A Boundary Integral Equation Related to the Ahlfors Map

The Ahlfors map is a mapping function that maps a multiply connected region onto a unit disk. This paper presents a new boundary integral equation related to the Ahlfors map of a bounded multiply connected region. The boundary integral equation is constructed from a boundary relationship satisfied by the Ahlfors map of a multiply connected region.

Stamps extraction using local adaptive k- means and ISODATA algorithms

<span>One of the main difficulties facing the certified documents documentary archiving system is checking the stamps system, but, that stamps may be contains complex background and surrounded by unwanted data. Therefore, the main objective of this paper is to isolate background and to remove noise that may be surrounded stamp. Our proposed method comprises of four phases, firstly, we apply k-means algorithm for clustering stamp image into a number of clusters and merged them using ISODATA algorithm. Secondly, we compute mean and standard deviation for each remaining cluster to isolate background cluster from stamp cluster. Thirdly, a region growing algorithm is applied to segment the image and then choosing the connected region to produce a binary mask for the stamp area. Finally, the binary mask is combined with the original image to extract the stamp regions. The results indicate that the number of clusters can be determined dynamically and the largest cluster that has minimum standard deviation (i.e., always the largest cluster is the background cluster). Also, show that the binary mask can be established from more than one segment to cover are all stamp’s disconnected pieces and it can be useful to remove the noise appear with stamp region.</span>

Weed and Corn Seedling Detection in Field Based on Multi Feature Fusion and Support Vector Machine

Detection of weeds and crops is the key step for precision spraying using the spraying herbicide robot and precise fertilization for the agriculture machine in the field. On the basis of k-mean clustering image segmentation using color information and connected region analysis, a method combining multi feature fusion and support vector machine (SVM) was proposed to identify and detect the position of corn seedlings and weeds, to reduce the harm of weeds on corn growth, and to achieve accurate fertilization, thereby realizing precise weeding or fertilizing. First, the image dataset for weed and corn seedling classification in the corn seedling stage was established. Second, many different features of corn seedlings and weeds were extracted, and dimensionality was reduced by principal component analysis, including the histogram of oriented gradient feature, rotation invariant local binary pattern (LBP) feature, Hu invariant moment feature, Gabor feature, gray level co-occurrence matrix, and gray level-gradient co-occurrence matrix. Then, the classifier training based on SVM was conducted to obtain the recognition model for corn seedlings and weeds. The comprehensive recognition performance of single feature or different fusion strategies for six features is compared and analyzed, and the optimal feature fusion strategy is obtained. Finally, by utilizing the actual corn seedling field images, the proposed weed and corn seedling detection method effect was tested. LAB color space and K-means clustering were used to achieve image segmentation. Connected component analysis was adopted to remove small objects. The previously trained recognition model was utilized to identify and label each connected region to identify and detect weeds and corn seedlings. The experimental results showed that the fusion feature combination of rotation invariant LBP feature and gray level-gradient co-occurrence matrix based on SVM classifier obtained the highest classification accuracy and accurately detected all kinds of weeds and corn seedlings. It provided information on weed and crop positions to the spraying herbicide robot for accurate spraying or to the precise fertilization machine for accurate fertilizing.

IRIS LOCATION METHOD BASED ON MATHEMATICAL MORPHOLOGY AND IMPROVED HOUGH TRANSFORM

An iris location method based on mathematical morphology and improved Hough transform is proposed in this paper. When locating the iris inner boundary, an iris inner boundary method based on mathematical morphology and circle fitting is proposed. The iris image is first preprocessed to get a binary image by the gray stretch transform or the circular region mean filter, and then the type of the connected region selected as the iris inner boundary in the binary image is determined by the ratio of the width to the length. On the foundation, the different method is used to extract the parameters of the connected region and the iris inner boundary is obtained. When locating the iris outer boundary, an iris outer boundary method based on the improved Hough transform is put forward. First, the Gaussian filter is used to deal with the iris image, the filtered image is reduced, Canny operator is employed for edge detection, an annular region centered at the center of the iris inner boundary of the reduced image is cropped to explore a circle by Hough transform, and the center and the radius of the iris outer boundary are worked out. The experimental results reveal that this proposed method has the advantages of fast speed, high accuracy, strong robustness and practicability.

Point cloud clustering and outlier detection based on spatial neighbor connected region labeling

Clustering analysis is one of the most important techniques in point cloud processing, such as registration, segmentation, and outlier detection. However, most of the existing clustering algorithms exhibit a low computational efficiency with the high demand for computational resources, especially for large data processing. Sometimes, clusters and outliers are inseparable, especially for those point clouds with outliers. Most of the cluster-based algorithms can well identify cluster outliers but sparse outliers. We develop a novel clustering method, called spatial neighborhood connected region labeling. The method defines spatial connectivity criterion, finds points connections based on the connectivity criterion among the k-nearest neighborhood region and classifies connected points to the same cluster. Our method can accurately and quickly classify datasets using only one parameter k. Comparing with K-means, hierarchical clustering and density-based spatial clustering of applications with noise methods, our method provides better accuracy using less computational time for data clustering. For applications in the outlier detection of the point cloud, our method can identify not only cluster outliers, but also sparse outliers. More accurate detection results are achieved compared to the state-of-art outlier detection methods, such as local outlier factor and density-based spatial clustering of applications with noise.

Connectivity of dynamic graphs in multiply connected spaces

Dynamic geometric graphs are natural mathematical models of many real-world systems placed and moving in space: computer ad hoc networks, transport systems, territorial distributed systems for various purposes. An important property of such graphs is connectivity, which is difficult to maintain during movement due to the presence of obstacles on the ground. In this paper, a model of a multiply connected region with obstacles of the “city blocks” type is constructed and the behavior of the characteristics of dynamic graphs located in such domains is studied. A probabilistic approach to the study of graphs is proposed, in which their characteristics are considered as random processes. For graphs of different scales, dependences of the connectivity probability, the number of components on the parameters of a multiply connected region, and the radius of stable signal reception / transmission were found. The mathematical expectation of the number of components in the starting random geometric graph is found. The significant influence not only of geometrical parameters, but also of the topological characteristics of a multiply-connected domain has been revealed. Graphs of changes in the probability of connectedness of a dynamic graph over time are constructed on the basis of calculating the average value over the set of realizations of the random process of moving network nodes. They are characterized by a periodic component that correlates with the structure of a multiply connected region, and a component that exponentially decreases with time. The dependence of the probability of connectedness of the graph on the direction of the network displacement vector was studied, which turned out to be very significant. The results obtained give an idea of the influence of a multiply-connected domain on the dynamics of graphs, and can be used in control algorithms for mobile distributed systems to ensure their spatial connectivity.