In this paper, we analyze the network structure of two SNSs, academic community system (ACS) and Amippy. From the viewpoint of network topology, the major characteristics of these data sets can be summarized as follows: low average shortest-path length, high clustering coefficient, presence of a power law degree distribution and negative assortativity. Based on our analysis, we propose a growth model of SNS networks. We conducted numerical simulations to compare actual data sets with networks generated by the proposed model. Results of simulations indicated that the processes of the CNN model and fitness model are needed to reproduce the networks of SNSs.

Recently, social networking services (SNS) have become a social phenomenon on the Internet. There are many small SNSs, including campus SNS, company SNS, and regional SNSs. Such user-limited SNSs are not fully utilized yet. To solve this problem, we propose a SNS model to simulate effective SNS encouragement methods. Simulations based on our proposed model were performed to confirm the influences of the encouragement methods on small SNSs. The simulations show that encouraging existing users' log-ins effectively encourages small SNSs.

The paper proposes a method for recognizing characters in gray-scale images with graphical designs and noise. The authors' previous method for binary images is robust against graphical designs, but it is easily affected by binarization conditions or scanning conditions. Therefore, they improve the method to be tolerant for such conditions by handling gray-scaled images directly. First, a projection profile for text-line extraction and a similarity value for character recognition are expanded for gray-scaled images. Next, a learning method of adaptive threshold values against the degree of degradation and variations of intensity levels are introduced to suppress spurious candidates during the recognition process. Experimental results for fifty Japanese newspaper headlines show that the proposed method achieves higher recognition rates than their previous method in the wide range of scanning brightness conditions.

This paper presents a new method for extracting the 3-D shape and texture of an object undergoing translational motion from image sequences captured through a monocular extra-wide picture viewing angle. The feature of this work is extracting this information from image sequences without requiring rigid environmental conditions. In this method, the relative positions between target and view position are estimated based on spatio-temporal image analysis, and shape is reconstructed from the multiple silhouette information. After reconstructing the 3-D shape, the voxel value of a surface point is determined by statistically analyzing those images that contain the surface point. The proposed method can extract 3-D shape and surface texture at a stroke from outdoor scenes. An experiment using real outdoor scenes confirms the effectiveness of the method.>

This paper presents a realtime pedestrian counting system based on x-t spacetime image analysis. The system counts the number of pedestrians through the combination of three processes: moving object extraction, object counting by region labeling, and direction detection by flow estimation. In order to extract objects reliably under various illumination changes, we propose new moving objects extraction methods that extract moving regions whose shadows have been eliminated by using adaptive background image reconstruction and the color information processing of the images. For labeling and flow estimation, we use the Ortho-sectioning method that analyzes extracted regions on a spacetime slice image of the original three dimensional volume. Experimental results confirm the robustness of the system against illumination changes.

The recovery of viewpoint-independent descriptions of 3D shapes from two-and-one-half dimensional images. A novel 3D shape representation called the smoothed local generalized cones (SLGCs) is proposed. This representation is suitable for recovery of the axis, because the local constraint that characterizes a data set corresponding to the same axis point, namely, the local generalized cone (LGC), is explicitly defined. The extracted axis can be used as a basis for determining a natural parameterization of the object surface. Using this parameterization, the deformable surface fitting problem results in a linear least-squares problem, so stable volumetric recovery is possible. Recovery experiments involving real 3D range images are reported.>

A formulation of the part decomposition problem motived by the minimum-description-length (MDL) criteria is presented. Unlike previous MDL approaches which use analytic functions, a general geometric constraint, convexity, is used as a part constraint. Therefore, the method is suitable for complex natural shapes such as human faces. The recovery process consists of a bottom-up grouping process and a subsequent optimization process based on the MDL criteria. The definite causal relations of part structure between different sensitivity levels are used to recover the hierarchy of part structure. Part decomposition experiments involving real 3-D range images are reported.>

A human action recognition method based on a hidden Markov model (HMM) is proposed. It is a feature-based bottom-up approach that is characterized by its learning capability and time-scale invariability. To apply HMMs, one set of time-sequential images is transformed into an image feature vector sequence, and the sequence is converted into a symbol sequence by vector quantization. In learning human action categories, the parameters of the HMMs, one per category, are optimized so as to best describe the training sequences from the category. To recognize an observed sequence, the HMM which best matches the sequence is chosen. Experimental results for real time-sequential images of sports scenes show recognition rates higher than 90%. The recognition rate is improved by increasing the number of people used to generate the training data, indicating the possibility of establishing a person-independent action recognizer.>

Presents a robust approach to estimate structure from an image sequence taken by a moving camera. This approach is based on a Hough-type weighted voting algorithm and has the following features: (1) needs no feature tracking and no image-to-image matching; and (2) allows arbitrary nonlinear motion of camera. Experimental results using actual complex scenes are presented.>

A new general purpose method for object extraction and detection, RONPaC (Robust Object Extraction (Detection) using NPaC Features) method is presented. RONPaC employs normalized principal component (NPaC) features as a measure of similarity between corresponding regions of a target image and a background image. No a priori knowledge of objects and no assumptions about the environment are required. The object extraction problem is dealt with as a discriminant problem of two classes, 'object' and 'background', in the feature space. The performance of the method is quantitatively evaluated using various real images and compared with conventional methods using two criteria, separability between two classes in feature space and ease of binarizing expressed by the maximum discriminant criterion. Experimental results confirm the extraction accuracy and applicability of the proposed method.>

Presents a robust method for extracting images of laterally moving vehicles from ITV image sequences. The information of occlusion, velocity and length of the vehicle is first extracted from the spatio-temporal image, and the background image is updated periodically. Overlapping images of other vehicles are then removed from each scene and a subtraction image sequence is superimposed. The new method has the following characteristics: (1) one can separate and extract images of two crossing vehicles from a sequence of images. (2) the complete image of a large or complex vehicle can be successfully extracted under typical daylight variations. In order to test the applicability of the extracted images, the authors classify passenger car types. Experiments with real data confirm the effectiveness of the method.>