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Edge Detection Techniques - An Overview
- INTERNATIONAL JOURNAL OF PATTERN RECOGNITION AND IMAGE ANALYSIS
, 1998
"... In computer vision and image processing, edge detection concerns the localization of significant variations of the grey level image and the identification of the physical phenomena that originated them. This information is very useful for applications in 3D reconstruction, motion, recognition, image ..."
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Cited by 131 (2 self)
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In computer vision and image processing, edge detection concerns the localization of significant variations of the grey level image and the identification of the physical phenomena that originated them. This information is very useful for applications in 3D reconstruction, motion, recognition, image enhancement and restoration, image registration, image compression, and so on. Usually, edge detection requires smoothing and differentiation of the image. Differentiation is an ill-conditioned problem and smoothing results in a loss of information. It is difficult to design a general edge detection algorithm which performs well in many contexts and captures the requirements of subsequent processing stages. Consequently, over the history of digital image processing a variety of edge detectors have been devised which differ in their mathematical and algorithmic properties. This paper is an account of the current state of our understanding of edge detection. We propose an overview of research...
Regularization, Scale-Space, and Edge Detection Filters
- Journal of Mathematical Imaging and Vision
"... . Computational vision often needs to deal with derivatives of digital images. Such derivatives are not intrinsic properties of digital data; a paradigm is required to make them well-defined. Normally, a linear filtering is applied. This can be formulated in terms of scale-space, functional mini ..."
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Cited by 39 (8 self)
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. Computational vision often needs to deal with derivatives of digital images. Such derivatives are not intrinsic properties of digital data; a paradigm is required to make them well-defined. Normally, a linear filtering is applied. This can be formulated in terms of scale-space, functional minimization, or edge detection filters. The main emphasis of this paper is to connect these theories in order to gain insight in their similarities and differences. We take regularization (or functional minimization) as a starting point, and show that it boils down to Gaussian scale-space if we require scale invariance and a semi-group constraint to be satisfied. This regularization implies the minimization of a functional containing terms up to infinite order of differentiation. If the functional is truncated at second order, the Canny-Deriche filter arises. 1 Introduction Given a digital signal in one or more dimensions, we want to define its derivatives in a well-posed way. This can...
A Modification of Deriche's Approach to Edge Detection
- In: 11th International Conference on Pattern Recognition
, 1992
"... In 1983 Canny presented criteria for measuring the quality of edge detectors and derived an optimal FIRFilter for step edges by optimizing them. Four years later Deriche proposed an approach to edge detection based on Canny design utilizing IIR-Filters which can be implemented very efficiently recur ..."
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Cited by 12 (6 self)
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In 1983 Canny presented criteria for measuring the quality of edge detectors and derived an optimal FIRFilter for step edges by optimizing them. Four years later Deriche proposed an approach to edge detection based on Canny design utilizing IIR-Filters which can be implemented very efficiently recursively. Unfortunately, using the Deriche-Filter leads to a distortion of the amplitudes of the edges depending on their direction. In this paper, it will be shown that these distortions are systematic errors which can be eliminated by a simple modification of the edge detection procedure. Because of its obvious "kinship" to the Deriche-filter the Shen-filter has been included in the investigations.
Scale-Space Generators and Functionals
"... In this chapter we will try to relate axiomatic formulations of scale-spaces with regularization theory. Most axiomatic formulations do not directly require regularity properties of scale-space. Nevertheless, an analytic filter is singled out so that scale-space appears to have strong regularity pr ..."
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Cited by 2 (0 self)
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In this chapter we will try to relate axiomatic formulations of scale-spaces with regularization theory. Most axiomatic formulations do not directly require regularity properties of scale-space. Nevertheless, an analytic filter is singled out so that scale-space appears to have strong regularity properties. These properties could also be stated as first principles in terms of Tikhonov regularization. In this chapter we study how scale-space properties as described in previous chapters and regularity properties interact in axiomatic formulations. In the previous chapters, we have seen how a set of axioms regarding a scalespace representation of an image can narrow down the set of admissible scale-space operators. We call an operator G a scale-space generator if it is parametrised by a scale parameter t so that
A Suitable Polygonal Approximation for Laser Rangefinder Data
"... This paper deals with indoor mobile robot localization using 2D laser rangefinder data. An indoor environment is mainly made of connected or nonconnected planes (e.g., walls, doors, desks...). Therefore the laser rangefinder data are a set of points belonging to straight lines. The more suitable pre ..."
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Cited by 1 (0 self)
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This paper deals with indoor mobile robot localization using 2D laser rangefinder data. An indoor environment is mainly made of connected or nonconnected planes (e.g., walls, doors, desks...). Therefore the laser rangefinder data are a set of points belonging to straight lines. The more suitable preprocessing of those data seems to be a polygonal approximation. However, classical approximation methods are not robust enough to provide a reliable local description. We propose a new method to provide a robust polygonal approximation. It means accurate, reliable and repeatable detection of dominant points, such as angular points or break points. We thus obtain a set of angular points and break points (e.g., segments) whose detection depends neither on relative sensor location nor on measure noise. We present experimental results that demonstrate successful map building. 1.
PARALLELIZING INFINITE IMPULSE RESPONSE FILTERS
"... Abstract: In this paper, we present a parallel algorithm for edge detection based on Infinite Impulse Response filter (IIR filter). In particular, the Infinite size Symmetric Exponential Filter (ISEF) that is an optimal IIR filter and computationally efficient smoothing filter is studied. The propos ..."
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Abstract: In this paper, we present a parallel algorithm for edge detection based on Infinite Impulse Response filter (IIR filter). In particular, the Infinite size Symmetric Exponential Filter (ISEF) that is an optimal IIR filter and computationally efficient smoothing filter is studied. The proposed algorithm exploits efficiently all aspects of potential parallelism (spatial parallelism, temporal parallelism and systolism) inherent in the studied edge detection algorithms. The designed concurrent algorithm is expressed in terms of a collection of concurrent processes communicating and synchronizing in an efficient way in order to speed up the low-level operations.
DOI: 10.1140/epjst/e2007-00061-7 THE EUROPEAN PHYSICAL JOURNAL SPECIAL TOPICS
"... vision at short latencies The quest for the neural code of vision at the first milliseconds L. Perrinet a ..."
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vision at short latencies The quest for the neural code of vision at the first milliseconds L. Perrinet a
Dynamical Neural Networks: . . .
, 2007
"... Our goal is to understand the dynamics of neural computations in low-level vision. We study how the substrate of this system, that is local biochemical neural processes, could combine to give rise to an efficient and global perception. We will study these neural computations at different scales fr ..."
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Our goal is to understand the dynamics of neural computations in low-level vision. We study how the substrate of this system, that is local biochemical neural processes, could combine to give rise to an efficient and global perception. We will study these neural computations at different scales from the single-cell to the whole visual system to infer generic aspects of the underlying neural code which may help to understand this cognitive ability. In fact, the architecture of cortical areas, such as the Primary Visual Cortex (V1), is massively parallel and we will focus on cortical columns as generic adaptive micro-circuits. To stress on the dynamical aspect of the processing, we will also focus on the transient response, that is during the first milliseconds after the presentation of a stimulus. In a generic model of a visual area, we propose to study the neural code as
