Informatics Publishing Limited

R. Sivagami ¹, V. Vaithiyanathan ¹, V. Sangeetha ¹, M. Ifjaz Ahmed ¹, K. Joseph Abraham Sundar ¹, K. Divya Lakshmi ¹

Affiliations
1 School of Computing, SASTRA University, Thanjavur - 613402, Tamil Nadu, IN

Abstract

Low resolution Multispectral images obtained from earth observation satellite interpreted directly have less information which is not suitable for remote sensing applications. The advancement of sensors onboard satellites provides panchromatic images which have more image details and low-resolution MS images lead the way for the researchers to develop algorithms suitable for fusing multi-sensor images which can improve the resolution of the MS images. In this paper review on various pixel based fusion algorithms and evaluation metrics are presented. From the literature review, it is inferred that the Multi resolution techniques will give better accuracy than all other traditional algorithms.

Keywords

Evaluation Metrics, Fusion Algorithms, Fusion Transforms, Image Fusion, Remote Sensing

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V. Vaithiyanathan ¹, K. Divya Lakshmi ¹, K. Joseph Abraham Sundar ¹, M. Ifjaz Ahmed ¹, V. Sangeetha ¹, R. Sivagami ¹

Affiliations
1 School of Computing, SASTRA University, Thanjavur - 613402, Tamil Nadu, IN

Abstract

Local features of an image are used in many computer vision applications such as object detection and scene matching. The gradient orientation histogram is used by many local features such as Scale Invariant Feature Transform (SIFT), a widely used image local feature. This paper discusses various distance functions that can be used to measure the similarity between the local features described by the gradient orientation histogram. A distance function, based on the quadratic form is proposed for the SIFT descriptor. The state of the art distance functions - Euclidean, Chi-square, Manhattan and the proposed quadratic form based distance function are calculated between the features extracted from the images. Nearest neighborhood ratio strategy is used to find the corresponding features based on the distance measure. Correct matches are estimated using the ground truth transformation function between the images, present in the form of homograph matrix. It is experimentally found that the proposed distance function has an execution time reduced by 21% compared to the Euclidean distance for a similar accuracy performance. The proposed distance retrieves more number of correct matches compared to the modified Earth Mover distance which is fastest among the evaluated distance functions. The future work will be aimed at improving the time taken for computing the distance matrix between the feature sets and a better strategy for computing the matches.

Keywords

Distance Functions, Gradient Orientation Histogram, Image Matching, Local Features, SIFT

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K. Divya Lakshmi ¹, V. Vaithiyanathan ¹

Affiliations
1 School of Computing, SASTRA University, Thirumalaisamudram, Thanjavur - 613401, Tamil Nadu, IN

Abstract

Image registration is an important preprocessing step for computer vision tasks such as object recognition by a robot or change detection over a natural terrain from satellite images. This paper discusses the basic steps for the feature based image registration, and surveys the various methodologies from literature applicable for each step. The evolution of algorithms on image features is explained with some of their mathematical concepts, along with their limitations. Two videos captured from an unmanned aerial vehicle were used for experimentation. The performance of intensity based image registration using normalized cross correlation and Speeded Up Robust Features was observed by registering various frames of the videos. Intensity based methods perform well for all type of images when the images to be registered vary by only a translation. Feature based methods are suitable when the possible transformation between the images are unknown. Feature based methods fail when the images lack distinct features such as corner points. The study presented would enable one to choose the right methodology for each step of the image registration algorithm, for various real time computer vision applications such as navigation of unmanned aerial vehicles.

Keywords

Image Matching, Image Registration, Local Features, Registration Algorithms, UAV Navigation.

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