Linear Feature Extraction using combined approach of Hough transform, Eigen values and Raster scan Algorithms
01 Dec 2006-pp 65-70
TL;DR: In this paper, the generalized standard Hough transform (HT), eigen value based statistical parameter analysis and Bresenham's raster scan algorithms are used for linear geometric primitive identification.
Abstract: In this paper we propose a new method for linear geometric primitive identification which uses the generalized standard Hough transform (HT), Eigen value based statistical parameter analysis and Bresenham 's raster scan algorithms In this method, we use the sparse matrix to find the Hough transform of the given image Since sparse matrices squeeze zero elements and contain a small number of nonzero elements they provide an advantage in matrix storage space and computational time Hough peaks are identified based on neighborhood suppression scheme After finding the meaningful and distinct Hough peaks, coordinates of linear features in Hough space can be obtained using Bresenham's raster scan algorithm Since quantization in parameter space of the HT gives both the real and false primitives because of quantization in the space of digital image, quantization in parameter space of HT as well as the fact that the edges in typical images are not perfectly constitutes the geometrical features, a statistical analysis is done using the eigen values to characterize and identifying the geometrical primitives The proposed method has the advantages of small storage, high speed, and accurate digitization of Hough space and less line extraction error ratio over previously presented HT based techniques and its invariants
Citations
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TL;DR: Experimental results show that the presented method can accurately measure industrial parts that are represented by various line segments and curves and can accurately reconstructed and measured with least squares errors.
Abstract: We present an effective method for the accurate three-dimensional (3D) measurement of small industrial parts under a complicated noisy background, based on stereo vision. To effectively extract the nonlinear features of desired curves of the measured parts in the images, a strategy from coarse to fine extraction is employed, based on a virtual motion control system. By using the multiscale decomposition of gray images and virtual beam chains, the nonlinear features can be accurately extracted. By analyzing the generation of geometric errors, the refined feature points of the desired curves are extracted. Then the 3D structure of the measured parts can be accurately reconstructed and measured with least squares errors. Experimental results show that the presented method can accurately measure industrial parts that are represented by various line segments and curves.
28Â citations
05 Mar 2009
TL;DR: An algorithm for the recognition of similar electrical poles from an aerial image by detecting the pole shadow is presented, which includes feature extraction, candidate position determination, and elimination of redundant candidates.
Abstract: This paper presents an algorithm for the recognition of similar electrical poles from an aerial image by detecting the pole shadow. One pole is used as a template (already identified by a human operator) for the algorithm. The algorithm includes feature extraction, candidate position determination, and elimination of redundant candidates. First, features of a pole shadow are extracted using standard filters and image processing techniques. Then the extracted features are used to design convolution filters tailored to emphasize possible locations for the shadows. Subsequently, an image candidate is submitted to Radon Transformation to verify adherence to expected shadow characteristics. Simulations show that most poles are made much more noticeable by the algorithm.
20Â citations
Journal Article•
TL;DR: An algorithm for detecting marks of road lane and road boundary with a view to the smart navigation of intelligent vehicles and shows the effectiveness of the proposed algorithm on both straight and slightly curved road scene images under different day light conditions and the presence of shadows on the roads.
Abstract: Automated road lane detection is the crucial part of vision-based driver assistance system of intelligent vehicles. This driver assistance system reduces the road accidents, enhances safety and improves the traffic conditions. In this paper, we present an algorithm for detecting marks of road lane and road boundary with a view to the smart navigation of intelligent vehicles. Initially, it converts the RGB road scene image into gray image and employs the flood-fill algorithm to label the connected components of that gray image. Afterwards, the largest connected component which is the road region is extracted from the labeled image using maximum width and no. of pixels. Eventually, the outside region is subtracted and the marks or road lane and road boundary are extracted from connected components. The experimental results show the effectiveness of the proposed algorithm on both straight and slightly curved road scene images under different day light conditions and the presence of shadows on the roads.
19Â citations
22 May 2010
TL;DR: This paper proposes a shape recognition method for detecting convex polygons (CVPs) in image planes, which is characterized by using Hough transformation (HT) and designing a set classifier, which has advantage of detecting the CVP shape with discontinuity and broken edges.
Abstract: This paper proposes a shape recognition method for detecting convex polygons (CVPs) in image planes, which is characterized by using Hough transformation (HT) and designing a set classifier. HT is used to extract the sides of a CVP by searching the peaks of accumulators in the Hough Parametric Space (HPS). A total set of the intersection points formed by all sides of CVP and their extending lines can be established based on the peaks in HPS, which includes a subset only containing the vertex of CVP. Based on the gradient distribution of the elements in the subset, the classifier for extracting the subset is designed. Compared with conventional method, the detection method has advantage of detecting the CVP shape with discontinuity and broken edges, and thus worthy of being promoted.
6Â citations
01 Oct 2018
TL;DR: A lane detection approach based on image processing that determines the painted lanes on road in challenging scenarios is proposed and it is evident from the detected response that this technique is effective in detecting straight and curved lanes in challenging hilly road scenarios.
Abstract: Automated lane identification is an essential component of perception based driver assistance system. These systems employed in intelligent vehicles minimize the fatal accidents and improve safety of driver as well as passenger and enhance the traffic scenarios. In this article, a lane detection approach based on image processing that determines the painted lanes on road in challenging scenarios is proposed. In the preprocessing stage, unique technique is used to detect and minimize the shadow and other illumination effects on the road which impose a crucial problem while detecting the lane lines. Two different thresholds are utilized to identify the probable lane boundaries and the outliers are removed in the post processing stage. The credible lane edges are obtained and superimposed on the original image. The tested results show the efficacy of the presented algorithm and it is evident from the detected response that this technique is effective in detecting straight and curved lanes in challenging hilly road scenarios.
5Â citations
References
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TL;DR: It is pointed out that the use of angle-radius rather than slope-intercept parameters simplifies the computation further, and how the method can be used for more general curve fitting.
Abstract: Hough has proposed an interesting and computationally efficient procedure for detecting lines in pictures. This paper points out that the use of angle-radius rather than slope-intercept parameters simplifies the computation further. It also shows how the method can be used for more general curve fitting, and gives alternative interpretations that explain the source of its efficiency.
6,693Â citations
TL;DR: It is shown how the boundaries of an arbitrary non-analytic shape can be used to construct a mapping between image space and Hough transform space, which makes the generalized Houghtransform a kind of universal transform which can beused to find arbitrarily complex shapes.
4,310Â citations
01 Aug 1988-Graphical Models \/graphical Models and Image Processing \/computer Vision, Graphics, and Image Processing
TL;DR: This survey will provide a useful guide to quickly acquaint researchers with the main literature in this research area and it seems likely that the Hough transform will be an increasingly used technique.
Abstract: We present a comprehensive review of the Hough transform, HT, in image processing and computer vision. It has long been recognized as a technique of almost unique promise for shape and motion analysis in images containing noisy, missing, and extraneous data but its adoption has been slow due to its computational and storage complexity and the lack of a detailed understanding of its properties. However, in recent years much progress has been made in these areas. In this review we discuss ideas for the efficient implementation of the HT and present results on the analytic and empirical performance of various methods. We also report the relationship of Hough methods and other transforms and consider applications in which the HT has been used. It seems likely that the HT will be an increasingly used technique and we hope that this survey will provide a useful guide to quickly acquaint researchers with the main literature in this research area.
2,099Â citations
TL;DR: This work proposes a new method for curve detection that has the advantages of small storage, high speed, infinite parameter space and arbitrarily high resolution, and the preliminary experiments have shown that the new method is quite effective.
1,080Â citations
TL;DR: This correspondence illustrates the ideas of the Adaptive Hough Transform, AHT, by tackling the problem of identifying linear and circular segments in images by searching for clusters of evidence in 2-D parameter spaces and shows that the method is robust to the addition of extraneous noise.
Abstract: We introduce the Adaptive Hough Transform, AHT, as an efficient way of implementing the Hough Transform, HT, method for the detection of 2-D shapes. The AHT uses a small accumulator array and the idea of a flexible iterative "coarse to fine" accumulation and search strategy to identify significant peaks in the Hough parameter spaces. The method is substantially superior to the standard HT implementation in both storage and computational requirements. In this correspondence we illustrate the ideas of the AHT by tackling the problem of identifying linear and circular segments in images by searching for clusters of evidence in 2-D parameter spaces. We show that the method is robust to the addition of extraneous noise and can be used to analyze complex images containing more than one shape.
671Â citations