Informatics Publishing Limited

S. N. Geethalakshmi ¹

Affiliations
1 Avinashilingam University of Women, Coimbatore, IN

Abstract

Project success and failure is a question of perception and that the criteria could vary from project to project. A project that has been perceived to be a failure by one stakeholder may be perceived as a success by another. Knowledge and understanding of success and failure factors, as well as how to measure them and the interactions between these factors have great importance for project management effectiveness. The success or failure of Software Project management consists of two components, namely the technical and non-technical components of software development. Non-technical related components of software development process tend to be under managed. Therefore a study was conducted in India among the industries that are into in-house software development, to investigate the influence of the non-technical components of the software development process, on success and failure of software development from the organizations' perspective (Practitioners' view). This study reveals that from organizational perspective the level of customer/user involvement, project manager/staff and software process management contribute most to project success and failure.

Keywords

Success, Failure, Software Development Process, In-House.

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P. Deepa ¹, S. N. Geethalakshmi ²

Affiliations
1 Department of Computer Science, Avinashilingam University for Women, IN
2 Avinashilingam University for Women, IN

Abstract

Image analysis is one of the important approaches in fruit grading. Since manual grading is more popular, if it done manually, the process is slow, labor expensive and grading is done by visual inspection that could be error prone. So automatic fruit grading is needed. Preprocessing in fruit image is a crucial initial step before image analysis is performed. Many preprocessing methods are available in the literature. Datasets are limited by laboratory constraints so that the need is for guidelines on quality and robustness of fruit, to proceed experimentation mango image is taken. In this paper, the performance of four preprocessing methods is compared namely contrast adjustment, Removing noise, Histogram equalization, and Binarization. The performances of these methods are evaluated using Peak Signal to Noise Ratio (PSNR) and Mean Square Error (MSE).

Keywords

Image Processing, Preprocessing, Image Enhancement, PSNR, MSE.

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N. Umadevi ¹, S. N. Geethalakshmi ²

Affiliations
1 Sri Jayendra Sarawsathi College of Arts & Science, Coimbatore, IN
2 Avinashilingam Deemed University, Coimbatore, IN

Abstract

Medical field produces huge volume of images, which are used during disease diagnosis. X-Rays are the oldest and most frequently used form of medical imaging. These images are used in many applications with prominent use found in fracture detection. The X-Ray images are normally affected by Poisson noise, which degrades the visual quality of the image and obscures important information required for accurate diagnosis. The current need is, thus, a method that removes noise while preserving important diagnostic data. This study proposed a method that combines Multiple Wavelet Denoising (MWD) Structure with ICA to remove Poisson noise from X-Ray images. The thresholding method used is Bayes Shrink and both soft, hard thresholding methods are analyzed. From the experimental results, it is evident that the proposed model produces images, which are visually clean and smooth, in fast manner. At the same time, the proposed method also preserves edges and other significant details of the image.

Keywords

Multiple Wavelet Denoising (MWD), Bi-Orthogonal Wavelet Transform, Bayesthresholding and Independent Component Analysis (ICA), Poisson Noise.

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N. Umadevi ¹, S. N. Geethalakshmi ²

Affiliations
1 Avinashilingam University for Women, Coimbatore–641 043
2 S. Avinashilingam University for Women, Coimbatore–641 043, IN

Abstract

Image retrieval is a technique to find similar images from an image archive by their textual or visual contents. Images can be retrieved from huge databases either by using text annotations or by analyzing the content of the images, in which case it is called Content Based Image retrieval (CBIR). CBIR can be applied to various applications like the Internet, healthcare industries, etc. CBIR is considered challenging in the medical field because the characteristics of medical images differ significantly from other general-purpose images. In the last decade, content-based image retrieval methods have been widely studied in different application domains and particularly, research in the medical field has taken special interest. This paper presents a highlight of recent researches in Content Based Medical Image Retrieval (CBMIR), techniques and trends, key issues and limitations.

Keywords

Commercial CBMIR, Content-Based Retrieval, Medical Based Retrieval, Visual Information Retrieval.

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N. Valliammal ¹, S. N. Geethalakshmi ¹

Affiliations
1 Department of Computer Science, Avinashilingam University for Women, Coimbatore-641043, IN

Abstract

The field of leaf recognition for plant classification has experienced an increased need for fast and efficient classification algorithms to aid in keeping track of our planet’s endangering inhabitants. This demand has led to the development of several techniques which have revolutionized the area of automatic plant classification. This increase in the number of techniques has given rise to the dilemma of deciding which of these methods possess the best properties and potentials for effective classification. This problem is of particular importance in the botany field where the distortion of information may lead to inaccurate and often neglect identification of important species. Thus, there exists a need to have knowledge about the different classification algorithms in order to see the advantages of each technique. Therefore this paper discusses the various techniques and methods used for plant classification through leaf recognition as reported by various researchers and academicians. In particular, this paper reviews research works based on two categories, namely, contour-based and region-based approaches.

Keywords

Contour-Based, Leaf Recognition, Plant Classification, Region-Based.

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