nils-herrmann/clean_scirepeval huggingface dataset:
A hyperdocument architecture for cardiac catheterisation documents

Medical documents consists of heterogeneous types of data generated from different diagnostic and monitoring modalities. Advances in multimedia technology have made possible the capturing, storage and visualisation of such multimedia documents. By incorporating hypermedia concept into the structure of the document, the physician is able to retrieve and view relevant information non-linearly via the hyperlinks embedded within the document. Central to the computerisation of such documents is the design of a suitable hypermedia document architecture which structures and organises the information inherent in the documents. This paper presents a hyperdocument architecture of a specific type of medical document, the cardiac catheterisation record, and a prototype multimedia document system which had been implemented on a PC-based platform.<<ETX>>
Approaches to decompose flat structuring element for fast overlapping search morphological algorithm

The fast morphological algorithm, overlapping search (OS) algorithm, recently proposed by Lam and Li (1998), can only be applied to a flat structuring element (FSE) whose 1D Euler-Poincare constants, N/sup (1)/(x) and N/sup (1)/(y), at any x or y coordinate are equal to 1. Thus, an arbitrarily shaped structuring element must be decomposed to a set of constrained components before employing the fast algorithm. In the paper, a decomposition algorithm, based on line-scanning process, and an improved algorithm, based on conditionally maximal convex polygon (CMCP), are proposed to generalize the use of the overlapping search morphological algorithm for any arbitrary flat structuring element.
Active learning for the prediction of phosphorylation sites

In this paper, we propose several active learning strategies to train classifiers for phosphorylation site prediction. When combined with support vector machine, we show that active learning with SVM is able to produce classifiers that give comparable or better phosphorylation site prediction performance than conventional SVM techniques and, at the same time, require a significantly less number of annotated protein training samples. The result has both conceptual and practical implications in protein prediction: it exploits information inherent in the large scale database of non-annotated protein samples and reduces the amount of manual labor required for protein annotation. To the best of our knowledge, active learning has not been explored in phosphorylation sites prediction. Several active learning strategies: single-running mode, batch-running mode with sample and support vector diversity, were investigated for phosphorylation sites prediction in this work. Our experiments have shown that active learning with SVM is able to reduce the effort of protein annotation by 6.6% to 25.7% to yield similar prediction performance as compared with conventional SVM technique.
Three-dimensional enhancement of confocal scanning laser fluorescence microscope images for vascular reconstruction

This paper presents a fast method for enhancing the images of the vascular network of zebrafish embryo from confocal scanning laser microscope (CSLM) stained with fluorescent probe. Specifically, we split the 3D enhancement process into two separate stages and adopt different methods respectively to deconvolve the lateral and optical directions in the observed images. The resulting algorithm increased both lateral and optical resolution and decreased the out of focus light of the images. The enhanced volumetric image is characterized by an improvement both in contrast and edge definition. The algorithm can be efficiently implemented on a general PC and the computation time is small even for the case of large volume of images. The value of the method for enhancing fluorescence images of biological objects is demonstrated by a comparison of the results of 3D vascular reconstruction.
An object-oriented framework for content-based image retrieval based on 5-tier architecture

Reports a generic object-oriented framework for content-based image retrieval (CBIR) systems. It is designed so that the basic data structures and functionality of a typical CBIR system are provided without sacrificing speed and flexibility. The framework is based on a five-tier architecture that allows modules in different tiers to be developed independently, and thus flexibility is ensured. We show that our framework is able to adapt to a wide range of CBIR applications by applying the framework to the development of two on-going projects: a trademark image retrieval system and a medical (histological) image retrieval system. These applications are briefly discussed.
Simulated patient for orthognathic surgery

Orthognathic surgery corrects a wide range of minor and major facial and jaw irregularities. This surgery will improve the patients' ability to chew, speak and breathe. In many cases, a better appearance will also result. With the recent advances in virtual reality (VR) and three-dimensional (3D) medical imaging technology, orthognathic surgery simulations typically requires costly volumetric data acquisition modalities such CT or MRI imaging for patient modeling. The authors present an approach for constructing 3D hard and soft tissue models of a patient based on colour portraits and conventional radiographs. This allows patient modeling to be done efficiently on low-cost platforms. Specifically, we extend the techniques developed by the author (H.S.H Ip and Lijin Yin, 1996) to hard tissue modeling. The extended technique employs a user-assisted approach to obtain the 3D coordinates of the feature points of the human face and jaw respectively from conventional photographs and radiographs. Then the displacement vectors of the feature points are computed by correspondence matching and interpolation against a generic head model and jaw bone model. The resulting combined hard and soft tissue models can be used for orthognathic surgical planning on a low-cost, PC based platform.
Integration of intelligent engines for a large scale medical image database

We present a semantic content representation scheme and the associated techniques for supporting (a) query by image examples or by natural language in a histological image database and (b) automatic annotation generation for images through image semantic analysis. In this research, various types of query are analysed by either a semantic analyser or a natural language analyser to extract high level concepts and histological information, which are subsequently converted into an internal semantic-content representation structure code-named "Papillon". Papillon serves not only as an intermediate representation scheme but also stores the semantic content of the image that will be used to match against the semantic index structure within the image database during query processing. During the image database population phase, all images that are going to be put into the database will go through the same processing so that every image would have its semantic content represented by a Papillon structure. Since the Papillon structure for an image contains high level semantic information of the image, it forms the basis of the technique that automatically generates textual annotation for the input images. Papillon bridges the gap between different media in the database, allows complicated intelligent browsing to be carried out efficiently and also provides a well-defined semantic content representation scheme for different content processing engines developed for content-based retrieval.
HACS: Hand Action Coding System for anatomy-based synthesis of hand gestures

Previously (H.S. Horace et al., 1997), we presented an anatomy based hierarchical model for hand motion simulation. We present a hand gesture coding system which we called Hand Action Coding System (HACS). It is an extension of our previous work. With HACS, the simulation allows specification of hand motion in a high level manner. The underlying computational complexities of motion simulation are separated from the high level motion descriptions. When humans want to perform some hand functions or hand gestures, the brain gives instructions and activates the corresponding hand muscles. HACS codifies hand gestures in terms of these hand muscle action units (HAU). When combined with our anatomy based hand model, HACS eases the specification of complex hand gestures/motion animation sequence.
Similarity measures for histological image retrieval

A gastro-intestinal (GI) tract histological image is usually composed of texture components with different dimensions and properties. To analyze a histological image, we divide it into an array of sub-images. A feature vector comprising a set of Gabor filters and the intensity statistics is computed in order to classify each sub-image to one of 63 histological labels. To retrieve an image from the database, we compare three similarity measures, shape, neighbour and sub-image frequency distribution. It is found that both neighbour and sub-image frequency distribution similarity measures perform similarly well but the shape similarity measure yields the worst result when retrieving images of different GI tract organs. In general, the sub-image frequency distribution measure is the best choice because it requires less time to compute than the neighbour measure.
Evolutionary Optimization of Feature Representation for 3D Point-based Model Classification

In this paper, we introduce a new approach for the classification of point-based 3D computer graphics models. We propose a new representation for 3D point cloud models based on a set of principal projection axes. The point set is then projected on to each of these axes, and a suitable summary statistics of the projected point set along each axis is calculated. The complete set of statistics is then adopted as the feature representation of the point set. Based on this representation, we need to search for the optimal set of projection axes which can best distinguish the different classes of point cloud models in the database. In general, this optimization problem is difficult due to the size of the search space. As a result, we propose to adopt evolutionary strategy (ES)(T. Back and H.-P. Schwefel, 1993) as the optimization technique. This is in view of the capability of ES to explore many regions of the search space in parallel. Our experiment results indicate that the proposed optimized feature representation based on only the point set can attain a classification accuracy which is comparable to alternative feature representations which require the availability of the original polygonal representation
A 3D geometric deformable model for tubular structure segmentation

In this paper, we present a relational-tubular (ReTu) deformable model for segmenting a complex and the entire tubular network structure with branches in close proximity of each other. Specifically, we incorporate a priori knowledge of the target anatomy structure as well as the spatial relationship between branches to reduce possible segmentation errors due to the effects of a variety of imaging artifacts and noise. To get more robust description of the data properties than a simple 3D edge map, a new data energy functional is proposed based on testing the volumetric density within the model cross-sections. The deformation process is formulated as a two-stage procedure: tubular medial axis deformation and tubular surface deformation. The efficiency of this approach is demonstrated by our experiments which show that satisfactory quantifications of the entire zebrafish vasculature recorded from the fluorescence confocal microscope. The experiments also demonstrate the robustness of our deformable model in the presence of complex issue structure that adhered to the vessel branches.
3-D Head Model Retrieval Using a Single Face View Query

In this paper, a novel 3D head model retrieval approach is proposed, in which only a single 2D face view query is required. The proposed approach will be important for multimedia application areas such as virtual world construction and game design, in which 3D virtual characters with a given set of facial features can be rapidly constructed based on 2D view queries, instead of having to generate each model anew. To achieve this objective, we construct an adaptive mapping through which each 2D view feature vector is associated with its corresponding 3D model feature vector. Given this estimated 3D model feature vector, similarity matching can then be performed in the 3D model feature space. To avoid the explicit specification of the complex relationship between the 2D and 3D feature spaces, a neural network approach is adopted in which the required mapping is implicitly specified through a set of training examples. In addition, for efficient feature representation, principal component analysis (PCA) is adopted to achieve dimensionality reduction for facilitating both the mapping construction and the similarity matching process. Since the linear nature of the original PCA formulation may not be adequate to capture the complex characteristics of 3D models, we also consider the adoption of its nonlinear counterpart, i.e., the so-called kernel PCA approach, in this work. Experimental results show that the proposed approach is capable of successfully retrieving the set of 3D models which are similar in appearance to a given 2D face view.
Web-enabling legacy applications

A recent research trend in Web applications is the integration of legacy applications on the World Wide Web. The motivations behind this research are the goals of producing a hybrid system where the Web can provide greater accessibility and distribution for legacy applications, and some standards to increase the interoperability and ease of use. For user interaction driven legacy applications, we propose a 3-tier conceptual architecture to support applications for the WWW, and present a approach to building sophisticated inactive Web systems. We benefit from this modelling approach in terms of universal accessibility, platform independency, modularity and migration efficiency. The interaction scenario between the client and server in the prototype implementation is an example to demonstrate the procedures for migrating a legacy application to the Web. Some basic technologies are reviewed and deployed for implementation of our design, including Java applet, servlet, JDBC and CORBA. Such an approach can also be extended to the newly developed Web based applications.
Affine-invariant sketch-based retrieval of images

The advent of the digital library and multimedia database require robust techniques for multimedia content searches. Content-based retrieval techniques have been developed to overcome some of the limitations associated with conventional keyword-based browsing or searching of visual data. We present an affine invariant shape-based retrieval technique for image retrieval which is efficient and robust. More importantly, the technique supports a query presented in the form of hand-drawn sketches and has the potential of supporting affine invariant partial shape retrieval.
Developing a Central Repository for Capturing Extracurricular Activities and Achievements Associated with Learning Outcomes

Recent surveys indicate that employers are looking for new graduates with soft skills more than purely academic achievements. There have been discussions on the importance of how participating in extracurricular activities and achievements contribute to the future success of a graduate in terms of personal development and outcome-based learning. However, current university transcripts fall flat in showing attributes and learning outcomes associated to extracurricular activities and achievements. In this paper, we identify the design issues against the personal development cycle and review a practical implementation in a university of Hong Kong. Potential benefits to institutions and learners in enhancing the employability, limitations and rooms for improvement are discussed.
Image categorization by learning with context and consistency

This paper presents a novel semi-supervised learning method which can make use of intra-image semantic context and inter-image cluster consistency for image categorization with less labeled data. The image representation is first formed with the visual keywords generated by clustering all the blocks that we divide images into. The 2D spatial Markov chain model is then proposed to capture the semantic context across these keywords within an image. To develop a graph-based semi-supervised learning approach to image categorization, we incorporate the intra-image semantic context into a kind of spatial Markov kernel which can be used as the affinity matrix of a graph. Instead of constructing a complete graph, we resort to a k-nearest neighbor graph for label propagation with cluster consistency. To the best of our knowledge, this is the first application of kernel methods and 2D Markov models simultaneously to image categorization. Experiments on the Corel and histological image databases demonstrate that the proposed method can achieve superior results.
Clustered Microcalcification detection based on a Multiple Kernel Support Vector Machine with Grouped Features (GF-SVM)

Clustered microcalcification is an important signal for breast cancer in the early stages. In this paper, we propose a multiple kernel SVM with group features (GF-SVM) to tackle problems associated with heterogeneous features of clustered microcalcification and normal breast tissues in suspicious regions. Specifically, different types of features such as being gradient, geometric and textural are grouped and modeled by different kernels, respectively. The prior knowledge from different resources is then combined into the framework of the multiple kernel SVM based classification scheme. Experimental results demonstrate that our classification scheme reduces the false positive rate significantly while maintaining the true positive rate.
A novel theorem on symmetries of 2D images

A theorem linking reflectional symmetry and rotational symmetry of the 2D images has been established. This theorem shows that, for a rotationally symmetric image with K/spl ges/1 fold(s) (K-RSI), its number of reflection-symmetric axes must be either K or zero. To the authors' knowledge no previous studies have shown the constraint relationship between reflectional symmetry and rotational symmetry. Demonstrations on some typical images have shown the exactness of our theorem.
Dynamic simulation of human hand motion using an anatomically correct hierarchical approach

We present a technique to realistically simulate the movements of human fingers. The technique utilises a hierarchical approach having real anatomical basis. With the dynamic simulation built upon the real human hand anatomy, not only physical correctness but also kinematics and biological correctness, which are often more important in human modelling, are guaranteed. The hierarchical approach here uses four layers of abstraction: anatomical, physical, geometry and image. The use of the hierarchical approach allows the animator to specify the hand motion without the knowledge of the underlying representations and computations. Since dynamic correctness alone is insufficient to guarantee realism in simulating human hand motion, we combine dynamics and kinematics computation to achieve naturalness. There are dependencies among motion of different fingers and realistic simulation cannot be achieved without taking the dependencies into consideration. We addressed this issue by using a mesh-based physical model of flesh around the lumbricales which simulates flesh deformation incurred in finger motion and activates execution of motion dependencies. A user interface is developed to facilitate changing of input parameters and visualisation of results.
Symmetry Detection by Generalized Complex (GC) Moments: A Close-Form Solution

This paper presents a unified method for detecting both reflection-symmetry and rotation-symmetry of 2D images based on an identical set of features, i.e., the first three nonzero generalized complex (GC) moments. This method is theoretically guaranteed to detect all the axes of symmetries of every 2D image, if more nonzero GC moments are used in the feature set. Furthermore, we establish the relationship between reflectional symmetry and rotational symmetry in an image, which can be used to check the correctness of symmetry detection. This method has been demonstrated experimentally using more than 200 images.
Image retrieval in digital library based on symmetry detection

We present a technique of content-based image retrieval based on the symmetric property of an image and its generalized complex moments. We propose a novel symmetry detection algorithm that is both fast and general compared with existing symmetry detection algorithms. Given the sensitivity of human perception to symmetry, the use of symmetry class helps to ensure that the retrieved images are visually similar. Experimental results show that these features are effective in retrieving binary images and our similarity function is able to rank them first place for nearly all the sample queries used in our experiment.
Relevance vector machine for content-based retrieval of 3D head models

In this paper, we propose a novel 3D head model retrieval approach in which the queries are 2D face views instead of less readily available 3D head models. The basic idea is to characterize the corresponding relations between 2D view feature and 3D model feature based on a machine learning approach. Thus the subsequent feature matching can be carried out in 3D feature space. As an effective solution to regression problems, relevance vector machine is used in this paper to establish an association between 2D and 3D features. Experimental results show that our proposed 2D query based method is comparable with the direct 3D query based one.
In the name of art

In building a digital library of Chinese calligraphy, it is desirable to preserve the dynamic writing as well as the aesthetic properties of the masterpieces. Compared with the work on computer synthesis of Chinese calligraphy, we are dealing with the reverse problem; it can be achieved by performing image analysis on the archaic Chinese calligraphic images to obtain a model-based parametric representation. In this article, we introduce our methodology to preserve archaic Chinese calligraphic images by automatically estimating the set of three-dimensional (3-D) geometric and ink model parameters such as the properties of the brush hair and the variations of ink deposition along the stroke trajectory. The dynamic brush writing of the archaic Chinese characters can then be reproduced with various brush and ink effects so that the calligraphy generation process can be visualized and the masterpieces can be reproduced with high-quality printing in scalable resolution.
AIMtechKinect: A Kinect Based Interaction-Oriented Gesture Recognition System Designed for Students with Severe Intellectual Disabilities

Microsoft Kinect sensors nowadays are widely used for real-time controller-free virtual reality (VR) interactions. However, there has been limited research on interaction-oriented analysis of motion data captured by Kinect from those who suffer from both intellectual and physical challenges. In this paper, a software framework based on Kinect is proposed to support a psycho-educational program (InSPAL) in a special school setting, which aims to enable students with severe intellectual disabilities (SID) to interact with the educational VR content and through which their pre-learning skills could be enhanced. Also, algorithms embedded in this framework are designed and modified in order to overcome limitations of the sensor and challenges originated from unique features of SID students' body movement.