Scale Invariant Feature Transform (SIFT) is a computer vision algorithm that is widely-used to extract features from images. We explored accelerating an existing implementation of this algorithm with message passing in order to analyze large data sets. We successfully tested two approaches to data decomposition in order to parallelize SIFT on a distributed memory cluster. Introduction In certai...

In 2004, David G. Lowe published his paper “Distinctive Image Features from ScaleInvariant Keypoints” (Lowe, 2004, [2]), outlining a method he developed for finding distinctive, scale and rotation invariant features in images that can be used to perform matching between different views of an object or scene. His method, Scale-Invariant Feature Transform (SIFT) combines scale-space theory and fe...

With explosive growth of multimedia data on internet, the effective information retrieval from a large scale of multimedia data becomes more and more important. To retrieve these multimedia data automatically, some features in them must be extracted. Hence, image feature extraction algorithms have been a fundamental component of multimedia retrieval. Among these algorithms, Scale Invariant Feat...

Most of the existing fingerprint retrieval systems are based on the overall characteristics and detailed features of fingerprints, and their performance is poor in the cases of low-quality fingerprint images, such as incomplete fingerprint images. In order to improve the recognition speed, accuracy, and robustness of automated fingerprint recognition systems based on large-scale fingerprint dat...

From the immense amount of images being sent to Earth by satellites, it takes too much time for a human to go through each image and classify what the image represents. Therefore, we are able to use classifiers to classify these images. In this paper, we used two different classification methods to classify satellite images. Between the two classifiers we used, both K-Nearest-Neighbor and Suppo...