There are often significant intensity variations between multispectral images, making automatic registration tasks difficult. Traditional feature matching methods, such as the scale-invariant feature transform (SIFT), are often sensitive to nonlinear variations of intensity between multispectral images. To solve this problem, an improved SIFT algorithm is introduced. First, the contrast limited adaptive histogram equalization algorithm is introduced in the feature extraction stage to improve the feature point extraction results. Then, the Sobel operator is used to enhance the main direction consistency between homologous feature point pairs. The experimental results suggest that the method can obtain reliable registration results on unmanned aerial vehicle multispectral images.
The matching of terrestrial laser intensity image and optical image is one of the active research areas in terrestrial laser
scanning technique. In this paper, we introduce an algorithm based on gray scale to match intensity image and optical
image. The similarity measure and the size of matching template are two crucial issues of this algorithm. Firstly, the
correlation coefficient based on gray values and the mutual information based on statistical distribution are combined.
Thus an integrated criterion is produced to extract corresponding points in an intensity image and an optical image.
Secondly, an experiment was done to perceive the relations between the correlation coefficient and the mutual
information of corresponding points and the size of a matching template.
This image matching algorithm has two steps, coarse image matching and fine image matching. The coarse image
matching step is carried out to search three precise pairs of corresponding points to calculate a mapping polynomial, by
which a primary search region can be obtained for reducing the possibility of false correspondence and increasing the
speed of image matching. While the fine image matching step aims at obtaining more precise corresponding points in the
region determined by the mapping polynomial.
In going from two-class to multi-class classification, most boosting algorithms have been restricted to reducing multiclass
problem to multiple two-class problems. In the paper, a direct multi-class AdaBoost algorithm is adopted to face
recognition. Then the weighted classification trees are extended from stumps as weak learners to fulfill the multi-class
learning. The multi-class boosting algorithm has the following features: A K-class classification problem is treated
simultaneously without reducing it to multiple binary classification problems; only one lost function per iteration is fitted;
the algorithmic structure is compact and easy to implement. The experimental results both on UCI dataset and YaleA
face dataset show the meanings of the proposed algorithm.
In this paper, a face recognition method using local qualitative representations is proposed to solve the problem of face
recognition in varying lighting. Based on the observation that the ordinal relationship between the average brightness of
image regions pair is invariant under lighting changes, Local Binary Mapping is defined as an illumination invariant for
face recognition based on Local Binary Pattern descriptor, which extracts the local variance features of an image. For the
'symbol' feature vector, hamming distance is used as similarity measurement. It has been proved that the proposed
method can provide the accuracy of 100 percent for subset 2, 3, 4 and 98.89 percent for subset 5 of the Yale facial
database B when all images in subset 1 are used as gallery.
KEYWORDS: Roads, Mechanics, Chemical elements, Finite element methods, Actinium, Process control, Space operations, Spatial resolution, Map generalization, Data processing
Spatial conflict resolution is an important part of cartographic generalization, and it can deal with the problems of having too much information competing for too little space, while feature displacement is a primary operator of map generalization, which aims at resolving the spatial conflicts between neighbor objects especially road features. Considering the road object, this paper explains an idea of displacement based on structural mechanics. In view of spatial conflict problem after road symbolization, it is the buffer zones that are used to detect conflicts, then we focus on each conflicting region, with the finite element method, taking every triangular element for analysis, listing stiffness matrix, gathering system equations and calculating with iteration strategy, and we give the solution to road symbol conflicts. Being like this until all the conflicts in conflicting regions are solved, then we take the whole map into consideration again, conflicts are detected by reusing the buffer zones and solved by displacement operator, so as to all of them are handled.
Extremely precise texture modeling of complex 3D objects is a significant problem worth of much research. Comparing the laser scanning technology and the digital photogrammetry with their combination results, the optimal solution is the fusion of the data. Surveying points are used to model the main shapes while laser scanning captured the fine details. Photogrammetry is used to register the texture images with the geometry and produce ortho-texture. This paper explains a reliable method based on Direct Linear Transformation (DLT) and ortho-rectification method to model 3D objects combined with 3D laser scanning data and high-resolution image data. This novel technology can produce precise texture and improves the efficiency and quality of highly detailed texture modeling.
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