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The invention discloses a novel automatic selection method of an optimal segmentation scale of a high-resolution remote sensing image. A multi-scale segmentation model of a high-resolution image is established by using a multi-scale MRF model, meanwhile image layer segmentation and image plane modeling are carried out at the same time, and context information between layer and layer objects and the spatial dependency of objects in the same layer are described. Spectrums, colors, textures, topological relations and other basic features of the objects are normalized in a Markov random field, a global optimal segmentation scale selection method capable of being automatically executed by a computer is realized by probabilistic information convergence calculation, parameter selection calculation and inference engineering are automatically executed by the computer, and an optimal segmentation scale parameter on theory is obtained. The technique has the advantages of high segmentation quality precision, high self adaptability and high computational efficiency.

Pavement crack detection plays an important role in pavement maintaining and management, nowadays, which could be performed through remote image analysis. Thus, edges of pavement crack should be extracted in advance; in general, traditional edge detection methods don’t consider phase information and the spatial relationship between the adjacent image areas to extract the edges. To overcome the deficiency of the traditional approaches, this paper proposes a pavement crack detection algorithm based on spectral clustering method. Firstly, a measure of similarity between pairs of pixels is taken into account through orientation energy. Then, spatial relationship is needed to find regions where similarity between pixels in a given region is high and similarity between pixels in different regions is low. After that, crack edge detection is completed with spectral clustering method. The presented method has been run on some real life images of pavement crack, experimental results display that the crack detection method of this paper could obtain ideal result.

This paper presented MMVD vanishing points detection algorithm which was inspired by multi-model estimation. It was operated in image pixel space, not parameter space. We first gave some vanishing points estimation model assumption based on the lines’ position and angle of each other. Then, the model assumption was used to construct the correlation matrix for line segments clustering and vanishing points estimation. Clustering algorithm produced vanishing points and they were refined by the EM algorithm to improve the accuracy. Experiments showed that the detection speed was good and the detection result can ensure good accuracy.

Based on the traditional iterative reconstruction workflow, a fast iterative reconstruction algorithm FIRA is proposed. First, using the image feature points extracted by SIFT algorithm, calculation of image similarity based on the minimum hash algorithm in LSH model is performed. Then, the iteration order is specified through hierarchical clustering. In the iterative process, the orientation estimation of images is carried through the clustering result coming from hierarchical tree. The optimization of parameter estimation is performed by bundle adjustment, and finally produce 3d mesh models. The experimental results show that the method could bring high efficiency and eliminate the accumulated error of adjustment calculation.

Generally, traditional edge detection methods don't consider the spatial relationship between the adjacent image areas to extract the edges. For the traditional algorithm insufficiency, this paper purposes a novel edge detection algorithm by introducing spatial relationship. This method can be divided into three main steps: firstly, a measure of similarity between pair wise pixels is taken into account by orientation energy. Then, the spatial relationship is needed to find regions where similarity between pixels in a given region is high and similarity between pixels in different regions is low. After that, edge detection is completed with spectral clustering method. Using IKONOS image, the experimental results show that the edge detection method of this paper gained ideal result.

Spectral clustering algorithms are newly developing technique in recent years. In this paper, we derive a new pairwise affinity function for spectral clustering based on a measure of texture features represented by Gaussian Markov Random Field (GMRF) model. This model is used to capture the statistical properties of the neighborhood at a pixel, and then pairwise affinities represented by it can cluster the pixels into coherent groups. Having obtained a local similarity measured by regions of coherent texture and brightness, we use the normalized cuts to find partitions of the image. Experimental results demonstrate that the proposed method is effective and robust for image segmentation.

This paper is concerned with the developing technique and the pattern of multimedia CAI(Computer Aided Instruction). The process involves educational psychology, computer science, experts system and artificial intelligence. The computer is mainly used to show teaching goal and content; note students’ studying status or control the studying schedule. All steps through teaching must be true of the pedagogical theory and technique; involve multimedia computer technology and modern instructional theory. Therefore, we can accomplish teaching assignment with high efficiency. This paper can play an important role on the design of this kind of software.

This paper presents a novel method for the solution to the complicated dynamic vibration, in which the measurement and quantification of the structural vibration signal is used. The universal formula for the electronic signal primary transform function’s correspondence matrix is inferred at some certain frequency.