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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TL;DR: Research in developing a dynamic quantitative phase imaging microscope providing instantaneous measurements of dynamic motions within and among live cells without labels or contrast agents is described.
Abstract: This paper describes research in developing a dynamic quantitative phase imaging microscope providing instantaneous measurements of dynamic motions within and among live cells without labels or contrast agents. It utilizes a pixelated phase mask enabling simultaneous measurement of multiple interference patterns derived using the polarization properties of light to track dynamic motions and morphological changes. Optical path difference (OPD) and optical thickness (OT) data are obtained from phase images. Two different processing routines are presented to remove background surface shape to enable quantification of changes in cell position and volume over time. Data from a number of different moving biological organisms and cell cultures are presented.
76 citations
TL;DR: An adaptive image thresholding technique via minimax optimization of a novel energy functional that consists of a non-linear convex combination of an edge sensitive data fidelity term and a regularization term that shows promising results to preserve edge/texture structures in different benchmark images over other competing methods is introduced.
66 citations
Additional excerpts
...Keywords: minimax principle, variational calculus, image thresholding....
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TL;DR: Because the proposed detection algorithm requires less user input and performs better than existing algorithms, the approach can quickly and accurately process neuron images without user intervention.
63 citations
TL;DR: An attempt is made to present the state of the art in automatic processing of handwritten cheque images and discusses the important results reported so far in preprocessing, extraction, recognition and verification of handwritten fields on bank cheques and highlights the positive directions of research till date.
Abstract: Bank cheques (checks) are still widely used all over the world for financial transactions. Huge volumes of handwritten bank cheques are processed manually every day in developing countries. In such a manual verification, user written information including date, signature, legal and courtesy amounts present on each cheque has to be visually verified. As many countries use cheque truncation systems (CTS) nowadays, much time, effort and money can be saved if this entire process of recognition, verification and data entry is done automatically using images of cheques. An attempt is made in this paper to present the state of the art in automatic processing of handwritten cheque images. It discusses the important results reported so far in preprocessing, extraction, recognition and verification of handwritten fields on bank cheques and highlights the positive directions of research till date. The paper has a comprehensive bibliography of many references as a support for researchers working in the field of automatic bank cheque processing. The paper also contains some information about the products available in the market for automatic cheque processing. To the best of our knowledge, there is no survey in the area of automatic cheque processing, and there is a need of such a survey to know the state of the art.
63 citations
Cites methods from "A new method for image segmentation..."
...that evaluation, algorithms of Niblack [28], Yanowitz and Bruckstein [29], White and Rohrer [30], Trier and Taxt [31]...
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...In that evaluation, algorithms of Niblack [28], Yanowitz and Bruckstein [29], White and Rohrer [30], Trier and Taxt [31] and Parker [32] produced high recognition rates....
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21 Feb 2015
TL;DR: In built in environment, both K-means and Otsu are unsuccessful to yield good standard of segmentation because of varying lightening on the image and complex surrounding.
Abstract: Segmentation is a method of partitioning an image or picture into different regions which has same attributes like Texture, intensity, gray level etc with the motive to yield object of interest from the background. It is a method in which we included the object belongs to the same category in one class and the objects that belong to other category are added in other class for separating the object and background. There are several image segmentation techniques namely traditional thresholding (Otsu) and clustering segmentation (K-means). By differentiating all these image segmentation techniques we have to find which segmentation technique is better on the characteristics of image segmented. Segmentation is done on built in environment which becomes more demanding. In built in environment, both K-means and Otsu are unsuccessful to yield good standard of segmentation because of varying lightening on the image and complex surrounding.
57 citations
Cites background from "A new method for image segmentation..."
...METHODOLOGY A. Image Segmentation The parts that human can easily separate and view as individual objects that information is the color information which is used to create histograms that indicates texture, boundaries or edge information....
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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