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This paper presents paper retrieval using the specific paper features chain and laid lines. Paper features are detected in digitized paper images and they are represented such that they could be used for retrieval. Optimal retrieval performance is achieved by means of a trainable similarity measure for a given set of paper features. By means of these methods a retrieval system is developed that art experts could use real-time in order to speed up their paper research. [ABSTRACT FROM AUTHOR]

In this paper we present a method for optical paper surface roughness measurement, which overcomes the disadvantages of the traditional methods. Airflow-based roughness measurement methods and profilometer require expensive special equipment, essential laboratory conditions, are contact-based and slow and unsuitable for on-line control purposes methods. We employed an optical microscope with a built-in CCD-camera to take images of paper surface. The obtained image is considered as a texture. We applied statistical brightness measures and fractal dimension analysis for texture analysis. We have found a strong correlation between the roughness and a fractal dimension. Our method is non-contact-based, fast and is suitable for on-line control measurements in the paper industry. [ABSTRACT FROM AUTHOR]

The Lost Cosmonaut is an interactive narrative based on digitally enhanced paper. This technology uses an electronic pen to mediate between paper and computer. Thus any actions of the pen on the paper can be captured and manipulated by a computer as well as we can map digitally controlled events onto paper. The story in this narrative environment reveals itself partially through written text and images on the paper surface just as any other printed story. However, additional information in form of digitally controlled outputs such as sound, light and projections can be accessed through interaction with pen and paper. Furthermore the audience is not only supposed to read and otherwise perceive information, we also want them to actively produce content for this environment by writing onto the paper. By doing so they also add content to the database containing the digital output at the same time. Hence we produce a complex multimedia environment that works on three levels: On paper, in a digitally controlled visual and acoustic environment and in the combination of both worlds. Last but not least this environment is an open system, which grows as a collaborative effort over time as each user adds his own entries to paper and database. We argue that using paper as an integrated part of a digital environment is a best-of-both-world approach that opens up new possibilities for producing and perceiving narrative. [ABSTRACT FROM AUTHOR]

Many documents exist in both paper and electronic forms. Paper has many well-known advantages, but electronic texts often contain useful information that is not easily accessible from printed paper versions. SGML texts, in particular, are rich sources of additional information. The Active Alice project shows how a reader can use a paper document to access information from its corresponding electronic version without having to manipulate the electronic version via a separate computer interface. The project makes use of a DigitalDesk. This is an ordinary desk augmented with a video camera and computer-driven projector. The camera captures images of the pages on the desk and detects simple user actions such as pointing to specific words on a page. The images are used to associate the pages with their SGML counterparts. Information from the SGML versions can then be conveyed directly to the reader via information projected onto the page or onto other areas of the desk. The project takes its name from the example text used—a version of Alice's Adventures in Wonderland. [ABSTRACT FROM AUTHOR]

We present a framework for generating paper-cutting effects on mesh models. Our system involves the construction of model defined paper-cutting patterns, extraction of the medial axis (MA) from the mesh models and position location for patterns on the mesh surface based on MA. Patch sets for placing patterns on the mesh surface are determined by the region growing algorithm. After patch sets parameterization, patterns are drawn on the parameterized domain and mapped onto the mesh surface by inverse sampling. [ABSTRACT FROM AUTHOR]

Paper keeps as a key information medium and this has motivated the development of new technologies for digitally augmented paper (DAP) that enable printed content to be linked with multimedia information. Among those technologies, one simplest approach is to print some visible patterns on paper (e.g., barcodes in the margin) as cross-media links. Due to the latest progress in printing industry, some more sophisticated methods have been developed, that is, some kinds of patterns printed on the background of a page in a high resolution are almost invisible and then we are affected little when reading. For all these pattern-embedding based approaches to integrate printed and multimedia information, we aim to present a unified publishing framework independent of particular patterns and readers(e.g., cameras to capture patterns) used to realize DAP. The presented framework manages semantic information about printed documents, multimedia resources, and patterns as links between them and users are provided with a platform for publishing DAP documents. [ABSTRACT FROM AUTHOR]

This paper presents way of generation color documents from elements extracted from an original document image. This scheme yields synthetic documents of similar visual information to the original one. This method has several advantages as it allows a far more efficient way of storing and transmitting the information. Its rationale is to decompose a color document into paper and ink texture parameters and textual information. The textual information may be either typed or handwritten and is stored as a compressed monochromatic image. [ABSTRACT FROM AUTHOR]

We describe a new binning technic for informed data hiding problem. In information theoretical point of view, the blind watermarking problem can be seen as transmitting a secret message M through a noisy channel on top of an interfered host signal S that is available only at the encoder. We propose an embedding scheme based on Low Density Parity Check(LDPC) codes, in order to quantize the host signal in an intelligent manner so that the decoder can extract the hidden message with a high probability. A mixture of erasure and symmetric error channel is realized for the analysis of the proposed method. [ABSTRACT FROM AUTHOR]

A novel method that is able to simulate artistic effects of ink-refusal and stroke-trace-reservation in ink paintings is developed. The main ingredients of ink are water, carbon particles, as well as glue. However, glue is not taken into account in other researches, although it plays an important role in ink diffusion. In our ink-diffusion model, we consider the number of fibers and the quantity of glue as parameters of the structure of paper. We simulate the physical interaction among water, carbon particles, glue, and fiber mesh of paper. The realistic renderings created from our models have demonstrated that our models are successful, and are able to imitate the special artistic effects of ink painting. [ABSTRACT FROM AUTHOR]

Automatic evaluation of visual print quality is addressed in this study. Due to many complex factors of perceived visual quality its evaluation is divided to separate parts which can be individually evaluated using standardized assessments. Most of the assessments however require active evaluation by trained experts. In this paper one quality assessment, missing dot detection from printed dot patterns, is addressed by defining sufficient hardware for image acquisition and method for detecting and counting missing dots from a test strip. The experimental results are evidence how the human assessment can be automated with the help of machine vision, thus making the test more repeatable and accurate. [ABSTRACT FROM AUTHOR]

This paper presents a novel physically-based model for virtual Chinese brush. Compared with previous works, the main advantage of our method lies in the use of physically based modeling methods that describe the behavior of the real brush's deformation in terms of the interaction of the external and internal forces with the virtual writing paper. Instead of simulating the brush using bristles, we use points to simulate the whole brush bundle, which can drastically decrease the complexity inherent in the conventional bristle-level approach. A spring network is derived to calculate the physical deflection of brush according to the force exerted on it. With this model, we can get a more effective simulation of real brush painting. [ABSTRACT FROM AUTHOR]

This paper proposes a kind of remote rendering method based on awareness model. This method takes the additional cost caused by the movement of the viewpoint into cost calculation and designs a cost prediction algorithm based on the vision field divided by awareness model. The simulation results show that the improved method can not only improve the quality of the remote rendering, but also make full use of the bandwidth of the network, as well as make the remote rendering more fluent when the viewpoint moves fast. [ABSTRACT FROM AUTHOR]

Several features of today's grid are based on centralized or hierarchical services. However, as the grid size increasing, some of their functions especially resource discovery should be decentralized to avoid performance bottlenecks and guarantee scalability. A novel grid resource discovery method based on adaptive k-Nearest Neighbors clustering is presented in this paper. A class is formed by a collection of nodes with some similarities in their characteristics, each class is managed by a leader and consists of members that serve as workers. Resource requests are ideally forwarded to an appropriate class leader that would then direct it to one of its workers. This method can handle resource requests by searching a small subset out of a large number of nodes by resource clustering which can improve the resource query efficiency; on the other hand, it also achieves well scalability by managing grid resources with adaptive mechanism. It is shown from a series of experiments that the method presented in this paper achieves more scalability and efficient lookup performance than other existing methods. [ABSTRACT FROM AUTHOR]

It is not easy to develop a game that applies physical laws. In this paper we proposed a physics editor to make it easy to produce a physics game. The physics editor uses XML for processing attribute values of each object. This paper presents the architecture of the physics editor, and describes its detailed components. The physics editor provides an efficient method that is easily applied to physical attributes of all objects in the game. In this paper, we showed the process of a car's creation, using the physics editor. The physics editor that is presented in this paper automatically creates physical objects, but it is limited to the rigid body for car racing. [ABSTRACT FROM AUTHOR]

This paper discusses two statistical shape descriptors, the Edge Co-occurrence Matrix (ECM) and the Contour Co-occurrence Matrix (CCM), and their use in surface defect classification. Experiments are run on two image databases, one containing metal surface defects and the other paper surface defects. The extraction of Haralick features from the matrices is considered. The descriptors are compared to other shape descriptors from e.g. the MPEG-7 standard. The results show that the ECM and the CCM give superior classification accuracies. [ABSTRACT FROM AUTHOR]

This paper proposes an adaptive motion mode selection method in H.264 frame skipping transcoding. In order to reduce the high complexity arising from variable block sizes in H.264, the proposed method exploits original motion information from incoming bitstreams. In addition, the paper also adopts Forward Dominant Vector Selection approach in MV composition of H.264 transcoding, in comparison with Bilinear Interpolation method. The simulation results show that the proposed method achieves good trade-off between computational complexity and video quality. [ABSTRACT FROM AUTHOR]

Graphics recognition for raster-based input discovers primitives such as lines, arrowheads, and circles. This paper focuses on graphics recognition of figures in vector-based PDF documents. The first stage consists of extracting the graphic and text primitives corresponding to figures. An interpreter was constructed to translate PDF content into a set of self-contained graphics and text objects (in Java), freed from the intricacies of the PDF file. The second stage consists of discovering simple graphics entities which we call graphemes, e.g., a pair of primitive graphic objects satisfying certain geometric constraints. The third stage uses machine learning to classify figures using grapheme statistics as attributes. A boosting-based learner (LogitBoost in the Weka toolkit) was able to achieve 100% classification accuracy in hold-out-one training/testing using 16 grapheme types extracted from 36 figures from BioMed Central journal research papers. The approach can readily be adapted to raster graphics recognition. Keywords: Graphics Recognition, PDF, Graphemes, Vector Graphics, Machine Learning, Boosting. [ABSTRACT FROM AUTHOR]

There are several researches on 2D gaze tracking techniques for the 2D screen for the Human-Computer Interaction. However, the researches for the gaze-based interaction to the stereo images or contents are not reported. The 3D display techniques are emerging now for the reality service. Moreover, the 3D interaction techniques are much more needed in the 3D contents service environments. This paper addresses the gaze-based 3D interaction techniques. It is well known that there are characteristics of eye vergence according to the depth of object to be focused by human. This paper presents our experimental research results about the characteristics of eye vergence according to stereo image depth. Our research result is evaluated and applied to the gaze-based 3D interaction with the parallax barrier stereo image display system. [ABSTRACT FROM AUTHOR]

In this paper, a framework for individualizing the learning material structure in adaptive learning system is introduced. It aims to utilize the learning characteristics and provide a personalized learning environment that exploit pedagogical model and fuzzy logic techniques. The learning material consists of 4 structures; 1) theory, 2) examples 3) exercises and 4) activities. The pedagogical model and learning characteristics are based on the student's personality factor (Myers-Briggs Type Indicator (MBTI)), whilst the fuzzy logic techniques are used to classify the structure of learning material which is based on student's personality factors. This paper tend to illustrate the construction of fuzzy set and fuzzy rules to find the best rules based on combination of two approaches; learning style approach and fuzzy logic approach for adapting the content to the user, allowing a learning system to dynamically adapt the choice of possible learning structure through the learning material based on the user's personality factor. [ABSTRACT FROM AUTHOR]

Due to the advance of e-community, collaborative learning becomes popular on the Web. Among the learning models for collaborative learning on the Web, there are seminar-based model and project-based model. The objective of this paper is to compare the two learning models by analyzing learners' activities. Among learners' activities, we focus on learners' interaction level, their collaborability, and their accountability. Also, we propose new assessment items for evaluating learners fairly. In this paper, the existing evaluation items in traditional classroom-based collaborative learning are firstly analyzed in order to develop new evaluation criteria for the Web. Based on these analyzed items, we propose new items for assessing learners' activities in Web-based collaborative learning. After two Web-based classes, each of which has a different learning model, are performed, the proposed items are applied to compare the two learning models in terms of learners' interaction level, collaborability, and accountability. By performing this research, we can understand learners' activities clearer and can develop a better instructional technique to make improved academic achievement, improved attendance, and increased self-confidence in collaborative e-learning. [ABSTRACT FROM AUTHOR]

This paper extends the upright face detection framework proposed by Viola et al. 2001 to handle in-plane rotated faces. These haar-like features work inefficiently on rotated faces, so this paper proposes a new set of ±26.565 ° haar-like features which can be calculated quickly to represent the features of rotated faces. Unlike previous face detection techniques in training quantities of samples to build different rotated detectors, with these new features, we address to build different rotated detectors by rotating an upright face detector directly so as to achieve in-plane rotated face detection. This approach is selected because of its computational efficiency, simplicity and training time saving. This proposed method is tested on CMU-MIT rotated test data and yields good results in accuracy and maintains speed advantage. [ABSTRACT FROM AUTHOR]

MPEG-4 system defines a binary format, called BIFS(BInary Format for Scene), and textual format, called XMT(eXtensible MPEG-4 Textual format) to represent the composition information of the scene featuring interactive content. BIFS was proposed for efficient transmission, and XMT based on XML, with the aim to support various playing environments and to enhance the reusability of the contents as it is converted into languages such as VRML, SMIL and etc. To provide interoperability of this XMT, this paper proposes the mechanism to convert XMT into SMIL using XSLT(XML Stylesheet Language Transformation). Further, this paper analyzes XMT and SMIL to propose a conversion method for various nodes, which do not match one to one, and defines XSLT for conversion. In addition, this paper represents various geometric objects that are not supported in the SMIL using SVG(Scalable Vector Graphics). [ABSTRACT FROM AUTHOR]

In this paper we tackle the specific problem of old documents recovery. Spots, print through, underlines and others ageing features are undesirable not only because they harm the visual appearance of the document, but also because they affect future Optical Character Recognition (OCR). This paper proposes a new method integrating fuzzy clustering of color properties of original images and mathematical morphology. We will show that this technique leads to higher quality of the recovered images and, at the same time, it delivers cleaned binary text for OCR applications. The proposed method was applied to books of XIX Century, which were cleaned in a very effective way. [ABSTRACT FROM AUTHOR]

Intelligent vehicle systems need to distinguish which objects are moving and which are static. A static concrete wall lying in the path of a vehicle should be treated differently than a truck moving in front of the vehicle. This paper proposes a new algorithm that addresses this problem, by providing dense dynamic depth information, while coping with real-time constraints. The algorithm models disparity and disparity rate pixel-wise for an entire image. This model is integrated over time and tracked by means of many pixel-wise Kalman filters. This provides better depth estimation results over time, and also provides speed information at each pixel without using optical flow. This simple approach leads to good experimental results for real stereo sequences, by showing an improvement over previous methods. [ABSTRACT FROM AUTHOR]

The tumor segmentation in Breast MRI image is difficult due to the complicated galactophore structure. The work in this paper attempts to accurately segment the abnormal breast mass in MRI(Magnetic resonance imaging) Images. The ROI (Region of Interest) is segmented using a novel DP (Dynamic Programming) based optimal edge detection technique. DP is an optimal approach in multistage decision-making. The method presented in this paper processes the object image to get the minimum cumulative cost matrix combining with LUM nonlinear enhancement filter, Gaussian preprocessor, non-maximum suppression and double-threshold filtering, and then trace the whole optimal edge. The experimental results show that this method is robust and efficient on image edge detection and can segment the breast tumor area more accurately. [ABSTRACT FROM AUTHOR]

This paper presents a gradient direction edge enhancement based contour (GDEEBC) detector to segment the nucleus and cytoplasm from each cervical smear image. In order to eliminate noise from the image, this paper proposes a trim-meaning filter that can effectively remove impulse and Gaussian noise but still preserve the edge sharpness of an object. In addition, a bi-group enhancer is proposed to make a clear-cut separation for the pixels lying between two objects. Finally, a gradient direction (GD) enhancer is presented to suppress the gradients of noise and to brighten the gradients of object contours as well. The experimental results show that all the techniques proposed above have impressive performances. In addition to cervical smear images, these proposed techniques can also be utilized in object segmentation of other types of images. [ABSTRACT FROM AUTHOR]

Hypothesis testing is an important way to detect the statistical difference between two populations. In this paper, we use the Fisher permutation and bootstrap tests to differentiate hippocampal shape between genders. These methods are preferred to traditional hypothesis tests which impose assumptions on the distribution of the samples. An efficient algorithm is adopted to rapidly perform the exact tests. We extend this algorithm to multivariate data by projecting the original data onto an "informative direction" to generate a scalar test statistic. This "informative direction" is found to preserve the original discriminative information. This direction is further used in this paper to isolate the discriminative shape difference between classes from the individual variability, achieving a visualization of shape discrepancy. [ABSTRACT FROM AUTHOR]

Nowadays, it is still the primary problem to find the inhibitors of retrovirus, protease and integrase in anti-AIDS drug design. However, the research and experimental results about anti-AIDS inhibitors mainly exist in large numbers of scientific literature, not in readable format for computer. In this paper, we introduce an Ontology-based Information Extraction (OIE) approach to extract anti-AIDS inhibitors from literature. Key to the approach is the construction of anti-AIDS inhibitors ontology, which provides a semantic framework for information extraction, and annotation of corpus. Consequently, this paper primarily focuses on the architecture of OIE, on which we construct the anti-AIDS ontology using Protégé tool and annotate corpus. Finally, we employ a demonstrated application scenario to show how to annotate the PubMed articles based on the ontology we have constructed. [ABSTRACT FROM AUTHOR]

H.264/AVC is the newest international video coding standard. This paper presents a novel hardware design for CABAC decoding in H.264/AVC. CABAC is the key innovative technology, but it brings huge challenge for high throughput implementation. The current bin decoding depends on the previous bin, which results in the long latency and limits the system performance. In this paper, the data hazards are analyzed and resolved using the algorithmic features. We present a new pipeline-based architecture using the standard look-ahead technique where the arithmetic decoding engine works in parallel with the context maintainer. An efficient finite state machine is developed to match the requirement of the pipeline controlling and the critical path is optimized for the timing. The proposed implementation can generate one bin per clock cycle at the 160-MHz working frequency. [ABSTRACT FROM AUTHOR]

The sub-block-based image retrieval method utilizes global and local image features to retrieve trademarks through separating individual an images into some blocks. This paper proposes a sub-block-based trademark image retrieval algorithm under the polar coordinate system. Experiment results show that our algorithm can keep excellent invariance in image translation, scaling and rotation and the retrieved results can satisfy human visual perception very well. [ABSTRACT FROM AUTHOR]

The heterogeneity in the contemporary multimedia environments requires a format-agnostic adaptation framework for the consumption of digital video content. Preferably, scalable bitstreams are used in order to satisfy as many circumstances as possible. In this paper, the scalable extension on the H.264/AVC specification is used to obtain the parent bitstreams. The adaptation along the combined scalability axis of the bitstreams must occur in a format-independent manner. Therefore, an abstraction layer of the bitstream is needed. In this paper, XML descriptions are used representing the high-level structure of the bitstreams by relying on the MPEG-21 Bitstream Syntax Description Language standard. The adaptation process is executed in the XML domain by transforming the XML descriptions considering the usage environment. Such an adaptation engine is discussed in this paper in which all communication is based on XML descriptions without knowledge of underlying coding format. From the performance measurements, one can conclude that the transformations in the XML domain and the generation of the corresponding adapted bitstream can be realized in real time. [ABSTRACT FROM AUTHOR]

In this paper, we propose a novel approach for facial expression analysis and recognition. The proposed approach relies on the distance vectors retrieved from 3D distribution of facial feature points to classify universal facial expressions. Neural network architecture is employed as a classifier to recognize the facial expressions from a distance vector obtained from 3D facial feature locations. Facial expressions such as anger, sadness, surprise, joy, disgust, fear and neutral are successfully recognized with an average recognition rate of 91.3%. The highest recognition rate reaches to 98.3% in the recognition of surprise. [ABSTRACT FROM AUTHOR]

With the development of computer science, there is an increasing demand on the object recognition in stereo images. As a binocular image pair contains larger and more complicated information than a monocular image, the stereo vision analysis has been a difficult task. Therefore how to extract the region of user's interest is a vital step to reduce the data redundancy and improve the robustness and reliability of the analysis. The original stereo sequences used in the paper are obtained from two parallel video cameras mounted on a vehicle driving in a residential area. This paper targets the problem of data segmentation of those stereo images. It proposes a set of algorithms to separate the foreground from the complicated changing background. Experiments show that the whole process is fast and efficient in reducing the data redundancy, and improves the overall performance for the further obstacle extraction. [ABSTRACT FROM AUTHOR]

Recent solutions to object classification have focused on the decomposition of objects into representative parts. However, the vast majority of these methods are based on single visual cue measurements. Psychophysical evidence suggests that humans use multiple visual cues to accomplish recognition. In this paper, we address the problem of integrating multiple visual information for object recognition. Our contribution in this paper is twofold. First, we describe a new probabilistic integration model of multiple visual cues at different spatial locations across the image. Secondly, we use the cue integration framework to classify images of objects by combining two-dimensional and three-dimensional visual cues. Classification results obtained using the method are promising. [ABSTRACT FROM AUTHOR]

In this paper, we present the results of the Acoustic Event Detection (AED) and Classification (AEC) evaluations carried out in February 2006 by the three participant partners from the CHIL project. The primary evaluation task was AED of the testing portions of the isolated sound databases and seminar recordings produced in CHIL. Additionally, a secondary AEC evaluation task was designed using only the isolated sound databases. The set of meeting-room acoustic event classes and the metrics were agreed by the three partners and ELDA was in charge of the scoring task. In this paper, the various systems for the tasks of AED and AEC and their results are presented. [ABSTRACT FROM AUTHOR]

Automatic story unit segmentation is an essential technique for content based video retrieval and summarization. A good video story unit has complete content and natural boundary in visual and acoustic perception, respectively. In this paper, a method of acoustic perception friendly story unit segmentation for broadcast soccer video is proposed. The approach combines replay detection, view pattern and non-speech detection to segment story units. Firstly, a replay detection method is implemented to find the highlight events in soccer video. Secondly, based on positions of replay clips, an FSM (Fine State Machine) is used to obtain rough starting points of story units. Finally, audio boundary alignment is employed to locate natural audio boundaries for acoustic perception. The algorithm is tested on several broadcast soccer videos. The story units segmented by algorithms with and without audio alignment are compared in acoustic perception. The experimental results indicate the performance of the proposed algorithm is encouraging and effective. [ABSTRACT FROM AUTHOR]

Supporting multimedia search has emerged as an important research topic. There are three paradigms on the research spectrum that ranges from the least automatic to the most automatic. On the far left end, there is the pure manual labeling paradigm that labels multimedia content, e.g., images and video clips, manually with text labels and then use text search to search multimedia content indirectly. On the far right end, there is the content-based search paradigm that can be fully automatic by using low-level features from multimedia analysis. In recent years, a third paradigm emerged which is in the middle: the annotation paradigm. Once the concept models are trained, this paradigm can automatically detect/annotate concepts in unseen multimedia content. This paper looks into this annotation paradigm. Specifically, this paper argues that within the annotation paradigm, the relationship-based annotation approach outperforms other existing annotation approaches, because individual concepts are considered jointly instead of independently. We use two examples to illustrate the argument. The first example is on image annotation and the second one is on video annotation. Experiments indeed show that relationship-based annotation approaches render superior performance. [ABSTRACT FROM AUTHOR]

This paper presents a novel shape recovery technique that combines photometric stereo with polarization information. First, a set of ambiguous surface normals are estimated from polarization data. This is achieved using Fresnel theory to interpret the polarization patterns of light reflected from dielectric surfaces. The process is repeated using three different known light source positions. Photometric stereo is then used to disambiguate the surface normals. The relative pixel brightnesses for the different light source positions reveal the correct surface orientations. Finally, the resulting unambiguous surface normal estimates are integrated to recover a depth map. The technique is tested on various objects of different materials. The paper also demonstrates how the depth estimates can be enhanced by applying methods suggested in earlier work. [ABSTRACT FROM AUTHOR]

A novel method for automatic occluded targets recognition in SAR images is proposed in this paper. Different SAR occluded targets are simulated based on actual vehicles from the MSTAR database, and are recognized using SVM classifier by grouping recognition based on the targets azimuth angles. It is shown that the proposed method outperforms the typical methods in accuracy at high occlusion, and robustness to occlusion with experiments considering accuracy and confusion matrix. [ABSTRACT FROM AUTHOR]

In this paper we propose a novel feature extracting method based on spatiotemporal slice analyzing. To date, video features are focused on the character of every single video frame. With our method, the video content is no longer represented with every single frame. The temporal variation of visual information is taken as an important feature of video in our method. We examined this kind of feature with experiments in this paper. The experiment results show that the proposed feature is effective and robust for variant video content and format. [ABSTRACT FROM AUTHOR]

Over the years, multimedia applications are getting increasingly more complex and large in scale. Multimedia Web Service is identified as one of the possible solutions to meet the challenges. The advantages of using Web Services are ease of application development, adaptive to changes, fault tolerance and etc. In the paper, a sample tracking application will be discussed and developed using multimedia Web Services (multimediaWS) approach. Throughout the paper, we will suggest some general rules on designing the multimediaWS as well as evaluate the pros and cons of using multimediaWS for multimedia application. [ABSTRACT FROM AUTHOR]

In this paper, a method for automatically indexing the contents to reduce the effort that used to be required for input paper information and constructing index is sought. Various contents formats for journals, which have different features from those for general documents, are described. The principal elements that we want to represent are titles, authors, and pages for each paper. Thus, the three principal elements are modeled according to the order of their arrangement, and then their features are generalized. The content analysis system is then implemented based on the suggested modeling method. The content analysis system, implemented for verifying the suggested method, gets its input in the form containing more than 300 dpi gray scale image and analyze structural features of the contents. It classifies titles, authors and pages using efficient projection method. The definition of each item is classified according to regions, and then is extracted automatically as index information. It also helps to recognize characters region by region. The experimental result is obtained by applying to some of the suggested 6 models, and the system shows 97.3% success rate for various journals. [ABSTRACT FROM AUTHOR]

This paper describes a simulation of the physical processes of coupled heat and moisture transfer in a clothing assembly containing phase change material (PCM). This paper focuses on the analysis of the effect of the PCM. The results of simulation show that the PCM can delay the decrease in temperature of the clothing. The experiment results are also shown in this paper. A reasonable agreement was found when comparing the results of the simulation with the experiment results. [ABSTRACT FROM AUTHOR]

The feature-line extraction of a 3D model is a key step in the model-based Non-Photorealistic Rendering. In this paper, we introduce a new algorithm that is based on a sharpening filter to extract the feature-lines of 3D models. Experiments of feature-line rendering where our sharpening filter is introduced as a pre-calculation step are shown to compare with the existing algorithms ([1][2][3]). From these experiments it can be found that our rendering results reserve more feature details and contain less noise. Furthermore, in our algorithm, the computation time of rendering is also reduced. [ABSTRACT FROM AUTHOR]

For e-Learning, traditional navigator or searching engine has inherent weaknesses, so individualized intelligent learning is difficult to be realized. This paper proposed a hybrid knowledge structure reflecting the relationships among knowledge modules. A series of association knowledge items were gathered by standardized inputting and knowledge clustering based on association rules. Based on the mapping of knowledge items to knowledge domain, the proposed knowledge clustering and representation could intelligently provide learner clues of interrelated learning. The simulation results showed that the proposed plan is an effective scheme of intelligent learning. [ABSTRACT FROM AUTHOR]

In this paper, Petri net theory is taken into analyzing the Virtual Experiment (VE) in the Virtual Experiment Environment (V.E.E.). After applied the Petri net to V.E.E., the V.E.E. then can monitor the experiment progress and measure the learning effects of students when the students are doing experiment. Petri net is an approach that can be used as a visual-communication aid similar to flow charts, block diagrams, and networks. Therefore, the experiment steps are easily modeled by Petri net. According to the data which is collected by the V.E.E., there are two major error types that a student might make during an experiment. The first error type is the procedural error and the other one is measuring error. In the end of this paper, an experiment system is implemented. There were 72 3rd year students in junior high school involved in the experiment. In this experiment, we did both of pre-test and post-test in order to make sure the Petri net-enhanced V.E.E. is useful. [ABSTRACT FROM AUTHOR]

We consider the problem of recognizing face or object when only single training image per class is available, which is typically encountered in law enforcement, passport or identification card verification, etc. In such cases, many discriminant subspace methods such as Linear Discriminant Analysis (LDA) fail because of the non-existence of intra-class variation. In this paper, we propose a novel framework called 2-Dimensional Kernel PCA (2D-KPCA) for face or object recognition from a single image. In contrast to conventional KPCA, 2D-KPCA is based on 2D image matrices and hence can effectively utilize the intrinsic spatial structure information of the images. On the other hand, in contrast to 2D-PCA, 2D-KPCA is capable of capturing part of the higher-order statistics information. Moreover, this paper reveals that the current 2D-PCA algorithm and its many variants consider only the row information or column information, which has not fully exploited the information contained in the image matrices. So, besides proposing the unilateral 2D-KPCA, this paper also proposes the bilateral 2D-KPCA which could exploit more information concealed in the image matrices Furthermore, some approximation techniques are developed for improving the computational efficiency. Experimental results on the FERET face database and the COIL-20 object database show that: 1) the performance of KPCA is not necessarily better than that of PCA; 2) 2D-KPCA almost always outperforms 2D-PCA significantly; 3) the kernel methods are more appropriate on 2D pattern than on 1D patterns. [ABSTRACT FROM AUTHOR]

For high-dimensional classification tasks such as face recognition, the number of samples is smaller than the dimensionality of the samples. In such cases, a problem encountered in Linear Discriminant Analysis-based (LDA) methods for dimension reduction is what is known as the Small Sample Size (SSS) problem. Recently, a number of approaches that attempt to solve the SSS problem have been proposed in the literature. In this paper, a different way of solving the SSS problem compared to these is proposed. It is one that employs a dissimilarity representation method where an object is represented based on the dissimilarity measures among representatives extracted from training samples instead of from the feature vector itself. Thus, by appropriately selecting representatives and by defining the dissimilarity measure, it is possible to reduce the dimensionality and achieve a better classification performance in terms of both speed and accuracy. Apart from utilizing the dissimilarity representation, in this paper simultaneously employing a fusion technique is also proposed in order to increase the classification accuracy. The rationale for this is explained in the paper. The proposed scheme is completely different from the conventional ones in terms of the computation of the transformation matrix, as well as in controlling the number of dimensions. The present experimental results, which to the best of the authors' knowledge, are the first such reported results, demonstrate that the proposed mechanism achieves nearly identical efficiency results in terms of the classification accuracy compared with the conventional LDA-extension approaches for well-known face databases involving AT&T and Yale databases. [ABSTRACT FROM AUTHOR]

In this paper, we present a scheme that segments triangular meshes into several meaningful patches using multi-scale normal variation. In differential geometry, there is a traditional scheme that segments smooth surfaces into several patches such as elliptic, hyperbolic, or parabolic regions, with several curves such as ridge, valley, and parabolic curve between these regions, by means of the principal curvatures of the surface. We present a similar segmentation scheme for triangular meshes. For this purpose, we develop a simple and robust scheme that approximates the principal curvatures on triangular meshes by multi-scale normal variation scheme. Using these approximated principal curvatures and modifying the classical segmentation scheme for triangular meshes, we design a scheme that segments triangular meshes into several meaningful regions. This segmentation scheme is implemented by evaluating a feature weight at each vertex, which quantifies the likelihood that each vertex belongs to one of the regions. We test our scheme on several face models and demonstrate its capability by segmenting them into several meaningful regions. [ABSTRACT FROM AUTHOR]