Scale Invariant Feature Transform (SIFT) has been widely applied, but extracting SIFT feature is complicated and time-consuming. In this paper, to meet the demand of the real-time applications, SIFT is parallelized and optimized on cluster system, which is named pSIFT. Redundancy storage and communication are used for boundary data to improve the performance, and before representation of featur...

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SIFT Feature with Relevance Feedback for Image Retrieval Written by Administrator Wednesday, 16 March 2011 08:52 Last Updated Monday, 21 March 2011 07:32 In this paper, we used the Scale Invariant Feature Transform (SIFT) feature for image retrieval. SIFT descriptors are invariant to image scaling, transformation,rotation and partially invariant to illumination changes and affine, gives the loc...

SIFT (Scale Invariant Feature Transform) [1] features, developed by David G. Lowe, have been found to be highly robust to translations, rotations and scaling, and have been the solution of choice for many when dealing with problems of robotic vision and object recognition. Object recognition using SIFT features involves detection of SIFT features in an image, and matching those features against...

The Scale Invariant Feature Transform (SIFT) operator's success for computer vision applications makes it an attractive solution for the intricate feature based SAR image registration problem. For SAR images, SIFT feature matching results into lot of false alarms. To overcome the mentioned problem, we propose to use mutual information (MI) along with the SIFT operator for SAR image registration...

We propose a video copy detection system which is sufficiently robust to detect transformed versions of original videos with ability to pinpoint location of copied segments. Precisely due to the good stability and discriminative capability, SIFT feature is used for visual content description. However SIFT based feature matching has high computational cost due to large number of keypoints and hi...

In the photogrammetry field, interest in region detectors, which are widely used in Computer Vision, is quickly increasing due to the availability of new techniques. Images acquired by Mobile Mapping Technology, Oblique Photogrammetric Cameras or Unmanned Aerial Vehicles do not observe normal acquisition conditions. Feature extraction and matching techniques, which are traditionally used in pho...

According to the rotational invariance of Harris corner detector and the robustness of Sift descriptor. An improved Harris-Sift corner descriptor was proposed. At first, the algorithm given multi-scale strategy to Harris corner, improved corner counting method and removed redundant points at the same time, then, the corner was directly applied to low-pass Gaussian filter image. Based on the his...

Scale Invariant Feature Transform (SIFT) algorithm is a widely used computer vision algorithm that detects and extracts local feature descriptors from images. SIFT is computationally intensive, making it infeasible for single threaded implementation to extract local feature descriptors for high-resolution images in real time. In this paper, an approach to parallelization of the SIFT algorithm i...

In machine vision, Scale-invariant feature transform (SIFT) and its variants have been widely used in image classification task. However, the high dimensionality nature of SIFT features, usually in the order of multiple thousands per image, would require careful consideration in place to achieve accurate and timely categorization of objects within images. This paper explores the possibility of ...