基于亮度颜色文理边缘检测2

1、Texture Featuresiiiiihghghg22)(21),( Texture Gradient TG(x,y,r, ) Pixel-texture values are computed using 13 filters Pixels are thus represented with a 13-feature vector Each disk-half is modeled by a point cloud of vectors in 13-dimensional space Problem: How does one compare two 13D spaces?0Textur

2、e Featuresiiiiihghghg22)(21),( Solution: Textons estimate joint distribution using adaptive bins Filter response vectors are clustered using k-means Cluster centers represent texture primitives (textons) Example Texton set K = 64 Trained using 200 images1Texture Features2iiiiihghghg22)(21),( Texture

3、 Gradient TG(x,y,r, ) Pixels in each disk-half are assigned to nearest texton 2 difference of texton histograms Textons are vector-quantized filter outputsTextonMapFeature Localization3Evaluation Boundary detector quality Used for optimizing parameters Comparing to other techniques Human-marked boun

4、daries as ground truth 1000 images, 5-10 segmentations Highly consistent4Alternate ApproachesCanny DetectorCanny 1986MATLAB implementationWith and without hysteresisSecond Moment MatrixNitzberg/Mumford/Shiota 1993cf. Frstner and Harris corner detectorsUsed by Konishi et al. 1999 in learning framewor

5、kLogistic model trained on eigenspectrum5Summary & Comments1.A simple linear model is sufficient for cue combinationAll cues weighted approximately equally in logistic2.Edge detection does not optimally for complex natural imagesTexture suppression is not sufficient3.Proposed method offers significant improvements Simple but powerful feature detectors Simple model for cue combination4.Surprisingly effective for a low level approach 1. Likely isnt robust to larger texturesOffset by using multiple scales2. Prohibitively sl