For visual word based location recognition in 3D models we propose a novel distance-weighted scoring scheme. Matching visual words are not treated as perfect matches anymore but are weighted with the distance of the original SIFT feature vectors before quantization. To maintain the scalability and efficiency of vocabulary tree based approaches PCA compressed SIFT feature vectors are used instea...

This paper investigates a face recognition system based on Scale Invariant Feature Transform (SIFT) feature and its distribution on feature space. The system takes advantage of SIFT which possess strong robustness to expression, accessory pose and illumination variations. Since we use each of SIFT keypoint as the feature of face and SIFT keypoints are very complicated in feature space, we apply...

This paper summarizes the three robust feature detection methods: Scale Invariant Feature Transform (SIFT), Principal Component Analysis (PCA)–SIFT and Speeded Up Robust Features (SURF). This paper uses KNN (K-Nearest Neighbor) and Random Sample Consensus (RANSAC) to the three methods in order to analyze the results of the methods’ application in recognition. KNN is used to find the matches, an...

According to SIFT algorithm does not have the property of affine invariance, and the high complexity of time and space, it is difficult to apply to real-time image processing for batch image sequence, so an improved SIFT feature extraction algorithm was proposed in this paper. Firstly, the MSER algorithm detected the maximally stable extremely regions instead of the DOG operator detected extrem...

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...