International Journal of Computer and Applications

Color science is a multidisciplinary field with broad applications in industries such as digital imaging, coatings and textiles, food, lighting, archiving, art, and fashion. Accurate definition and measurement of color appearance is a challenging task that directly affects color reproduction in such applications. Color Appearance Models addresses those challenges and offers insight into the preferred solutions. Extensive research on the human visual system (HVS) and color vision has been performed in the last century, and this book contains a good overview of the most important and relevant literature regarding color appearance models.

https://www.spiedigitallibrary.org/journals/Journal-of-Electronic-Imaging/volume-23/issue-2/029901/Color-Appearance-Models/10.1117/1.JEI.23.2.029901.pdf

Color Appearance Models

Due to the recent technology development, the multimedia complexity is noticeably increased and new research areas are opened relying on similar multimedia content retrieval. Content-based image retrieval (CBIR) systems are used for the retrieval of images related to the Query Image (QI) from huge databases. The CBIR systems available today have confined efficiency as they extract only limited feature sets. This paper demonstrates the extraction of vast robust and important features from the images database and the storage of these features in the repository in the form of feature vectors. The feature repository contains color signature, the shape features and texture features. Here, features are extracted from specific QI. Accordingly, an innovative similarity evaluation with a metaheuristic algorithm (genetic algorithm with simulating annealing) has been attained between the QI features and those belonging to the database images. For an image entered as QI from a database, the distance metrics are used to search the related images, which is the main idea of CBIR. The proposed CBIR techniques are described and constructed based on RGB color with neutrosophic clustering algorithm and Canny edge method to extract shape features, YCbCr color with discrete wavelet transform and Canny edge histogram to extract color features, and gray-level co-occurrence matrix to extract texture features. The combination of these methods increases the image retrieval framework performance for content-based retrieval. Furthermore, the results’ precision–recall value is calculated to evaluate the system’s efficiency. The CBIR system proposed demonstrates better precision and recall values compared to other state-of-the-art CBIR systems.

Content-Based Image Retrieval Using Color, Shape and Texture Descriptors and Features

With the recent evolution of technology, the number of image archives has increased exponentially. In Content-Based Image Retrieval (CBIR), high-level visual information is represented in the form of low-level features. The semantic gap between the low-level features and the high-level image concepts is an open research problem. In this paper, we present a novel visual words integration of Scale Invariant Feature Transform (SIFT) and Speeded-Up Robust Features (SURF). The two local features representations are selected for image retrieval because SIFT is more robust to the change in scale and rotation, while SURF is robust to changes in illumination. The visual words integration of SIFT and SURF adds the robustness of both features to image retrieval. The qualitative and quantitative comparisons conducted on Corel-1000, Corel-1500, Corel-2000, Oliva and Torralba and Ground Truth image benchmarks demonstrate the effectiveness of the proposed visual words integration.

/pdf/a-novel-image-retrieval-based-on-visual-words-integration-of-4nkbztiyoi.pdf

A Novel Image Retrieval Based on Visual Words Integration of SIFT and SURF.

The necessity of image fusion is growing in recently in image processing applications due to the tremendous amount of acquisition systems. Fusion of images is defined as an alignment of noteworthy Information from diverse sensors using various mathematical models to generate a single compound image. The fusion of images is used for integrating the complementary multi-temporal, multi-view and multi-sensor Information into a single image with improved image quality and by keeping the integrity of important features. It is considered as a vital pre-processing phase for several applications such as robot vision, aerial, satellite imaging, medical imaging, and a robot or vehicle guidance. In this paper, various state-of-art image fusion methods of diverse levels with their pros and cons, various spatial and transform based method with quality metrics and their applications in different domains have been discussed. Finally, this review has concluded various future directions for different applications of image fusion.

/pdf/image-fusion-techniques-a-survey-4bx3psydco.pdf

Image Fusion Techniques: A Survey

Augmented episodes of melanoma, a curable skin cancer variety of antagonistic nature have stimulated the advancements in designing systems for computer aided diagnosis of the disease. Clinical diagnosis includes primary vetting of the symptoms followed by a biopsy and necessary medical examinations. However, computer based classification of the clinical images of dermoscopy have the potential to diminish the exertion of the dermatologist by offering a computer aided opinion independent of medical know-how. Assorted methods have been proposed in recent times including the deep learning techniques for computer based melanoma recognition. But, most of the techniques have enhanced feature dimension which has added to the computational complexity of the entire system. In this work, the authors have attempted to design robust light-weight feature extraction techniques from high level bit planes of dermoscopic images. The noisy lower bit planes are ignored for lack of significant feature information. The proposed method of feature extraction is tested with three different classifiers for specificity and sensitivity outputs of the dermoscopic images. The results of classification have outclassed the performance of state-of-the-art feature extraction techniques.

Significant Bit Contribution in Robust Feature Extraction for Dermoscopic Image Classification

/pdf/group-key-management-model-for-worldwide-wireless-mesh-78oagki2jc.pdf

Group-Key Management Model for Worldwide Wireless Mesh Networks

Recent developments in the areas of smart phones and wearable devices have paved the way to many emerging applications in Mobile Crowd Sensing (MCS) domain. There has been a phenomenal increase in the number of people using these devices. In this work we consider an MCS application to cover a set of points of interest (targets) with the objective of selecting a minimum set of participants among the mobile devices. This problem, coined as Participant Selection Problem (PSP) to provide desired coverage can be solved optimally using an Integer Linear Programming (ILP). But for large problem size, solving the ILP becomes impractical from the point of execution time as well as the requirement of having the necessary information about all the participants in one place. In this paper, we have proposed a distributed participants selection algorithm (DPSA) to be run at each participants where, the participants after exchanging messages with its neighbors, decides to remain active or idle. We have shown that, those participants which selects themselves as active suffice to cover all the desired targets. We have run a series of experiments to measure the performance of the proposed algorithm where we measure along with number of participants selected also the total energy consumption. Simulation results reveals the close proximity of DPSA compared to the optimal one providing the same coverage.

Cost Effective Algorithms for Participant Selection Problem in Mobile Crowd Sensing Environment

Recent times have witnessed a considerable number of inference drawing applications of Wireless Sensor Networks (WSN) in the areas of monitoring and surveillance. Sensor nodes are battery powered and have limited computational capability thereby mandating data gathering in WSN be done in an energy efficient manner. At the same time deriving inference requires reliable and confident estimate of input dataset for increasing prediction accuracy. In our work we have incorporated both the issues by using a lightweight Bayesian Estimator process with a reduction ratio of 10:1, 20:1 and 30:1 to be executed in sensor nodes in a temporal manner. In the base station we use Naive Bayes, Support Vector Machine (SVM) and Multilayer Perceptron (MLP) based inference systems to generate decision based on the estimated data send by the sensor nodes. We compare our work in terms of energy efficiency and accuracy with a similar kind of system without using an estimator. MATLAB 2018a is used to simulate our results indicating significant improvement of our proposal using Bayesian Estimator over the naive method i.e. without using it, in terms of energy efficiency and accuracy. Further we perform paired-t test to show the statistical significance of our results.

Bayesian Estimator Based Weather Forecasting using WSN

Routing protocols in MANET such as OLSR-INRIA, DSR and ZRP finds out the path between a given source destination node pair without considering the reliability of the links in the selected path. Some links in MANET are unreliable due to interference from transmissions from adjacent links, ambient noise system noise, jamming signals from intruder nodes all of which results in low throughput, packet delivery ratio, high jitter and end-to-end delay. In our work, we use Signal-to-Noise Ratio (SNR) as a measure of the link reliability. We propose modified secure version of the of three protocols namely OLSR-INRIA, DSR & ZRP coined as SOLSR-INRIA , SDSR, & SZRP which takes into account the link SNR value as a measure of link reliability in addition to the other parameters as in the original method in the route discovery phase. QualNet network simulator have been extensively used to evaluate the performance of our modified secure routing protocol over two different network scenarios consisting of 52 and 72 mobile nodes respectively considering random waypoint (RWP) mobility model. The results indicate high throughput, high packet delivery ratio and low jitter and end-to-end delay in comparison to the original protocols which do not account for wireless links reliability.

/pdf/performance-evaluation-of-proactive-reactive-and-hybrid-lybp1ox70u.pdf

Saurav Ghosh

Papers

Significant Bit Contribution in Robust Feature Extraction for Dermoscopic Image Classification

Group-Key Management Model for Worldwide Wireless Mesh Networks

Cost Effective Algorithms for Participant Selection Problem in Mobile Crowd Sensing Environment

Bayesian Estimator Based Weather Forecasting using WSN

Performance Evaluation of Proactive, Reactive and Hybrid Routing Protocols considering Link SNR Value using Qualnet