IOSR Journal of Electronics and Communication Engineering 

About: IOSR Journal of Electronics and Communication Engineering is an academic journal. The journal publishes majorly in the area(s): Microstrip antenna & Patch antenna. It has an ISSN identifier of 2278-2834. Over the lifetime, 1149 publications have been published receiving 3010 citations.

Color Image Segmentation for Medical Images using L*a*b* Color Space

Abstract: Image segmentation is always a fundamental but challenging problem in computer vision. The simplest approach to image segmentation may be clustering of pixels. Our works in this paper address the problem of image segmentation under the paradigm of clustering. A robust clustering algorithm is proposed and utilized to do clustering on the L*a*b* color feature space of pixels. Image segmentation is straightforwardly obtained by setting each pixel with its corresponding cluster. We test our segmentation method on medical images and Mat lab standard images.The experimental results clearly show region of interest object segmentation.

Improved Face Recognition Rate Using HOG Features and SVM Classifier

Abstract: A novel face recognition algorithm is presented in this paper. Histogram of Oriented Gradient features are extracted both for the test image and also for the training images and given to the Support Vector Machine classifier. The detailed steps of HOG feature extraction and the classification using SVM is presented. The algorithm is compared with the Eigen feature based face recognition algorithm. The proposed algorithm and PCA are verified using 8 different datasets. Results show that in all the face datasets the proposed algorithm shows higher face recognition rate when compared with the traditional Eigen feature based face recognition algorithm. There is an improvement of 8.75% face recognition rate when compared with PCA based face recognition algorithm. The experiment is conducted on ORL database with 2 face images for testing and 8 face images for training for each person. Three performance curves namely CMC, EPC and ROC are considered. The curves show that the proposed algorithm outperforms when compared with PCA algorithm. IndexTerms: Facial features, Histogram of Oriented Gradients, Support Vector Machine, Principle Component Analysis.

Real Time Artifact-Free Image Upscaling

Abstract: Image upscaling (and more generally image interpolation) is the process of resizing a digital image. Enlarging an image is generally common for making smaller imagery fit a bigger screen in full screen mode, for example. In "zooming" an image, it is not possible to discover any more information in the image than already exists, and image quality inevitably suffers, for that reason several methods have been proposed to obtain better results, involving simple heuristics, edge modeling or statistical learning. The most powerful ones, however, present a high computational complexity and are not suitable for real time applications, while fast methods, even if edge-adaptive, are not able to provide artifacts-free images. So that's why a new method for image upscaling is proposed i.e. Iterative Curvature Based Interpolation (ICBI), it is based on a two-step grid filling and an iterative correction of the interpolated pixels obtained by minimizing an objective function depending on the second order directional derivatives of the image intensity. These are implemented in a variety of computer tools like printers, digital TV, media players, image processing packages, graphics renderers and so on.

Remote Area Plant Disease Detection Using Image Processing

Abstract: India being an agro-based economy, farmers experince alot of problem in detecting andpreventing diseases in fauna. So there is a necessacity in detecting diseases in fauna which proves to be effective and conviennent for researchers. Relying on pure naked-eye observation to detect and classify diseases can be very unprecise and cumbersome. The color and texture features are used to recognize and classify different agriculture/horticulture produce into normal and affected regions. The combinations of features prove to be very effective in disease detection. The experimental results indicate that proposed approach significantly enhances accuracy in automatic detection of normal and affected produce. This paper presents an effective method for detection of diseases in Malus Domestica using methods like K-meanclustering, color and texture analysis. Keywords-CCM method, color & texture segmentation, K-mean clustering, Malus Domestica, nuclei segmentation

Efficient Majority Logic Fault Detection with Difference-Set Codes for Memory Applications

Abstract: In this Paper, we focus on a class of LDPC codes known as Euclidean Geometric (EG) LDPC codes, which are constructed deterministically using the points and lines of a Euclidean geometry. Minimum distances for EG codes are also reasonably good and can be derived analytically. memory error correction code has been implemented using pipelined cyclic corrector where majority logic gate determined the error .LDPC soft error decoding is also implemented for the same memory error detection and correction comparison of the results are done .as the majority gate can detect only upto 2 error the extending majority gate with ldpc soft decoding can decrease the bit error rate.