How do we retrieve cartoon characters accurately? Or how to synthesize new cartoon clips smoothly and efficiently from the cartoon library? Both questions are important for animators and cartoon enthusiasts to design and create new cartoons by utilizing existing cartoon materials. The first key issue to answer those questions is to find a proper representation that describes the cartoon character effectively. In this paper, we consider multiple features from different views, i.e., color histogram, Hausdorff edge feature, and skeleton feature, to represent cartoon characters with different colors, shapes, and gestures. Each visual feature reflects a unique characteristic of a cartoon character, and they are complementary to each other for retrieval and synthesis. However, how to combine the three visual features is the second key issue of our application. By simply concatenating them into a long vector, it will end up with the so-called "curse of dimensionality," let alone their heterogeneity embedded in different visual feature spaces. Here, we introduce a semisupervised multiview subspace learning (semi-MSL) algorithm, to encode different features in a unified space. Specifically, under the patch alignment framework, semi-MSL uses the discriminative information from labeled cartoon characters in the construction of local patches where the manifold structure revealed by unlabeled cartoon characters is utilized to capture the geometric distribution. The experimental evaluations based on both cartoon character retrieval and clip synthesis demonstrate the effectiveness of the proposed method for cartoon application. Moreover, additional results of content-based image retrieval on benchmark data suggest the generality of semi-MSL for other applications.

Abstract Stroke correspondence construction is a precondition for generating inbetween frames from a set of key frames. In our case, each stroke in a key frame is a vector represented as a Disk B‐Spline Curve (DBSC) which is a flexible and compact vector format. However, it is not easy to construct correspondences between multiple DBSC strokes effectively because of the following points: (1) with the use of shape descriptors, the dimensionality of the feature space is high; (2) the number of strokes in different key frames is usually large and different from each other and (3) the length of corresponding strokes can be very different. The first point makes matching difficult. The other two points imply ‘many to many’ and ‘part to whole’ correspondences between strokes. To solve these problems, this paper presents a DBSC stroke correspondence construction approach, which introduces a manifold learning technique to the matching process. Moreover, in order to handle the mapping between unequal numbers of strokes with different lengths, a stroke reconstruction algorithm is developed to convert the ‘many to many’ and ‘part to whole’ stroke correspondences to ‘one to one’ compound stroke correspondence.

Correspondence construction of objects in key frames is the precondition for inbetweening and coloring in 2-D computer-assisted animation production. Since each frame of an animation consists of multiple layers, objects are complex in terms of shape and structure. Therefore, existing shape-matching algorithms specifically designed for simple structures such as a single closed contour cannot perform well on objects constructed by multiple contours with an open shape. This paper introduces a semisupervised patch alignment framework for complex object correspondence construction. In particular, the new framework constructs local patches for each point on an object and aligns these patches in a new feature space, in which correspondences between objects can be detected by the subsequent clustering. For local patch construction, pairwise constraints, which indicate the corresponding points (must link) or unfitting points (cannot link), are introduced by users to improve the performance of correspondence construction. This kind of input is convenient for animation software users via user-friendly interfaces. A dozen of experimental results on our cartoon data set that is built on industrial production suggest the effectiveness of the proposed framework for constructing correspondences of complex objects. As an extension of our framework, additional shape retrieval experiments on MPEG-7 data set show that its performance is comparable with that of a prominent algorithm published in T-PAMI 2009.

Abstract To reduce tedious works in cartoon animation, some computer‐assisted systems including automatic inbetweening and cartoon reusing systems have been proposed. In existing automatic inbetweening systems, accurate correspondence construction, which is a prerequisite for inbetweening, cannot be achieved. For cartoon reusing systems, the lack of efficient similarity estimation method and reusing mechanism makes it impractical for the users. The Transductive Graph based Cartoon Synthesis (TGCS) approach proposed in this paper aims at synthesizing smooth cartoons from the existing data. In this approach, the similarity between cartoon frames can be accurately evaluated by calculating the distance based on local shape context, which is rotation, and scaling invariant. According to the similarity, the label propagation based graph transduction method is adopted to generate cartoon clips, which is smoother than the clips generated by the shortest path method used in previous cartoon reusing approaches. Besides, the synthesized cartoon clips can be applied in accurate correspondence building, based on which the inbetweening method can be used to refine the results. Experimental results on our cartoon dataset suggest the effectiveness of the proposed approach for cartoon synthesis. Additional experiments on correspondence show our approach's performance on accurate correspondence building. Copyright © 2010 John Wiley & Sons, Ltd.

Visual clustering should be one of the basic tools for time-dependent data analysis in cyberworlds. This paper describes a novel approach to spatial clustering and boundary detection based on geometric modeling and visualization. Datasets and boundaries of clusters are visualized as 3D points and surfaces of reconstructed solids changing over time. Our approach applies the concepts of geometric solid modeling and uses density as clustering criteria that comes from traditional density-based clustering techniques. Visual clustering allows the user to analyze results of clustering the data changing over time and to interactively choose appropriate parameters.

Databases and warehouses play significant role in real world providing the human with the necessary information for solving problems in all areas of life from marketing to bioinformatics. Therefore, databases and warehouses are essential components of all intelligent information systems in cyberworlds. This paper describes a geometric approach to clustering and querying data in databases and warehouses. Data are interpreted geometrically as multidimensional points. A geometric definition of the cluster as a multidimensional solid defined with implicit functions is introduced. A query window is a query solid of any shape specified by its location. The queries are formulated with geometric objects and operations over them. The geometric objects and operations are described with implicit functions. With the uniform geometric model of the clustering and querying, 3D visualization tools can be naturally incorporated in one system that allows us to visualize and query clusters in 3D space. The user clusters the data and poses the queries through a graphics interface accessing dynamically multidimensional points and solids.