| Performance Evaluation of Histogram Equalization and Fuzzy image Enhancement Techniques on Low Contrast Images |
| Pages: 144-150 (7) | [Full Text] PDF (580 KB) |
| E Onyedinma, I Onyenwe, H Inyiama |
| Department of Computer Science, Nnamdi Azikiwe University, Awka. Anambra State, Nigeria |
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Abstract - Image enhancement aims at improving the information content of original image for a specific purpose. This purpose could be for visual interpretation or for effective extraction of required details. Nevertheless, some acquired images are often associated with pixels of low dynamic range and as such result in low contrast images. Enhancing the contrast therefore tends to increase the dynamic range of the gray levels in the acquired image so as to span the full intensity range. Techniques such as Histogram Equalization (HE) and fuzzy technique can be adopted for contrast enhancement. HE adjusts the contrast of an input image by modifying the intensity distribution of its histogram. It is characterized by providing a global approach to image enhancement, computationally fast and easy to implement approach but can introduce unnatural artifacts and other undesirable elements to the resulting image. Fuzzy technique on its part enhances image by mapping the image gray level intensities into a fuzzy plane using membership functions; modifying the membership functions as desired and mapping back into the gray level plane. Thus, details at desired areas can be enhanced at the expense of increase in computational cost. This paper explores the effect of the use of HE and fuzzy technique to enhance low contrast images. Their performances are evaluated using the Mean squared error (MSE), Peak to signal noise ratio (PSNR), entropy and Absolute mean brightness error (AMBE). |
| Index Terms - Histogram Equalization, Fuzzy, Intensity, Gray Level, Membership Function |
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C itation - E Onyedinma, I Onyenwe, H Inyiama. "Performance Evaluation of Histogram Equalization and Fuzzy image Enhancement Techniques on Low Contrast Images." International Journal of Computer Science and Software Engineering 8, no. 7 (2019): 144-150. |
| Comparative Study: 2D Image Processing In Gray Scale Using Wavelets |
| Pages: 151-160 (10) | [Full Text] PDF (1.5 MB) |
| JA Acuna-Garcia, SL Canchola-Magdaleno, FAJ Garcia |
| Faculty of Informatics, Autonomous University of Queretaro, Santiago de Queretaro, Queretaro 76230, Mexico |
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Abstract - The digitization of images has many advantages that could not be taken in past times when occupying film for record images. Image processing allows operations that facilitate the modification of their presentation by means of mathematical treatments that enhancement their visual characteristics, dedicated to specific purposes. The present work is an experimental comparative study to understand the information that can result from Discrete Wavelet Transform of an image in 2D in grayscale, using kernel Wavelet Daubechies. This analysis focus on how many resolutions can get from an original image, convolved with Wavelet Transform and how can the coefficients be modified in different wavelet resolutions to get expected results of a future research Project. |
| Index Terms - Image processing, Wavelet, Multi-resolution, DWT, Daubechies |
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C itation - JA Acuna-Garcia, SL Canchola-Magdaleno, FAJ Garcia. "Comparative Study: 2D Image Processing In Gray Scale Using Wavelets." International Journal of Computer Science and Software Engineering 8, no. 7 (2019): 151-160. |
| Improving Entity-Attribute-Value Architecture Using Middleware Layer |
| Pages: 161-165 (5) | [Full Text] PDF (406 KB) |
| M Khodairy, F ALqurashi |
| Computer Science Department, Faculty of Computing and Information Technology King Abdulaziz University, Jeddah, Saudi Arabia |
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Abstract - The Entity-Attribute-Value (EAV) data model, is widely used solution to represent high dimensional and sparse data it is actually provides high flexibility, but EAV does not explicitly provide the constrains as a traditional database (e.g. data type, length, default value, allow null, etc.). In this paper, we implemented middleware layer that managing EAV operations to reflect the features that applied on traditional database and more, for purpose of improve complexity and efficiency. The experiments were done using Microsoft EntityFramework ORM tool, Microsoft SQL Server database, and implementation language was C#. |
| Index Terms - EAV, Open Schema, Sparse Data, High Dimensional Data, Improving EAV |
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C itation - M Khodairy, F ALqurashi. "Improving Entity-Attribute-Value Architecture Using Middleware Layer." International Journal of Computer Science and Software Engineering 8, no. 7 (2019): 161-165. |
| Detection and Classification of Cassava Diseases Using Machine Learning |
| Pages: 166-176 (11) | [Full Text] PDF (1.66 MB) |
| O Emuoyibofarhe, JO Emuoyibofarhe, S Adebayo, A Ayandiji, O Demeji, O James |
| Department of Computer Science and Information Technology, Bowen University, Iwo, Osun State, NigeriaDepartment of Computer Science and Engineering, Ladoke Akintola University, Ogbomosho, Oyo State, Nigeria |
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Abstract - In this work, we develop and trained machine learning models for the detection and classification of cassava (Manihot esculenta Crantz) disease as Blight or Mosaic. Our emphasis here was on two major cassava diseases that occur in Nigeria which are the Cassava Mosaic Disease (CMD) and the Cassava Bacterial Blight disease (CBBD). A total of 46 models were trained in two categories from over 18,000 cassava leaf images was collected at different times of day containing leaves at different levels of symptom manifestation. One model diagnosed the healthy leaf and the other model detected the diseases that are present on the leaf when diagnosed as an unhealthy leaf and two most accurate models were exported. A 5-fold cross-validation was used to test the Cubic Support Vector Machine (CSVM) model developed for health diagnosis and the Coarse Gaussian Support Vector Machine (CGSVM) model developed for disease detection which yielded accuracies of 83.9% and 61.6% respectively. |
| Index Terms - Classification of Cassava Diseases, Machine Learning, learning models, Vector Machine, Coarse Gaussian Support Vector Machine |
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C itation - O Emuoyibofarhe, JO Emuoyibofarhe, S Adebayo, A Ayandiji, O Demeji, O James. "Detection and Classification of Cassava Diseases Using Machine Learning." International Journal of Computer Science and Software Engineering 8, no. 7 (2019): 166-176. |