Recently it is relatively easy to produce digital point sampled 3D geometric models. In sight of the increasing capability of 3D scanning systems to produce models with millions of points, compression efficiency is of paramount importance. In this paper, we propose a novel competition-based predictive method for single-rate compression of 3D models represented as point cloud. In particular we aim at 3D scanning methods based on grid pattern. The proposed method takes advantage of the pattern characteristic made of vertical and horizontal lines, by assuming that the object surface is sampled in curve of points. We then designed and implemented a predictive coder driven by this curve-based point representation. Novel prediction techniques are specifically designed for a curve-based cloud of points, and been competing between them to achieve high quality 3D reconstruction. Experimental results demonstrate the effectiveness of the proposed method.

Dense 3D reconstruction of extremely fast moving objects could contribute to various applications such as body structure analysis and accident avoidance and so on. The actual cases for scanning we assume are, for example, acquiring sequential shape at the moment when an object explodes, or observing fast rotating turbine's blades. In this paper, we propose such a technique based on a one-shot scanning method that reconstructs 3D shape from a single image where dense and simple pattern are projected onto an object. To realize dense 3D reconstruction from a single image, there are several issues to be solved; e.g. instability derived from using multiple colors, and difficulty on detecting dense pattern because of influence of object color and texture compression. This paper describes the solutions of the issues by combining two methods, that is (1) an efficient line detection technique based on de Bruijn sequence and belief propagation, and (2) an extension of shape from intersections of lines method. As a result, a scanning system that can capture an object in fast motion has been actually developed by using a high-speed camera. In the experiments, the proposed method successfully captured the sequence of dense shapes of an exploding balloon, and a breaking ceramic dish at 300–1000 fps.

Structure analysis of table form document is importantbecause printed documents and also electronical documentsonly provide geometrical layout and lexical information explicitly.To handle these documents automatically, logicalstructure information is necessary. In this paper, we firstpropose a general representation of table form documentbased on XML, which contains both structure and layoutinformation. Next, we present structure analysis systembased on graph grammar which represents document structureknowledge. As the relation between adjacent fields intable form documents become two dimensional, two dimensionalnotation is necessary to denote structural knowledge.Therefore, we adopt two dimensional graph grammar to denotethem. By using grammar notation, we can easily modifyand keep consistency of it, as the rules are relatively simple.Another advantage of using grammar notation is that,it can be used for generating documents only from logicalstructure. Experimental results have shown that the systemsuccessfully analyzed several kinds of table forms.

Since shadows in a real picture imply the geometric constraint between lights, objects and viewpoint, the augmented reality using the picture should include the consistent shadows with the real situation. This paper proposes a method to remove shadows from a real picture based on the RGB color space analysis and shows experimental results of actual shadow removal and virtual shadow addition.

Document structure is an important issue not only for document analysis but for document synthesis. This paper presents a computer assisted document synthesis system based on the grammar-based structure analysis. The system is designed to accomplish the analysis and synthesis of table form documents cooperatively by a user and computer; namely, the user interprets the document meaning and gives the entry data to be filled in, while the computer detects the boxes formed by horizontal and vertical rules and determines the logical relations of adjacent boxes. First, the document is decomposed into a set of boxes and they are classified semi-automatically into four types: blank, insertion, indication and explanation. Then the box relations between the indication box and its associated entry one are analyzed based on the semantic and geometric knowledge defined in the document structure grammar. Finally, the system generates LATEX codes of the synthesized documents whose blank and insertion boxes are filled with the text and image data given by user. Experimental results have shown that the system analyzed successfully several kinds of table forms and yielded synthesized documents as expected.

Camera calibration is an important issue not only for real image analysis but also for realistic image synthesis. This paper proposes a new scheme for image synthesis that we call "calibrated computer graphics" and presents a method to synthesize blurred images based on camera parameters obtained by calibrating a real camera. Using the reversed projection blurring model, we have developed the multiple Z-buffer algorithm to generate images of arbitrary focus settings. Experimental verification has demonstrated that the quality of reality of the generated images is as high as that of real images. Finally, we have shown a series of synthesized images that include virtual objects embedded in real images.

This paper analyzes photometric properties of occluding edges and proves that an object surface behind a nearer object is partially observable beyond the occluding edges. We first discuss a limitation of the image blurring model using the convolution, and then present an optical flux based blurring model named the reversed projection blurring (RPB) model. Unlike the multicomponent blurring model proposed by Nguyen et al., the RPB model enables us to explore the optical phenomena caused by a shift-variant point spread function that appears at a depth discontinuity. Using the RPB model, theoretical analysis of occluding edge properties are given and two characteristic phenomena are shown: (1) a blurred occluding edge produces the same brightness profiles as would be predicted for a surface edge on the occluding object when the occluded surface radiance is uniform and (2) a nonmonotonic brightness transition would be observed in blurred occluding edge profiles when the occluded object has a surface edge. Experimental results using real images have demonstrated the validity of the RPB model as well as the observability of the characteristic phenomena of blurred occluding edges.

This paper describes a calibration method of zoom lens systems to eliminate the photometric distortion due to vignetting. A novel camera model including the variable cone is proposed to account for the marginal intensity reduction observed in images. Using the theoretical model, we first show that the serious distortion due to vignetting appears when using a zoom lens that has open aperture and long focal length. Then we present the calibration method to estimate the intensity reduction from the lens parameters of zoom and iris. The experimental results demonstrate the validity of our theoretical model and the effectiveness of the photometric calibration of zoom lens systems.

This paper analyzes photometric properties of occluding edges and proves that (1) we can observe surface edges on the farther object located close to the occluding edge even if they are occluded by the nearer object, (2) the image of an occluding edge coincides with that of a surface edge on the nearer object if the brightness of the farther object is uniform around the occluding edge. First, we propose a blurring model named the reversed projection blurring model to analyze photometric properties of blurring phenomena of an occluding edge. Using this model, the theoretical proof of the two properties mentioned above is given. Finally, experimental results in real world environments demonstrate the validity of our blurring model as well as the observability of the photometric properties of occluding edges.>

A set of images taken by systematically varying camera parameters conveys useful information which cannot or can hardly be extracted from a single image. This paper proposes a method to obtain the reliable color information, i.e. chromaticity and brightness from multiple color images taken with different aperture sizes, i.e. multi-iris color images. Based on the fundamental characteristics of optical systems, the authors first define an evaluation function to measure the reliability of color information at each pixel. They then synthesize a color image which consists of those pixels with the most reliable color information. In the experiments, they quantitatively examine the stability and reliability of color information in the synthesized image. They also demonstrate the usefulness of multi-iris color images: estimation of light source chromaticity and recovery of depth information from blurred edges.>