With the register detection problem, the color textile image segmentation algorithm based on MeanShift and the block matching algorithm based on Harris corner detection were proposed. The extended MeanShift algorithm was used to segment the textile image, and then different color regions were extracted from the segmented standard image; then the feature points were detected by Harris operator, ...

We present a steganalysis scheme for LSB matching steganography based on feature vectors extracted from integer wavelet transform (IWT). In integer wavelet decomposition of an image, the coefficients will be integer, so we can calculate co-occurrence matrix of them without rounding the coefficients. Before calculation of co-occurrence matrices, we clip some of the most significant bitplanes of ...

Matching laser range scans observed at different points in time is a crucial component of many robotics tasks, including mobile robot localization and mapping. While existing techniques such as the Iterative Closest Point (ICP) algorithm perform well under many circumstances, they often fail when the initial estimate of the offset between scans is highly uncertain. This paper presents a novel a...

In this paper, we propose a method for retinal image matching that can be used in image matching for person identification or patient longitudinal study. Vascular invariant features are extracted from the retinal image and a feature vector is constructed for each of the vessel-segments in the retinal blood vessels. The feature vectors are represented in a tree structure with maintaining the ves...

SIFT (Scale invariant feature transform) descriptor is one of the most effective local features that is used for scale, rotation and illumination invariant of automatic image registration. In this paper a fast bi-directional matching algorithm based on SIFT was proposed for improving match accuracy and reducing match time. Firstly the single-feature points and multi-feature points in two images...

In the study, it presents an efficient algorithm based on SURF (Speeded Up Robust Features). The method applies the SURF algorithm in the detection and description for image features, first it applies the SURF feature detector in extracting reference images and matching feature points in the image, respectively, then it uses DAISY algorithm instead of the SURF algorithm to calculate the charact...

Abstact Point/feature matching in 2D images has been well studied in the computer vision community. With the huge advancement in shape scanning and acquisition technology, feature matching has recently found usefulness in 3D shape matching and searching. We study the general problem of feature correspondence in 3D shapes represented by triangular meshes. This project is a survey and comparison ...

Recently, Scale Invariant Feature Transform (SIFT) algorithm is widely used in feature extraction and image matching. However, it has some defects, such as large volume of computational data and low efficiency of image matching. To address these defects, adaptive feature extraction and image matching based on Haar Wavelet Transform and SIFT (AHWT-SIFT) is proposed in this paper. In view of the ...

Geometric distortions and intensity differences always exist in multi-source optical satellite imagery, seriously reducing the similarity between images, making it difficult to obtain adequate, accurate, stable, and well-distributed matches for image registration. With the goal of solving these problems, an effective image matching method is presented in this study for multi-source optical sate...

The human ear is unique and stability and it has broad application prospects in the field of identity verification. Ear image matching is an important part of the ear recognition, has gained widely research. SIFT(Scale invariant feature transform) descriptor is one of the most effective local features that is used for scale, rotation and illumination invariant. In this paper a fast bi-direction...