Vicky Sintunata

3D object retrieval system using skewness database

This paper presents a novel 3D object retrieval system from a single query image. A 3D object retrieval system is one of the most difficult systems to archive. It will be much easier if users can input 3D model of the object and use it as a query to the retrieval system. Unfortunately, creating a 3D model from an object is not an easy task. Usually a 2D rendered image of the object (photos, sketches, etc.) that needs to be retrieved is used and the system will find matching (or nearly matching) objects based on the query image. This will take a high computational cost since every image in the database must be compared with the query image. By using the proposed method, not only that every image in the database need not to be compared with the query image, but also the horizontal and vertical angles of a 3D object can be inferred immediately. Each 3D object image inside the database will be represented by a set of cubic spline function. We also use the skewness value as a feature when constructing the database. In order to retrieve the data, we use a simple thresholding method. The experimental results show that the proposed method has a good accuracy in retrieving the information within the databases.

Automatic Image Colorization based on Feature Lines

Automatic image colorization is one of the attractive research topics in image processing. The most crucial task in this field is how to design an algorithm to define appropriate color from the reference image(s) for propagating to the target image. In other words, we need to determine whether two pixels in reference and target images have similar color. In previous methods, many approaches have been introduced mostly based on local feature matching algorithms. However, they still have some defects as well as time-consuming. In this paper, we will present a novel automatic image colorization method based on Feature Lines. Feature Lines is our new concept, which enhances the concept of Color Lines. It represents the distribution of each pixel feature vector as being elongated around the lines so that we are able to assemble the similar feature pixels into one feature line. By introducing this new technique, pixel matching between reference and target images performs precisely. The experimental achievements show our proposed method achieves smoother, evener and more natural color assignment than the previous methods.

Skeleton Extraction in Cluttered Image Based on Delaunay Triangulation

Conventional skeleton extraction method usually limits its applicability due to the close curve or complete boundary constraint. Unfortunately in cluttered image or natural image, the close curve or complete boundary constraint happens all the time. Therefore a skeleton extraction algorithm with an incomplete boundary condition is proposed. The proposed method is based on the Delaunay triangulation of some sampled points from the input image. By using the proposed method, the incomplete boundary condition can be relaxed to some extends.

Improved image quality in fast inpainting with omnidirectional filling

Image inpainting is an active research field of image processing. Previous inpainting methods often require a long computational time to give sufficient results, especially due to the extensive search process of exemplar-based methods. This work improves a previous fast inpainting method based on local similarity, which achieves runtimes measured in tens of milliseconds per image, but often results in unacceptable artifacts. We improve the resulting image quality by allowing pixels to be filled at any angle, determining the angle based only on the vicinity of the target region, and cross-fading between source pixels from opposite sides of the target region. The proposed method is shown to eliminate the two drawbacks, while retaining a fast runtime.

Template Matching Skeletonization Based on Gauss Sphere Representation

Skeletonization, or automatic skeleton extraction, is a technology in 3DCG which automatically extract skeletons (i.e. bones, joints and their hierarchical structures) from 3D models. Such skeletons are important shape and pose descriptors for object representation, object recognition etc. Some existing skeletonization methods have difficulties in correctly extracting the position of joints. Some other methods are able to extract joints correctly to some extent, but controlling the number of bones and joints in their structure is not allowed. Therefore applying motion data acquired from motion capture devices to 3D models still involves a lot of manual work. In this paper, we propose a novel animation skeletonization method suited for CG animation based on Gauss sphere representation. By applying vertex Gauss sphere representation first and then applying template matching approach regardless of the objects shape, the proposed method is able to extract the same numbers of joints or bones in the same structure as in given motion data, i.e. one can directly apply existing motion data without the need of manual adjustment. Experimental results showed that the proposed method achieves 90% accuracy of pose estimation and 73% accuracy of joint estimation.