A new mathematical model for mapping indoor environment

This paper presents a mathematical model as a new approach to object mapping, the system is proscribed to indoor and applied to approach a landmark. The contribution of this paper is to propose a new mathematical model for object mapping, the landmark is captured at varying distant points, the Scale invariant Feature Transform (SIFT) to extract object options, at the side of their uncertainty, from camera sensors. The (SIFT) features are invariant to image scaling, translation, and rotation, and partially invariant to illumination changes and affine or 3D projection, which is suitable for our application. As image options do not seem to be noise-free, the error analysis of the landmark positions and a preprocessing to obtained information which is incorporated into a model, using curve fitting techniques. Predictions createdby our model square measure well and correlate with experimental knowledge. This has eliminated correspondence Problem also known as a data association problem

Effect of masking techniques on computational complexity reduction of scale invariant feature transform

The Scale Invariant Feature Transform (SIFT) is algorithm use in feature detection and description, it is famous and has dominated the research community. The SIFT is a standard compared to others. Notwithstanding its sturdiness, SIFT has limitation of computational complexity, this has pose great limitation to real time and on-line implementations. Effort has been done by researchers to improve on SIFT performance because of it robustness, the computational cost was reduced to some extent but the distinctiveness cannot be compared with others(Descriptors). Thus, the aim of this research is to propose masking techniques to images, by eliminating areas with no or sparse keypoints in the feature extraction process, thereby reducing the computational cost of SIFT as the dominant descriptor in computer and robotic vision. Performance of the proposed approach was able to reduce the computational time to 47.27% at 0.7 Threshold with 17.94% in keypoint reduction. However, the masked SIFT was used in place categorization for performance evaluation. The evaluation was conducted using two classifiers in pattern recognition on the classification of Royal Institute of Technology-Image Data for Robot Localization (KTH-IDOL2) database. The classifiers are Nearest Neighbor (NN) and Multilayer Perceptron (MLP). Comparison of the categorization and classification accuracy produced 70% with the nearest neighbor(NN) and improved to 80% with Multilayer perceptron. In conclusion, the proposed approach was able to improved the computational time of SIFT higher than the recent published work in literature