Automatic quantification of cell nuclei in immunostained images is highly desired by pathologists diagnosis. In this paper, we present a new approach for the segmentation severely clustered overlapping nuclei. The proposed first involves applying combined global and local threshold method to extract foreground regions. order segment regions, seed markers are obtained utilizing morphological filtering intensity based region growing. Seeded watershed then applied separated. As pixels...

Multi-object tracking is a crucial problem for autonomous vehicle. Most state-of-the-art approaches adopt the tracking-by-detection strategy, which two-step procedure consisting of detection module and module. In this paper, we improve both steps. We by incorporating temporal information, beneficial detecting small objects. For module, propose novel compressed deep Convolutional Neural Network (CNN) feature based Correlation Filter tracker. By carefully integrating these two modules,...

Abstract For autonomous driving, traversability analysis is one of the most basic and essential tasks. In this paper, we propose a novel LiDAR‐based terrain modeling approach, which could output stable, complete, accurate models results. As an inherent property environment that does not change with different view angles, our approach adopts multiframe information fusion strategy for modeling. Specifically, normal distributions transform mapping adopted to accurately model by fusing from...

For autonomous driving, it is important to navigate in an unknown environment. In this paper, we propose a fully automated 2D simultaneous localization and mapping (SLAM) system based on lidar working large-scale outdoor environments. To improve the accuracy robustness of scan matching module, improved Correlative Scan Matching (CSM) algorithm proposed. efficient place recognition, design AdaBoost loop closure detection which can efficiently reject false closures. SLAM back-end, light-weight...

Federated Learning (FL) mitigates privacy leakage in decentralized machine learning by allowing multiple clients to train collaboratively locally. However, dynamic mobile networks with high mobility, intermittent connectivity, and bandwidth limitation severely hinder model updates the cloud server. Although previous studies have typically addressed user mobility issue through task reassignment or predictive modeling, frequent migrations may result communication overhead. Addressing this...

For autonomous driving, it is important to obtain precise and high-frequency localization information. This paper proposes a novel method in which the Inertial Measurement Unit (IMU), wheel encoder, lidar odometry are utilized together estimate ego-motion of an unmanned ground vehicle. The IMU fused with encoder motion prior, involved three levels odometry: Firstly, we use information rectify intra-frame distortion scan, caused by vehicle’s own movement; secondly, provides better initial...

High-precision real-time 3D object detection based on the LiDAR point cloud is an important task for autonomous driving. Most existing methods utilize grid-based convolutional networks to handle sparse and cluttered clouds. However, performance of limited by coarse grid quantization expensive computational cost. In this paper, we propose a more efficient representation clouds SCNet, single-stage, end-to-end subdivision coding network that learns finer feature representations vertical grids....

This paper introduces the covariance matrix of visually salient image features as a compact and robust descriptor for near duplicate video copy detection. We make two novel contributions. first present fast method computing information theoretic based visual saliency maps using data independent transform to replace conventional dependent computationally demanding transforms. then introduce (SCOV) - various within regions use SCOV experimental results show that our new computation technique...

Range images are commonly used representations for 3D LiDAR point cloud in the field of autonomous driving. The approach generating a range image is generally regarded as standard approach. However, there do exist two different types approaches to image: In one approach, row defined laser ID, and other elevation angle. We named first Projection By Laser ID (PBID), second Elevation Angle (PBEA). Few previous works have paid attention difference these approaches. this work, we quantitatively...

For autonomous driving, drivable region detection is one of the most basic and essential tasks. In this paper, a novel LiDAR-based algorithm which could output complete, accurate stable result proposed. To promote completeness result, Bayesian generalized kernel inference bilateral filtering are utilized to estimate attribute those unobserved cells. ensure traversability, growing operator performed on normal vector map reflects slope terrain, thus closely related traversability vehicle....

Three-dimensional (3D) point cloud maps are widely used in autonomous driving scenarios. These usually generated by accumulating sequential LiDAR scans. When generating a map, moving objects (such as vehicles or pedestrians) will leave long trails on the assembled map. This is undesirable and reduces map quality. In this paper, we propose MapCleaner, an approach that can effectively remove from MapCleaner first estimates dense continuous terrain surface, based which then divided into noisy...

In this paper, we propose a conformal momentum-preserving method to solve damped nonlinear Schrödinger (DNLS) equation. Based on its multi-symplectic formulation, the DNLS system can be split into Hamiltonian part and dissipative part. For part, average vector field (AVF) implicit midpoint are employed in spatial temporal discretizations, respectively. it exactly. The proposed conserves momentum conservation law any local time–space region. With periodic boundary conditions, also preserves...

Abstract Robust localization is an essential capability for autonomous land vehicles. While a lot of work focused on structured environments, this article focuses navigation in off‐road environments. In the environment, due to lack salient features, scan matching algorithms tend degenerate. Therefore, first contribution paper propose reliable degeneracy indicator which can evaluate performance. The evaluated then integrated into factor graph optimization framework used both offline mapping...

This paper introduces random forest as a computational and data structure paradigm for fusing low-level visual features high-level semantic concepts image retrieval. We use to split the tree nodes labels supervise splitting make images located at same node share similar well similarities. exploit such define neighbor set (SNS) of given union all in leaf that this falls onto. From SNS we further similarity measure (SSM) between two number trees which they within SNS. With SSM, example-based...

Accurately localizing the vehicle against a pre-built high precision map is an essential step for Autonomous Land Vehicle (ALV). This paper proposes efficient scan-to-map matching approach based on multi-channel lidar. We firstly advocate usage of both lidar reflectance and height map, as these two maps contain complementary information. Then, borrowing ideas from image optical flow literature, we formulate problem computation problem, propose gradient descent to solve it. Finally, proposed...

High-precision 3D maps play an important role in autonomous driving. The current mapping system performs well most circumstances. However, it still encounters difficulties the case of Global Navigation Satellite System (GNSS) signal blockage, when surrounded by too many moving objects, or a featureless environment. In these challenging scenarios, either global navigation approach local will degenerate. With aim developing degeneracy-aware robust system, this paper analyzes possible...