Reflectance and texture of real-world surfaces
ACM Transactions on Graphics (TOG)
The Analysis and Recognition of Real-World Textures in Three Dimensions
IEEE Transactions on Pattern Analysis and Machine Intelligence
Shape from Periodic Texture Using the Eigenvectors of Local Affine Distortion
IEEE Transactions on Pattern Analysis and Machine Intelligence
Adapting Spectral Scale for Shape from Texture
ECCV '00 Proceedings of the 6th European Conference on Computer Vision-Part I
Analysis of Curved Textured Surfaces Using Local Spectral Distortion
ICAPR '01 Proceedings of the Second International Conference on Advances in Pattern Recognition
Improved Orientation Estimation for Texture Planes Using Multiple Vanishing Points
CAIP '99 Proceedings of the 8th International Conference on Computer Analysis of Images and Patterns
Pattern Recognition Letters
Object material classification by surface reflection analysis with a time-of-flight range sensor
ISVC'10 Proceedings of the 6th international conference on Advances in visual computing - Volume Part II
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Uniformly textured surfaces in 3D scenes provide important cues for image understanding. Texture can be used for both segmentation and for 3D shape inference. Unfortunately, virtually all current algorithms are based on assumptions that make it impossible to do texture segmentation and shape-from-texture in the same image. Texture segmentation algorithms rely on an absence of 3D effects that tend to distort the texture. Shape-from-texture algorithms depend on these effects, relying instead on the texture being already segmented. To really understand texture in images, texture segmentation and shape-from-texture must be viewed as a combined problem to be solved simultaneously. We present a solution to this problem with a region-growing algorithm that explicitly accounts for perspective distortions of otherwise uniform texture. We use the image spectrogram to compute local surface normals, which are in turn used to "frontalize" the texture. These frontalized texture patches are then subjected to a region-growing algorithm based on similarity in the local frequency domain and a minimum description length criteria. We show results of our algorithm on real texture images taken in the lab and outdoors.