#166: CSANG: Continuous scale anisotropic Gaussians for robust linear structure extraction

V. B. S. Prasath, S. Surineni, K. Gao, G. Seetharaman, and K. Palaniappan

Proc. IEEE Applied Imagery Pattern Recognition (AIPR), 2016

arst, edge detection, gpu

PlainText, Bibtex, Google Scholar


Robust estimation of linear structures such as edges and junction features in digital images is an important problem. In this paper, we use an adaptive robust structure tensor (ARST) method where the local adaptation process uses spatially varying adaptive Gaussian kernel that is initialized using the total least-squares structure tensor solution. An iterative scheme is designed with size, orientation, and weights of the Gaussian kernel adaptively changed at each iteration step. Such an adaptation and continuous scale anisotropic Gaussian kernel change for local orientation estimation helps us obtain robust edge and junction features. We consider an efficient graphical processing unit (GPU) implementation which obtained 30x speed improvement over traditional central processing unit (CPU) based implementations. Experimental results on noisy synthetic and natural images indicate that we obtain robust edge detections and further comparison with other edge detectors shows that our approach obtains better localization and accuracy under noisy conditions.