Contact Analysis Between a Moving Solid and the Boundary of Its Swept Volume

2008 ◽  
Author(s):  
Hu¨seyin Erdim ◽  
Horea T. Ilies¸
Keyword(s):  
Moving Objects ◽  
Tool Path ◽  
Computational Cost ◽  
Moving Object ◽  
Reconstruction Algorithms ◽  
Functional Changes ◽  
Affine Motion ◽  
The Family ◽  
Data Output ◽  
Swept Volume

The modeling of many practical problems in design and manufacturing involving moving objects relies on sweeps and their boundaries, which are mathematically described by the envelopes to the family of shapes generated by the moving object. In many problems, such as the design and analysis of higher pairs or tool path planning, contact changes between the moving object and the boundary of its swept volume become critical because they often translate into functional changes of the system under consideration. However, the difficulty of this task quickly escalates beyond the reach of existing approaches as the complexity of the shape and motion increases. We recently proposed a sweep boundary evaluator for general sweeps in conjunction with efficient point sampling and surface reconstruction algorithms that relies on our novel point membership classification (PMC) test for general solid sweeps. In this paper we describe a new approach that automates the prediction of changes in the state of contact between a shape of arbitrary complexity moving according to an affine motion, and the boundary of its swept set. We show that we can predict when and where such contact changes occur with only minimal additional computational cost by exploiting the data output by our sweep boundary evaluator. We discuss the problem and the associated computational issues in a 2D framework, and we conclude by discussing the extension of our approach to 3D moving objects.

Comparative Study of Statistical background Modeling and Subtraction

2017 ◽  
Vol 8 (2) ◽  
pp. 287
Author(s):  
Mourad Moussa ◽  
Maha Hmila ◽  
Ali Douik
Keyword(s):  
Object Detection ◽  
Background Subtraction ◽  
Moving Objects ◽  
Computational Cost ◽  
Gaussian Mixture ◽  
Traffic Monitoring ◽  
Human Motion ◽  
Moving Object Detection ◽  
Moving Object ◽  
Dynamic Scenes

Background subtraction methods are widely exploited for moving object detection in videos in many computer vision applications, such as traffic monitoring, human motion capture and video surveillance. The two most distinguishing and challenging aspects of such approaches in this application field are how to build correctly and efficiently the background model and how to prevent the false detection between; (1) moving background pixels and moving objects, (2) shadows pixel and moving objects. In this paper we present a new method for image segmentation using background subtraction. We propose an effective scheme for modelling and updating a background adaptively in dynamic scenes focus on statistical learning. We also introduce a method to detect sudden illumination changes and segment moving objects during these changes. Unlike the traditional color levels provided by RGB sensor aren’t the best choice, for this reason we propose a recursive algorithm that contributes to select very significant color space. Experimental results show significant improvements in moving object detection in dynamic scenes such as waving tree leaves and sudden illumination change, and it has a much lower computational cost compared to Gaussian mixture model.

scholarly journals MOVING OBJECT DETECTION USING SPATIAL CORRELATION IN LAB COLOUR SPACE

2019 ◽  
Vol XLII-2/W12 ◽  
pp. 173-177
Author(s):  
A. Roshan ◽  
Y. Zhang
Keyword(s):  
Object Detection ◽  
Spatial Correlation ◽  
Background Subtraction ◽  
Moving Objects ◽  
Computational Cost ◽  
Moving Object Detection ◽  
Moving Object ◽  
Subtraction Technique ◽  
Colour Space ◽  
Image Thresholding

<p><strong>Abstract.</strong> Background subtraction-based techniques of moving object detection are very common in computer vision programs. Each technique of background subtraction employs image thresholding algorithms. Different thresholding methods generate varying threshold values that provide dissimilar moving object detection results. A majority of background subtraction techniques use grey images which reduce the computational cost but statistics-based image thresholding methods do not consider the spatial distribution of pixels. In this study, authors have developed a background subtraction technique using Lab colour space and used spatial correlations for image thresholding. Four thresholding methods using spatial correlation are developed by computing the difference between opposite colour pairs of background and foreground frames. Out of 9 indoor and outdoor scenes, the object is detected successfully in 7 scenes whereas existing background subtraction technique using grey images with commonly used thresholding methods detected moving objects in 1–5 scenes. Shape and boundaries of detected objects are also better defined using the developed technique.</p>

RETROSPECTIVE ANALYSIS OF THE MEDICAL DOCUMENTATION OF PATIENTS WHO APPLIED TO THE AMBULATORY OF GENERAL PRACTICE - FAMILY MEDICINE

2019 ◽  
Vol 72 (5) ◽  
pp. 938-941
Author(s):  
Оlexander Ye. Kononov ◽  
Liliana V. Klymenko ◽  
Ganna V. Batsiura ◽  
Larysa F. Matiukha ◽  
Olha V. Protsiuk ◽  
...  
Keyword(s):  
Family Medicine ◽  
Middle Ages ◽  
Risk Groups ◽  
Medical Documentation ◽  
Aged Patients ◽  
Family Doctors ◽  
Functional Changes ◽  
Medicine Clinic ◽  
Leading Role ◽  
The Family

Introduction: In today’s realities of health care reform in Ukraine family doctors play a leading role. The aim of our work was to analyze the medical cards of patients who applied for medical care to the family medicine clinic. Materials and methods: It was analyzed outpatient medical cards of 87 patients who applied to the family medicine clinic in the Khotov village, Kyiv region. The study included people aged 18 to 60 years, which corresponded to the groups of young and middle ages according to the WHO classification. Review: Our findings indicate the prevalence of functional changes among young people: somatoform dysfunction of the autonomic nervous system - 9 (37,5%) and the development of organic manifestations at middle-aged patients: arterial hypertension - 32 (62,7%) and coronary artery disease - 17 (33,3%). Conclusions: This study is important for determining the risk groups, early diagnosis and prevention of diseases.

scholarly journals U2-ONet: A Two-Level Nested Octave U-Structure Network with a Multi-Scale Attention Mechanism for Moving Object Segmentation

2020 ◽  
Vol 13 (1) ◽  
pp. 60
Author(s):  
Chenjie Wang ◽  
Chengyuan Li ◽  
Jun Liu ◽  
Bin Luo ◽  
Xin Su ◽  
...  
Keyword(s):  
Moving Objects ◽  
Object Segmentation ◽  
Contextual Information ◽  
Attention Mechanism ◽  
Moving Object ◽  
Feature Maps ◽  
Moving Object Segmentation ◽  
Practical Applications ◽  
Multi Scale ◽  
Spatial Redundancy

Most scenes in practical applications are dynamic scenes containing moving objects, so accurately segmenting moving objects is crucial for many computer vision applications. In order to efficiently segment all the moving objects in the scene, regardless of whether the object has a predefined semantic label, we propose a two-level nested octave U-structure network with a multi-scale attention mechanism, called U2-ONet. U2-ONet takes two RGB frames, the optical flow between these frames, and the instance segmentation of the frames as inputs. Each stage of U2-ONet is filled with the newly designed octave residual U-block (ORSU block) to enhance the ability to obtain more contextual information at different scales while reducing the spatial redundancy of the feature maps. In order to efficiently train the multi-scale deep network, we introduce a hierarchical training supervision strategy that calculates the loss at each level while adding knowledge-matching loss to keep the optimization consistent. The experimental results show that the proposed U2-ONet method can achieve a state-of-the-art performance in several general moving object segmentation datasets.

scholarly journals Combining Acceleration Techniques for Low-Dose X-Ray Cone Beam Computed Tomography Image Reconstruction

2017 ◽  
Vol 2017 ◽  
pp. 1-10
Author(s):  
Hsuan-Ming Huang ◽  
Ing-Tsung Hsiao
Keyword(s):  
Computed Tomography ◽  
Image Reconstruction ◽  
Low Dose ◽  
Computational Cost ◽  
Reconstruction Algorithm ◽  
Reconstruction Algorithms ◽  
Acceleration Techniques ◽  
Reconstruction Methods ◽  
Speed Up ◽  
Phantom Studies

Background and Objective. Over the past decade, image quality in low-dose computed tomography has been greatly improved by various compressive sensing- (CS-) based reconstruction methods. However, these methods have some disadvantages including high computational cost and slow convergence rate. Many different speed-up techniques for CS-based reconstruction algorithms have been developed. The purpose of this paper is to propose a fast reconstruction framework that combines a CS-based reconstruction algorithm with several speed-up techniques.Methods. First, total difference minimization (TDM) was implemented using the soft-threshold filtering (STF). Second, we combined TDM-STF with the ordered subsets transmission (OSTR) algorithm for accelerating the convergence. To further speed up the convergence of the proposed method, we applied the power factor and the fast iterative shrinkage thresholding algorithm to OSTR and TDM-STF, respectively.Results. Results obtained from simulation and phantom studies showed that many speed-up techniques could be combined to greatly improve the convergence speed of a CS-based reconstruction algorithm. More importantly, the increased computation time (≤10%) was minor as compared to the acceleration provided by the proposed method.Conclusions. In this paper, we have presented a CS-based reconstruction framework that combines several acceleration techniques. Both simulation and phantom studies provide evidence that the proposed method has the potential to satisfy the requirement of fast image reconstruction in practical CT.

Generating Swept Volumes With Instantaneous Screw Axes

1994 ◽  
Author(s):  
Zeng-Jia Hu ◽  
Zhi-Kui Ling
Keyword(s):  
Moving Object ◽  
Ruled Surfaces ◽  
Screw Axis ◽  
Spherical Surfaces ◽  
Swept Volumes ◽  
Instantaneous Screw Axis ◽  
Swept Volume ◽  
Instantaneous Screw ◽  
Plane Polygon ◽  
Boundary Surfaces

Abstract The instantaneous screw axis is used in the generation of the swept volume of a moving object. The envelope theory is used to determine the boundary surfaces of the swept volume. Specifically, the envelope surfaces generated by a plane polygon, cylindrical and spherical surfaces are presented. Furthermore, the ruled surfaces generated by edges of the moving object are discussed.

Influence of Ball and Background Patterns on Perception of Visual Direction of a Moving Object

1979 ◽  
Vol 49 (2) ◽  
pp. 343-346 ◽  
Author(s):  
Marcella V. Ridenour
Keyword(s):  
Analysis Of Variance ◽  
Moving Objects ◽  
Dimensional Space ◽  
Three Dimensional ◽  
Moving Object ◽  
Visual Direction ◽  
Visual Patterns ◽  
Prediction Time ◽  
The Subject ◽  
Three Dimensional Space

30 boys and 30 girls, 6 yr. old, participated in a study assessing the influence of the visual patterns of moving objects and their respective backgrounds on the prediction of objects' directionality. An apparatus was designed to permit modified spherical objects with interchangeable covers and backgrounds to move in three-dimensional space in three directions at selected speeds. The subject's task was to predict one of three possible directions of an object: the object either moved toward the subject's midline or toward a point 18 in. to the left or right of the midline. The movements of all objects started at the same place which was 19.5 ft. in front of the subject. Prediction time was recorded on 15 trials. Analysis of variance indicated that visual patterns of the moving object did not influence the prediction of the object's directionality. Visual patterns of the background behind the moving object did not influence the prediction of the object's directionality except during the conditions of a light nonpatterned moving object. It was concluded that visual patterns of the background and that of the moving object have a very limited influence on the prediction of direction.

scholarly journals Optimized Multi-layer Machine Learning Technique for Movement Detection and Tracking in Video Data Stream

2019 ◽  
Vol 8 (10) ◽  
pp. 1151-1159
Keyword(s):  
Object Detection ◽  
Moving Objects ◽  
Error Rates ◽  
Moving Object Detection ◽  
Video Data ◽  
Moving Object ◽  
Detection Accuracy ◽  
Efficient System ◽  
Detection And Tracking ◽  
The Given

With the advent in technology, security and authentication has become the main aspect in computer vision approach. Moving object detection is an efficient system with the goal of preserving the perceptible and principal source in a group. Surveillance is one of the most crucial requirements and carried out to monitor various kinds of activities. The detection and tracking of moving objects are the fundamental concept that comes under the surveillance systems. Moving object recognition is challenging approach in the field of digital image processing. Moving object detection relies on few of the applications which are Human Machine Interaction (HMI), Safety and video Surveillance, Augmented Realism, Transportation Monitoring on Roads, Medical Imaging etc. The main goal of this research is the detection and tracking moving object. In proposed approach, based on the pre-processing method in which there is extraction of the frames with reduction of dimension. It applies the morphological methods to clean the foreground image in the moving objects and texture based feature extract using component analysis method. After that, design a novel method which is optimized multilayer perceptron neural network. It used the optimized layers based on the Pbest and Gbest particle position in the objects. It finds the fitness values which is binary values (x_update, y_update) of swarm or object positions. Method and output achieved final frame creation of the moving objects in the video using BLOB ANALYSER In this research , an application is designed using MATLAB VERSION 2016a In activation function to re-filter the given input and final output calculated with the help of pre-defined sigmoid. In proposed methods to find the clear detection and tracking in the given dataset MOT, FOOTBALL, INDOOR and OUTDOOR datasets. To improve the detection accuracy rate, recall rate and reduce the error rates, False Positive and Negative rate and compare with the various classifiers such as KNN, MLPNN and J48 decision Tree.

scholarly journals Moving Object Detection for Dynamic Background Scenes Based on Spatiotemporal Model

2017 ◽  
Vol 2017 ◽  
pp. 1-9 ◽  
Author(s):  
Yizhong Yang ◽  
Qiang Zhang ◽  
Pengfei Wang ◽  
Xionglou Hu ◽  
Nengju Wu
Keyword(s):  
Object Detection ◽  
Moving Objects ◽  
Moving Object Detection ◽  
Moving Object ◽  
Background Model ◽  
Dynamic Background ◽  
Spatiotemporal Model ◽  
Temporal Features ◽  
Common Technique ◽  
Public Datasets

Moving object detection in video streams is the first step of many computer vision applications. Background modeling and subtraction for moving detection is the most common technique for detecting, while how to detect moving objects correctly is still a challenge. Some methods initialize the background model at each pixel in the first N frames. However, it cannot perform well in dynamic background scenes since the background model only contains temporal features. Herein, a novel pixelwise and nonparametric moving object detection method is proposed, which contains both spatial and temporal features. The proposed method can accurately detect the dynamic background. Additionally, several new mechanisms are also proposed to maintain and update the background model. The experimental results based on image sequences in public datasets show that the proposed method provides the robustness and effectiveness in dynamic background scenes compared with the existing methods.

On the Exact Representation of the Boundary Surfaces of the Swept Volume of a Cylinder Undergoing Rational Bézier and B-Spline Motions

1999 ◽  
Author(s):  
J. Xia ◽  
Q. J. Ge
Keyword(s):  
Tool Path ◽  
Traditional Approach ◽  
Object Motion ◽  
Exact Representation ◽  
Tool Path Planning ◽  
New Approach ◽  
B Spline ◽  
Volume Analysis ◽  
Swept Volume ◽  
Boundary Surfaces

Abstract This paper develops methods for the exact analysis and representation of the swept volume of a circular cylinder undergoing rational Bézier and B-spline motions. Instead of following the traditional approach of analyzing the point trajectory of an object motion for swept volume analysis, this paper seeks to develop a new approach to swept volume analysis by studying the plane trajectory of a rational motion. It seeks to bring together recent work in swept volume analysis, plane representation of developable surfaces, as well as computer aided synthesis of freeform rational motions. The results have applications in design and approximation of freeforms surfaces as well as tool path planning for 5-axis machining of freeform surfaces.

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