containing pixels are detected.