Search

Image warping is a technology to transform input images to be suitable for given conditions and has been recently utilized in changing face shape of characters in the field of movies or animation. Mesh warping which is one of warping methods that change shapes based on the features of face forms warping images by forming rectangular mesh groups around the eyes, nose, and mouth and matching them 1:1. This method has a problem in the resultant images are distorted in the segments of boundaries between meshes when there are errors in mesh control points or when meshes have been formed as many small area meshes. This study proposes a triangle based image interpolation technique to minimize the occurrence of errors in the process of forming natural warping images of face and process accurate results with a small amount of arithmetic operation and a short time. First, feature points that represent the face are found and these points are connected to form basic triangle meshes. The fact that the proposed method can reduce errors occurring in the process of warping while reducing the amount of arithmetic operation and time is shown through experiments. Keywords : Facial image warping(얼굴 영상 워핑), Feature-based method(특징기반 방법), Triangle subdivision(삼각형 반복분할), Image interpolation(영상보간법), Mesh warping(메쉬워핑)

This study proposes a novel procedural modeling method using convolution sums of divisor functions to model a variety of natural trees in a virtual ecosystem efficiently. The basic structure of the modeling method defines the growth grammar, including the branch propagation, a growth pattern of branches and leaves, and a process of growth deformation for various tree generation. Here, the proposed procedural method for trees is to utilize convolution sums of divisor functions as a novel approach. The structure of convolution sums has branch propagation of a uniform pattern, which is controllable, so that it is efficient for real-time virtual ecosystem construction. Furthermore, it can process changes of environment factors or growth deformation for various and unique tree generation simply through the properties of divisor functions. Finally, an experiment is performed in order to evaluate our proposed modeling method whether it can generate natural and various tree models, and a real-time virtual ecosystem of a large area where a variety of trees are presented using the modeling method can be constructed efficiently.Copyright © 2013 John Wiley & Sons, Ltd.

In this study, a tree growth model is designed to represent a variety of trees consisting of a outdoor terrain of game efficiently and naturally. The proposed tree growth model is an integrated tree growth model, and is configured using the following approaches: (1) the tree modeling method based on growth volume and the convolution sums of divisor functions, which is used to model a variety kind of trees more intuitively and naturally; (2) a rendering method using a level of detail of branch based on instancing for real-time processing of numerous trees with complicated structures; and (3) a combination of the above methods to efficiently implement a game landscape. The natural and diverse growths of trees that emerged using the proposed tree growth model is evaluated through experimentation, along with the possibility of implementing the natural game landscape and the efficiency of real-time processing.

Recently the 3D modeling techniques were developed rapidly due to rapid development of computer vision, computer graphics with the excellent performance of hardware. With the advent of a variety of 3D contents, 3D modeling technology becomes more in demand and it`s quality is increased. 3D face models can be applied widely to such contents with high usability. In this paper, a 3D face modeling is attempted from a given single 2D frontal face image. To achieve the goal, we thereafter the feature points using AAM are extracted from the input frontal face image. With the extracted feature points we deform the 3D general model by 2-pass mesh warping, and also the depth extraction based on intensity values is attempted to. Throughout those processes, a universal 3D face modeling method with less expense and less restrictions to application environment was implemented and it`s validity was shown through experiments.

This study suggests a growth volume system for the efficient modeling of numerous trees for a real-time virtual ecosystem. To increase the efficiency of the real-time system and to allow natural tree generation, the basic tree modeling system obtains its growth rule by means of botany-based self-organizing under a recursive hierarchy structure. On the basis of this rule, growth volume was introduced and controlled to generate numerous trees in a virtual ecosystem more intuitively, which allows the generation of various trees. The reality of the expression was increased by including the effects of external factors, such as light, physical obstacles, and other trees. Finally, an instancing-based branch control procedure was added to enable an efficient rendering of the real-time ecosystem. Experiments were performed to verify whether various and natural trees could be generated and numerous trees in the virtual ecosystem can be easily modeled on the basis of the suggested growth volume, which shows the efficiency of the real-time ecosystem. Copyright © 2012 John Wiley & Sons, Ltd.

As one of elements to be able to endow more exciting and higher degree of completion for game, the feeling of hit is realized by image, sound and body-sensing (vibration) effects. When the feeling of hit is realized by game developer, most proper effects will be chosen with regard to genre, system and standpoint of world for the game. In general, most of choices for the effects are performed by the experience of game developer or referring the other games. Nevertheless the related studies are not significant in comparison with the importance for the feeling of hit, and the fundamental studies are mostly not accomplished. This paper introduces a study on efficiency and important factors for the feeling of hit by analyzing the properties and degrees of feeling for all effects to represent the feeling of hit through experiments. For this, a software simulator was implemented to test all effects and therewith the final results are presented through questionnaires for the feeling of hit sent to gamers. Our results are expected to be used to accomplish higher degree of completion for mobile games or web games with limited resources.

We propose a tree modeling method of expressing realistically and efficiently numerous trees distributed on a broad terrain. This method combines and simplifies the recursive hierarchy of tree branch and branch generation process through self-organizing from buds, allowing users to generate trees that can be used more intuitively and efficiently. With the generation process the leveled structure and the appearance such as branch length, distribution and direction can be controlled interactively by user. In addition, we introduce an environment-adaptive model that allows to grow a number of trees variously by controlling at the same time and we propose an efficient application method of growing environment. For the real-time rendering of the complex tree models distributed on a broad terrain, the rendering process, the LOD(level of detail) for the branch surfaces, and shader instancing are introduced through the GPU(Graphics Processing Unit). Whether the numerous trees are expressed realistically and efficiently on wide terrain by proposed models are confirmed through simulation.

For browsing, searching, and manipulating video documents, an indexing technique to describe video contents is required. Until now, the indexing process is mostly carried out by specialists who manually assign a few keywords to the video contents and thereby this work becomes an expensive and time consuming task. Therefore, automatic classification of video content is necessary. We propose a fully automatic and computationally efficient method for analysis and summarization of spots news video for 5 spots news video such as soccer, golf, baseball, basketball and volleyball. First of all, spots news videos are classified as anchor-person Shots, and the other shots are classified as news reports shots. Shot classification is based on image preprocessing and color features of the anchor-person shots. We then use the dominant color of the field and motion features for analysis of sports shots, Finally, sports shots are classified into five genre type. We achieved an overall average classification accuracy of 75% on sports news videos with 241 scenes. Therefore, the proposed method can be further used to search news video for individual sports news and sports highlights.

In 3D animation most people adjust the moving motion of their characters on various terrains by using motion data acquired with the motion capture equipment. The motion data can be used to present real human motions naturally, but the data must be captured again to apply to the different terrains from those given at acquiring mode. In addition, there would be a difficulty when applying the data to other characters, in that case the motion data must be captured newly or the existing motion data must be heavily edited manually. In this paper we propose a unified method to generate human motions of walking and running for various terrains such as flat plane, inclined plane, stairway and irregular face. With these methods we are able to generate human motions controlled by the parameters : body height, moving speed, stride, etc. In the proposed methods, the positions and angles of joint can be calculated by using inverse kinematics, and we calculate the trajectory of the swing leg and pelvis according to the cubic spline. With these methods we were presented moving motions using a model of a human body.

In this paper, we propose a new method in which interpolation images are created by using a small number of axiai T2-weighted images instead of using many sectional images for 3D visualization of brain MR images. For image Interpolation, an important part of this process, we first segment a region of interest (ROI) that we wish to apply 3D reconstruction and extract the boundaries of segmented ROIs and MBR information. After the image size of interpolation layer is determined according to the changing rate of MBR size between top slice and bottom slice of segmented ROI, we find the corresponding pixels in segmented ROI images. Then we calculate a pixel`s intensity of interpolation image by assigning to each pixel intensity weights detected by cube interpolation method. Finally, 3D reconstruction is accomplished by exploiting feature points and 3D voxels in the created interpolation images.

In this paper, we present an extended scheme of human action recognition with nearness information between hands and other body parts for the purpose of automatically analyzing nonverbal actions of human beings. First, based on the principle that a human action can be defined as a combination of multiple articulation movements, we apply the inference of stochastic grammars. We measure and quantize each human action in 3D coordinates and make two sets of 4-chain-code for xy and zy projection planes, so that they are appropriate for the stochastic grammar inference method. Next, we extend the stochastic grammar inferring method by applying nearness information. We confirm that various physical actions are correctly classified against a set of real-world 3D temporal data with this method in experiments. Our experiments show that this extended method reveals comparatively successful achievement with a 92.7% recognition rate of 60 movements of the upper body.

In this paper, we present a new method of recognizing human actions by inference of stochastic grammars for the purpose of automatic analysis of nonverbal actions of human beings. We applied the principle that a human action can be defined as a combination of multiple articulation movements. We measure and quantize each articulation movements in 3D and represent two sets of 4-connected chain code for xy and zy projection planes, so that they are appropriate for the stochastic grammar inference method. This recognition method is tested by using 900 actions of human upper body. The result shows a comparatively successful achievement of 93.8% recognition rate through the experiments of 8 action types of head and 84.9% recognition rate of 60 action types of upper body.

In den letzten Jahren ist das Interesse an der automatischen Erkennung von Korperbewegungen auf der Basis optischer Information ganz erheblich gestiegen. Die Vielfalt der Einsatzmoglichkeiten beweist, das es sich nicht nur um das Interesse einer kleinen Schar von Experten handelt, z.B.: Ganganalyse, automatische Kommentierung von Sequenzen in Video-Datenbanken, die die Bewegungen von Menschen beinhalten [Cle95], drahtlose Mensch-Maschine-Schnittstellen fur Virtual- Reality-Anwendungen [Bec97], Uberwachungs-systeme, Interpretation von Zeichensprachen [Bra96][Sut96] und Choreographie von Tanz und Ballett [Cam95]. Fur die automatische Erkennung der Korperbewegungen in Videobildern mussen mehrere Phasen durchgefuhrt werden: Initiale Detektion einer Person, Verfolgen der Person, Segmentierung des Korpers, Extrahierung der einzelnen Korperteile und Erkennung der Korperbewegungsmuster. Es sind schon mehrere Systeme in speziellen Gebieten entwickelt worden, die die Korperbewegungen erkennen konnen, z.B. Sensei [Bec97], ARGo [Bra96], First Sight [Leu95] usw. Zur Klassifikationsmethode von Bewegungsmustern wurden Phase Space Methode [Cam95], HMM (Hidden Markov Model) [Bec97][Bra96], Dynamic Time Warping [Gav95], Template Matching [Sut96] usw. angewendet. Der Schwerpunkt dieses Beitrags liegt auf der Klassifikationsmethode von Korperbewegungsmustern mit dem Ziel, die nonverbalen Korpersignale zu erkennen. Eine Korperbewegung enthalt gleichzeitig mehrere Korperteilbewegungen mit verschiedenen Bewegungsvektoren.