A new method for image segmentation
01 Nov 2009-Vol. 2, pp 123-125
TL;DR: A new segmentation method which is based on the morphology method, fuzzy K-means algorithm and some parts operator of the Canny algorithm, and the course of Canny operator that calculating the value and direction of grads, non-maxima suppression to the grad value and lag threshold process into the post-treatment process is introduced.
Abstract: On the basis of analyzing the blur images with noise, this paper presents a new segmentation method which is based on the morphology method, fuzzy K-means algorithm and some parts operator of the Canny algorithm. Because of the Canny's good performance on good detection, good localization and only one response to a single edge, we introduce the course of Canny operator that calculating the value and direction of grads, non-maxima suppression to the grad value and lag threshold process into our post-treatment process. Through experiments, it is demonstrated that the image segmentation method in this paper is very effective.
Citations
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01 Jan 2016
TL;DR: A chronology of key events and figures leading up to and including the publication of the encyclopaedia Britannica, vol.
Abstract: iii PUBLIC ABSTRACT vi ACKNOWLEDGMENTS vii LIST OF TABLES x LIST OF FIGURES xi CHAPTER
2 citations
Cites background from "A new method for image segmentation..."
...Text segmentation [41] is an inherent part of an OCR system irrespective of the domain of application....
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Journal Article•
TL;DR: This study is to improve image data compression performance based on variable block-size quadtree image segmentation applied to double predictor differential pulse code modulation (DP–DPCM) image compressive algorithm to moderate the effect from the fed-back quantization error and not to augment the system complexity.
Abstract: This study is to improve image data compression performance based on variable block-size quadtree image segmentation applied to double predictor differential pulse code modulation (DP–DPCM) image compressive algorithm. The quadtree segmentation method is applied to better allocate image characteristics. A variable block-size double predictor DPCM (VBDP–DPCM) image coding system works on an image been preprocessed into segments of variable size, square blocks, and each block is separately encoded by a DP–DPCM algorithm. Quadtree segmentation method is utilized to divide a given real-world image into variable size image blocks. The detail regions comprise more image features of a given image is segmented into blocks with smaller block size, and the background regions of the image will be assigned larger block size to the image blocks. After quadtree segmentation process, the average dissimilar values between the nearby pixels within an image block are abridged. Therefore, we can decrease the distribution range of the prediction error anddiminish the quantization levels as well as the coding bit rate. We then adopt the double predictor DPCM technique to moderate the effect from the fed-back quantization error and not to augment the system complexity. The compression performance of this proposed method is about 5dB (or greater) coding gain in Signal-to-Noise Ratio (SNR) than that of a conventional DPCM system.
2 citations
Dissertation•
05 Jun 2018
TL;DR: La societe GENI collabore avec le laboratoire LIRIS, afin d’automatiser le processus de gradation a partir de photos de pieces de monnaie, compose de quatre phases segmentation des monnaies, identification du type monetaire, detection and reconnaissance du millesime and gradation desmonnaies.
Abstract: Objets de collection depuis les temps anciens, de nos jours les pieces de monnaie constituent un marche de plus en plus important. L’evaluation par des experts de l’etat de conservation des pieces de monnaie, que nous nommons gradation, joue un role important pour determiner leur valeur sur le marche. Dans le but de grader des pieces de monnaie de maniere efficace et objective, la societe GENI collabore avec le laboratoire LIRIS, afin d’automatiser le processus de gradation a partir de photos de pieces de monnaie.
L’objectif principal de cette these est de fournir une aide a la gradation des pieces de monnaie a partir des photos de qualite. Le projet est compose de quatre phases :segmentation des monnaies, identification du type monetaire, detection et reconnaissance du millesime et gradation des monnaies.
Dans la premiere phase, la piece de monnaie est segmentee de sa photo de maniere precise a l’aide d’un modele parametrique deformable. Ce dernier permet egalement d’extraire des caracteristiques de la piece de monnaie telles que sa taille, son nombre de coins, de pans, etc.
Lors de la deuxieme phase, nous cherchons dans une base de donnees le type monetaire de reference correspondant a la piece de monnaie requete a l’aide de scores de similarite bases sur des graphes. Le premier score se base sur des caracteristiques locales des contours
en relief, et le second, qui est semi-global, permet de mettre en evidence des differences de motifs.
La troisieme phase concernent la reconnaissance du millesime. Il s’agit d’un sujet difficile car les caracteres, dans ce contexte, ont un premier plan de couleur tres similaire a l’arriere-plan. Apres avoir localise la zone du millesime et l’avoir decoupee en imagettes de chiffres, nous proposons une methode de reconnaissance de chiffres a l’aide de caracteristiques « topologiques ».
Enfin, concernant la gradation des monnaies, nous proposons une methode se basant sur une quantification des « elements inattendus » comme les rayures et les taches. La piece de monnaie est d’abord recalee sur une monnaie de reference, puis, nous detectons les
« elements inattendus » significatifs sur des zones d’interet. Enfin, concernant les « elements inattendus » tenus difficiles a reperer individuellement, nous detectons les zones granuleuses a l’aide du Deep Learning. Le resultat obtenu par cette methode, proche de ce que l’expert realise « a la main », servira d’aide aux numismates.
1 citations
29 Oct 2019
TL;DR: A method based on neighborhood maximum threshold is proposed, named NMT (Neighborhood Maximum Thresholding), which aimed at rust segmentation problem for workpiece with low saturation, because it is difficult to select the appropriate threshold in RGB space.
Abstract: As a kind of defect on the surface of metal, rust greatly affects the quality of workpiece. However, it's usually difficult to segment rust with small area. Aiming at the segmentation threshold in small rust area detection, a method based on neighborhood maximum threshold is proposed, named NMT (Neighborhood Maximum Thresholding), which aimed at rust segmentation problem for workpiece with low saturation. Because it is difficult to select the appropriate threshold in RGB space, the S channel in HSV color space is selected for rust segmentation. The method includes calculating the maximum saturation in a certain block size area of each pixel in the image, using the least square method to simulate the distribution function of the maximum value, getting the background threshold according to the function distribution. Experiments show the final accuracy in dataset is 96.29%.
1 citations
Cites methods from "A new method for image segmentation..."
...[11] proposed an interpolation method based on the maximum local gradient, which uses the position and gray level of the pixels with the maximum local gradient to interpolate on the image, and then obtains the gray level threshold surface....
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Journal Article•
TL;DR: Different type of methods like as Monte Carlo method, water displacement method and color image segmentation technique and algorithm like as Image analysis algorithm and Canny Edge Detection algorithm for volume estimation of fruits are reviewed.
Abstract: Image processing is a process of understanding, analysis and modification on the image. Based on image processing some of the techniques were developed for volume estimation of fruits like as lemon, orange, mango etc. Volume estimation of fruit is use in packaging industries. This paper review different type of methods like as Monte Carlo method, water displacement method and color image segmentation technique and algorithm like as Image analysis algorithm and Canny Edge Detection algorithm for volume estimation of fruits. This paper includes advantages and disadvantages of the all methods of volume estimation of the fruits and also describes comparison of all methods. Volume estimation of fruits has some of the problems like it is time consuming process and accuracy of result. Keywords— Fruit, Volume measurement, Segmentation.
1 citations
References
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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.
28,073 citations
"A new method for image segmentation..." refers methods in this paper
...Canny operator[2] transforms the edge detection problem into the problem of unit function maximum detection....
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TL;DR: This work presents a simple and efficient implementation of Lloyd's k-means clustering algorithm, which it calls the filtering algorithm, and establishes the practical efficiency of the algorithm's running time.
Abstract: In k-means clustering, we are given a set of n data points in d-dimensional space R/sup d/ and an integer k and the problem is to determine a set of k points in Rd, called centers, so as to minimize the mean squared distance from each data point to its nearest center. A popular heuristic for k-means clustering is Lloyd's (1982) algorithm. We present a simple and efficient implementation of Lloyd's k-means clustering algorithm, which we call the filtering algorithm. This algorithm is easy to implement, requiring a kd-tree as the only major data structure. We establish the practical efficiency of the filtering algorithm in two ways. First, we present a data-sensitive analysis of the algorithm's running time, which shows that the algorithm runs faster as the separation between clusters increases. Second, we present a number of empirical studies both on synthetically generated data and on real data sets from applications in color quantization, data compression, and image segmentation.
5,288 citations
"A new method for image segmentation..." refers methods in this paper
...Fuzzy K-means algorithm[3] that divides the samples on various categories of membership according to the data is a clustering method in more common use....
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Book•
15 Sep 1994
TL;DR: The fundamental principles of Digital Image Processing are explained, as well as practical suggestions for improving the quality and efficiency of image processing.
Abstract: What Is Image Processing?. Fundamentals of Digital Image Processing. The Digital Image. PROCESSING CONCEPTS. Image Enhancement and Restoration. Image Analysis. Image Compression. Image Synthesis. PROCESSING SYSTEMS. Image Origination and Display. Image Data Handling. Image Data Processing. PROCESSING IN ACTION. Image Operation Studies. Appendices. Glossary. Index.
457 citations
12 May 1998
TL;DR: A novel method for measuring the orientation of an edge is introduced and it is shown that it is without error in the noise-free case, and the wreath product transform edge detection performance is shown to be superior to many standard edge detectors.
Abstract: Wreath product group based spectral analysis has led to the development of the wreath product transform, a new multiresolution transform closely related to the wavelet transform. We derive the filter bank implementation of a simple wreath product transform and show that it is in fact, a multiresolution Roberts (1965) Cross edge detector. We also derive the relationship between this transform and the two-dimensional Haar wavelet transform. We prove that, using a non-traditional metric for measuring edge amplitude with the wreath product transform, yields a rotation and translation invariant edge detector. We introduce a novel method for measuring the orientation of an edge and show that it is without error in the noise-free case. The wreath product transform edge detection performance is shown to be superior to many standard edge detectors.
19 citations