Subpixel Line Localization With Normalized Sums of Gradients and Location Linking With Straightness and Omni-Directionality

This paper presents a method to localize line locations with subpixel accuracy and a method to link the locations based on a linking distance. This paper first proposes a subpixel line localization method based on normalized sums of gradients (NSG) calculated by dividing pixel sum of gradients by the sum of gradient lengths within the pixel neighborhood. The proposed NSG method is compared with current state-of-the art based on a Taylor series approximation of intensity surface and the normal vector derived from the Hessian matrix. Comparative experiments for subpixel line localization methods were performed with simulated and natural images and confirmed the proposed subpixel localization method provided superior accuracy and faster localization under most combinations of varying line width and noise strengths than the state-of-the art localization method. The proposed linking method was also designed to have more straightness and omni-directionality than a current state-of-the art method. Experimental comparison of linking methods confirmed the proposed linking method provided superior linking quality than current state-of-the art.

[1]  Chang-Hong Lin,et al.  Lane-mark extraction for automobiles under complex conditions , 2014, Pattern Recognit..

[2]  Chien-Chang Chen,et al.  Edge detection improvement by ant colony optimization , 2008, Pattern Recognit. Lett..

[3]  Cláudio Rosito Jung,et al.  Lane following and lane departure using a linear-parabolic model , 2005, Image Vis. Comput..

[4]  John M. Gauch,et al.  Multiresolution Analysis of Ridges and Valleys in Grey-Scale Images , 1993, IEEE Trans. Pattern Anal. Mach. Intell..

[5]  Li Chen,et al.  Efficient discrete spatial techniques for blur support identification in blind image deconvolution , 2006, IEEE Trans. Signal Process..

[6]  Carsten Steger,et al.  An Unbiased Detector of Curvilinear Structures , 1998, IEEE Trans. Pattern Anal. Mach. Intell..

[7]  Cuneyt Akinlar,et al.  CannySR: Using smart routing of edge drawing to convert Canny binary edge maps to edge segments , 2015, 2015 International Symposium on Innovations in Intelligent SysTems and Applications (INISTA).

[8]  Andreas E. Savakis,et al.  Blur identification by residual spectral matching , 1993, IEEE Trans. Image Process..

[9]  Owen Robert Mitchell,et al.  Edge Location to Subpixel Values in Digital Imagery , 1984, IEEE Transactions on Pattern Analysis and Machine Intelligence.

[10]  Kwanghoon Sohn,et al.  Real-time illumination invariant lane detection for lane departure warning system , 2015, Expert Syst. Appl..

[11]  Nanning Zheng,et al.  PSF Estimation via Gradient Domain Correlation , 2012, IEEE Transactions on Image Processing.

[12]  Russell M. Mersereau,et al.  Blur identification by the method of generalized cross-validation , 1992, IEEE Trans. Image Process..

[13]  Debasish Kundu,et al.  An Automated Graphical User Interface based System for the Extraction of Retinal Blood Vessels using Kirsch's Template , 2015 .

[14]  Angel D. Sappa,et al.  Edge Point Linking by Means of Global and Local Schemes , 2008 .

[15]  Qiuming Zhu,et al.  Edge linking by a directional potential function (DPF) , 1996, Image Vis. Comput..

[16]  Haibo Wang,et al.  Automatic blur-kernel-size estimation for motion deblurring , 2014, The Visual Computer.

[17]  Frank Y. Shih,et al.  Adaptive mathematical morphology for edge linking , 2004, Inf. Sci..

[18]  Y. Shan,et al.  Sub-pixel location of edges with non-uniform blurring: a finite closed-form approach , 2000 .

[19]  Weisi Lin,et al.  Blind Blur Assessment for Vision-Based Applications , 2007, 2007 IEEE International Conference on Multimedia and Expo.

[20]  Cuneyt Akinlar,et al.  CEDContours: A high speed contour detector for color images , 2016, Image Vis. Comput..

[21]  Antonio M. López,et al.  On Ridges and Valleys , 2000 .

[22]  Nan Yang,et al.  A review of road extraction from remote sensing images , 2016 .

[23]  Paul F. Whelan,et al.  Computational approach for edge linking , 2002, J. Electronic Imaging.

[24]  Deepa Kundur,et al.  Blind Image Deconvolution , 2001 .

[25]  Cuneyt Akinlar,et al.  Edpf: a Real-Time parameter-Free Edge Segment Detector with a False Detection Control , 2012, Int. J. Pattern Recognit. Artif. Intell..

[26]  Youngjoon Han,et al.  Real-time Lane Detection Based on Extended Edge-linking Algorithm , 2010 .

[27]  Cuneyt Akinlar,et al.  PEL: A Predictive Edge Linking algorithm , 2016, J. Vis. Commun. Image Represent..

[28]  P. Kulla,et al.  Edge Detection with Sub-pixel Accuracy Based on Approximation of Edge with Erf Function , 2011 .

[29]  Bhabatosh Chanda,et al.  On edge and line linking with connectionist models , 1994, IEEE Trans. Syst. Man Cybern..

[30]  A. Murat Tekalp,et al.  Maximum likelihood image and blur identification: a unifying , 1990 .

[31]  Hong Zhang,et al.  Edge linking using geodesic distance and neighborhood information , 2008, 2008 IEEE/ASME International Conference on Advanced Intelligent Mechatronics.

[32]  Xiangjing An,et al.  Real-time lane departure warning system based on a single FPGA , 2013, EURASIP J. Image Video Process..

[33]  Aly A. Farag,et al.  Edge linking by sequential search , 1995, Pattern Recognit..

[34]  Suyoung Seo Subpixel Edge Localization Based on Adaptive Weighting of Gradients , 2018, IEEE Transactions on Image Processing.

[35]  Yitzhak Yitzhaky,et al.  Identification of Blur Parameters from Motion Blurred Images , 1997, CVGIP Graph. Model. Image Process..

[36]  Bahadir Karasulu AUTOMATIC EXTRACTION OF RETINAL BLOOD VESSELS: A SOFTWARE IMPLEMENTATION , 2012 .

[37]  Jorge L. Flores,et al.  Edge linking and image segmentation by combining optical and digital methods , 2013 .

[38]  Robert M. Haralick,et al.  Ridges and valleys on digital images , 1983, Comput. Vis. Graph. Image Process..