A Self-Calibration Method of Zooming Camera

In this article we proposed a novel approach to self- calibrate a camera with variable focal length. We show that the estimation of camera’s intrinsic parameters is possible from only two points of an unknown planar scene. The projection of these points by using the projection matrices in two images only permit us to obtain a system of equations according to the camera’s intrinsic parameters . From this system we formulated a nonlinear cost function which its minimization allows us to estimate the camera’s intrinsic parameters in each view. The results on synthetic and real data justify the robustness of our method in term of reliability and convergence

Per-Pixel Extrusion Mapping with Correct Silhouette

Per-pixel extrusion mapping consists of creating a virtual geometry stored in a texture over a polygon model without increasing its density. There are four types of extrusion mapping, namely, basic extrusion, outward extrusion, beveled extrusion, and chamfered extrusion. These different techniques produce satisfactory results in the case of plane surfaces, but when it is about the curved surfaces, the silhouette is not visible at the edges of the extruded forms on the 3D surface geometry because they not take into account the curvature of the 3D meshes. In this paper, we presented an improvement that consists of using a curved ray-tracing to correct the silhouette problem by combining the per-pixel extrusion mapping techniques and the quadratic approximation computed at each vertex of the 3D mesh

A Feature Selection Approach Based on Archimedes’ Optimization Algorithm for Optimal Data Classification

Feature selection is an active research area in data mining and machine learning, especially with the increase in the amount of numerical data. FS is a search strategy to find the best subset of features among a large number of subsets of features. Thus, FS is applied in most modern applications and in various domains, which requires the search for a powerful FS technique to process and classify high-dimensional data. In this paper, we propose a new technique for dimension reduction in feature selection. This approach is based on a recent metaheuristic called Archimedes’ Optimization Algorithm (AOA) to select an optimal subset of features to improve the classification accuracy. The idea of the AOA is based on the steps of Archimedes' principle in physics. It explains the behavior of the force exerted when an object is partially or fully immersed in a fluid. AOA optimization maintains a balance between exploration and exploitation, keeping a population of solutions and studying a large area to find the best overall solution. In this study, AOA is exploited as a search technique to find an optimal feature subset that reduces the number of features to maximize classification accuracy. The K-nearest neighbor (K-NN) classifier was used to evaluate the classification performance of selected feature subsets. To demonstrate the superiority of the proposed method, 16 benchmark datasets from the UCI repository are used and also compared by well-known and recently introduced meta-heuristics in this context, such as: sine-cosine algorithm (SCA), whale optimization algorithm (WOA), butterfly optimization algorithm (BAO), and butterfly flame optimization algorithm (MFO). The results prove the effectiveness of the proposed algorithm over the other algorithms based on several performance measures used in this paper

Using features of local densities, statistics and HMM toolkit (HTK) for offline Arabic handwriting text recognition

This paper presents an analytical approach of an offline handwritten Arabic text recognition system. It is based on the Hidden Markov Models (HMM) Toolkit (HTK) without explicit segmentation. The first phase is preprocessing, where the data is introduced in the system after quality enhancements. Then, a set of characteristics (features of local densities and features statistics) are extracted by using the technique of sliding windows. Subsequently, the resulting feature vectors are injected to the Hidden Markov Model Toolkit (HTK). The simple database âArabic-Numbersâ and IFN/ENIT are used to evaluate the performance of this system. Keywords: Hidden Markov Models (HMM) Toolkit (HTK), Sliding window

Contribution of Collaborative Filtering Approach on Environmental Big Data Analytics Context

In recent years and following the spread of internet using, the way of life in our society as well as the management of the main production sectors have changed towards digitalization (E-Commerce, E-Surgery, road traffic management, etc.) to minimize the. This change challenged researchers and organizations to come up with solutions to manage, maintain, classify and make the best decision from the gigantic flows of data created daily while reducing environmental Damage (Consumption of Energical resources….), From which was born the Collaborative filtering approach which facilitate decision making based on the experience feedback of Internet users. This approach is used generally the big Data Analytics Algorithm to predict and classify dat

A Self-Calibration Method of Zooming Camera

In this article we proposed a novel approach to self- calibrate a camera with variable focal length. We show that the estimation of camera’s intrinsic parameters is possible from only two points of an unknown planar scene. The projection of these points by using the projection matrices in two images only permit us to obtain a system of equations according to the camera’s intrinsic parameters . From this system we formulated a nonlinear cost function which its minimization allows us to estimate the camera’s intrinsic parameters in each view. The results on synthetic and real data justify the robustness of our method in term of reliability and convergence

New approch of opinion analysis from big social data environment using a supervised machine learning algirithm

Sentiment analysis is a very substantial area of research in our environment. Many studies have focused on the topic in recent years. It has rapidly gained interest due to the unusual volume of opinion-bearing data on the Internet (Big Social Data). In this paper, we focus on sentiment environment analysis from Amazon customer reviews shared by a machine learning based approach. This process starts with the collection of reviews and their annotation followed by a text pre-processing phase in order to extract words that are reduced to their root. These words will be used for the construction of input variables using several combinations of extraction and weighting schemes. Classification is then performed by a supervised Machine Learning classifier. The results obtained from the experiments are very promising

Securing Images Using High Dimensional Chaotic Maps and DNA Encoding Techniques

With the growing need for secure multimedia data transmission, image encryption has become an important research area. Traditional encryption algorithms like RSA are not well-suited for this purpose, leading researchers to explore new approaches such as chaotic maps. The present study introduces a new image encryption algorithm that utilizes an improved Rossler system as a keystream generator. The improved Rossler system is an enhanced version of the original Rossler system, which has been optimized for better chaotic behavior and improved security. For the confusion part, we combine DNA encoding techniques with Baker maps to ensure high levels of security. Various performance metrics, including NPCR, UACI, correlation coefficient, histogram analysis, and key sensitivity analysis, were used to evaluate the proposed scheme. The results showed that the proposed method surpassed several existing image encryption methods in terms of both security and efficiency