Velagapudi Ramakrishna Siddhartha Engineering College

About: Velagapudi Ramakrishna Siddhartha Engineering College is a based out in . It is known for research contribution in the topics: Computer science & Antenna (radio). The organization has 1307 authors who have published 1155 publications receiving 6163 citations.

Wavelet Based Saliency Detection for Stereoscopic Images Aided by Disparity Information

Abstract: In the field of Computer vision, dependable assessment of visual saliency permits suitable processing of pictures deprived of earlier learning of their substance, and therefore sustains as an imperative stride in numerous errands including segmentation, object identification, and Compression. In this paper, we present a novel saliency recognition model for 3D pictures in view of highlight difference from luminance, color, surface texture, and depth. Difference of the stereo pair is extricated utilizing sliding window strategy. Then we present a contrast based saliency identification method that assesses global contrast divergences and spatial lucidness at the same time. This calculation is straightforward, proficient, and produces full determination saliency maps by combination of the considerable number of elements removed. Our calculation reliably performed better than existing saliency discovery strategies, yielding higher accuracy. We likewise show how the extricated saliency guide can be utilized to make top notch division covers for ensuing picture handling.

An Efficient Integrated ERP System Using Multilayer Perceptron

Abstract: Maintenance of data flow from one department to another is difficult in any organization currently, because of rapid growth in data. In order to maintain the data and to provide better services and enhance the organization’s revenue, enterprise resource planning system has been introduced. As the organization contains massive amount of data and to reduce up-front investment, secure cloud-based ERP systems have been used prominently. Apart from securely maintaining the data, goal of any organization is to increase their revenue and services to the customers. This paper proposes a novel method using artificial neural network multilayer perceptron to forecast the future to increase human resources, machinery and inventory in the organization. It has tested on clinical data repository and analysed the system performance by considering standard measures like RAE, MAPE, etc.

Saliency detection in stereoscopic images using adaptive Gaussian Kernel and Gabor filter

Abstract: So far a number of saliency detection designs have been developed for various applications in multimedia scenario. However, certain applications of stereoscopic imaging necessitate enhancing saliency detection designs to extract the salient areas accurately. Saliency detection strategy still faces many challenges like complexity in natural images as well as lesser-scale patterns in salient objects. This paper is set to obtain the saliency detection technique in two stages: feature extraction and depth saliency detection. Gaussian Kernel model is used to extract the features and Gabor filter to get the depth saliency map. This paper optimizes two coefficients namely feature difference among image patches $$ U $$ , from feature evaluation and fine scale $$ c $$ , from which the accurate detection can be achieved. For this, a renowned optimization algorithm named grey wolf optimization (GWO) is used, which is a recently proposed intelligent optimization method inspired by hunting behavior of grey wolves. The saliency detection model detection model is implemented in MATLAB 2015a. To the next of implementation, the performance of the proposed model, GWO-3-dimensional saliency mapping is compared over other the conventional algorithms in terms of receiver operator curve, Pearson correlation coefficient, Kullback–Leibler divergence and area under the curve. The overall performance analysis in terms of above measures and a valuable statistical analysis are carried out for validating the effectiveness of proposed model. The entire analysis proves that the proposed saliency detection performs better than the conventional models.