Long-term Action Quality Assessment (AQA) is of significant importance in applications such as sports analysis and medical rehabilitation. Compared with short-term AQA, long-term AQA involves longer temporal spans and more complex motion structures, posing greater challenges to spatio-temporal modeling. Existing methods typically rely on a single modality, either video or skeleton data. Video-based approaches are easily affected by background noise, while skeleton-based methods lack visual appearance information, making it difficult to comprehensively represent action quality. To address these limitations, we propose a multimodal framework termed Skeleton-Guided Spatio-Temporal Video Representation (SG-STVR). By introducing skeletal priors, we develop a Spatial Prior Injection (SPI) strategy that utilizes human foreground masks generated from skeleton data to guide the model to focus on action-relevant regions and suppress background interference. In addition, we propose a Temporal Prior Guidance (TPG) strategy, which enhances the temporal modeling capability of video features based on the motion sensitivity of skeleton sequences. Experimental results on the RG and Fis-V datasets demonstrate that SG-STVR outperforms other approaches, reflecting its competitiveness in long-term AQA task.

Prior work on few-shot action recognition predominantly adopts two strategies: spatio-temporal separated frame matching and multi-stream multi-modal networks. However, each suffering from either incomplete spatio-temporal modeling or an over-reliance on additional annotation data. To address these limitations, we propose a Contrastive Single-Stream Spatio-Temporal joint modeling Few-Shot Action Recognition (CS3T-FSAR) model. In terms of spatio-temporal modeling, our approach directly constructs high-quality three-dimensional spatio-temporal representations to fully capture the global associations among video frames. Regarding the loss function design, we integrate a triplet loss to achieve precise matching while reducing both inference cost and computational complexity. Ultimately, our method achieves significant performance improvements across four benchmark datasets, demonstrating its competitiveness in few-shot action recognition.

The large scale and extensive coverage of point cloud data make large-scale airborne laser scanning (ALS) point cloud semantic segmentation a highly challenging task. Although transformers have shown impressive performance in large-scale point cloud semantic segmentation task, their quadratic complexity limits the processing capacity. To alleviate this issue, we propose Local and Global Mamba (LGMamba)—a novel state-space model (SSM)-based network for large-scale point cloud semantic segmentation. Specifically, we propose Local Mamba module to extract fine-grained local features by effectively capturing local dependencies. Then, we propose Global Mamba module to refine the learned local features by capturing the global long-distance dependencies of whole scenes. The validation of our method on the DALES datasets was conducted. Extensive experimental results demonstrate the effectiveness of LGMamba, with mean intersection over union (mIoU) of 82.3% and overall accuracy (OA) of 97.7% on DALES.

Transformer networks have demonstrated remarkable performance in point cloud processing tasks. However, balancing local feature aggregation with long-range dependency modeling remains a challenging issue. In this work we present a local enhanced Transformer network (LETNet) for land cover classification with multispectral LiDAR data. Specifically, we first rethink position encoding in 3D Transformers and design a novel feature encoding module that embeds comprehensive geometric and semantic information, serving a similar purpose. Then, the proposed local enhanced Transformer module is used to capture the accurate global attention weights and refine the features. Finally, to effectively extract and integrate global features across various scales, an attention-based pooling module is introduced. This module extracts global features from each encoder and decoder layer and constructs a feature pyramid to fuse these multi-scale global features. Both quantitative assessments and comparative analyses demonstrate the competitive capability and advanced performance of the LETNet in land cover classification task.

Accurate recognition and localization of 3D objects is a fundamental research problem in 3D computer vision. Benefiting from transformation-free point cloud processing and flexible receptive fields, point-based methods have become accurate in 3D point cloud modeling, but still fall behind voxel-based competitors in 3D detection. We observe that the set abstraction module, commonly utilized by point-based methods for downsampling points, tends to retain excessive irrelevant background information, thus hindering the effective learning of features for object detection tasks. To address this issue, we propose MSSA, a Multi-representation Semantics-augmented Set Abstraction for 3D object detection. Specifically, we first design a backbone network to encode different representation features of point clouds, which extracts point-wise features through PointNet to preserve fine-grained geometric structure features, and adopts VoxelNet to extract voxel features and BEV features to enhance the semantic features of key points. Second, to efficiently fuse different representation features of keypoints, we propose a Point feature-guided Voxel feature and BEV feature fusion (PVB-Fusion) module to adaptively fuse multi-representation features and remove noise. At last, a novel Multi-representation Semantic-guided Farthest Point Sampling (MS-FPS) algorithm is designed to help set abstraction modules progressively downsample point clouds, thereby improving instance recall and detection performance with more important foreground points. We evaluate MSSA on the widely used KITTI dataset and the more challenging nuScenes dataset. Experimental results show that compared to PointRCNN, our method improves the AP of “moderate” level for three classes of objects by 7.02%, 6.76%, and 5.44%, respectively. Compared to the advanced point-voxel-based method PV-RCNN, our method improves the AP of “moderate” level by 1.23%, 2.84%, and 0.55% for the three classes, respectively.

Multilabel feature selection (MFS) has received widespread attention in various big data applications. However, most of the existing methods either explicitly or implicitly assume that all labels are given in advance before feature selection starts; or that all labels are independent. In fact, in many practical applications, the available labels usually arrive dynamically, and they may be interdependent with each other. Moreover, labels may be generated dynamically in a minibatch manner, which makes it more difficult to explore label dependency. In this article, we propose a novel fuzzy mutual information-based multilabel feature selection approach MSDS, which is able to solve single streaming label, minibatch streaming labels, and exploit label dependency simultaneously. In specific, we first promote fuzzy mutual information to be suitable for multilabel learning. This model can effectively consider the relationship between two labels, and has good applicability for measuring the relationship between multiple labels. Then, we analyze feature relevance and feature redundancy based on the combination of label dependency and streaming labels, which helps to facilitate the selection of high-quality feature subsets. Finally, a feature conversion is designed to fuse the representative features of new arrival streaming labels. Comprehensive experiments on twelve multilabel datasets clearly reveal the superiority of the proposed method against two streaming labels based algorithms and five state-of-the-art static label space based algorithms.

Transformer-based and interaction point-based methods have demonstrated promising performance and potential in human-object interaction detection. However, due to differences in structure and properties, direct integration of these two types of models is not feasible. Recent Transformer-based methods divide the decoder into two branches: an instance decoder for human-object pair detection and a classification decoder for interaction recognition. While the attention mechanism within the Transformer enhances the connection between localization and classification, this paper focuses on further improving HOI detection performance by increasing the intrinsic correlation between instance and action features. To address these challenges, this paper proposes a novel Transformer-based HOI Detection framework. In the proposed method, the decoder contains three parts: learnable query generator, instance decoder, and interaction classifier. The learnable query generator aims to build an effective query to guide the instance decoder and interaction classifier to learn more accurate instance and interaction features. These features are then applied to update the query generator for the next layer. Especially, inspired by the interaction point-based HOI and object detection methods, this paper introduces the prior bounding boxes, keypoints detection and spatial relation feature to build the novel learnable query generator. Finally, the proposed method is verified on HICO-DET and V-COCO datasets. The experimental results show that the proposed method has the better performance compared with the state-of-the-art methods.

Describing video content using natural language is an important part of video understanding. It needs to not only understand the spatial information on video, but also capture the motion information. Meanwhile, video captioning is a cross-modal problem between vision and language. Traditional video captioning methods follow the encoder-decoder framework that transfers the video to sentence. But the semantic alignment from sentence to video is ignored. Hence, finding a discriminative visual representation as well as narrowing the semantic gap between video and text has great influence on generating accurate sentences. In this paper, we propose an approach based on multi-feature fusion refine network (MFRN), which can not only capture the spatial information and motion information by exploiting multi-feature fusion, but also can get better semantic aligning of different models by designing a refiner to explore the sentence to video stream. The main novelties and advantages of our method are: (1) multi-feature fusion: Both two-dimension convolutional neural networks and three-dimension convolutional neural networks pre-trained on ImageNet and Kinetic respectively are used to construct spatial information and motion information, and then fused to get better visual representation. (2) Sematic alignment refiner: the refiner is designed to restrain the decoder and reproduce the video features to narrow semantic gap between different modal. Experiments on two widely used datasets demonstrate our approach achieves state-of-the-art performance in terms of BLEU@4, METEOR, ROUGE and CIDEr metrics.

Current methods for human-object interaction detection often use the spatial relation between a human and an object as an interaction pattern. However, this strategy is relatively simple and has low discrimination in similar interactions. To solve this drawback, the spatial relation between skeletons and objects is proposed to model the interaction pattern and improve the detection accuracy. First, the skeleton-object interaction pattern image is extracted for each interaction proposal. Second, a deep neural network is applied to learn the interaction features from these images. Finally, the interaction feature is added to the human-object interaction detection network by a multistream structure. In the experiments, we evaluate the proposed method on the HICO-DET and V-COCO datasets. Experimental results show that the proposed method can achieve the best performance compared with state-of-art methods.

The local key features in video are important for improving the accuracy of human action recognition. However, most end-to-end methods focus on global feature learning from videos, while few works consider the enhancement of the local information in a feature. In this article, we discuss how to automatically enhance the ability to discriminate the local information in an action feature and improve the accuracy of action recognition. To address these problems, we assume that the critical level of each region for the action recognition task is different and will not change with the region location shuffle. We therefore propose a novel action recognition method called the shuffle-invariant network. In the proposed method, the shuffled video is generated by regular region cutting and random confusion to enhance the input data. The proposed network adopts the multitask framework, which includes one feature backbone network and three task branches: local critical feature shuffle-invariant learning, adversarial learning, and an action classification network. To enhance the local features, the feature response of each region is predicted by a local critical feature learning network. To train this network, an L 1-based critical feature shuffle-invariant loss is defined to ensure that the ordered feature response list of these regions remains unchanged after region location shuffle. Then, the adversarial learning is applied to eliminate the noise caused by the region shuffle. Finally, the action classification network combines these two tasks to jointly guide the training of the feature backbone network and obtain more effective action features. In the testing phase, only the action classification network is applied to identify the action category of the input video. We verify the proposed method on the HMDB51 and UCF101 action datasets. Several ablation experiments are constructed to verify the effectiveness of each module. The experimental results show that our approach achieves the state-of-the-art performance.

Large-scale data clustering is an essential key for big data problem. However, no current existing approach is “optimal” for big data due to high complexity, which remains it a great challenge. In this article, a simple but fast approximate DBSCAN, namely, KNN-BLOCK DBSCAN, is proposed based on two findings: 1) the problem of identifying whether a point is a core point or not is, in fact, a kNN problem and 2) a point has a similar density distribution to its neighbors, and neighbor points are highly possible to be the same type (core point, border point, or noise). KNN-BLOCK DBSCAN uses a fast approximate kNN algorithm, namely, FLANN, to detect core-blocks (CBs), noncore-blocks, and noise-blocks within which all points have the same type, then a fast algorithm for merging CBs and assigning noncore points to proper clusters is also invented to speedup the clustering process. The experimental results show that KNN-BLOCK DBSCAN is an effective approximate DBSCAN algorithm with high accuracy, and outperforms other current variants of DBSCAN, including ρ-approximate DBSCAN and AnyDBC.

Abstract Generally, it is regarded as challenge work to grasp the drawing style of an ancient masterpiece in Chinese painting learning. This paper presents a novel approach to the generation of a Chinese ink painting in a certain style and animating its brushwork process with expert skills. In order to demonstrate the techniques of brush and ink inside a stroke, a serials of geometric properties of a brush stroke, are first extracted, then through rational deformation calculation, the best stroke source is mapped onto the stroking path, which is sketched by the user, and finally a new Chinese painting can be synthesized by style migration and natural stroke composition. So with the generated strokes, the lifelike brushwork process of the new painting can be represented dramatically. Actually, by showing the authentic painting process, the tool we implemented helps the learners, who have no profound skills and knowledge in domain of Chinese painting, master the essence of a great painting style, and also provides an easy way to art creation and the comprehension of mysterious Chinese traditional art.

Road extraction from remote sensing images is an attractive but difficult task. Gray-value distribution and structure feature information are both crucial for road extraction task. However, existing methods mainly focus on structure feature information which contains morphological shape features and machine learning features, suffering from lots of false positives which are generated at positions having similar structure features but different gray-value distribution with roads. To effectively fuse the two complementary gray-value distribution and structure feature information, we propose a coarse-to-fine road extraction algorithm from remote sensing images. First, at the coarse level, we introduce a local Dirichlet mixture models (LDMM) which utilizing gray-value distribution information to pre-segment images into potential roads and backgrounds. Thus, most backgrounds having different gray-value distribution with roads can be removed firstly. Compared with original Dirichlet mixture models, the LDMM is much faster and more accurate. Next, at the fine level, we introduce a multiscale-high-order deep learning strategy based on ResNet model which can learn robust structure context features for final road extraction step. Based on the results of LDMM, the multiscale-high-order strategy can further remove false positives which have different structure features with roads. Compared with a single scanning size ResNet, our multiscale-high-order strategy can learn higher-order context information, leading to better performances. We test our algorithm on Shaoshan dataset. Experiments illustrate our better performance compared with other six state-of-the-art methods.

Cross-modal hashing has recently gained significant popularity to facilitate retrieval across different modalities. With limited label available, this paper presents a novel Semi-Supervised Semantic-Preserving Hashing (S3PH) for flexible cross-modal retrieval. In contrast to most semi-supervised cross-modal hashing works that need to predict the label of unlabeled data, our proposed approach groups the labeled and unlabeled data together, and integrates the relaxed latent subspace learning and semantic-preserving regularization across different modalities. Accordingly, an efficient relaxed objective function is proposed to learn the latent subspaces for both labeled and unlabeled data. Further, an orthogonal rotation matrix is efficiently learned to transform the latent subspace to hash space by minimizing the quantization error. Without sacrificing the retrieval performance, the proposed S3PH method can benefit various kinds of retrieval tasks, i.e., unsupervised, semi-supervised and supervised. Experimental results compared with several competitive algorithms show the effectiveness of the proposed method and its superiority over state-of-the-arts.

BACKGROUND: Benefiting from big data, powerful computation and new algorithmic techniques, we have been witnessing the renaissance of deep learning, particularly the combination of natural language processing (NLP) and deep neural networks. The advent of electronic medical records (EMRs) has not only changed the format of medical records but also helped users to obtain information faster. However, there are many challenges regarding researching directly using Chinese EMRs, such as low quality, huge quantity, imbalance, semi-structure and non-structure, particularly the high density of the Chinese language compared with English. Therefore, effective word segmentation, word representation and model architecture are the core technologies in the literature on Chinese EMRs. RESULTS: In this paper, we propose a deep learning framework to study intelligent diagnosis using Chinese EMR data, which incorporates a convolutional neural network (CNN) into an EMR classification application. The novelty of this paper is reflected in the following: (1) We construct a pediatric medical dictionary based on Chinese EMRs. (2) Word2vec adopted in word embedding is used to achieve the semantic description of the content of Chinese EMRs. (3) A fine-tuning CNN model is constructed to feed the pediatric diagnosis with Chinese EMR data. Our results on real-world pediatric Chinese EMRs demonstrate that the average accuracy and F1-score of the CNN models are up to 81%, which indicates the effectiveness of the CNN model for the classification of EMRs. Particularly, a fine-tuning one-layer CNN performs best among all CNNs, recurrent neural network (RNN) (long short-term memory, gated recurrent unit) and CNN-RNN models, and the average accuracy and F1-score are both up to 83%. CONCLUSION: The CNN framework that includes word segmentation, word embedding and model training can serve as an intelligent auxiliary diagnosis tool for pediatricians. Particularly, a fine-tuning one-layer CNN performs well, which indicates that word order does not appear to have a useful effect on our Chinese EMRs.

This paper proposes a Bayesian nonparametric framework for clustering axially symmetric data. Our approach is based on a Dirichlet processes mixture model with Watson distributions, which can also be considered as the infinite Watson mixture model. In this paper, first, we extend the finite Watson mixture model into its infinite counterpart based on the framework of truncated Dirichlet process mixture model with a stick-breaking representation. Second, we propose a coordinate ascent mean-field variational inference algorithm that can effectively learn the parameters of our model with closed-form solutions; Third, to cope with a massive data set, we develop a stochastic variational inference algorithm to learn the proposed model through the method of stochastic gradient ascent; Finally, the proposed nonparametric Bayesian model is evaluated through simulated axially symmetric data sets and a real-world application, namely, gene expression data clustering.

A fast exact nearest neighbor search algorithm over large scale data is proposed based on semi-convex hull tree, where each node represents a semi-convex hull, which is made of a set of hyper planes. When performing the task of nearest neighbor queries, unnecessary distance computations can be greatly reduced by quadratic programming. GPUs are also used to accelerate the query process. Experiments conducted on both Intel(R) HD Graphics 4400 and Nvidia Geforce GTX1050 TI, as well as theoretical analysis show that the proposed algorithm yields significant improvements and outperforms current k-d tree based nearest neighbor query algorithms and others.

The availability of large motion capture (mocap) data has sparked a great motivation for computer animation, and the task of automatically annotating complex mocap sequences plays an important role in the efficient motion analysis. To this end, this study presents an efficient human mocap data annotation approach by using multi‐view spatiotemporal feature fusion. First, the authors exploit an improved hierarchical aligned cluster analysis algorithm to divide the unknown human mocap sequence into several sub‐motion clips, and each sub‐motion clip incorporates a particular semantic meaning. Then, the two kinds of multi‐view features, namely most informative central distances and most informative geometric angles, are discriminatively extracted and temporally modelled by a Fourier temporal pyramid to complementarily characterise each motion clip. Finally, the authors utilise the discriminant correlation analysis to fuse these two types of motion features and further employ an extreme learning machine to annotate each sub‐motion clip. The extensive experiments tested on the public available database have demonstrated the effectiveness of the proposed approach in comparison with the existing counterparts.

Density-based clustering is one of the most popular paradigms of existing clustering approaches, most approaches of this kind, such as DBSCAN, recognize clusters of data characterized by a fixed scanning radius. However, some flaws are caused by the fixed scanning radius, e.g., the determination of a proper scanning radius is nontrivial. In order to solve these problems, we revise DBSCAN, Meanshift, DPeak, etc. based on two new features, i.e., effective radius and density heat (DHeat). Generally, we name these revised clustering algorithms as DHeat. The underlying idea is based on two assumptions: 1) the existence of clusters is raised by the nonuniformity of data distribution, and the density of one data point within its r-neighborhood is proportional to the volume of the neighborhood provided the density distribution is uniform and 2) each cluster can be divided into different density layers, such as edges, shallow inner, deep inner, etc.; the deeper inner of a point locates, the higher density of that point. The experiments conducted on various test cases show that the advantage of DHeat lies in its good performance and the self-adapting scanning radius.

Facial retouching has been increasingly applied in current social media and entertainment industries. In this study, the authors propose an efficient approach to automatically detect and retouch the facial flaws by using discriminative structure tensor. First, a non‐linear structure tensor associated with saliency model is exploited to discriminatively and automatically detect the significant facial flaws. Then, a Gaussian skin model is constructed in YCbCr space and the OSTU operation is simultaneously utilised to precisely mark the facial skin regions, in which the mouth, eyebrows and nostril parts are excluded. Subsequently, diverse structure tensor is employed to discriminatively adjust the inpainting priority and propose a structure tensor‐based inpainting algorithm to retouch the detected flaws. Without manual intervention, the extensive experiments have shown its effectiveness in marking the freckles, blemishes and moles in face images, and the retouching performance is visually pleasing in comparison with state‐of‐the‐art counterparts.

Label imbalance and the insufficiency of labeled training samples are major obstacles in most methods for counting people in images or videos. In this work, a sparse representation-based semi-supervised regression method is proposed to count people in images with limited data. The basic idea is to predict the unlabeled training data, select reliable samples to expand the labeled training set, and retrain the regression model. In the algorithm, the initial regression model, which is learned from the labeled training data, is used to predict the number of people in the unlabeled training dataset. Then, the unlabeled training samples are regarded as an over-complete dictionary. Each feature of the labeled training data can be expressed as a sparse linear approximation of the unlabeled data. In turn, the labels of the labeled training data can be estimated based on a sparse reconstruction in feature space. The label confidence in labeling an unlabeled sample is estimated by calculating the reconstruction error. The training set is updated by selecting unlabeled samples with minimal reconstruction errors, and the regression model is retrained on the new training set. A co-training style method is applied during the training process. The experimental results demonstrate that the proposed method has a low mean square error and mean absolute error compared with those of state-of-the-art people-counting benchmarks.

Beta-Liouville mixture models have achieved measurable success in many computer vision and pattern recognition applications. In this paper, we develop a novel algorithm to learn this particular kind of models that have been shown to be very efficient for the clustering of proportional data. Our algorithm is based on an accelerated version of the variational Bayes approach. Experiments show that the developed algorithm work very well for the categorization of 3D shapes.

In this study, the authors investigate the possibility of boosting action recognition performance by exploiting the associated scene context. Towards this end, the authors model a scene as a mid‐level ‘middle layer’ in order to bridge action descriptors and action categories. This is achieved via a scene topic model, in which hybrid visual descriptors, including spatial–temporal action features and scene descriptors, are first extracted from a video sequence. Then, the authors learn a joint probability distribution between scene and action using a naive Bayes nearest neighbour algorithm, which is adopted to jointly infer the action categories online by combining off‐the‐shelf action recognition algorithms. The authors demonstrate the advantages of their approach by comparing it with state‐of‐the‐art approaches using several action recognition benchmarks.

Many traditional dark channel prior based haze removal schemes often suffer from the colour distortion and generate halo artefacts in the remote scenes. To tackle these issues, the authors present an efficient scene‐adaptive single image dehazing approach via opening dark channel model (ODCM). First, the authors detect the image depth information and separate it into close view and distant view. Then, an ODCM is proposed to optimise the whole atmospheric veil, in which the values of close view are regularised by a minimum channel image while the distant parts are estimated by an appropriate lower constant. Accordingly, the transmission map can be further optimised by guide filter and smoothed by domain transform filter. Finally, the haze degraded image can be well restored by the atmosphere scattering model. The extensive experiments have shown that the proposed image dehazing approach has significantly increased the perceptual visibility of the scene and achieved a better colour fidelity visually.

Exploiting Dirichlet process mixture models (also known as infinite mixture models) to model visual and textual data is now standard weapon in the arsenal of machine learning. This paper proposes a new accelerated variational inference approach to learn Dirichlet process mixture models with Dirichlet distributions. The choice of using Dirichlet distribution as the basic distribution is mainly due to its flexibility for modeling proportional data. Indeed, this kind of data is naturally generated by several applications involving the representation of texts, images and videos using the bag-of-words (or "visual words" in the case of images and videos) approach. The potential of the developed learning framework is shown using a challenging real application namely human action recognition in videos.

Most recently, a DE framework with neighborhood and direction information (NDi-DE) was proposed to exploit the information of population and was demonstrated to be effective for most of the DE variants. However, the performance of NDi-DE heavily depends on the selection of direction information. In order to alleviate this problem, two adaptive operator selection (AOS) mechanisms are introduced to adaptively select the most suitable type of direction information for the specific mutation strategy during the evolutionary process. The new method is named as adaptive direction information based NDi-DE (aNDi-DE). In this way, the good balance between exploration and exploitation can be dynamically achieved. To evaluate the effectiveness of aNDi-DE, the proposed method is applied to the well-known DE/rand/1 algorithm. Through the experimental study, we show that aNDi-DE can effectively improve the efficiency and robustness of NDi-DE.

In this study, we present an efficient active contour with a joint and region‐scalable distribution metric for interactive natural image segmentation. First, the authors project a red–green–blue image into the CIELab colour space and employ independent component analysis to select two subspace channels. Then, by initialising the evolving curve interactively in terms of a polygonal curve or multiple polygonal curves, they compute a joint probability distribution associated with a region‐scalable mask to model the regional statistics and propose a simple but effective distribution metric to regularise the active contours. Subsequently, they convert the resultant level set function into binary pattern and find the larger 8‐connected regions as the desired objects. Finally, the selected regions are smoothed with a circular averaging filter such that the final segmentation results can be obtained. The proposed approach not only can deal with the complex appearance and intensity in homogeneity, but also has the advantages of fast convergence and easy implementation. The experiments have shown the precise and reliable segmentation results in comparison with the state‐of‐the‐art competing approaches.

In this paper, a novel heuristic structure optimization methodology for radial basis probabilistic neural networks (RBPNNs) is proposed. First, a minimum volume covering hyperspheres (MVCH) algorithm is proposed to select the initial hidden-layer centers of the RBPNN, and then the recursive orthogonal least square algorithm (ROLSA) combined with the particle swarm optimization (PSO) algorithm is adopted to further optimize the initial structure of the RBPNN. The proposed algorithms are evaluated through eight benchmark classification problems and two real-world application problems, a plant species identification task involving 50 plant species and a palmprint recognition task. Experimental results show that our proposed algorithm is feasible and efficient for the structure optimization of the RBPNN. The RBPNN achieves higher recognition rates and better classification efficiency than multilayer perceptron networks (MLPNs) and radial basis function neural networks (RBFNNs) in both tasks. Moreover, the experimental results illustrated that the generalization performance of the optimized RBPNN in the plant species identification task was markedly better than that of the optimized RBFNN.

In this paper, a novel model of elliptical basis function neural networks (EBFNN) based on a hybrid optimization algorithm is proposed. Firstly, a geometry analytic algorithm is applied to construct the hyper-ellipsoid units of hidden layer of the EBFNN, i.e., an initial structure of the EBFNN, which is further pruned by the particle swarm optimization (PSO) algorithm. And the shape parameters of kernel function for the hidden layer are also optimized by the PSO simultaneously. Finally, the hybrid learning algorithm (HLA) is further applied to adjust the hidden centers and the shape parameters of kernel function for the hidden layer. The experimental results demonstrated the proposed hybrid optimization algorithm for the EBFNN model is feasible and efficient, and the EBFNN is not only parsimonious but also has better generalization performance than the RBFNN.

A new method for selecting features from protein sequences is proposed in this paper. First, the protein sequences are converted into fixed-dimensional feature vectors. Then, a subset of features is selected using relative entropy method and used as the inputs for Support Vector Machine (SVM). Finally, the trained SVM classifier is utilized to classify protein sequences into certain known protein families. Experimental results over proteins obtained from PIR database and GPCRs have shown that our proposed approach is really effective and efficient in selecting features from protein sequences.

In this paper a new method for recognition of 2D occluded shapes based on neural network using generalized differential evolution training algorithm is proposed. Firstly, a generalized differential evolution (GDE) algorithm is introduced. And this GDE algorithm is applied to train multilayer perceptron neural networks. Then a new shape feature, refer to as multiscale Fourier descriptors (MFDs) is proposed. Finally, the superiority of GDE training method over traditional approaches to train networks is demonstrated by experiment. The experimental results show that our proposed GDE training method is much efficient and effective. And they also showed that the MFDs method is suitable for the shape recognition.

In this paper an efficient shape matching approach based on fuzzy discrete particle swarm optimization (FDPSO) is proposed. Based on fuzzy theory and PSO method, we applied this optimization method to a special combinatorial optimization problem: shape matching and recognition. Firstly, an original shape is approximated to a polygone and a shape representation of invariant attributes sequence is used. Then fuzzy matrices were adopted to represent the position and velocity of the particles in PSO. Finally, the superiority of our proposed method over traditional approaches to shape matching is demonstrated by experiments. The experimental results showed that our proposed method can achieve good results due to its robustness.

This paper introduces the research project of robot assisted minimally invasive neurosurgery. Using visualization technology, the system reconstructs and displays the 3D model of the patient's inner structure in the computer. Thus the surgeons can plan the surgery on the model in the computer. Marker registration is used to create the mapping between the patient's head and the 3D-brain model. Robot arm is used as a navigator to direct the surgery planning and as an instrument platform to assist surgeons to accomplish the operation. The results of the clinical application are given to show that the robotic system is effective.