Abstract Due to the federal Kalman filter is used to directly fuse the measurement information into the main filter without processing, resulting in the problem of reduced filtering accuracy. An adaptive weighted federated Kalman filtering based on Mahalanobis distance was proposed in this paper. By calculating the Mahalanobis distance between the predicted value and the measurements of the system, the random fluctuation of the measurements is detected. The statistical characteristics of the system measurement noise are adjusted at any time according to random fluctuations in the measurements. And then by using a adaptive amplification factor to dynamically adjust the measurement noise in the subsystems, and reduce the impact of measurement information contamination in subfilters on the main filter. The adaptive federated information distribution coefficient is used to realize the global information fusion of the federal Kalman filter method, to reduce the influence of inaccurate estimation of subfilters on the main filter.Simulation results and comparison analysis prove that the filtering performance of the proposed is better than the traditional federated Kalman filter (FKF) and adaptive FKF, which can improve the accuracy of the integrated navigation system.

Remote sensing image retrieval (RSIR) has broad application prospects, but related challenges still exist. One of the most important challenges is how to obtain discriminative features. In recent years, although the powerful feature learning ability of convolutional neural networks (CNNs) has significantly improved RSIR, their performance can be restricted by the complexity of remote sensing (RS) images, such as small objects, varying scales, and wide scope. To address these problems, we propose a novel wide-context attention network (W-CAN). It leverages two attention modules to adaptively learn local features correlated in the spatial and channel dimensions, respectively, which can obtain discriminative features with extensive context information. During training, a hybrid loss is introduced to enhance the intraclass compactness and interclass separability of the features. Moreover, we add a branch to learn binary descriptors and realize the end-to-end descriptor aggregation. Experiments on four RS benchmark data sets demonstrate that the proposed method can outperform some state-of-the-art RSIR methods.

Currently, reliable and accurate ship detection in optical remote sensing images is still challenging. Even the state-of-the-art convolutional neural network (CNN)-based methods cannot obtain very satisfactory results. To more accurately locate the ships in diverse orientations, some recent methods conduct the detection via the rotated bounding box. However, it further increases the difficulty of detection because an additional variable of ship orientation must be accurately predicted in the algorithm. In this article, a novel CNN-based ship-detection method is proposed by overcoming some common deficiencies of current CNN-based methods in ship detection. Specifically, to generate rotated region proposals, current methods have to predefine multioriented anchors and predict all unknown variables together in one regression process, limiting the quality of overall prediction. By contrast, we are able to predict the orientation and other variables independently, and yet more effectively, with a novel dual-branch regression network, based on the observation that the ship targets are nearly rotation-invariant in remote sensing images. Next, a shape-adaptive pooling method is proposed to overcome the limitation of a typical regular region of interest (ROI) pooling in extracting the features of the ships with various aspect ratios. Furthermore, we propose to incorporate multilevel features via the spatially variant adaptive pooling. This novel approach, called multilevel adaptive pooling, leads to a compact feature representation more qualified for the simultaneous ship classification and localization. Finally, a detailed ablation study performed on the proposed approaches is provided, along with some useful insights. Experimental results demonstrate the great superiority of the proposed method in ship detection.

Tracking and mapping functions in a monocular SLAM system remain active due to their challenging nature. In this paper, we propose a novel approach to perform the accurate and robust ego-motion estimation and provide the detail-preserving reconstruction in indoor environments. More specifically, we design a new algorithm called synchronous event measurement (SEM) to create event-based difference images (EDIs) so as to highlight frame-to-frame (F2F) difference. The observation indicates that F2F difference is highly correlated with the camera's motion change. We hereby feed EDIs into a deep convolutional neural network, in order to infer ego-motion of the camera. Subsequently, based on a monocular reconstruction framework (REMODE), we devise an algorithm named event region search or briefly ERS, to reduce possibility of mismatch on the depth estimation stage. Evaluations on a variety of datasets demonstrate the satisfactory performance of our proposed method: the ego-motion estimation is more accurate than some geometric based Visual Odometry (VO) and learning based approaches. The results are robust under extreme situations, such as brightness variation and motion blur. Meanwhile, our approach can provide more precise depth map with relatively rich textural information.

Abstract Semantic surface decomposition (SSD) facilitates various geometry processing and product re‐design tasks. Filter‐based techniques are meaningful and widely used to achieve the SSD, which however often leads to surface either under‐fitting or over‐fitting. In this paper, we propose a reliable rolling‐guided point normal filtering method to decompose textures from a captured point cloud surface. Our method is built on the geometry assumption that 3D surfaces are comprised of an underlying shape (US) and a variety of bump ups and downs (BUDs) on the US. We have three core contributions. First, by considering the BUDs as surface textures, we present a RANSAC‐based sub‐neighborhood detection scheme to distinguish the US and the textures. Second, to better preserve the US (especially the prominent structures), we introduce a patch shift scheme to estimate the guidance normal for feeding the rolling‐guided filter. Third, we formulate a new position updating scheme to alleviate the common uneven distribution of points. Both visual and numerical experiments demonstrate that our method is comparable to state‐of‐the‐art methods in terms of the robustness of texture removal and the effectiveness of the underlying shape preservation.

Abstract This paper addresses the nontraditional but practically meaningful reversibility problem of mesh filtering. This reverse‐filtering approach (termed a DeFilter) seeks to recover the geometry of a set of filtered meshes to their artifact‐free status. To solve this scenario, we adapt cascaded normal regression (CNR) to understand the commonly used mesh filters and recover automatically the mesh geometry that was lost through various geometric operations. We formulate mesh defiltering by an extreme learning machine (ELM) on the mesh normals at an offline training stage and perform it automatically at a runtime defiltering stage. Specifically, (1) to measure the local geometry of a filtered mesh, we develop a generalized reverse Filtered Facet Normal Descriptor (grFND) in the consistent neighbors; (2) to map the grFNDs to the normals of the ground‐truth meshes, we learn a regression function from a set of filtered meshes and their ground‐truth counterparts; and (3) at runtime, we reversely filter the normals of a filtered mesh, using the learned regression function for recovering the lost geometry. We evaluate multiple quantitative and qualitative results on synthetic and real data to verify our DeFilter's performance thoroughly. From a practical point of view, our method can recover the lost geometry of denoised meshes without needing to know the exact filter used previously, and can act as a geometry‐recovery plugin for most of the state‐of‐the‐art methods of mesh denoising.

3-D pose estimation for texture-less objects remains a challenging problem. Previous works either focus on a template matching method to find the nearest template as a candidate, or construct a Hough forest, which utilizes the offset of patches to vote for the object location and pose. By contrast, in this paper, we propose a comprehensive framework to directly regress 3-D poses for the candidates, in which a convolutional neural network-based triplet network is trained to extract discriminating features from the binary images. To make the features suitable for the regression task, a pose-guided method and a regression constraint are employed with the constructed triplet network. We show that the constraint reaches the goal of creating the correlation between the features and 3-D poses. Once the expected features are obtained, the object pose could be efficiently regressed, by training a regression network with a simple structure. For symmetric objects, depth images are treated as an additional channel to feed the triplet network. Experiments on the LineMOD and our own datasets demonstrate our method with high regression precision and efficiency.