Study On The Detecting And Matching Technique Of Local Invariant Feature And Its Applications

Image feature extraction, which serves as a key for many areas of image processing, plays an important role in image analysis, pattern recognition and computer vision. Since the images are always suffered from a series of transformations, such as rotation, viewpoint, scale, lightness, blur and so on, the issue that how to detect stable features becomes an emphasis in related research fields. In recent years, a kind of local features, invariant to a class of image transformations, has been proved to be successful in a wide range of applications, such as image registration, image stitching, object recognition, target tracking, watermarking, image retrieval and so on. The kind of methods based on local invariant features mainly consists of feature extraction (including feature detection and description) and feature matching. In this paper, some theories about invariant features were analyzed thoroughly and some existing methods of detecting and matching local invariant features have been studied. Then several novel algorithms based on original ones with better performance in image registration, object recognition and tarket tracking have been proposed.A kind of methods about multi-scale feature extraction based on scale-space theory has been studied and some shortcomings of these methods were analyzed. On the basis of the analysis, a feature detector named improved Harris-Laplace is proposed to obtain higher repeatability than that of original Harris-Laplace. In this novel method, the Harris feature points in each scale are extracted respectively first, and all points detected in each scale are tracked and grouped beginning with the largest scale in the scale-space to make each group represent one local structure. Then the point in each group which simultaneously leads to the maxima of corner points measuring and scale normalization Laplace function is selected. Finally, these points are described and matched by SIFT descriptor successfully. To some image with the transformations, such as scale, viewpoint, JPEG compression and blur, experimental results indicate that the proposed method has higher repeatability than original Harris-Laplace. Moreover, comparing with original Harris-Laplace, a more accurate registration precision of multi-sensor remote sensing images was obtained by the advanced method.Scale Invariant Feature Transform (SIFT) is a widely used descriptor for local invariant feature. However, since this descriptor uses the gradient information in the neighborhood of one feature point, some mismatches may appear when the extracted feature points locate in some similar structures of one image. So a novel method based on a kind of spatial distribution descriptor is proposed to correct the mismatches caused by SIFT. In the proposed method, the feature points were detected and matched first by SIFT and then each matched point can be described again to generate a more distinctive descriptor using the spatial distribution of the pixels on the image contour to the matched point. Finally, two kinds of mismatchs were corrected by the new descriptor. The experimental results indicate that, comparing with the Random sample consensus (RANSAC), the proposed algorithm shows the ability to exclude more false matches while retain more of the original correct matches.Meanwhile, a new local affine invariant feature descriptor is proposed. First, a new kind of feature named as Multi-scale Auto-convolution Entropy (MSAE) is constructed based on MSA and proved to be affine invariant. Then the MSA is combined with MSAE using the Generalized Canonical Correlation Analysis (GCCA) to obtain a new feature with more information. This combined feature can be seen as a new local affine invariant feature descriptor. Finally, the whole image and the Maximally Stable Extremal Region (MSER) extracted from the image are described by the new descriptor, respectively. Two recognition experiments verify that the proposed combined affine invariant feature is more distinctive than MSA.Furthermore, a kind of algorithm, based on epipolar geometry constraint, now is known as a mainstream method for discarding mismatches. Among them, M-Estimators, with fast computation speed and robustness to Gaussian noise, has good application prospects in discarding mismatches. Because this algorithm depends entirely on the primary matrix obtained by the method of least squares, its precision and stability of detection is not very well. Then an improved M-Estimators algorithm for estimating the fundamental matrix was studied. The improved method calculates the primary matrix by seven-point technique first. Then the quadratic sum of the distances between the matching points and the corresponding epipolar lines is set as a metric to calculate a more precise initial fundamental matrix than M-Estimators. In the following step, this obtained initial matrix is used to eliminate the mismatches included in the original point set. Finally, a nonlinear optimization for the new matched points set is carried out with Torr-M-Estimators and some finally matched point pairs are obtained. Through a mass of experiments performed in the case of mismatches and Gaussian noise, the experimental results indicate that the proposed algorithm not only improves the estimation precision but also shows a well robustness, comparing with M-Estimators and Torr-M-Estimators.In the last part, two algorithms, Mean Shift and its derivative Camshift, have been researched and a new target tracking method combining SIFT and Camshift is proposed to overcome the shortcomings of Camshift. In the first step, the target region is transformed to HSV color space, then the feature points are extracted from H, S and V channels by SIFT algorithm respectively. All SIFT points, most of which are located on targets in view of the weak texture of target background, can be used to generate hue histogram. Secondly, color probability distribution of the next frame image is obtained based on the hue histogram. Thirdly, SIFT algorithm is used to detect points in the searching region from H, S and V and then these points are matched with those points in the target region. Finally, the pixels within the region obtained by the matched points located in searching region can be used to calculate the new center and size of the searching region. Three image sequences including testing environment indoors and outdoors are used to evaluate the propose method. Experimental results indicate that the new method has better validity.