Assessment of ZTD Derived from COSMIC Occultation Data with ECWMF, Radiosondes, and GNSS.

Global Navigation Satellite System (GNSS) signals generate slant tropospheric delays when they pass through the atmosphere, which is recognized as the main source of error in many spatial geodetic applications. The zenith tropospheric delay (ZTD) derived from radio occultation data is of great significance to atmospheric research and meteorology and needs to be assessed in the use of precision positioning. Based on the atmPrf, sonPrf, and echPrf data from the Constellation Observing System for Meteorology, Ionosphere, and Climate (COSMIC) Data Analysis and Archiving Center (CDAAC) from 1 January to 31 December 2008 and 2012, we obtained the ZTDs of the radio occultation data (occZTD) and the corresponding radiosonde (sonZTD) and ECWMF data (echZTD). The ZTDs derived from ground-based global positioning system (GPS) observations from the International GNSS Service (IGS) were corrected to the lowest tangent point height of the matched radio occultation profile by the barometric height formula (gnsZTD). The statistical results show that the absolute values of the bias between occZTD and echZTD, sonZTD, or gnsZTD are less than 5 mm, and the standard deviations are approximately 20 mm or less, indicating that occZTD had significant accuracy in the GNSS positioning model even when the local spherical symmetry assumption error was introduced when the Abel inversion algorithm was used to obtain the refractive index profile of atmPrf. The effects of the horizontal/vertical matching resolution and the variation in the station height/latitude on the biases of occZTD and gnsZTD were analyzed. The results can be used to quantify the performance of radio occultation data for tropospheric delay error correction in dynamic high-precision positioning.

Real and Pseudo Pedestrian Detection Method with CA-YOLOv5s Based on Stereo Image Fusion.

With the development of convolutional neural networks, the effect of pedestrian detection has been greatly improved by deep learning models. However, the presence of pseudo pedestrians will lead to accuracy reduction in pedestrian detection. To solve the problem that the existing pedestrian detection algorithms cannot distinguish pseudo pedestrians from real pedestrians, a real and pseudo pedestrian detection method with CA-YOLOv5s based on stereo image fusion is proposed in this paper. Firstly, the two-view images of the pedestrian are captured by a binocular stereo camera. Then, a proposed CA-YOLOv5s pedestrian detection algorithm is used for the left-view and right-view images, respectively, to detect the respective pedestrian regions. Afterwards, the detected left-view and right-view pedestrian regions are matched to obtain the feature point set, and the 3D spatial coordinates of the feature point set are calculated with Zhengyou Zhang's calibration method. Finally, the RANSAC plane-fitting algorithm is adopted to extract the 3D features of the feature point set, and the real and pseudo pedestrian detection is achieved by the trained SVM. The proposed real and pseudo pedestrian detection method with CA-YOLOv5s based on stereo image fusion effectively solves the pseudo pedestrian detection problem and efficiently improves the accuracy. Experimental results also show that for the dataset with real and pseudo pedestrians, the proposed method significantly outperforms other existing pedestrian detection algorithms in terms of accuracy and precision.

An Improved Stereo Matching Algorithm for Vehicle Speed Measurement System Based on Spatial and Temporal Image Fusion.

This paper proposes an improved stereo matching algorithm for vehicle speed measurement system based on spatial and temporal image fusion (STIF). Firstly, the matching point pairs in the license plate area with obviously abnormal distance to the camera are roughly removed according to the characteristic of license plate specification. Secondly, more mismatching point pairs are finely removed according to local neighborhood consistency constraint (LNCC). Thirdly, the optimum speed measurement point pairs are selected for successive stereo frame pairs by STIF of binocular stereo video, so that the 3D points corresponding to the matching point pairs for speed measurement in the successive stereo frame pairs are in the same position on the real vehicle, which can significantly improve the vehicle speed measurement accuracy. LNCC and STIF can be used not only for license plate, but also for vehicle logo, light, mirror etc. Experimental results demonstrate that the vehicle speed measurement system with the proposed LNCC+STIF stereo matching algorithm can significantly outperform the state-of-the-art system in accuracy.

Disparity level identification using the voxel-wise Gabor model of fMRI data.

Perceiving disparities is the intuitive basis for our understanding of the physical world. Although many electrophysiology studies have revealed the disparity-tuning characteristics of the neurons in the visual areas of the macaque brain, neuron population responses to disparity processing have seldom been investigated. Many disparity studies using functional magnetic resonance imaging (fMRI) have revealed the disparity-selective visual areas in the human brain. However, it is unclear how to characterize neuron population disparity-tuning responses using fMRI technique. In the present study, we constructed three voxel-wise encoding Gabor models to predict the voxel responses to novel disparity levels and used a decoding method to identify the new disparity levels from population responses in the cortex. Among the three encoding models, the fine-coarse model (FCM) that used fine/coarse disparities to fit the voxel responses to disparities outperformed the single model and uncrossed-crossed model. Moreover, the FCM demonstrated high accuracy in predicting voxel responses in V3A complex and high accuracy in identifying novel disparities from responses in V3A complex. Our results suggest that the FCM can better characterize the voxel responses to disparities than the other two models and V3A complex is a critical visual area for representing disparity information.

Stereoscopic processing of crossed and uncrossed disparities in the human visual cortex.

BACKGROUND: Binocular disparity provides a powerful cue for depth perception in a stereoscopic environment. Despite increasing knowledge of the cortical areas that process disparity from neuroimaging studies, the neural mechanism underlying disparity sign processing [crossed disparity (CD)/uncrossed disparity (UD)] is still poorly understood. In the present study, functional magnetic resonance imaging (fMRI) was used to explore different neural features that are relevant to disparity-sign processing. METHODS: We performed an fMRI experiment on 27 right-handed healthy human volunteers by using both general linear model (GLM) and multi-voxel pattern analysis (MVPA) methods. First, GLM was used to determine the cortical areas that displayed different responses to different disparity signs. Second, MVPA was used to determine how the cortical areas discriminate different disparity signs. RESULTS: The GLM analysis results indicated that shapes with UD induced significantly stronger activity in the sub-region (LO) of the lateral occipital cortex (LOC) than those with CD. The results of MVPA based on region of interest indicated that areas V3d and V3A displayed higher accuracy in the discrimination of crossed and uncrossed disparities than LOC. The results of searchlight-based MVPA indicated that the dorsal visual cortex showed significantly higher prediction accuracy than the ventral visual cortex and the sub-region LO of LOC showed high accuracy in the discrimination of crossed and uncrossed disparities. CONCLUSIONS: The results may suggest the dorsal visual areas are more discriminative to the disparity signs than the ventral visual areas although they are not sensitive to the disparity sign processing. Moreover, the LO in the ventral visual cortex is relevant to the recognition of shapes with different disparity signs and discriminative to the disparity sign.

Projection-based disparity control for toed-in multiview images.

The multiview images captured by toed-in camera array can reproduce the 3D scene vividly with appropriate positive, negative, and zero disparities. However, it is a challenging task to adjust the depth of the scene according to requirements of visual effects. In this paper, we propose a novel disparity control method based on projection to solve this problem. With the relationship between the world coordinate system and camera coordinate system, the zero disparity point in reference view is projected into other views. Thus, disparities of different views are obtained through matched corresponding points and views are shifted with calculated disparities. The proposed method is easy to implement, and the depth of toed-in multiview images can be adjusted as requirements. Experiment results show that the proposed method is effective in comparison to the conventional method, and the processed multiview images present desirable stereoscopic visual quality.

Defocus map estimation from a single image via spectrum contrast.

We present an effective method for defocus map estimation from a single natural image. It is inspired by the observation that defocusing can significantly affect the spectrum amplitude at the object edge locations in an image. By establishing the relationship between the amount of spatially varying defocus blur and spectrum contrast at edge locations, we first estimate the blur amount at these edge locations, then a full defocus map can be obtained by propagating the blur amount at edge locations over the entire image with a nonhomogeneous optimization procedure. The proposed method takes into consideration not only the affect of light refraction but also the blur texture of an image. Experimental results demonstrate that our proposed method is more reliable in defocus map estimation compared to various state-of-the-art methods.

Reasoning and Tuning: Graph Attention Network for Occluded Person Re-identification.

Occluded person re-identification (re-id) aims to match occluded person images to holistic ones. Most existing works focus on matching collective-visible body parts by discarding the occluded parts. However, only preserving the collective-visible body parts causes great semantic loss for occluded images, decreasing the confidence of feature matching. On the other hand, we observe that the holistic images can provide the missing semantic information for occluded images of the same identity. Thus, compensating the occluded image with its holistic counterpart has the potential for alleviating the above limitation. In this paper, we propose a novel Reasoning and Tuning Graph Attention Network (RTGAT), which learns complete person representations of occluded images by jointly reasoning the visibility of body parts and compensating the occluded parts for the semantic loss. Specifically, we self-mine the semantic correlation between part features and the global feature to reason the visibility scores of body parts. Then we introduce the visibility scores as the graph attention, which guides Graph Convolutional Network (GCN) to fuzzily suppress the noise of occluded part features and propagate the missing semantic information from the holistic image to the occluded image. We finally learn complete person representations of occluded images for effective feature matching. Experimental results on occluded benchmarks demonstrate the superiority of our method.

Video Saliency Detection via Sparsity-Based Reconstruction and Propagation.

Video saliency detection aims to continuously discover the motion-related salient objects from the video sequences. Since it needs to consider the spatial and temporal constraints jointly, video saliency detection is more challenging than image saliency detection. In this paper, we propose a new method to detect the salient objects in video based on sparse reconstruction and propagation. With the assistance of novel static and motion priors, a single-frame saliency model is first designed to represent the spatial saliency in each individual frame via the sparsity-based reconstruction. Then, through a progressive sparsity-based propagation, the sequential correspondence in the temporal space is captured to produce the inter-frame saliency map. Finally, these two maps are incorporated into a global optimization model to achieve spatio-temporal smoothness and global consistency of the salient object in the whole video. The experiments on three large-scale video saliency datasets demonstrate that the proposed method outperforms the state-of-the-art algorithms both qualitatively and quantitatively.

An Iterative Co-Saliency Framework for RGBD Images.

As a newly emerging and significant topic in computer vision community, co-saliency detection aims at discovering the common salient objects in multiple related images. The existing methods often generate the co-saliency map through a direct forward pipeline which is based on the designed cues or initialization, but lack the refinement-cycle scheme. Moreover, they mainly focus on RGB image and ignore the depth information for RGBD images. In this paper, we propose an iterative RGBD co-saliency framework, which utilizes the existing single saliency maps as the initialization, and generates the final RGBD co-saliency map by using a refinement-cycle model. Three schemes are employed in the proposed RGBD co-saliency framework, which include the addition scheme, deletion scheme, and iteration scheme. The addition scheme is used to highlight the salient regions based on intra-image depth propagation and saliency propagation, while the deletion scheme filters the saliency regions and removes the non-common salient regions based on interimage constraint. The iteration scheme is proposed to obtain more homogeneous and consistent co-saliency map. Furthermore, a novel descriptor, named depth shape prior, is proposed in the addition scheme to introduce the depth information to enhance identification of co-salient objects. The proposed method can effectively exploit any existing 2-D saliency model to work well in RGBD co-saliency scenarios. The experiments on two RGBD co-saliency datasets demonstrate the effectiveness of our proposed framework.

Semi-Supervised Salient Object Detection Using a Linear Feedback Control System Model.

To overcome the challenging problems in saliency detection, we propose a novel semi-supervised classifier which makes good use of a linear feedback control system (LFCS) model by establishing a relationship between control states and salient object detection. First, we develop a boundary homogeneity model to estimate the initial saliency and background likelihoods, which are regarded as the labeled samples in our semi-supervised learning procedure. Then in order to allocate an optimized saliency value to each superpixel, we present an iterative semi-supervised learning framework which integrates multiple saliency cues and image features using an LFCS model. Via an innovative iteration method, the system gradually converges an optimized stable state, which is associating with an accurate saliency map. This paper also covers comprehensive simulation study based on public datasets, which demonstrates the superiority of the proposed approach.

Combining Local and Global Measures for DIBR-Synthesized Image Quality Evaluation.

Depth-Image-Based-Rendering (DIBR) techniques are significant for three-dimensional (3D) video applications, e.g., 3D television and free viewpoint video (FVV). Unfortunately, the DIBR-synthesized image suffers from various distortions, which induce an annoying viewing experience for the entire FVV. Proposing a quality evaluator for DIBR-synthesized images is fundamental for the design of perceptual friendly FVV systems. Since the associated reference image is usually not accessible, full-reference (FR) methods cannot be directly applied for quality evaluation of the synthesized image. In addition, most traditional no-reference (NR) methods fail to effectively measure the specifically DIBR-related distortions. In this paper, we propose a novel NR quality evaluation method accounting for two categories of DIBR-related distortions, i.e., geometric distortions and sharpness. First, the disoccluded regions, as one of the most obvious geometric distortions, are captured by analyzing local similarity. Then, another typical geometric distortion (i.e., stretching) is detected and measured by calculating the similarity between it and its equal-size adjacent region. Second, considering the property of scale invariance, the global sharpness is measured as the distance between the distorted image and its downsampled version. Finally, the perceptual quality is estimated by linearly pooling the scores of two geometric distortions and sharpness together. Experimental results verify the superiority of the proposed method over the prevailing FR and NR metrics. More specifically, it is superior to all competing methods except APT in terms of effectiveness, but greatly outmatches APT in terms of implementation time.

Depth Super-resolution from RGB-D Pairs with Transform and Spatial Domain Regularization.

This paper proposes a depth super-resolution method with both transform and spatial domain regularization. In the transform domain regularization, nonlocal correlations are exploited via an auto-regressive model, where each patch is further sparsified with a locally-trained transform to consider intra-patch correlations. In the spatial domain regularization, we propose a multi-directional total variation (MTV) prior to characterize the geometrical structures spatially orientated at arbitrary directions in depth maps. To achieve adaptive regularization, the MTV is weighted for each directional finite difference considering local characteristics of RGB-D data. We develop an accelerated proximal gradient algorithm to solve the proposed model. Quantitative and qualitative evaluations compared with state-of-the-art methods demonstrate that the proposed method achieves superior depth super-resolution performance for various configurations of magnification factors and datasets.

Reconstruction of Structurally-Incomplete Matrices With Reweighted Low-Rank and Sparsity Priors.

Most matrix reconstruction methods assume that missing entries randomly distribute in the incomplete matrix, and the low-rank prior or its variants are used to well pose the problem. However, in practical applications, missing entries are structurally rather than randomly distributed, and cannot be handled by the rank minimization prior individually. To remedy this, this paper introduces new matrix reconstruction models using double priors on the latent matrix, named Reweighted Low-rank and Sparsity Priors (ReLaSP). In the proposed ReLaSP models, the matrix is regularized by a low-rank prior to exploit the inter-column and inter-row correlations, and its columns (rows) are regularized by a sparsity prior under a dictionary to exploit intra-column (-row) correlations. Both the low-rank and sparse priors are reweighted on the fly to promote low-rankness and sparsity, respectively. Numerical algorithms to solve our ReLaSP models are derived via the alternating direction method under the augmented Lagrangian multiplier framework. Results on synthetic data, image restoration tasks, and seismic data interpolation show that the proposed ReLaSP models are quite effective in recovering matrices degraded by highly structural missing and various types of noise, complementing the classic matrix reconstruction models that handle random missing only.

Depth Map Super-Resolution Considering View Synthesis Quality.

Accurate and high-quality depth maps are required in lots of 3D applications, such as multi-view rendering, 3D reconstruction and 3DTV. However, the resolution of captured depth image is much lower than that of its corresponding color image, which affects its application performance. In this paper, we propose a novel depth map super-resolution (SR) method by taking view synthesis quality into account. The proposed approach mainly includes two technical contributions. First, since the captured low-resolution (LR) depth map may be corrupted by noise and occlusion, we propose a credibility based multi-view depth maps fusion strategy, which considers the view synthesis quality and interview correlation, to refine the LR depth map. Second, we propose a view synthesis quality based trilateral depth-map up-sampling method, which considers depth smoothness, texture similarity and view synthesis quality in the up-sampling filter. Experimental results demonstrate that the proposed method outperforms state-of-the-art depth SR methods for both super-resolved depth maps and synthesized views. Furthermore, the proposed method is robust to noise and achieves promising results under noise-corruption conditions.

Fabricating and Tailoring Polyaniline (PANI) Nanofibers with High Aspect Ratio in a Low-Acid Environment in a Magnetic Field.

In a 0.010 m HCl solution, we successfully transformed irregular polyaniline (PANI) agglomerates into uniform PANI nanofibers with a diameter of 46-145 nm and a characteristic length on the order of several microns by the addition of superparamagnetic Fe3 O4 microspheres in a magnetic field. The PANI morphological evolution showed that the PANI nanofibers stemmed from the PANI coating shell synthesized on the surface of the Fe3 O4 microsphere chains. It was found that the magnetic field could optimize the PANI nanofibers with a narrow diameter size distribution, and effectively suppressed secondary growth. When compared with other microspheres (like silica and polystyrene), only the use of superparamagnetic Fe3 O4 microspheres resulted in the appearance of PANI nanofibers. Attempts to form these high-quality PANI nanofibers in other concentrations of HCl solution were unsuccessful. This deficiency was largely attributed to the inappropriate quantity of aniline cations.

Amino-Fe3O4 Microspheres Directed Synthesis of a Series of Polyaniline Hierarchical Nanostructures with Different Wettability.

We demonstrated polyaniline (PANI) dimensional transformation by adding trace amino-Fe3O4 microspheres to aniline polymerization. Different PANI nanostructures (i.e., flowers, tentacles, and nanofibers) could be produced by controlling the nucleation position and number on the surface of Fe3O4 microspheres, where hydrogen bonding were spontaneously formed between amino groups of Fe3O4 microspheres and aniline molecules. By additionally introducing an external magnetic field, PANI towers were obtained. These PANI nanostructures displayed distinctly different surface wettability in the range from hydrophobicity to hydrophilicity, which was ascribed to the synergistic effect of their dimension, hierarchy, and size. Therefore, the dimension and property of PANI nanostructures can be largely rationalized and predicted by adjusting the PANI nucleation and growth. Using PANI as a model system, the strategies presented here provide insight into the general scheme of dimension and structure control for other conducting polymers.

Graph-based segmentation for RGB-D data using 3-D geometry enhanced superpixels.

With the advances of depth sensing technologies, color image plus depth information (referred to as RGB-D data hereafter) is more and more popular for comprehensive description of 3-D scenes. This paper proposes a two-stage segmentation method for RGB-D data: 1) oversegmentation by 3-D geometry enhanced superpixels and 2) graph-based merging with label cost from superpixels. In the oversegmentation stage, 3-D geometrical information is reconstructed from the depth map. Then, a K-means-like clustering method is applied to the RGB-D data for oversegmentation using an 8-D distance metric constructed from both color and 3-D geometrical information. In the merging stage, treating each superpixel as a node, a graph-based model is set up to relabel the superpixels into semantically-coherent segments. In the graph-based model, RGB-D proximity, texture similarity, and boundary continuity are incorporated into the smoothness term to exploit the correlations of neighboring superpixels. To obtain a compact labeling, the label term is designed to penalize labels linking to similar superpixels that likely belong to the same object. Both the proposed 3-D geometry enhanced superpixel clustering method and the graph-based merging method from superpixels are evaluated by qualitative and quantitative results. By the fusion of color and depth information, the proposed method achieves superior segmentation performance over several state-of-the-art algorithms.

Estimation of signal-dependent noise level function in transform domain via a sparse recovery model.

This paper proposes a novel algorithm to estimate the noise level function (NLF) of signal-dependent noise (SDN) from a single image based on the sparse representation of NLFs. Noise level samples are estimated from the high-frequency discrete cosine transform (DCT) coefficients of nonlocal-grouped low-variation image patches. Then, an NLF recovery model based on the sparse representation of NLFs under a trained basis is constructed to recover NLF from the incomplete noise level samples. Confidence levels of the NLF samples are incorporated into the proposed model to promote reliable samples and weaken unreliable ones. We investigate the behavior of the estimation performance with respect to the block size, sampling rate, and confidence weighting. Simulation results on synthetic noisy images show that our method outperforms existing state-of-the-art schemes. The proposed method is evaluated on real noisy images captured by three types of commodity imaging devices, and shows consistently excellent SDN estimation performance. The estimated NLFs are incorporated into two well-known denoising schemes, nonlocal means and BM3D, and show significant improvements in denoising SDN-polluted images.

Color-guided depth recovery from RGB-D data using an adaptive autoregressive model.

This paper proposes an adaptive color-guided autoregressive (AR) model for high quality depth recovery from low quality measurements captured by depth cameras. We observe and verify that the AR model tightly fits depth maps of generic scenes. The depth recovery task is formulated into a minimization of AR prediction errors subject to measurement consistency. The AR predictor for each pixel is constructed according to both the local correlation in the initial depth map and the nonlocal similarity in the accompanied high quality color image. We analyze the stability of our method from a linear system point of view, and design a parameter adaptation scheme to achieve stable and accurate depth recovery. Quantitative and qualitative evaluation compared with ten state-of-the-art schemes show the effectiveness and superiority of our method. Being able to handle various types of depth degradations, the proposed method is versatile for mainstream depth sensors, time-of-flight camera, and Kinect, as demonstrated by experiments on real systems.