Data Mining - Concepts and Techniques.

We conduct an exhaustive survey of image thresholding methods, categorize them, express their formulas under a uniform notation, and finally carry their performance comparison. The thresholding methods are categorized according to the information they are exploiting, such as histogram shape, measurement space clustering, entropy, object attributes, spatial correlation, and local gray-level surface. 40 selected thresholding methods from various categories are compared in the context of nondestructive testing applications as well as for document images. The comparison is based on the combined performance measures. We identify the thresholding algorithms that perform uniformly better over nonde- structive testing and document image applications. © 2004 SPIE and IS&T. (DOI: 10.1117/1.1631316)

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Survey over image thresholding techniques and quantitative performance evaluation

From the Publisher: 
The accessible presentation of this book gives both a general view of the entire computer vision enterprise and also offers sufficient detail to be able to build useful applications. Users learn techniques that have proven to be useful by first-hand experience and a wide range of mathematical methods. A CD-ROM with every copy of the text contains source code for programming practice, color images, and illustrative movies. Comprehensive and up-to-date, this book includes essential topics that either reflect practical significance or are of theoretical importance. Topics are discussed in substantial and increasing depth. Application surveys describe numerous important application areas such as image based rendering and digital libraries. Many important algorithms broken down and illustrated in pseudo code. Appropriate for use by engineers as a comprehensive reference to the computer vision enterprise.

Computer vision : a modern approach = 计算机视觉 : 一种现代的方法

MUCKE aims to mine a large volume of images, to structure them conceptually and to use this conceptual structuring in order to improve large-scale image retrieval. The last decade witnessed important progress concerning low-level image representations. However, there are a number problems which need to be solved in order to unleash the full potential of image mining in applications. The central problem with low-level representations is the mismatch between them and the human interpretation of image content. This problem can be instantiated, for instance, by the incapability of existing descriptors to capture spatial relationships between the concepts represented or by their incapability to convey an explanation of why two images are similar in a content-based image retrieval framework. We start by assessing existing local descriptors for image classification and by proposing to use co-occurrence matrices to better capture spatial relationships in images. The main focus in MUCKE is on cleaning large scale Web image corpora and on proposing image representations which are closer to the human interpretation of images. Consequently, we introduce methods which tackle these two problems and compare results to state of the art methods. Note: some aspects of this deliverable are withheld at this time as they are pending review. Please contact the authors for a preview.

http://ieeexplore.ieee.org/iel2/4524/12820/00592460.pdf

Image Processing

https://cyberleninka.org/article/n/566686.pdf

A Review on Predicting Student's Performance Using Data Mining Techniques

Predicting the performance of a student is a great concern to the higher education managements. The scope of this paper is to identify the factors influencing the performance of students in final examinations and find out a suitable data mining algorithm to predict the grade of students so as to a give timely and an appropriate warning to students those who are at risk. In the present investigation, a survey cum experimental methodology was adopted to generate a database and it was constructed from a primary and a secondary source. The obtained results from hypothesis testing reveals that type of school is not influence student performance and parents’ occupation plays a major role in predicting grades. This work will help the educational institutions to identify the students who are at risk and to and provide better additional training for the weak students.

/pdf/predicting-student-performance-a-statistical-and-data-mining-5e6g3pf9cc.pdf

Predicting Student Performance: A Statistical and Data Mining Approach

Multilevel thresholding for segmentation of medical brain images using real coded genetic algorithm

Early detection of breast cancer in the mammograms is very essential in the field of medicine Contrast enhancement of mammograms based on Histogram Equalization (HE) is presented Histogram equalization is an effective and simple technique for contrast enhancement The standard histogram equalization (HE) usually results in excessive contrast enhancement because of lack of control on the level of enhancement The Histogram Modified Local Contrast Enhancement (HM-LCE) is introduced in this paper to adjust the level of contrast enhancement, which in turn gives the resultant image a strong contrast and also brings the local details present in the original image for more relevant interpretation It incorporates a two stage processing both histogram modifications as an optimization technique and a local contrast enhancement technique This method is tested for Mias mammogram images The performance of this method is determined using three parameters like Enhancement Measure (EME), Absolute Mean Brightness Error (AMBE) and Discrete Entropy (H) for all 22 numbers of Mias mammogram images with microcalcification It's enhancement potential is also tested by sobel and otsu methods for the detection of microcalcification in the mammogram image From the subjective and quantitative measures it is interesting that this proposed technique provides optimum results by giving better contrast enhancement and preserving the local information of the original mammogram images in the Mias data base and the method has increased the detectability of micro calcifications present in the given mammogram image

Short Communication: Histogram Modified Local Contrast Enhancement for mammogram images

Early detection of breast cancer in the mammograms is very essential in the field of medicine. Contrast enhancement for the detection of micro calcification of mammograms based on the Histogram Modified Contrast Limited Adaptive Histogram Equalization (HM-CLAHE) is presented. Histogram equalization is an effective and simple technique for contrast enhancement. The standard histogram equalization (HE) usually results in excessive contrast enhancement because of lack of control on the level of enhancement. The Histogram Modified Contrast Limited Adaptive Histogram Equalization (HM-CLAHE) is proposed in this paper to adjust the level of contrast enhancement, which in turn gives the resultant image a strong contrast and brings the local details for more relevant interpretation. It incorporates both histogram modifications as an optimization technique and Contrast Limited Adaptive Histogram Equalization. This method is tested for Mias mammogram images. The performance of this method is determined using the parameter like Enhancement Measure (EME). From the subjective and quantitative measures it is interesting that this proposed technique provides better contrast enhancement with preserving the local information of the mammogram images.

Histogram based contrast enhancement for mammogram images

Multicast network support is becoming an increasingly important technology for both military and commercial distributed and group based applications. The security services such as confidentiality, authenticity and data integrity are necessary for both wired and wireless networks to protect basic applications. In this paper we present a simulation based study of the impact of different types of attacks in mobile ad hoc networks. We consider the most common type of attacks namely Gray hole attack and Worm hole attack. Specifically, we study how these attacks affect the performance metrics of a multicast session such as packet delivery ratio, packet latency and packet-consumed energy.

http://www.jatit.org/volumes/research-papers/Vol10No1/8Vol10No1.pdf

K. Ramar

Papers

Predicting Student Performance: A Statistical and Data Mining Approach

Multilevel thresholding for segmentation of medical brain images using real coded genetic algorithm

Short Communication: Histogram Modified Local Contrast Enhancement for mammogram images

Histogram based contrast enhancement for mammogram images

Study of different attacks on multicast mobile ad hoc network