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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
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Journal ArticleDOI
TL;DR: Experimental results showed significant improvements in OCR recognition rates compared to other well-established segmentation algorithms, and improving the structural quality of the characters' skeleton facilitates better feature extraction and classification.
Abstract: The iterative cross section sequence graph (ICSSG) is an algorithm for handwritten character segmentation. It expands the cross section sequence graph concept by applying it iteratively at equally spaced thresholds. The iterative thresholding reduces the effect of information loss associated with image binarization. ICSSG preserves the characters' skeletal structure by preventing the interference of pixels that causes flooding of adjacent characters' segments. Improving the structural quality of the characters' skeleton facilitates better feature extraction and classification, which improves the overall performance of optical character recognition (OCR). Experimental results showed significant improvements in OCR recognition rates compared to other well-established segmentation algorithms.

25 citations

Journal ArticleDOI
TL;DR: A fast and general multiclass image segmentation method consisting of a computation of superpixels, extraction of superpixel-based descriptors, and calculating image-based class probabilities in a supervised or unsupervised manner that outperform the baseline results.
Abstract: Image segmentation is widely used as an initial phase of many image analysis tasks. It is often advantageous to first group pixels into compact, edge-respecting superpixels, because these reduce the size of the segmentation problem and thus the segmentation time by an order of magnitudes. In addition, features calculated from superpixel regions are more robust than features calculated from fixed pixel neighborhoods. We present a fast and general multiclass image segmentation method consisting of the following steps: (i) computation of superpixels; (ii) extraction of superpixel-based descriptors; (iii) calculating image-based class probabilities in a supervised or unsupervised manner; and (iv) regularized superpixel classification using graph cut. We apply this segmentation pipeline to five real-world medical imaging applications and compare the results with three baseline methods: pixelwise graph cut segmentation, supertexton-based segmentation, and classical superpixel-based segmentation. On all datasets, we outperform the baseline results. We also show that unsupervised segmentation is surprisingly efficient in many situations. Unsupervised segmentation provides similar results to the supervised method but does not require manually annotated training data, which is often expensive to obtain.

25 citations

Proceedings ArticleDOI
13 May 2013
TL;DR: Object counting in an image is one of the major challenges in image processing and marker controlled watershed segmentation along with thresholding technique gives satisfactory result.
Abstract: Object counting in an image is one of the major challenges in image processing. Image segmentation is used to segregate similar particles which help counting approximate total number of particles. Watershed segmentation technique is considered to be most efficient technique to solve problems of segregation contiguous objects. Thresholding technique is needed for counting objects in an image. Counting only with thresholding technique can give wrong impression. Using marker controlled watershed segmentation along with thresholding technique gives satisfactory result.

24 citations


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

  • ...Image segmentation is used to cluster pixels into relevant image regions, i.e. regions corresponding to individual surfaces, objects, or natural parts of objects [3]....

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Journal ArticleDOI
TL;DR: A windowing post-processing method is proposed that takes into account the neighbourhood of each minutia within defined window and check for minutian validation and invalidation to eliminate a large number of false extracted minutiae from skeletonised fingerprint images.
Abstract: Automatic Fingerprint Identification Systems (AFIS) are widely used for personal identification due to uniqueness of fingerprints. Minutiae-based fingerprint matching techniques are normally used for fingerprint matching. Fingerprint matching results and their accuracy depends on presence of valid minutiae. In this paper, we present a new technique for fingerprint image post-processing. This post-processing is used to eliminate a large number of false extracted minutiae from skeletonised fingerprint images. We propose a windowing post-processing method that takes into account the neighbourhood of each minutia within defined window and check for minutia validation and invalidation. We also present a complete pre-processing system including new segmentation technique that is required to extract region of interest (ROI) accurately from a fingerprint image. The results are confirmed by visual inspections of validated minutiae of the FVC2004 reference fingerprint image database. Experimental results obtained by the proposed approach show efficient reduction of false minutiae.

24 citations


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

  • ...Her research interests include signal processing, biometrics study, image processing, and pattern recognition....

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  • ...Pre-processing techniques mainly include: segmentation which refers to the separation foreground from the image background (Maltoni et al., 2003; Yanowitz and Bruckstein, 1989); normalisation is used to standardise the image values in a particular range; orientation field estimation is done to…...

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Proceedings ArticleDOI
28 Aug 2007
TL;DR: Experiments show that the proposed thresholding technique is fast, robust, and efficient for the binarization of badly illuminated document images.
Abstract: This paper presents a document image thresholding technique that binarizes badly illuminated document images by the photometric correction. Based on the observation that illumination normally varies smoothly and document images often contain a uniformly colored background, the global shading variation is estimated by using a two-dimensional Savitzky-Golay filter that fits a least square polynomial surface to the luminance of a badly illuminated document image. With the knowledge of the global shading variation, shading degradation is then corrected through a compensation process that produces animage with roughly uniform illumination. Badly illuminated document images are accordingly binarized through the global thresholding of the compensated ones. Experiments show that the proposed thresholding technique is fast, robust, and efficient for the binarization of badly illuminated document images.

23 citations

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....

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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

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

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How to Train an image segmentation model?

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