Creating realistic and fully detailed 3D human models using a minimal number of views has long been a challenging goal in 3D human reconstruction. Reconstructing a realistic human model from only two images (front and back) is particularly difficult due to the limited 3D information available, leading to two major challenges: (1) it is difficult to establish spatial consistency for reconstruction due to the lack of sufficient images for reliable matching, and (2) incomplete field of view results in missing color information. To address these challenges, we propose DuetGS, a novel pipeline that divides the reconstruction process into two stages: geometry reconstruction and color reconstruction. For geometry reconstruction, we employ a data-driven neural network to recover a full-body mesh from the front and back images, providing the spatial positioning for Gaussians. For color reconstruction, we adapt Gaussian Splatting and integrate our proposed unsupervised color propagation method to establish the color details of the Gaussians. Furthermore, our Gaussians are directly mapped to the mesh, allowing us to control their rotation and translation through mesh manipulation. This mapping ensures compatibility with various animation techniques. Extensive experiments on the THUman, CustomHumans, and PeopleSnapshot datasets demonstrate that our approach outperforms existing methods in terms of reconstruction accuracy and visual quality.

Infrared-visible image fusion for object detection (IVIF-OD) aims to utilize complementary information in the two modalities to synthesize new images with richer information to serve object detection. Most existing works focus on how to better fuse pixel-level details while ignoring object-related information required for detection and introducing redundant and object-irrelevant information in the fused images. To address the limitations of previous studies, this paper proposes a diffusion-based denoising fusion for object detection in infrared-visible images, termed DDFD. Specifically, DDFD treats image fusion as a diffusion-based denoising process to generate fused images that are informative yet non-redundant. Since visible imaging is easily affected by adverse conditions, DDFD exploits an image-adaptive enhancement (IAE) module that adaptively improves visible images to achieve better fusion. To extract key fusion features and remove redundancy, DDFD uses an image-aware noise estimator (INE) to determine the noise in the input infrared-visible images for promoting the diffusion denoising network. To take advantage of both the fusion network and object detection network, DDFD jointly optimizes them such that the fusion network can receive object information to improve the fused images, and the improved images can provide high-quality features to enhance object detection performance. Extensive experiments on the M3FD, DroneVehicle, and VEDAI public datasets reveal the superior object detection performance of DDFD and confirm the effectiveness of IVIF-based object detection under challenging weather conditions.

Integrating dynamic effects has shown its significance in enhancing the accuracy and robustness of Visual-Inertial Odometry (VIO) systems in dynamic scenarios. Existing methods either prune dynamic features or rely heavily on prior semantic knowledge or kinetic models, proved unfriendly to scenes with a multitude of dynamic elements. This work proposes a novel dynamic feature fusion method for monocular VIO, named DFF-VIO, which requires no prior models or scene preference. By combining IMU-predicted poses with visual clues, it initially identifies dynamic features during the tracking stage by constraints of consistency and degree of motion. Then, we innovatively design a Dynamic Transformation Operation (DTO) to separate the effect of dynamic features on multiple frames into pairwise effects and construct a Dynamic Feature Cell (DFC) to preserve the eligible information. Subsequently, we reformulate the VIO nonlinear optimization problem and construct dynamic feature residuals with the transformed DFC as a unit. Based on the proposed inter-frame model of moving features, a so-called motion compensation is developed to resolve the reprojection issue of dynamic features, allowing their effects to be incorporated into the VIO’s tight coupling optimization, thereby realizing robust positioning in dynamic scenarios. We conduct accuracy evaluations on ADVIO and VIODE, degradation tests on EuRoC dataset, as well as ablation studies to highlight the joint optimization of dynamic residuals. Results reveal that DFF-VIO outperforms state-of-the-art methods in pose accuracy and robustness across various dynamic environments.

Anonymous access is an effective method for preserving privacy in access control. This study assumes that anonymous access control requires both frameworks and policies. Numerous solutions have been proposed for anonymous access at the framework level. In this study, these solutions are analyzed and quantified using a unified attribute-based access control (ABAC) anonymous access reference framework. Anonymous access at the framework level is the first line of defense, and inappropriate policies may undermine subject anonymity. An anonymity metric is proposed at the policy level to prevent authorization authority from re-identification using specific attributes and policies. The anonymity metric evaluates the risk of re-identifying a subject due to inappropriate access requests, as well as subject attribute assignment schemes and policies. This study is the first to focus on anonymity at the policy level in ABAC. Furthermore, a formal definition of anonymity suitable for ABAC is proposed. The feasibility of the proposed anonymity metric is verified through simulations.

This paper discusses probe configuration algorithm in the dual anechoic chamber setup, which establishes an end-to-end link without any cable connection on both ends for the multiprobe over-the-air (OTA) radiated testing. Under this setup, there exist no suitable probe configuration algorithm to meet the required accuracy of the spatial profile in the test zone. Therefore, an improved multi-objective evolutionary algorithm based on decomposition (MOEA/D) is proposed to configure the probes in terms of number, position and power weighting. Simulation results show that the proposed algorithm significantly improves the reconstruction accuracy of the spatial profile compared with the existing methods in the literature under the same test settings.

Point cloud segmentation is a crucial fundamental step in 3D reconstruction, object recognition and scene understanding. This paper proposes a supervoxel-based point cloud segmentation algorithm in region growing principle to solve the issues of inaccurate boundaries and nonsmooth segments in the existing methods. To begin with, the input point cloud is voxelized and then pre-segmented into sparse supervoxels by flow constrained clustering, considering the spatial distance and local geometry between voxels. Afterwards, plane fitting is applied to the over-segmented supervoxels and seeds for region growing are selected with respect to the fitting residuals. Starting from pruned seed patches, adjacent supervoxels are merged in region growing style to form the final segments, according to the normalized similarity measure that integrates the smoothness and shape constraints of supervoxels. We determine the values of parameters via experimental tests, and the final results show that, by voxelizing and pre-segmenting the point clouds, the proposed algorithm is robust to noises and can obtain smooth segmentation regions with accurate boundaries in high efficiency.

Palm-related biometrics have been widely studied for a long time, as the palm contains many distinctive patterns. However, most of the existing systems are designed to work within an ideal environment, such as in front of a unicolor background or in a large enclosure. Those preconditions can avoid influences of ambient light and hand distance change, but at the same time, they also limit the applications of palm recognition. In the work reported in this paper, we designed a novel red-green-blue and depth-based four-camera system that can capture the palm-related images separately in real time. The techniques of region-of-interest (ROI) location, ROI alignment, and light-source intensity optimization were studied. The ROI location method is modified to increase the robustness of hand gesture variation. Based on the depth information, we proposed the coordinate mapping and inclination rectification methods to obtain aligned ROI pairs. Using this device, we collected a video-based multimodal palm image database. After the parameter optimization and information fusion, the equal-error-rate of our approach on this database is lower than 0.47%. The recognition rate obtained from the support-vector-machine-based fusion is higher than 99.8%. The experimental results prove that the proposed system achieves advantages of anti-spoofing, high speed, high accuracy, and small size.

With the rapid development of mobile Internet and smart devices, VoD (video on demand) providers build media cloud to offer multi-bitrate video streaming services to users at a reduced cost, called as cloud-based multi-version VoD. In cloud-based multi-version VoD, we need to solve the problem of allocating appropriate resources for virtual streaming media server cluster with the aim of optimizing the user experience and reducing the service cost. To address this problem, a resource allocation for virtual streaming media server cluster in cloud-based multi-version VoD is proposed in this paper. We firstly analyze the user historical learning logs to mine the user behavior characteristics, including the average user request arrival rate, the video playing time distribution, and the video popularity distribution, etc. Then, based on the user behavior characteristics and the queueing theory, a resource allocation model for the virtual streaming media server cluster is introduced. It predicts the user arrival rate at first and then allocates appropriate resources dynamically to solve the resources allocation irrationality problem. Simulation results have proved the proposed method can allocate appropriate resources for virtual streaming media server cluster, which can ensure the user experience satisfaction and improve the resources utilization.

This study focuses on fast and robust outlier matches removal strategy to improve the efficiency and precision of initial alignment and further the quality of pairwise registration. Starts from the point matches obtained via feature detecting and matching, the distance disparity matrix derived from Euclidean invariants of rigid transformation is introduced, based on which a fast and effective pruning method is proposed to eliminate the outlier correspondences, especially the sharp ones. Then, the remaining matches are sent into the enhanced least‐square backward method to estimate an initial transformation in lesser attempts. Since most of the outliers are rejected, presented backward method could provide a finer alignment to input point clouds in higher efficiency than existing methods, and the following refining procedure converges to a more precise registration consuming fewer iterations, which have been proved in designed experiments. The thresholds employed in the pipeline are all automatically determined according to the actual resolution of input point clouds. Users are just required to control the error precision through a scale factor, in which way the inaccuracy and inconvenience of manually threshold defining are avoided.

In this study, we present a novel selection process to solve the multiperson decision making (MPDM) problems with different preference representation structures. This selection process is based on the prospect theory, which is one of the most influential psychological behavior theories, and seeks to maximize the satisfactory of all decision makers. Specifically, the individual selection methods associated with different preference structures are used to obtain individual preference orderings. Then, the preference-approval structures are used to determine the reference points of the prospect theory, according to the obtained individual preference orderings. Next, the gains and losses are calculated based on the prospect theory and the established reference points. Finally, the prospect values of the alternatives are obtained to rank the alternatives.

There are increasing interests to learn Chinese all over the world due to the fast economic growth of China. Intrinsically, learning Chinese is challenging to most foreigners and Chinese students as well due to the complex structures of Chinese Characters, the writing of characters in correct stroke sequences, and their appropriate usage and pronunciation, etc. Even with the guidance of an experienced Chinese teacher, there is often insufficient time to practise the writing or pronunciation during classes. However, mobile devices such as the Android-based smartphones, iPads or iPhones may open up numerous opportunities facilitated by the latest interface and sensing technologies for students to learn anytime and anywhere. Therefore, we propose here an extendible and intelligent mobile application namely the Intelligent Chinese Explorer (iCExplorer) based on learning objects and fully utilizing the smart sensors including the GPS, touch and image/video sensors of smartphones to facilitate foreigners or Chinese students to learn Chinese more effectively. In particular, we have designed an intelligent algorithm to aid all learners in writing Chinese characters with the correct stroke sequences. To demonstrate the feasibility of our proposal, a prototype of our proposed mobile application was successfully built on the iOS platform for a thorough evaluation with a detailed plan. Furthermore, there are many interesting directions for the future investigations of our proposal.

Pollen tube growth is an essential part of the sexual reproductive process in plants. It is the result of a complex interaction of cytoplasmic contents (proteins, ions, cellular structures, etc.). Existing pollen tube models use differential equations to represent these complex intra-cellular interactions that lead to growth. As a result of this complex nature, these models are not used to verify the shape and growth behavior observed in living cells. We present a method of analyzing the growth behavior of pollen tubes in experimental videos through affine transformations on the detected cell tip. The method relies on underlying biological knowledge about the growth process and leverages these processes to determine tip morphology. Experimental results on videos of growing pollen tube cells show that our method is superior to the current method of treating cell tip morphology as well as adaptive active appearance models.

Head pose estimation is important in human-machine interfaces. However, illumination variation, occlusion and low image resolution make the estimation task difficult. Hence, a Dirichlet-tree distribution enhanced Random Forests approach (D-RF) is proposed in this paper to estimate head pose efficiently and robustly under various conditions. First, PCA based sub-features space from Gabor features and histogram distributions of the facial patches are extracted to eliminate the influence of occlusion and noise. Then, the D-RF is proposed to estimate the head pose in a coarse-to-fine way. In order to improve the discrimination capability of the approach, an adaptive Gaussian mixture model is introduced in the tree distribution. The proposed method has been evaluated with different data sets spanning from -90° to 90° in vertical and horizontal directions under various conditions. The experimental results demonstrate the approach’s robustness and efficiency.

One of the important characteristics of pulsar radiation is polarization. It is considered not only as a probe for recognizing the structure of a magnetic field, but also as a lighthouse for estimating spacecraft attitude via orientation information between the pulsar and the detector. Although polarization of a pulsar has been studied for decades, until recently applications to determination of spacecraft attitude have been seldom reported. This paper deals with analysis of the feasibility of applying polarization information to attitude estimation. The stability factor (SFR) and observation fluctuation factor (OFR) are introduced to analyze the stability of a pulsar’s polarized position angle. Based on European Pulsar Network (EPN) data, several simulated instances are used to demonstrate that the accuracy requirement of attitude determination can be met via polarization measurement. The SFR of a pulsar is evaluated using simulated polarization data, and the OFR is used to analyze the relationship between fluctuation extent and observation time. Simulation results show that the polarized measurement of candidate pulsars PSR B0470-28 and PSR B2319+60 reaches the specification for attitude determination.

Palmprints have been widely studied for personal authentication because they are highly accurate and incur low costs. Most of the previous work has focused on two-dimensional (2-D) palmprint identification. However, the inner surfaces of palms contain not only texture information but also shape information. Unfortunately, 2-D palmprint systems lose the shape information when capturing palmprint images. Hence, three-dimensional (3-D) information is important for palmprint systems. In this paper, we have designed and developed a novel 3-D palmprint acquisition system based on structured-light imaging technology. The acquisition system can obtain 3-D palmprint information and, at the same time, the corresponding 2-D texture, which are used for personal authentication. A 3-D palmprint database that contains 8000 samples has been established by using the developed acquisition system, and the test results illustrate the effectiveness of our system.

Palmprint has proved to be one of the most unique and stable biometric characteristics. Almost all the current palmprint recognition techniques capture the 2-D image of the palm surface and use it for feature extraction and matching. Although 2-D palmprint recognition can achieve high accuracy, the 2-D palmprint images can be counterfeited easily and much 3-D depth information is lost in the imaging process. This paper explores a 3-D palmprint recognition approach by exploiting the 3-D structural information of the palm surface. The structured light imaging is used to acquire the 3-D palmprint data, from which several types of unique features, including mean curvature image, Gaussian curvature image, and surface type, are extracted. A fast feature matching and score-level fusion strategy are proposed for palmprint matching and classification. With the established 3-D palmprint database, a series of verification and identification experiments is conducted to evaluate the proposed method. The results demonstrate that 3-D palmprint technique has high recognition performance. Although its recognition rate is a little lower than 2-D palmprint recognition, 3-D palmprint recognition has higher anticounterfeiting capability and is more robust to illumination variations and serious scrabbling in the palm surface. Meanwhile, by fusing the 2-D and 3-D palmprint information, much higher recognition rate can be achieved.

Sweat pores have been recently employed for automated fingerprint recognition, in which the pores are usually extracted by using a computationally expensive skeletonization method or a unitary scale isotropic pore model. In this paper, however, we show that real pores are not always isotropic. To accurately and robustly extract pores, we propose an adaptive anisotropic pore model, whose parameters are adjusted adaptively according to the fingerprint ridge direction and period. The fingerprint image is partitioned into blocks and a local pore model is determined for each block. With the local pore model, a matched filter is used to extract the pores within each block. Experiments on a high resolution (1200dpi) fingerprint dataset are performed and the results demonstrate that the proposed pore model and pore extraction method can locate pores more accurately and robustly in comparison with other state-of-the-art pore extractors.