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Glossary of Computer Vision Terms
Published in Edward R. Dougherty, Digital Image Processing Methods, 2020
Robert M. Haralick, Linda G. Shapiro
The Hough transform is a transform which can aid in the detection of image arcs of a given shape or form or 3D object shapes. Each shape or form has some free parameters which when specified precisely define the arc, shape or form. The shape having free parameter q corresponds to the set {x ∈ X\F(x, q) = 0}. The free parameters constitute the transform domain or the parameter space of the Hough transform. Depending on the information available to the Hough transform, each neighborhood of the image or object surface being transformed will map to a point or a set of points in the Hough parameter space. The Hough transform discretizes the Hough parameter space into bins and counts for each bin how many neighborhoods on the image or object surface has one of its transformed points lie in the volume assigned to the bin.
Image Descriptors and Features
Published in Manas Kamal Bhuyan, Computer Vision and Image Processing, 2019
Edge detection is a fundamental step in image processing and computer vision, particularly for feature extraction. The aim is to identify points in an image at which the image brightness changes sharply or has discontinuities. Different techniques of edge detection viz., gradient-based edge detection (Robert, Prewitt, Sobel, Compass and Laplacian operators) and model-based edge detection (LOG operator and Canny edge detector) are briefly discussed in this chapter. The Hough transform is a feature extraction technique used in image analysis. The purpose of this technique is to detect lines or other shapes by a voting procedure. This voting procedure is carried out in a parametric space, from which object candidates are obtained as local maxima in an accumulator space. The procedure of line and other shapes detection by Hough Transform is discussed in this chapter.
Machine Vision
Published in Jerry C. Whitaker, Microelectronics, 2018
David A. Kosiba, Rangachar Kasturi
The Hough transform can also be defined to recognize other types of curves. For example, points on a circle can be detected by searching through a three-dimensional parameter space of (xc, yc, r) where the first two parameters define the location of the center of the circle and r represents the radius. The Hough transform can also be generalized to detect arbitrary shapes. However, one problem with the Hough transform is the large parameter search space required to detect even simple curves. This problem can be alleviated somewhat by the use of additional information that may be available in the spatial domain. For example, in detecting circles by the brute-force method, searching must be done in a three-dimensional parameter space; however, when the direction of the curve gradient is known, the search is restricted to only one dimension.
Study of raw coal identification method by dual-energy X-ray and dual-view visible light imaging
Published in International Journal of Coal Preparation and Utilization, 2023
Lei He, Shuang Wang, Yongcun Guo, Kun Hu, Gang Cheng, Xinquan Wang
The Hough transform can be used to detect shapes that can be accurately resolved to define, for example, linear features, circles, and ellipses in images (Mandela 2020). There are significant differences between pseudo-medium coal and gangue surfaces. For instance, pseudo-medium coal generally has a layered and reflective structure, while gangue surfaces are usually flat or have concave features. Therefore, there were differences when we used Hough transform to detect straight lines on pseudo-medium coal and gangue surfaces, which could be used as distinguishing features. The 20 most remarkable Hoff peaks were selected, and the distance between the detected lines reached a 10-pixel distance, and the minimum line length reached a 20-pixel distance. Eventually, the number of detected straight lines cl, the sum of straight-line length l, and the maximum straight-line length ml were extracted from the two-side images. As shown in Fig. 9, the cyan line denotes the total straight-line length ml.
Evaluating the effect of MIPM on vehicle detection performance
Published in Transportation Letters, 2020
Nastaran Yaghoobi Ershadi, José Manuel Menéndez, David Jiménez Bermejo
The Hough transform is most commonly used for the detection of regular curves such as lines, circles, ellipses, and others. According to Hough Transform, every single pixel in an image space corresponds to a line inside a parameter space. Parameters and are defined in the equation of the line as follows: