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Sobel operator

About: Sobel operator is a(n) research topic. Over the lifetime, 3535 publication(s) have been published within this topic receiving 58627 citation(s).
Papers
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Journal ArticleDOI
John Canny1Institutions (1)
TL;DR: There is a natural uncertainty principle between detection and localization performance, which are the two main goals, and with this principle a single operator shape is derived which is optimal at any scale.
Abstract: This paper describes a computational approach to edge detection. The success of the approach depends on the definition of a comprehensive set of goals for the computation of edge points. These goals must be precise enough to delimit the desired behavior of the detector while making minimal assumptions about the form of the solution. We define detection and localization criteria for a class of edges, and present mathematical forms for these criteria as functionals on the operator impulse response. A third criterion is then added to ensure that the detector has only one response to a single edge. We use the criteria in numerical optimization to derive detectors for several common image features, including step edges. On specializing the analysis to step edges, we find that there is a natural uncertainty principle between detection and localization performance, which are the two main goals. With this principle we derive a single operator shape which is optimal at any scale. The optimal detector has a simple approximate implementation in which edges are marked at maxima in gradient magnitude of a Gaussian-smoothed image. We extend this simple detector using operators of several widths to cope with different signal-to-noise ratios in the image. We present a general method, called feature synthesis, for the fine-to-coarse integration of information from operators at different scales. Finally we show that step edge detector performance improves considerably as the operator point spread function is extended along the edge.

26,639 citations


Book
26 May 2000
Abstract: This book claims to fill a niche in the provision of textbooks devoted to image processing by being devoted to colour aspects. It is aimed at researchers and practitioners working in the area of colour image processing, particularly graduates in Computer Science and Electrical and Computer Engineering. The book is structured in such a way that, after reading an introductory chapter on colour, readers can work their way through the following four chapters on image processing, or omit them and move directly to the final three chapters that deal with colour image analysis and coding. It is not immediately apparent from reading the preface that companion image processing software is available online from the publisher's website, and this is not made use of as an integral part of the book. Regrettably, the book shows much evidence of a lack of rigorous proof reading and editing. A number of errors can be found, particularly in the first chapter; this provides the fundamentals in colour science on which the book is based. For example, on the first page the visible spectrum is incorrectly reproduced with the wrong wavelength scale and photoreceptors are referred to as `roads'. A few pages on there is a complete mismatch between the explanatory text and diagrams concerning the CIE, XYZ and RGB colour matching functions. One diagram appears to have been reproduced twice, is incorrectly titled and not annotated. Also, the CIE chromaticity diagram lacks a wavelength scale and the colours of the diagram are incorrectly reproduced. Unfortunately, these fundamental errors appear in the first ten pages and have the unfortunate effect of detracting from the authoritative nature of the book as a whole. A further example of poor proof reading/editing can be found towards the end of the chapter in which photographic film is defined as follows: `The film which is used by conventional cameras contains three emulsion layers which are sensitive to red and blue light, which enters through the camera lens.' The chapters do improve, however, as one goes through the book, although in chapter 2 the description of the origins of photographic noise is incorrect and incomplete (`the noise is mainly due to the silver grains that precipitate during film exposure'). Also, the origins of noise in photoelectronic sensors are incompletely described. Each chapter is accompanied by a substantial number of references to the primary sources of information, many of which are to recent literature in the field, in very useful summary or conclusion sections. It is puzzling that in view of the significance of the Fourier transform in image processing, it is not included, other than a brief mention of the Discrete Fourier Transform in the chapter on image compression. Adaptive image filters are described at length in chapter 3, which is followed by chapters dedicated to colour edge detection, enhancement and restoration, image segmentation, image compression and emerging applications. The latter makes interesting reading but is based almost exclusively on the detection and automatic location of the human face. The index is not very exhaustive and as a consequence it is difficult to find many items that are discussed in the text but are not indexed. A few examples include: Wiener filter, Sobel, Prewitt and Robert's edge detection, all of which appear in the text and in the indices of most books on image processing but not in the index to this book. Also, most of the existing texts on image processing include many aspects of colour, which detracts somewhat from this book's claimed uniqueness, although it does contain more state-of-the-art aspects. Ralph Jacobson

929 citations


Journal ArticleDOI
N. Kanopoulos1, N. Vasanthavada1, R.L. Baker1Institutions (1)
TL;DR: The architecture of the edge detector presented is highly pipeline to perform the computations of gradient magnitude and direction for the output image samples and has been demonstrated with a prototype system that is performing image edge detection in real time.
Abstract: The architecture of the edge detector presented is highly pipeline to perform the computations of gradient magnitude and direction for the output image samples The chip design is based on a 2- mu m, double-metal, CMOS technology and was implemented using a silicon compiler system in less than 2 man-months It is designed to operate with a 10-MHz two-phase clock, and it performs approximately 200*10/sup 6/ additions/s to provide the required magnitude and direction outputs every clock cycle The function of the chip has been demonstrated with a prototype system that is performing image edge detection in real time >

489 citations


Journal ArticleDOI
Danian Zheng1, Yannan Zhao1, Jiaxin Wang1Institutions (1)
TL;DR: A real time and robust method of license plate location that first extracts out the vertical edges of the car image using image enhancement and Sobel operator, then removes most of the background and noise edges by an effective algorithm, and finally searches the plate region by a rectangle window in the residual edge image.
Abstract: License plate location is an important stage in vehicle license plate recognition for automated transport system. This paper presents a real time and robust method of license plate location. License plate area contains rich edge and texture information. We first extract out the vertical edges of the car image using image enhancement and Sobel operator, then remove most of the background and noise edges by an effective algorithm, and finally search the plate region by a rectangle window in the residual edge image and segment the plate out from the original car image. Experimental results demonstrate the great robustness and efficiency of our method.

380 citations


Journal ArticleDOI
Ali J. Tabatabai1, O. Robert Mitchell1Institutions (1)
TL;DR: It is shown that the edge location is related to the so-called ``Christoffel numbers'' and is compared with Sobel and Hueckel edge detectors in presence and absence of noise.
Abstract: A new method for locating edges in digital data to subpixel values and which is invariant to additive and multiplicative changes in the data is presented For one-dimensional edge patterns an ideal edge is fit to the data by matching moments It is shown that the edge location is related to the so-called ``Christoffel numbers'' Also presented is the study of the effect of additive noise on edge location The method is extended to include two-dimensional edge patterns where a line equation is derived to locate an edge This in turn is compared with the standard Hueckel edge operator An application of the new edge operator as an edge detector is also provided and is compared with Sobel and Hueckel edge detectors in presence and absence of noise

352 citations


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Performance
Metrics
No. of papers in the topic in previous years
YearPapers
20225
2021155
2020182
2019235
2018204
2017189