Visual knowledge is a new form of knowledge representation that can encapsulate visual concepts and their relations in a succinct, comprehensive, and interpretable manner, with a deep root in cognitive psychology. As the knowledge of the visual world has been identified as an indispensable component of human cognition and intelligence, visual knowledge is poised to have a pivotal role in establishing machine intelligence. With the recent advance of artificial intelligence (AI) techniques, large AI models (or foundation models) have emerged as a potent tool capable of extracting versatile patterns from broad data as implicit knowledge, and abstracting them into an outrageous amount of numeric parameters. To pave the way for creating visual knowledge empowered AI machines in this coming wave, we present a timely review that investigates the origins and development of visual knowledge in the pre-big-model era, and accentuates the opportunities and unique role of visual knowledge in the big model era.

Traditional Chinese medicine (TCM) is an interesting research topic in China’s thousands of years of history. With the recent advances in artificial intelligence technology, some researchers have started to focus on learning the TCM prescriptions in a data-driven manner. This involves appropriately recommending a set of herbs based on patients’ symptoms. Most existing herb recommendation models disregard TCM domain knowledge, for example, the interactions between symptoms and herbs and the TCM-informed observations (i.e., TCM formulation of prescriptions). In this paper, we propose a knowledge-guided and TCM-informed approach for herb recommendation. The knowledge used includes path interactions and co-occurrence relationships among symptoms and herbs from a knowledge graph generated from TCM literature and prescriptions. The aforementioned knowledge is used to obtain the discriminative feature vectors of symptoms and herbs via a graph attention network. To increase the ability of herb prediction for the given symptoms, we introduce TCM-informed observations in the prediction layer. We apply our proposed model on a TCM prescription dataset, demonstrating significant improvements over state-of-the-art herb recommendation methods.

Visual recognition of cardiac images is important for cardiac pathology diagnosis and treatment. Due to the limited availability of annotated datasets, traditional methods usually extract features directly from two-dimensional slices of three-dimensional (3D) heart images, followed by pathological classification. This process may not ensure the overall anatomical consistency in 3D heart. A new method for classification of cardiac pathology is therefore proposed based on 3D parametric model reconstruction. First, 3D heart models are reconstructed based on multiple 3D volumes of cardiac imaging data at the end-systole (ES) and end-diastole (ED) phases. Next, based on these reconstructed 3D hearts, 3D parametric models are constructed through the statistical shape model (SSM), and then the heart data are augmented via the variation in shape parameters of one 3D parametric model with visual knowledge constraints. Finally, shape and motion features of 3D heart models across two phases are extracted to classify cardiac pathology. Comprehensive experiments on the automated cardiac diagnosis challenge (ACDC) dataset of the Statistical Atlases and Computational Modelling of the Heart (STACOM) workshop confirm the superior performance and efficiency of this proposed approach.

We digitally reproduce the process of resource collaboration, design creation, and visual presentation of Chinese seal-carving art. We develop an intelligent seal-carving art-generation system (Zhejiang University Intelligent Seal-Carving System, http://www.next.zju.edu.cn/seal/ ; the website of the seal-carving search and layout system is http://www.next.zju.edu.cn/seal/search_app/ ) to deal with the difficulty in using a visual knowledge guided computational art approach. The knowledge base in this study is the Qiushi Seal-Carving Database, which consists of open datasets of images of seal characters and seal stamps. We propose a seal character generation method based on visual knowledge, guided by the database and expertise. Furthermore, to create the layout of the seal, we propose a deformation algorithm to adjust the seal characters and calculate layout parameters from the database and knowledge to achieve an intelligent structure. Experimental results show that this method and system can effectively deal with the difficulties in the generation of seal carving. Our work provides theoretical and applied references for the rebirth and innovation of seal-carving art.

认知心理学早已指出, 人类知识记忆中的重要部分是视觉知识, 被用来进行形象思维. 因此, 基于视觉的人工智能 (AI) 是AI绕不开的课题, 且具有重要意义. 本文继《论视觉知识》一文, 讨论与之相关的5个基本问题: (1) 视觉知识表达; (2) 视觉识别; (3) 视觉形象思维模拟; (4) 视觉知识的学习; (5) 多重知识表达. 视觉知识的独特优点是具有形象的综合生成能力, 时空演化能力和形象显示能力. 这些正是字符知识和深度神经网络所缺乏的. AI 与计算机辅助设计/图形学/视觉的技术联合将在创造、 预测和人机融合等方面对 AI 新发展提供重要的基础动力. 视觉知识和多重知识表达的研究是发展新的视觉智能的关键, 也是促进 AI 2.0 取得重要突破的关键理论与技术. 这是一块荒芜、 寒湿而肥沃的“北大荒”, 也是一块充满希望值得多学科合作勇探的 “无人区”.

Object detection is one of the hottest research directions in computer vision, has already made impressive progress in academia, and has many valuable applications in the industry. However, the mainstream detection methods still have two shortcomings: (1) even a model that is well trained using large amounts of data still cannot generally be used across different kinds of scenes; (2) once a model is deployed, it cannot autonomously evolve along with the accumulated unlabeled scene data. To address these problems, and inspired by visual knowledge theory, we propose a novel scene-adaptive evolution unsupervised video object detection algorithm that can decrease the impact of scene changes through the concept of object groups. We first extract a large number of object proposals from unlabeled data through a pre-trained detection model. Second, we build the visual knowledge dictionary of object concepts by clustering the proposals, in which each cluster center represents an object prototype. Third, we look into the relations between different clusters and the object information of different groups, and propose a graph-based group information propagation strategy to determine the category of an object concept, which can effectively distinguish positive and negative proposals. With these pseudo labels, we can easily fine-tune the pre-trained model. The effectiveness of the proposed method is verified by performing different experiments, and the significant improvements are achieved.

提出一种多重知识表示框架, 探讨了其对推动大数据人工智能技术在各个领域中发展的重要意义及深远影响. 传统知识表达和现代基于深度学习的知识表达通常着眼于利用特定变换方式, 将输入转换为符号编码或者向量. 例如, 知识图谱关注于描述各个概念之间的语义联系, 而深度神经网络更像是感知原始信号输入的工具. 多重知识表达是一种更为先进的人工智能表征框架, 具备更完整的智能功能, 比如原始信号感知、 特征提取及向量化、 知识符号化和逻辑推断. 多重知识表达有如下两点优势: (1) 与现有以深度学习为主导的人工智能技术相比, 具有更强的解释性以及更好的泛化能力; (2) 将多重知识表达集成于现有人工智能技术, 有利于各种表征 (例如原始信号感知以及符号化编码) 发挥互补优势. 我们希望多重知识表达相关研究以及应用能够驱动新一代人工智能蓬勃发展.

In July 2017, the Chinese government issued a guideline on developing artificial intelligence (AI), namely, the 'New-Generation Artificial Intelligence Development Plan', through 2030 to the public, setting a goal of becoming a global innovation center in this field by 2030.According to the development plan, breakthroughs should be made in basic theories of AI in terms of big data intelligence, cross-media computing, human-machine hybrid intelligence, collective intelligence, autonomous unmanned decisionmaking, brain-like computing, and quantum intelligent computing.The next-generation AI would be never-ending (self) learning from data and experience, intuitive reasoning and adaptation (Pan, 2016(Pan, , 2017)).From the perspective of overcoming the limitation of existing AI, it is generally recognized that the crossdisciplinary collaboration is a key for AI having real impact on the world.Thanks for the efforts from researchers in computer science, statistics, robotics, and psychiatry, the topics in this special issue consist mainly of five subjects: (1) fundamental issues in AI such as interpretable deep learning and unsupervised learning (i.e., domain adaptation and generative adversarial learning); (2) brain-like learning such as spiking neural network and memory-augmented reasoning; (3) human-in-the-loop learning such as crowdsourcing design and digital brain with crowd power; (4) creative applications such as social chatbots (i.e., XiaoICe) and automatic speech recognition; (5) Dr.

In this paper, we review recent emerging theoretical and technological advances of artificial intelligence (AI) in the big data settings. We conclude that integrating data-driven machine learning with human knowledge (common priors or implicit intuitions) can effectively lead to explainable, robust, and general AI, as follows: from shallow computation to deep neural reasoning; from merely data-driven model to data-driven with structured logic rules models; from task-oriented (domain-specific) intelligence (adherence to explicit instructions) to artificial general intelligence in a general context (the capability to learn from experience). Motivated by such endeavors, the next generation of AI, namely AI 2.0, is positioned to reinvent computing itself, to transform big data into structured knowledge, and to enable better decision-making for our society.

We present a new framework for multimedia content analysis and retrieval which consists of two independent algorithms. First, we propose a new semi-supervised algorithm called ranking with Local Regression and Global Alignment (LRGA) to learn a robust Laplacian matrix for data ranking. In LRGA, for each data point, a local linear regression model is used to predict the ranking scores of its neighboring points. A unified objective function is then proposed to globally align the local models from all the data points so that an optimal ranking score can be assigned to each data point. Second, we propose a semi-supervised long-term Relevance Feedback (RF) algorithm to refine the multimedia data representation. The proposed long-term RF algorithm utilizes both the multimedia data distribution in multimedia feature space and the history RF information provided by users. A trace ratio optimization problem is then formulated and solved by an efficient algorithm. The algorithms have been applied to several content-based multimedia retrieval applications, including cross-media retrieval, image retrieval, and 3D motion/pose data retrieval. Comprehensive experiments on four data sets have demonstrated its advantages in precision, robustness, scalability, and computational efficiency.

Classification can be regarded as dividing the data space into decision regions separated by decision boundaries. In this paper we analyze decision tree algorithms and the NBTree algorithm from this perspective. Thus, a decision tree can be regarded as a classifier tree, in which each classifier on a non-root node is trained in decision regions of the classifier on the parent node. Meanwhile, the NBTree algorithm, which generates a classifier tree with the C4.5 algorithm and the naive Bayes classifier as the root and leaf classifiers respectively, can also be regarded as training naive Bayes classifiers in decision regions of the C4.5 algorithm. We propose a second division (SD) algorithm and three soft second division (SD-soft) algorithms to train classifiers in decision regions of the naive Bayes classifier. These four novel algorithms all generate two-level classifier trees with the naive Bayes classifier as root classifiers. The SD and three SD-soft algorithms can make good use of both the information contained in instances near decision boundaries, and those that may be ignored by the naive Bayes classifier. Finally, we conduct experiments on 30 data sets from the UC Irvine (UCI) repository. Experiment results show that the SD algorithm can obtain better generalization abilities than the NBTree and the averaged one-dependence estimators (AODE) algorithms when using the C4.5 algorithm and support vector machine (SVM) as leaf classifiers. Further experiments indicate that our three SD-soft algorithms can achieve better generalization abilities than the SD algorithm when argument values are selected appropriately.

Since its inception at Zhejiang University in 2005, the International Conference on the Universal Digital Library (ICUDL) has been held around the globe in Alexandria, Egypt, Carnegie Mellon University, USA, and at Allahabad in India. This annual event has been a strong driving force for exchange in digital library technologies, international scientific and cultural cooperation, as well as an influence on the development of the Universal Digital Library. Now the sixth ICUDL is being held in Hangzhou (China) again, which is bound to become a good opportunity for us to summarize the past while casting our eyes into the future. The theme of this year’s conference is ‘Data Ocean and Cloud Computing’. Nowadays, two major changes are taking place in digital library technologies. One is the transition from database into Data Ocean. This not only means a rapid increase in the number of books being digitalized, from 1 million to 5 million or even 10 million books, but also an enrichment in the forms of information, from books, pictures, calligraphy, videos, paintings and photos to a wide variety of relationships between them and their derivatives. The other change is the transition from digital library to Smart Library, where advanced services beyond the means of conventional libraries such as personalized services, hypertext services, computer-aided design (CAD) services, translation services, knowledge mining services, cross-media services are provided. This means that from the Data Ocean, new and various smart cloud services will arise to make the digital library more active, professional and intelligent. It has been nearly ten years since the launching of the China-America Digital Academic Library (CADAL) with the ultimate goal of the digitalization all the library resources of humanity to allow everybody to access the knowledge, anywhere at any time. On completion of Phase I of CADAL, China has scanned over 1 million books in Chinese and English, which were released on the CADAL portal website for users from over 70 countries and regions, which is a great contribution to the conservation and sharing of human intellectual wealth. CADAL has already been marked as a milestone in the history of the Universal Digital Library. It is my great pleasure to inform you that the Chinese Government has decided to invest 150 million Yuan to initiate Phase II of CADAL starting from April, 2010. The project will further increase the coverage and quantity of the Journal of Zhejiang University-SCIENCE C (Computers & Electronics) ISSN 1869-1951 (Print); ISSN 1869-196X (Online) www.zju.edu.cn/jzus; www.springerlink.com E-mail: [email protected]

Cartoons play important roles in many areas, but it requires a lot of labor to produce new cartoon clips. In this paper, we propose a gesture recognition method for cartoon character images with two applications, namely content-based cartoon image retrieval and cartoon clip synthesis. We first define Edge Features (EF) and Motion Direction Features (MDF) for cartoon character images. The features are classified into two different groups, namely intra-features and inter-features. An Unsupervised Bi-Distance Metric Learning (UBDML) algorithm is proposed to recognize the gestures of cartoon character images. Different from the previous research efforts on distance metric learning, UBDML learns the optimal distance metric from the heterogeneous distance metrics derived from intra-features and inter-features. Content-based cartoon character image retrieval and cartoon clip synthesis can be carried out based on the distance metric learned by UBDML. Experiments show that the cartoon character image retrieval has a high precision and that the cartoon clip synthesis can be carried out efficiently.

Abstract To imitate personal handwritings is non‐trivial. In this paper, we attempt to address the challenging problem of automatic handwriting facsimile. We focus on Chinese calligraphic writings due to their rich variation in style, high artistic values and also the fact that they are among the most difficult candidates for the problem. We first analyze the structures and shapes of the constituent components, i.e., strokes and radicals, of characters in sample calligraphic writings by the same writer. To generate calligraphic writing in the style of the writer, we facsimile the individual character elements as well as the layout relationships used to compose the character, both in the writer's personal writing style. To test our algorithm, we compare our facsimileing results of Chinese calligraphic writings with the original writings. Our results are found to be acceptable for most cases, some of which are difficult to differentiate from the real ones. More results and supplementary materials are provided in our project website at http://www.cs.hku.hk/~songhua/facsimile/ .

In this paper, we consider the problem of multimedia document (MMD) semantics understanding and content-based cross-media retrieval. An MMD is a set of media objects of different modalities but carrying the same semantics and the content-based cross-media retrieval is a new kind of retrieval method by which the query examples and search results can be of different modalities. Two levels of manifolds are learned to explore the relationships among all the data in the level of MMD and in the level of media object respectively. We first construct a Laplacian media object space for media object representation of each modality and an MMD semantic graph to learn the MMD semantic correlations. The characteristics of media objects propagate along the MMD semantic graph and an MMD semantic space is constructed to perform cross-media retrieval. Different methods are proposed to utilize relevance feedback and experiment shows that the proposed approaches are effective.

Abstract We propose a generic pigment model suitable for digital painting in a wide range of genres including traditional Chinese painting and water‐based painting. The model embodies a simulation of the pigment‐water solution and its interaction with the brush and the paper at the level of pigment particles; such a level of detail is needed for achieving highly intricate effects by the artist. The simulation covers pigment diffusion and sorption processes at the paper surface, and aspects of pigment particle deposition on the paper. We follow rules and formulations from quantitative studies of adsorption and diffusion processes in surface chemistry and the textile industry. The result is a pigment model that spans a continuum from the very wet to the very dry brush stroke effects. We also propose a new pigment mixing method based on machine learning techniques to emulate pigment mixing in real life as well as to support the creation of new artificial pigments. To experiment with the proposed model, we embedded the model in a sophisticated digital brush system. The combined system exhibits interactive speed on a modest PC platform. http://www.cs.hku.hk/~songhua/pigment provides supplementary materials for this paper.

Product identity can affect a new mobile phone's acceptance and an enterprise's succession. This paper, from the identity of features and styles, studied the mobile phone's computer-supported cooperative design. To solve the disagreements appearing in this cooperative process objectively, we introduced the methods of cognitive psychology and Kansei engineering and conducted an Internet/intranet recognition experiment. The experiment results confirmed the differences quantitatively among manufacturers, industrial designers and consumers on the recognition of mobile phone's features and styles, and expressed the differences in a visual way. Based on the experiment results, by communicating with each other, industrial designers generated the accepted solutions, which proved the positive function of ours methods in cooperative design

In traditional product design, designer just has user as source of design information. However, when designer misunderstands user information, he can't correct it in time. Thus it damages product seriously. To improve the accuracy of design result, it is necessary to introduce user and user experience in the whole product design process. So, in this paper, we introduce user experience into design process by applying web technology that make designer and user co-operate on the basis of the characters of user experience. Then we bring out the notion of co-operative work based on user experience and co-operative model. In the case study, we have tap's operation part as example and explain how to apply the model to product design

Chinese calligraphy is pictographic and each calligraphist has his own writing style. People often feel difficult in writing a demanded beautiful calligraphy style. In order to help people enjoy the art of calligraphy and learn how it is written step-by-step we present a new approach to animate its writing process by 3-D visualization method. In this paper some novel algorithms used in the approach are presented to solve the following problems: 1) estimate varied stroke's thickness 2) extract strokes order from an offline Chinese calligraphic writing. Through this approach we implement a system. Experimental result is given to demonstrate the application finally.

This article proposes a technique to animate a Chinese style painting given its image. We first extract descriptions of the brush strokes that hypothetically produced it. The key to the extraction process is the use of a brush stroke library, which is obtained by digitizing single brush strokes drawn by an experienced artist. The steps in our extraction technique are first to segment the input image, then to find the best set of brush strokes that fit the regions, and, finally, to refine these strokes to account for local appearance. We model a single brush stroke using its skeleton and contour, and we characterize texture variation within each stroke by sampling perpendicularly along its skeleton. Once these brush descriptions have been obtained, the painting can be animated at the brush stroke level. In this article, we focus on Chinese paintings with relatively sparse strokes. The animation is produced using a graphical application we developed. We present several animations of real paintings using our technique.

One of the problems in building collaborative and intelligent design systems is the difficulty in integrating computer supported collaborative work (CSCW) with knowledge to generate results to the satisfaction of designers who often have high demands on aesthetics. In order to assist designers in the early stages of a product development this paper develops an intelligent methodology for integrating user knowledge and design knowledge. Taken the mobile phone form design as an example, a prototype intelligent conceptual design system is implemented, which consists essentially of the knowledge agents, each of which is a knowledge system with the capabilities of problem reasoning and learning. With this system, product designer and user can carry out simultaneously and intelligently in an Internet-based computer-aided product concurrent design system.

Detecting objects of interest from a video sequence is a fundamental and critical task in automated visual surveillance. Those objects can either be moving or stationary. However, most of current approaches only focus on discriminating moving objects by background subtraction. In this work, we propose layers segmentation to detect both of moving and stationary target objects from surveillance video. We first construct a codebook with set of codewords for each pixel and then extend the Matrix Entropy statistical model to segment layers with codewords features. Our experimental results are presented in terms of success layer segmentation rate.

Wireless networked sensors raise a great number of programming challenges in applications. In this paper, we present AntC, a systems programming language, which focuses on the super-micro embedded system that supports wireless networked sensors. It provides four broad characteristics: it is a component-oriented application design; it integrates an embedded OS with two-level dispatcher; it improves the fault tolerance in run-time; furthermore, the improvement on multiplatform adaptability allows AntC program to be easily transplanted to other platforms with different embedded processors.

Collaborative virtual environment model and role scheme are two research branches in computer science. Collaborative activities need an effective mechanism to support participants' roles and rights 'while role schemes can be applied to this field. In this paper, we present a new generic role-based and agent-oriented model for collaborative virtual environment based on discussion of collaborative activity as an essential role in the research of CVE. It introduces role schemes to establish more efficient collaborative virtual environments. The proposed model includes two important parts: collaborative entity and collaborative event. It also advances intelligent entity in running state and collaborative federation in this model to describe cooperation in collaborative virtual environment.

The design of user friendly and expressive virtual brush systems for interactive digital painting and calligraphy has attracted a lot of attention and effort in both computer graphics and human-computer interaction circles for a long time. Providing a digital environment for paper-less artwork creation is not only challenging in terms of algorithmic design, but also promising for its potential market values. This paper proposes a novel algorithmic framework for interactive digital painting and calligraphy based a novel virtual hairy brush model. The algorithms in the kernel of our simulation framework are built upon solid modeling techniques. Implementing the algorithms, we have developed a virtual hairy brush prototype system with which end users can interactively produce high-quality digital paintings and calligraphic artwork. (The latest progress of our virtual brush project is reported at the website "http://www.cs.hku.hk/~songhua/e-brush/".) Copyright by Science in China Press 2005.

We present a novel approach for automatic 3D face verification from range data. The method consists of range data registration and comparison. There are two steps in the registration procedure: a coarse step conducting normalization by exploiting a priori knowledge of the human face and facial features; a fine step aligning the input data with the model stored in the database by the partial directed Hausdorff distance. To speed up the registration, a simplified version of the model is generated for each model in the model database. During the face comparison, the partial Hausdorff distance is employed as the similarity metric. Experiments have been carried out on a database with 30 individuals, and the best EER of 3.24% is achieved.

In this paper, we presented a novel approach for automatic 3D face verification from range data. The method consists of range data registration and comparison. There are two steps in registration procedure: the coarse step conducting the normalization by exploiting a priori knowledge of the human face and facial features, and the fine step aligning the input data with the model stored in the database by the partial directed Hausdorff distance. To speed up the registration, a simplified version of the model is generated for each model in the model database. During the face comparison, the partial Hausdorff distance is employed as the similarity metric. The experiments are carried out on a database with 30 individuals and the best EER of 3.24% is achieved.

No abstract available.

Better information leads to better design. In landscape design, the designers care for what plans affect the urban more than what their plans look like. In this paper, we discuss some key techniques in a GIS based landscape design system. With the advantage of the GIS, designers can make better decisions. A prototype named landscape evaluation system (LES) has been implemented. In LES system, designers can build up their virtual evaluation world, and establish the interrelationship of the entities in their virtual world.

It is well known that music can convey emotion and modulate mood, to retrieve music by mood is sometimes the exclusive manner people select music to enjoy. We concentrate on music retrieval by detecting mood. Mood detection is implemented on the viewpoint of computational media aesthetics, that is, by analyzing two music dimensions, tempo and articulation, in the procedure of making music, we derive four categories of mood, happiness, anger, sadness and fear. Concretely, with regard to music in the format of raw audio, after tempo is detected using a multiple agent approach, a feature called relative tempo is calculated, and after the mean and standard deviation of the feature called average silence ratio in the presented computational articulation model are calculated, a simple BP neural network classifier is trained to detect mood. Users retrieval music by browsing the 3D visualization of feature space associated with specific mood. We report the experimental result on a test corpus of 353 pieces of popular music with various genres.

Abstract This paper proposes a novel algorithmic framework for an advanced virtual brush to be used in interactive digitalpainting. The framework comprises the following components: a geometry model of the brush using a hierarchicalrepresentation that leads to substantial savings in every step of the painting process; fast online brush motionsimulation assisted by offline calibration that guarantees an accurate and stable simulation of the brush's dynamicbehavior; a new pigment model based on a diffusion process of random molecules that considers delicateand complex pigment behavior at dipping time as well as during painting; and a user‐adaptation component thatenables the system to cater for the personal painting habits of different users. A prototype system has been implementedbased on this framework. Compared with other virtual brushes, this new system is designed to presenta realistic brush in the sense that the system accurately and stably simulates the complex painting functionalityof a running brush, and therefore is capable of creating high‐quality digital paintings with minute aesthetic detailsthat can rival the real artwork. The advanced features also give rise to a high degree of expressiveness ofthe virtual brush that the user can comfortably manipulate. http://www.csis.hku.hk/songhuale‐brush/ providessupplementary materials for this paper. Categories and Subject Descriptors (according to ACM CCS): I.3.6 [Methodology and Techniques]: Interactiontechniques; I.3.5 [Computational Geometry and Object Modeling]: Physically based modeling; I.3.4 [GraphicsUtilities]: Paint systems;

This paper proposes a novel scheme to virtually restore the colors of ancient murals. Our approach integrates artificial intelligence techniques with digital image processing methods. The knowledge related to the mural colors is first categorized into four types. A hybrid frame and rule-based approach is then developed to represent knowledge and to inter colors. An algorithm that takes into account color similarity and spatial proximity is developed to segment mural images. A novel color transformation method based on color histograms is finally proposed to restore the colors of murals. A number of experiments based on real images have demonstrated the validity of the proposed scheme for color restoration.

The few-color images are often synthetic graphics without complicated color and texture variation, which makes the embedding of invisible digital watermark difficult. This paper proposes a data hiding method that can hide a moderate amount of data in a few-color image, such as cartoon images, binary images, without introducing noticeable artifacts. To achieve least visual quality reduction, the prioritized pattern matching scheme is employed to embed the invisible data in the pixels those are close to the boundaries. The block permutation is also exploited before embedding. No additional color is introduced and the palette keeps unchanged after embedding. Extracting of the hidden data does not require the knowledge of the original image. The experimental results show that the approach achieves a quite great performance in visibility transparency. It is applicable to invisible annotation, covert communication etc.

To effectively track incomplete motion features, a novel feature tracking algorithm for motion capture is presented. According to feature attributes and relationship among features, extracted features are classified as four types of features. Then different strategies are applied to track different kinds of features. To verify the tracks, cross correlation test and predicted 3D model based test are used to test and remove outliers. Experimental results demonstrate the effectiveness of our algorithm.

Many traditional relevance feedback approaches for content-based image retrieval (CBIR) can only achieve limited short-term performance improvement without benefiting long-term performance. To remedy this limitation, we propose a graphic-theoretic model for incremental relevance feedback in image retrieval. Firstly, a two-layered graph model is introduced that describes the correlations between images. A teaming strategy is then suggested to enrich the graph model with semantic correlations between images derived from user feedback. Based on the graph model, we propose a link analysis approach for image retrieval and relevance feedback. Experiments conducted on real-world images have demonstrated the advantage of our approach over traditional approaches in both short-term and long-term performance.

In this paper, we present a novel algorithm Opportune Project for mining complete set of frequent item sets by projecting databases to grow a frequent item set tree. Our algorithm is fundamentally different from those proposed in the past in that it opportunistically chooses between two different structures, array-based or tree-based, to represent projected transaction subsets, and heuristically decides to build unfiltered pseudo projection or to make a filtered copy according to features of the subsets. More importantly, we propose novel methods to build tree-based pseudo projections and array-based unfiltered projections for projected transaction subsets, which makes our algorithm both CPU time efficient and memory saving. Basically, the algorithm grows the frequent item set tree by depth first search, whereas breadth first search is used to build the upper portion of the tree if necessary. We test our algorithm versus several other algorithms on real world datasets, such as BMS-POS, and on IBM artificial datasets. The empirical results show that our algorithm is not only the most efficient on both sparse and dense databases at all levels of support threshold, but also highly scalable to very large databases.

No abstract available.

We present the detailed modeling of the hairy brush used typically in Chinese calligraphy. The complex model, which includes also a model for the ink and the paper, covers the various stages of the brush going through a calligraphy process. The model relies on the concept of writing primitives, which are the smallest units of hair clusters, to reduce the load on the simulation. Each such primitive is constructed through the general sweeping operation in CAD and described by a NURBS surface. The writing primitives dynamically adjust themselves during the virtual writing process, leaving an imprint on the virtual paper as they move. The behavior of the brush is an aggregation of the behavior of all the writing primitives. A software system based on the model has been built and tested. Samples of imitation artwork from using the system were obtained and found to be nearly indistinguishable from the real artwork. Categories and Subject Descriptors (according to ACM CCS): I.3.6 [Methodology and Techniques]: Interaction techniques I.3.5 [Computational Geometry and Object Modeling]: Physically based modeling I.3.4 [Graphics Utilities]: Paint systems

Using a group of ellipses to approach the shape contour, a new shape retrieval method is presented in this paper. In order to keep shape-based retrieval invariant to its position, orientation and size, the shape normalization method is presented. From our research, any closed shape contour can be uniquely decomposed into a group of ellipses, and the original shape contour can be re-constructed using the decomposed ellipses. The ellipse-based shape description and similar retrieval method is introduced in this paper. Based on ellipse’s contribution to shape contour, the decomposed ellipses are parted into low-order ellipses and high-order ellipses. The low-order ellipses measure the macroscopic feature of a shape contour, and the high-order ellipses measure the microscopic feature. The two-phase shape matching method is given. Through the experiment test, our method has better shape retrieval effect.

Abstract One of the major problems of the motion capture‐based computer animation technique is the relatively high cost of equipment and low reuse rate of data. To overcome this problem, many motion‐editing methods have been developed. However, most of them can only handle one character whose motions are preset, and hence cannot interact with its environment automatically. In this paper, we construct a new architecture of multiple animated character motion fusion, which not only enables the characters to perceive and respond to the virtual environment, but also allows them to interact with each other. We will also discuss in detail the key issues, such as motion planning, coordination of multiple animated characters and generation of vivid continuous motions. Our experimental results will further testify to the effectiveness of the new methodology. Copyright © 2002 John Wiley & Sons, Ltd.

Presents a human-like, non-photorealistic rendering approach. A typical process of how human engineers learn to paint a technical illustration is as follows. First, they are trained in how to paint separate primitives, such as cubes and spheres, and accordingly accumulate empirical drawing principles and skills during their continuous practice, and finally they can freely express complicated shapes by composing the related primitives' drawings together. We manage to mimic this human-like approach by embedding established illustration rules into primitives' lighting models and drawing algorithms, and implement it in an illustration system called RETOUCH.

A new classification using support vectors is presented. Support vector machines that learn classification problem are specific to use hyperplane. We propose a novel approach that contains support vectors describing the hypersphere to separate the samples. We also generalize it in multi-class classification phrase. The experiment results on UCI datasets are presented.

Image sequence based tracking is the pivotal technique of human motion. In the paper, we first propose an appropriate human model and color model. Second, two approaches are proposed aiming at two different levels of color-block information including the boundary and the inner-area: the extraction of the boundary algorithm is based on the Robert operator and the clustering algorithm is based on self-adaptation. Third, we unite two regions, which are processed by different approaches. This step counteracts the ambiguity of obtaining information from each level. Finally, after the rough area of the color-block is obtained, the boundary of the color-block is determined by calculating the histogram of the X,Y coordinate of every point on the boundary of the block. Experimental results are presented.

This paper introduces a virtual Dunhuang mural restoration system in collaborative network environment. It describes the style of Dunhuang mural, analyzes the reasons of mural spoilage, and presents the necessity to develop a collaborative mural restoration GroupWare. It describes the components and the workflow of mural restoration in detail, solves some key technologies in the system. In the end, it introduces the system architecture, and presents the system interface and some restored results.

In the domain of computer vision, there exists a very wide application for the research of human motion capture. This paper proposes a new approach to do motion capture in video. It is composed of image sequence based tracking of human feature points and the reconstruction of three dimension (3D) motion skeleton. First, we track every part of human body from top to bottom on the basis of a human model. The Kalman filter and a morph-block similarity algorithm based on subpixel are used. Then we do camera calibration using the line correspondences between the 3D model and the image. Finally the 3D motion skeleton is established by using the model knowledge. This approach does not aim at a given mode of human motion. Rather, it analyzes large motion from frame to frame in complex, variational background, and sets up a 3D motion skeleton under the perspective projection. We also present the experimental result at the end of the paper.

Human animation is a challenging domain in computer animation. To aim at many shortcomings in conventional techniques, this paper proposes a new video based human animation technique. Given a clip of video, firstly human joints are tracked with the support of Kalman filter and morph-block based match in the image sequence. Then corresponding sequence of three-dimension (3D) human motion skeleton is constructed under the perspective projection using camera calibration and human anatomy knowledge. Finally a motion library is established automatically by annotating multiform motion attributes, which can be browsed and queried by the animator. This approach has the characteristic of rich source material, low computing cost, efficient production, and realistic animation result. We demonstrate it on several video clips of people doing full body movements, and visualize the results by re-animating a 3D human skeleton model.

The similarity measure between video clips is a key issue in video retrieval. In the developing of our video retrieval system, we propose a new video similarity model. In contrast to existing algorithms, it proposes many influencing factors, such as order factor, speed factor, disturbance factor, etc, based on the subjective visual judgement of human. So this algorithm embodies the degree of similarity completely and systematically. On the other hand, it has resolution adaptation because it can be applied to every level of video structure. In the retrieval system, it can be used to process video query by example clip. This paper introduces it in detail and presents experiment results at the end of the paper.

Proposes a new approach to capture human motion in video. This approach does not aim at a given human motion mode, but instead analyzes large-scale motion from frame to frame in a complex variational background and sets up a 3D motion skeleton under perspective projection. This approach is composed of two steps. First, we track every part of a human body from top to bottom on the basis of a human model. Then we perform a camera calibration using the line correspondences between the 3D model and the image, and establish the 3D motion skeleton by using the human model knowledge. The experimental results are presented at the end of the paper.

This paper mainly deals with efficient retrieval of Dunhuang Frescoes from large image databases based on similarity calculation of color and texture features. In this paper, we firstly give out a survey of research on Dunhuang Frescoes by artists from all over the world. Therefore, these research achievements can provide us a new clue to organize and manage Dunhuang Frescoes databases, and guide us to find an efficient way to retrieve Dunhuang Frescoes from large-volume image databases. We present a similarity calculation method from multi-source analogy. Then we describe several techniques for extracting the color and texture features of Dunhuang Frescoes. Finally, we calculate the comprehensive similarity between Frescoes using color and texture features. By means of the similarity computation, we can carry on an efficient and fast image database retrieval. A Dunhuang Frescoes retrieval and analysis system demonstrates that retrieval using the combination of color and texture features can assure high precision and robustion.