Scale Estimation Algorithm Research Articles

An Object SLAM Framework for Association, Mapping, and High-Level Tasks

Object SLAM is considered increasingly significant for robot high-level perception and decision-making. Existing studies fall short in terms of data association, object representation, and semantic mapping and frequently rely on additional assumptions, limiting their performance. In this paper, we present a comprehensive object SLAM framework that focuses on object-based perception and object-oriented robot tasks. First, we propose an ensemble data association approach for associating objects in complicated conditions by incorporating parametric and nonparametric statistic testing. In addition, we suggest an outlier-robust centroid and scale estimation algorithm for modeling objects based on the iForest and line alignment. Then a lightweight and object-oriented map is represented by estimated general object models. Taking into consideration the semantic invariance of objects, we convert the object map to a topological map to provide semantic descriptors to enable multi-map matching. Finally, we suggest an object-driven active exploration strategy to achieve autonomous mapping in the grasping scenario. A range of public datasets and real-world results in mapping, augmented reality, scene matching, relocalization, and robotic manipulation have been used to evaluate the proposed object SLAM framework for its efficient performance.

Joint estimation of SOC and SOH of lithium battery based on multi-time scale EKF-ASRCKF algorithm

To address the low accuracy of battery charged of state (SOC) estimation due to fixed model parameters and capacity, a joint multi-time scale estimation algorithm (EKF-ASRCKF) is proposed in this paper, which consists of the adaptive square root cubature Kalman filter (ASRCKF) algorithm and the extended Kalman filter (EKF) algorithm. Firstly, the model parameters and capacity on the macroscopic time scale are identified by the EKF algorithm; meanwhile, the battery capacity estimation results characterize the battery health of state (SOH). Then the ASRCKF algorithm estimates the SOC on the microscopic time scale. Finally, the experimental validation is carried out under the BBDST condition. The validation result indicates that the EKF-ASRCKF algorithm provides a good estimation of the model parameters and capacity, which further improves the estimation precision of the battery SOC.

Design of Intelligent Early Warning Robot System for Warehouse Autonomous Patrol based on Scale Estimation Algorithm of Probability Theory

In view of the backward monitoring status in the traditional safety management of hazardous chemicals warehouse, a patrol robot for hazardous chemicals warehouse with autonomous patrol stereo detection, intelligent early warning and remote monitoring functions is designed, and the design process of related functions of autonomous patrol navigation subsystem, environmental detection subsystem and remote monitoring subsystem is discussed from two aspects of hardware and software. Because both the camera and the target may be in motion, it is necessary to consider the scale change of target influence caused by relative distance. In order to overcome the shortcoming of invariable scale in Mean Shift algorithm, this paper adopts a scale adaptive method to estimate the size of the target, adjusts the parameters of kernel function by using ellipse feature, and takes the parameters of the current frame as the initial data of the next frame, so as to achieve the effect of adaptively adjusting the target scale. The test results show that the system can effectively traverse the map of hazardous chemicals warehouse, accurately assess the leakage of hazardous chemicals and the environmental safety of warehouse, and give early warning. The remote monitoring platform greatly facilitates the remote management of the warehouse.

A high-speed feature matching method of high-resolution aerial images

This paper presents a novel corner detection and scale estimation algorithm for image feature description and matching. Inspired by Adaboost’s weak classifier, a series of sub-detectors is elaborately designed to obtain reliable corner pixels. The new corner detection algorithm is more robust than the FAST and HARRIS algorithm, and it is especially suitable for the implementation in FPGA. The new scale estimation method can be directly implemented in the original image without building Gaussian pyramid and searching max response value in each level, which not only increase computational efficiency but also greatly reduces memory requirement. Based on the proposed algorithm, a CPU-FPGA cooperative parallel processing architecture is presented. The architecture overcomes the memory space limitation of FPGA and achieves high-speed feature matching for massive high-resolution aerial images. The speed of the CPU-FPGA cooperative process is hundred times faster than SIFT algorithm running on CPU, and dozens of times faster than SIFT running in CPU + GPU system.

High-Speed Railway Intruding Object Image Generating with Generative Adversarial Networks.

Foreign object intrusion is a great threat to high-speed railway safety operations. Accurate foreign object intrusion detection is particularly important. As a result of the lack of intruding foreign object samples during the operational period, artificially generated ones will greatly benefit the development of the detection methods. In this paper, we propose a novel method to generate railway intruding object images based on an improved conditional deep convolutional generative adversarial network (C-DCGAN). It consists of a generator and multi-scale discriminators. Loss function is also improved so as to generate samples with a high quality and authenticity. The generator is extracted in order to generate foreign object images from input semantic labels. We synthesize the generated objects to the railway scene. To make the generated objects more similar to real objects, on scale in different positions of a railway scene, a scale estimation algorithm based on the gauge constant is proposed. The experimental results on the railway intruding object dataset show that the proposed C-DCGAN model outperforms several state-of-the-art methods and achieves a higher quality (the pixel-wise accuracy, mean intersection-over-union (mIoU), and mean average precision (mAP) are 80.46%, 0.65, and 0.69, respectively) and diversity (the Fréchet-Inception Distance (FID) score is 26.87) of generated samples. The mIoU of the real-generated pedestrian pairs reaches 0.85, and indicates a higher scale of accuracy for the generated intruding objects in the railway scene.

Quantitative analysis of regional endocardial geometry dynamics from 4D cardiac CT images: endocardial tracking based on the iterative closest point with an integrated scale estimation

Regional cardiac function analysis is important for the diagnosis and treatment planning of ischemic heart disease, but has not been sufficiently developed in the field of computed tomography (CT). Therefore, we propose a 3D endocardial tracking framework for cardiac CT using local point cloud registration based on the iterative closest point with an integrated scale estimation algorithm. We also introduce regional function descriptors that express the curvature and stretching of the endocardium: Surface distortion (E) and Scaling rate (S). For a region-to-region comparison, we propose endocardial segmentation according to coronary perfusion territories defined by the Voronoi partition based on coronary distribution. Our study of 65 endocardial segments in ten subjects showed that global endocardial deformation has a positive relationship with the stroke volume index (r = 0.896 and 0.829 in and , respectively) and ejection fraction (r = 0.804 and 0.835), and a positive relationship with the brain natriuretic peptide level (r = 0.690 and 0.776). A positive relationship between segmental E and S (r = 0.845), a higher value of E in ischemic segments (p = 0.021) that are determined by fractional flow reserve estimated from coronary CT data, and a higher value of S in the left circumflex artery territory (p < 0.05) were also observed. The required radiation dose was 5.0 ± 0.7 mSv and the computation time was 7.2 ± 1.1 min. The result suggests that proposed endocardial deformation analysis using CT can be conducted on site and in time for the acute setting, and may be useful for the diagnosis of cardiac dysfunction or myocardial ischemia.

High-Accuracy Image Rotation and Scale Estimation Using Radon Transform and Sub-Pixel Shift Estimation

Rotation and scale estimation of images are fundamental tasks in image registration. The conventional estimation method uses log-polar transform and 1D shift estimation to estimate rotation and scale regardless of the shift of images. However, this transform requires interpolation of the frequency components, which causes estimation error. We propose a rotation and scale estimation algorithm based on the Radon transform and sub-pixel shift estimation. The Radon transform can estimate the rotation independent of the shift and can reduce the influence of interpolation error. In addition, sub-pixel shift estimation using a linear approximation of the phase component improves the precision of 1D shift estimation and achieves accurate rotation estimation. The proposed method was evaluated on test images, and the results demonstrate that the proposed method has higher accuracy compared with the log-polar transform.

A compressed multiple feature and adaptive scale estimation method for correlation filter-based visual tracking

The core part of the popular tracking-by-detection trackers is the discriminative classifier, which distinguishes the tracked target from the surrounding environment. Correlation filter-based visual tracking methods have the advantage of computing efficiency over the traditional methods by exploiting the properties of circulant matrix in learning process, and the significant progress in efficiency has been achieved by making use of the fast Fourier transform at detection and learning stages. But most existing correlation filter-based approaches are mainly restricted to translation estimation, which are susceptible to drifting in long-term tracking. In this article, a compressed multiple feature and adaptive scale estimation method is presented, which uses multiple features, including histogram of orientation gradients, color-naming, and raw pixel value to further improve the stability and accuracy of translation estimation. And for the scale estimation, another correlation filter is trained, which uses the compressed histogram of orientation gradients and raw pixel value to construct a multiscale pyramid of the target, and the optimal scale is obtained by exhaustively searching. The translation and scale estimation are unified with an iterative searching strategy. Extensively experimental results on the benchmark data set of scale variation show that the performance of the proposed compressed multiple feature and adaptive scale estimation algorithm is competitive against state-of-the-art methods with scale estimation capabilities in terms of robustness and accuracy.

On the improvement of foreground–background model‐based object tracker

In this study, the authors propose two kinds of improvements to a baseline tracker that employs the tracking‐by‐detection framework. First, they explore different feature spaces by employing features commonly used in object detection to improve the performance of detector in feature space. Second, they propose a robust scale estimation algorithm that estimates the size of the object in the current frame. Their experimental results on the challenging online tracking benchmark‐13 dataset show that reduced dimensionality histogram of oriented gradients boosts the performance of the tracker. The proposed scale estimation algorithm provides a significant gain and reduces the failure of the tracker in challenging scenarios. The improved tracker is compared with 13 state‐of‐the‐art trackers. The quantitative and qualitative results show that the performance of the tracker is comparable with the state of the art against initialisation errors, variations in illumination, scale and motion, out‐of‐plane and in‐plane rotations, deformations and low resolution.

Invariant foreground occupation ratio for scale adaptive mean shift tracking

The mean shift algorithm has been introduced successfully into the field of computer vision to be an efficient approach for visual tracking but the tracker has been awkward in handling the scale change of the object. This study addresses the scale estimation problem of the mean shift tracker, and proposes a novel method which is based on invariant foreground occupation ratio to solve this problem. The foreground occupation ratio is defined as the proportion of the foreground pixels in an image region. By taking an analysis of the foreground occupation ratio, the authors obtain its three simple properties. With its property of scale invariance, an iterative approximation approach is employed to estimate the scale of the foreground in the current image. The scale value is modified by a weighting function, and it is adjusted along the two axes with respect to the width and the height of the target. The scale estimation algorithm is then employed in the mean shift tracker to obtain the ability of scale adaptation for tracking. Experimental results show that, using the authors method for object scale estimation, the mean shift tracker performs well in tracking the target efficiently when its scale continuously changes.

Image-based magnification calibration for electron microscope

Magnification calibration is a crucial task for the electron microscope to achieve accurate measurement of the target object. In general, magnification calibration is performed to obtain the correspondence between the scale of the electron microscope image and the actual size of the target object using the standard calibration samples. However, the current magnification calibration method mentioned above may include a maximum of 5 % scale error, since an alternative method has not yet been proposed. Addressing this problem, this paper proposes an image-based magnification calibration method for the electron microscope. The proposed method employs a multi-stage scale estimation approach using phase-based correspondence matching. Consider a sequence of microscope images of the same target object, where the image magnification is gradually increased so that the final image has a very large scale factor $$S$$ (e.g., $$S=1\!,\!\!000$$ ) with respect to the initial image. The problem considered in this paper is to estimate the overall scale factor $$S$$ of the given image sequence. The proposed scale estimation method provides a new methodology for high-accuracy magnification calibration of the electron microscope. This paper also proposes a quantitative performance evaluation method of scale estimation algorithms using Mandelbrot images which are precisely scale-controlled images. Experimental evaluation using Mandelbrot images shows that the proposed scale estimation algorithm can estimate the overall scale factor $$S=1\!,\!\!000$$ with approximately 0.1 % scale error. Also, a set of experiments using image sequences taken by an actual scanning transmission electron microscope (STEM) demonstrates that the proposed method is more effective for magnification calibration of a STEM compared with a conventional method.

3D Target Scale Estimation and Target Feature Separation for Size Preserving Tracking in PTZ Video

To achieve size preserving tracking, in addition to controlling the camera's pan and tilt motions to keep the object of interest in the camera's field of view (FOV), the camera's focal length is adjusted automatically to compensate for the changes in the target's image size caused by the relative motion between the camera and the target. The estimation accuracy of these changes determines the effectiveness of the resulting zoom control. The existing method of choice for real-time target scale estimation applies structure from motion (SFM) based on the weak perspective projection model. In this paper we propose a target scale estimation algorithm with a linear solution based on the more advanced paraperspective projection model, which improves the accuracy of scale estimation by considering center offset. Another key issue in SFM based algorithms is the separation of target and background features, especially when composite camera (pan/tilt/zoom) and target motions are involved. This paper designs a fast target feature separation/grouping algorithm, the 3D affine shape method. The resulting separation automatically adapts to the target's 3D geometry and motion and is able to accommodate a large amount of off-plane rotation, which most existing separation/grouping algorithms find difficult to achieve. Experimental results illustrate the effectiveness of the proposed scale estimation and feature separation algorithms in tracking translating and rotating objects with a PTZ camera while preserving their sizes. In comparison with the leading size preserving tracking algorithm described by Tordoff and Murray, our algorithm is able to reduce the cumulative tracking error significantly from 17.4% to 3.3%.