Quaternion Photometric Stereo for Rotation Invariant Surface Texture Classification

Abstract

Problem statement: The escalating growth of computer vision applications has increased the need for faster and more accurate image analysis algorithms. One application of image analysis that has been studied for a long time is texture analysis. The majority of existing texture analysis methods makes the explicit or implicit assumption that texture images are acquired from the same viewpoint. This study presents a rotationally invariant descriptor for textures with different orientations based on the Quaternion Representation. Approach: A novel Quaternion Photometric Stereo (QPS) was proposed for Rotation invariant classification of 3D surface textures. QPS was constructed by placing each pixel of three images of same texture with different orientation into the three imaginary parts of the quaternion, leaving the real part zero. The Peak Distribution Norm Vector (PDNV) was extracted from the radial plot of the Quaternion Fourier spectrum as rotation invariant texture signature used for texture classification. Results: The quaternion representation of stereo images was to be effective in the context of Rotation Invariant Texture classification. Conclusion: The proposed Quaternion approach gives a successful classification rate with computational advantages than the previously developed Monochrome and Color Photometric Stereo Methods.