The 18th International Conference on Pattern Recognition : ICPR 2006, slides ; Place : Hong Kong, China ; Date : August 20-24, 200

Suzuki, Hiroshi

Tamaki, Toru

Yamamoto, Masanobu

Hiroshima University Institutional Repository

1ICPR2006 @ Hong KongString-like Occluding Region Extraction for Background RestorationToru Tamaki, Hiroshi Suzuki, Masanobu Yamamoto2ICPR2006 @ Hong KongString-like Occluding Region Extraction for Background RestorationToru Tamaki, Hiroshi Suzuki, Masanobu Yamamoto3ICPR2006 @ Hong KongString-like Occluding Region Extraction for Background RestorationToru Tamaki, Hiroshi Suzuki, Masanobu Yamamoto4ICPR2006 @ Hong KongString-like Occluding Region Extraction for Background RestorationToru Tamaki, Hiroshi Suzuki, Masanobu Yamamoto5ICPR2006 @ Hong KongString-like Occluding Region Extraction for Background RestorationToru Tamaki, Hiroshi Suzuki, Masanobu Yamamoto6ICPR2006 @ Hong KongString-like Occluding Region Extraction for Background RestorationToru Tamaki, Hiroshi Suzuki, Masanobu Yamamoto7modal / amodal completionmodalcompletionamodalcompletion8amodal completion in real scenes9Our Goal■ Objective Find occluding regions: given an image only Recover the background scene■ What’s “occlusion” ? difficult to define…■ Related Researches task-depend object detection➔ glasses➔ rain➔ fences➔ etc.10Our Goal■ Objective Find occluding regions: given an image only Recover the background scene■ What’s “occlusion” ? difficult to define…■ Our Target string-like regions➔ strings, wires, fences, branches, etc. properties➔ long and narrow➔ small, but not tiny➔ contrast with background➔ same background in both sides11Our Goal■ Objective Find occluding regions: given an image only Recover the background scene■ What’s “occlusion” ? difficult to define…■ Our Target string-like regions➔ strings, wires, fences, branches, etc. properties➔ long and narrow➔ small, but not tiny➔ contrast with background➔ same background in both sides12Our Goal■ Objective Find occluding regions: given an image only Recover the background scene■ What’s “occlusion” ? difficult to define…■ Our Target string-like regions➔ strings, wires, fences, branches, etc. properties➔ long and narrow➔ small, but not tiny➔ contrast with background➔ same background in both sides13Our Goal■ Objective Find occluding regions: given an image only Recover the background scene■ What’s “occlusion” ? difficult to define…■ Our Target string-like regions➔ strings, wires, fences, branches, etc. properties➔ long and narrow➔ small, but not tiny➔ contrast with background➔ same background in both sidesOX14Our Goal■ Objective Find occluding regions: given an image only Recover the background scene■ What’s “occlusion” ? difficult to define…■ Our Target string-like regions➔ strings, wires, fences, branches, etc. properties➔ long and narrow➔ small, but not tiny➔ contrast with background➔ same background in both sides15Circle Contrast: a proposed featureCircle Contrast :16Circle Contrast: a proposed featureCircle Contrast :17Circle Contrast: a proposed featureCircle Contrast :18Circle Contrast: a proposed featureCircle Contrast :19Circle Contrast in a flat region= 0= 0~~20Circle Contrast at side of a string= 0= 0= 0, but small21Circle Contrast at side of a string= 0= 0= 0, but small22Circle Contrast on the string= 0= 0= 0, but LARGE23Circle Contrast on the string= 0= 0= 0, but LARGEA point x where is● far from the region: 0● near to the region: not 0, but small● on the region: not 0, but large24Circle Contrast across the string= 0< 0black(0)white(255)black(0)< 025Circle Contrast across the string= 0> 0black(0)white(255)> 0black(0)26Circle Contrast across the string= 0< 0black(0)white(255)< 0black(0)The circle contrast alters its signas crossing a string-like region(from positive to negative, or vise versa) 27Circle Contrast as a spatial filterwr1=w/2r1=3w/4r1=wr1=5w/4r1=3w/2r1=2wHow large isthe circle enough?28Response across the stringw=20wblack(0)white(255)r1=10r1=15r1=20r1=25r1=30r1=4029Response across the stringw=20wblack(0)white(255)30Response across the stringw=20w31Absolute with locally largest valuepositivenegativesuppress to 0enhance peakslocal abs maxima32An exampleoriginal image Circle contrastwith sign decisionCircle contrastafter enhancementbinalizatoin removingsmall regionsstring-like regionextraction33Evaluation for Extractionprinted sheetcaptureoriginal image occluded image extracted imagedifference imageS TFP:FN:34Evaluation for Extractionstringsprinted sheetcaptureoriginal image occluded image extracted imagedifference imageS TFP:FN:35Evaluation for Extractionstringsprinted sheetcaptureoriginal image occluded image extracted imagedifference imageS TFP:FN:36Images for EvaluationTotal: over 180 images37ROC Curveover detectionmisdetection38Circle contrast as the radius changesw = 5r1=3wappropriate parameter value:r1=10 r1=15r1=5 r1=2039Binarization threshold of circle contrastThreshold range: [0, 1] Results are very sensitive to thresholdth=0.002 th=0.003 th=0.004 th=0.005th=0.006 th=0.007 th=0.010 th=0.02040Experimental resultsoriginal image Circle contrast extraction interpolation41Experimental resultsoriginal image Circle contrast extraction interpolation42Limitations■ Circle Contrast the circle radius r1 by user is requred➔ depends on images given sign determination is not enough➔ many artifacts➔ inappropriate decision at cross sections binarization threshold is the critical parameter43Conclusions■ String-like occluding object detection proposed and analyzed the Circle Contrast➔ simple model➔ good properties➔ needs parameter tuning evaluated by experimental results with images➔ quantitatively with ROC curve➔ qualitatively as changing parameters showed results images➔ with an simplest interpolation method■ Future works make the circle contrast more robust employ sophisticated binarization consider color and texture

String-like occluding region extraction for background restoration

Name not available

1ICPR2006 @ Hong KongString-like Occluding Region Extraction for Background RestorationToru Tamaki, Hiroshi Suzuki, Masanobu Yamamoto2ICPR2006 @ Hong KongString-like Occluding Region Extraction for Background RestorationToru Tamaki, Hiroshi Suzuki, Masanobu Yamamoto3ICPR2006 @ Hong KongString-like Occluding Region Extraction for Background RestorationToru Tamaki, Hiroshi Suzuki, Masanobu Yamamoto4ICPR2006 @ Hong KongString-like Occluding Region Extraction for Background RestorationToru Tamaki, Hiroshi Suzuki, Masanobu Yamamoto5ICPR2006 @ Hong KongString-like Occluding Region Extraction for Background RestorationToru Tamaki, Hiroshi Suzuki, Masanobu Yamamoto6ICPR2006 @ Hong KongString-like Occluding Region Extraction for Background RestorationToru Tamaki, Hiroshi Suzuki, Masanobu Yamamoto7modal / amodal completionmodalcompletionamodalcompletion8amodal completion in real scenes9Our Goal■ Objective? Find occluding regions: given an image only? Recover the background scene■ What’s “occlusion” ?? difficult to define…■ Related Researches? task-depend object detection➔ glasses➔ rain➔ fences➔ etc.10Our Goal■ Objective? Find occluding regions: given an image only? Recover the background scene■ What’s “occlusion” ?? difficult to define…■ Our Target? string-like regions➔ strings, wires, fences, branches, etc.? properties➔ long and narrow➔ small, but not tiny➔ contrast with background➔ same background in both sides11Our Goal■ Objective? Find occluding regions: given an image only? Recover the background scene■ What’s “occlusion” ?? difficult to define…■ Our Target? string-like regions➔ strings, wires, fences, branches, etc.? properties➔ long and narrow➔ small, but not tiny➔ contrast with background➔ same background in both sides12Our Goal■ Objective? Find occluding regions: given an image only? Recover the background scene■ What’s “occlusion” ?? difficult to define…■ Our Target? string-like regions➔ strings, wires, fences, branches, etc.? properties➔ long and narrow➔ small, but not tiny➔ contrast with background➔ same background in both sides13Our Goal■ Objective? Find occluding regions: given an image only? Recover the background scene■ What’s “occlusion” ?? difficult to define…■ Our Target? string-like regions➔ strings, wires, fences, branches, etc.? properties➔ long and narrow➔ small, but not tiny➔ contrast with background➔ same background in both sidesOX14Our Goal■ Objective? Find occluding regions: given an image only? Recover the background scene■ What’s “occlusion” ?? difficult to define…■ Our Target? string-like regions➔ strings, wires, fences, branches, etc.? properties➔ long and narrow➔ small, but not tiny➔ contrast with background➔ same background in both sides15Circle Contrast: a proposed featureCircle Contrast :16Circle Contrast: a proposed featureCircle Contrast :17Circle Contrast: a proposed featureCircle Contrast :18Circle Contrast: a proposed featureCircle Contrast :19Circle Contrast in a flat region= 0= 0~~20Circle Contrast at side of a string= 0= 0= 0, but small21Circle Contrast at side of a string= 0= 0= 0, but small22Circle Contrast on the string= 0= 0= 0, but LARGE23Circle Contrast on the string= 0= 0= 0, but LARGEA point x where is● far from the region: 0● near to the region: not 0, but small● on the region: not 0, but large24Circle Contrast across the string= 0< 0black(0)white(255)black(0)< 025Circle Contrast across the string= 0> 0black(0)white(255)> 0black(0)26Circle Contrast across the string= 0< 0black(0)white(255)< 0black(0)The circle contrast alters its signas crossing a string-like region(from positive to negative, or vise versa) 27Circle Contrast as a spatial filterwr1=w/2r1=3w/4r1=wr1=5w/4r1=3w/2r1=2wHow large isthe circle enough?28Response across the stringw=20wblack(0)white(255)r1=10r1=15r1=20r1=25r1=30r1=4029Response across the stringw=20wblack(0)white(255)30Response across the stringw=20w31Absolute with locally largest valuepositivenegativesuppress to 0enhance peakslocal abs maxima32An exampleoriginal image Circle contrastwith sign decisionCircle contrastafter enhancementbinalizatoin removingsmall regionsstring-like regionextraction33Evaluation for Extractionprinted sheetcaptureoriginal image occluded image extracted imagedifference imageS TFP:FN:34Evaluation for Extractionstringsprinted sheetcaptureoriginal image occluded image extracted imagedifference imageS TFP:FN:35Evaluation for Extractionstringsprinted sheetcaptureoriginal image occluded image extracted imagedifference imageS TFP:FN:36Images for EvaluationTotal: over 180 images37ROC Curveover detectionmisdetection38Circle contrast as the radius changesw = 5r1=3wappropriate parameter value:r1=10 r1=15r1=5 r1=2039Binarization threshold of circle contrastThreshold range: [0, 1] Results are very sensitive to thresholdth=0.002 th=0.003 th=0.004 th=0.005th=0.006 th=0.007 th=0.010 th=0.02040Experimental resultsoriginal image Circle contrast extraction interpolation41Experimental resultsoriginal image Circle contrast extraction interpolation42Limitations■ Circle Contrast? the circle radius r1 by user is requred➔ depends on images given? sign determination is not enough➔ many artifacts➔ inappropriate decision at cross sections? binarization threshold is the critical parameter43Conclusions■ String-like occluding object detection? proposed and analyzed the Circle Contrast➔ simple model➔ good properties➔ needs parameter tuning? evaluated by experimental results with images➔ quantitatively with ROC curve➔ qualitatively as changing parameters? showed results images➔ with an simplest interpolation method■ Future works? make the circle contrast more robust? employ sophisticated binarization? consider color and texture

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String-like occluding region extraction for background restoration

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