This paper presents a new face identification system based on Graph Matching Technique on SIFT features extracted from face images. Although SIFT features have been successfully used for general object detection and recognition, only recently they were applied to face recognition. This paper further investigates the performance of identification techniques based on Graph matching topology drawn...

Scale Invariant Feature Transform (SIFT) is a popular image feature extraction algorithm. SIFT’s features are invariant to many image related variables including scale and change in viewpoint. Despite its broad capabilities, it is computationally expensive. This characteristic makes it hard for researchers to use SIFT in their works especially in real time application. This is a common problem ...

If a physical object has a smooth or piecewise smooth boundary, its images obtained by cameras in varying positions undergo smooth apparent deformations. These deformations are locally well approximated by affine transforms of the image plane. In consequence the solid object recognition problem has often been led back to the computation of affine invariant image local features. Such invariant f...

Representing and understanding the three-dimensional (3D) structural information of protein-ligand complexes is a critical step in the rational drug discovery process. Traditional analysis methods are proving inadequate and inefficient in dealing with the massive amount of structural information being generated from X-ray crystallography, NMR, and in silico approaches such as structure-based do...

The chosen solution is based on a client-server architecture and the object recognition is based on local features. The study focuses on the comparison, in terms of time and performance, of the Scale-Invariant Feature Transform (SIFT), the Speeded Up Robust Features (SURF), the Nearest Neighbor Search (NNS) match and a k-means trees based search. It was found that SIFT outperforms SURF in terms...

In this paper, the well-known SIFT detector is extended with a bivariate feature localization. This is done by using function models that assume a Gaussian feature shape for the detected features. As function models we propose (a) a bivariate Gaussian and (b) a Difference of Gaussians. The proposed detector has all properties of SIFT, but provides invariance to affine transformations and blurri...

Image Registration is an important part of computer vision. We propose a method of image registration by obtaining best similarity of local geometric figure that utilizes opposite core difference (OCD) of corresponding local figure. This method gets initial matching after describing precisely SIFT points by constructing feature subspaces based on the detection of SIFT feature points. Then we de...

In this paper, Hematoxylin and Eosin (H&E) stained liver images are classified by using both Local Binary Patterns (LBP) and one dimensional SIFT (1-D SIFT) algorithm. In order to obtain more meaningful features from the LBP histogram, a new feature vector extraction process is implemented for 1-D SIFT algorithm. LBP histograms are extracted with different approaches and concatenated with color...

Ear recognition is an emerging biometric technology. This paper proposes a new ear recognition method based on SIFT(Scale-invariant feature transform) and Harris corner detection. Firstly, Harris corner points and SIFT keypoints are detected respectively. Then taking Harris corner into the SIFT algorithm to calculate their descriptor as the image feature vectors. Finally the feature vectors are...

Texture is a powerful cue for describing structures that show a high degree of similarity in their image intensity patterns. This paper describes the use of Self-Invariant Feature Transform (SIFT), both as low-level and high-level descriptors, applied to differentiate the tissues present in breast US images. For the low-level texture descriptors case, SIFT descriptors are extracted from a regul...