scispace - formally typeset
Search or ask a question
Proceedings ArticleDOI

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
More filters
01 Jan 2011
TL;DR: An extension of the standard Gaussian mixture model for image segmentation is proposed, which utilizes a novel approach to incorporate the spatial relationships between neighboring pixels into the standard GMM and requires lesser number of parameters.
Abstract: Segmentation of images has found widespread applications in image recognition systems. Over the last two decades, there has been a growing research interest in model-based technique. In this technique, standard Gaussian mixture model (GMM) is a well-known method for image segmentation. The model assumes a common prior distribution, which independently generates the pixel labels. In addition, the spatial relationship between neighboring pixels is not taken into account of the standard GMM. For this reason, its segmentation result is sensitive to noise. To reduce the sensitivity of the segmented result with respect to noise, Markov Random Field (MRF) models provide a powerful way to account for spatial dependencies between image pixels. However, their main drawback is that they are computationally expensive to implement. Based on these considerations, in the first part of this thesis (Chapter 4), we propose an extension of the standard GMM for image segmentation, which utilizes a novel approach to incorporate the spatial relationships between neighboring pixels into the standard GMM. The proposed model is easy to implement and compared with the existing MRF models, requires lesser number of parameters. We also propose a new method to estimate the model

19 citations


Cites background from "A new method for image segmentation..."

  • ...Adaptive thresholding [20], [21], [22] often is taken as a solution to this....

    [...]

Journal ArticleDOI
TL;DR: The purpose of this work is to provide the capability to automatically evaluate standard textual display information, so that tasks that involve visual user verification can be performed without human intervention.
Abstract: In this paper text segmentation in generic displays is proposed through learning the best binarization values for a commercial optical character recognition (OCR) system. The commercial OCR is briefly introduced as well as the parameters that affect the binarization for improving the classification scores. The purpose of this work is to provide the capability to automatically evaluate standard textual display information, so that tasks that involve visual user verification can be performed without human intervention. The problem to be solved is to recognize text characters that appear on the display, as well as the color of the characters' foreground and background. The paper introduces how the thresholds are learnt through: (a) selecting lightness or hue component of a color input cell, (b) enhancing the bitmaps' quality, and (c) calculating the segmentation threshold range for this cell. Then, starting from the threshold ranges learnt at each display cell, the best threshold for each cell is gotten. The input and output data sets for testing the algorithms proposed are described, as well as the analysis of the results obtained.

18 citations


Cites methods or result from "A new method for image segmentation..."

  • ...A surface fitted to the gray-level landscape is used as a local threshold in Yanowitz and Bruckstein (1989), Shen and Ip (1997)....

    [...]

  • ...Different adaptive binarization methods can for instance be (Yanowitz & Bruckstein, 1989); and in Trier and Taxt (1995) several methods are compared with respect to the obtained recognition results....

    [...]

Proceedings ArticleDOI
29 Nov 2010
TL;DR: The global and local thresholds are utilized to enhance the image binarization and a novel criterion for effectively detecting edges in the images is presented, which can provide a better solution than existing binary algorithms.
Abstract: The identification of ballistics specimens from imaging systems is of paramount importance in criminal investigation. Binarization plays a key role in preprocess of recognizing cartridges in the ballistic imaging systems. Unfortunately, it is very difficult to get the satisfactory binary image using existing binary algorithms. In this paper, we utilize the global and local thresholds to enhance the image binarization. Importantly, we present a novel criterion for effectively detecting edges in the images. Comprehensive experiments have been conducted over sample ballistic images. The empirical results demonstrate the proposed method can provide a better solution than existing binary algorithms.

17 citations


Cites background or methods or result from "A new method for image segmentation..."

  • ...Yanowitz and Bruckstein[17] use edges and gray level information to obtain threshold surface....

    [...]

  • ..., Chow and Kaneko[15] , Yanowitz and Bruckstein[17]....

    [...]

  • ...In order to make a comparison with the proposed algorithm, we compare performance of the proposed algorithm with those of Otsu[14], Chow and Kaneko[15] and Yanowitz and Bruckstein[17]....

    [...]

  • ...Yanowitz and Bruckstein[17] take pixels with local gradient maxima as boundary pixels between object and background....

    [...]

  • ...The local thresholding methods choose variable thresholds based on the local information in an image[15-18]....

    [...]

Journal ArticleDOI
TL;DR: This paper presents a methodology for separating handwritten foreground pixels, from background pixels, in carbon copied medical forms, which is a vital step in automating emergency medical health surveillance systems.

16 citations

Journal ArticleDOI
TL;DR: In this paper, a new surface thresholding method is introduced to detect defects in radiographic images of welding joints, and the proposed method obtained a promising result with high precision, but it is not suitable for welding joints with uneven illumination, contrast and noise.
Abstract: Automated detection of welding defects in radiographic images becomes non-trivial when uneven illumination, contrast and noise are present. In this paper, a new surface thresholding method is introduced to detect defects in radiographic images of welding joints. In the first stage, several image processing techniques namely fuzzy c means clustering, region filling, mean filtering, edge detection, Otsu's thresholding and morphological operations method are utilised to locate the area in which defects might exist. This is followed by the implementation of inverse surface thresholding with partial differential equation to locate isolated areas that represent the defects in the second stage. The proposed method obtained a promising result with high precision.

15 citations


Cites methods from "A new method for image segmentation..."

  • ...An adaptive thresholding method based on a surface derived from performing secondorder PDE was proposed in Refs [13,14]....

    [...]

References
More filters
Journal ArticleDOI
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....

    [...]

Journal ArticleDOI
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....

    [...]

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

Proceedings ArticleDOI
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

Trending Questions (1)
How to Train an image segmentation model?

Through experiments, it is demonstrated that the image segmentation method in this paper is very effective.