TRACKING MOVING CONTOURS USING ENERGY-MINIMIZING ELASTIC CONTOUR MODELS

1995 ◽  
Vol 09 (03) ◽  
pp. 465-484 ◽  
Author(s):  
NAONORI UEDA ◽  
KENJI MASE
Keyword(s):  
A Priori ◽  
Optimal Solution ◽  
Target Object ◽  
Contour Tracking ◽  
Minimization Problems ◽  
Programming Framework ◽  
Object Contour ◽  
Contour Models ◽  
Object Shapes ◽  
Elastic Contour

This paper proposes a robust method for tracking an object contour in a sequence of images. In this method, both object extraction and tracking problems can be solved simultaneously. Furthermore, it is applicable to the tracking of arbitrary shapes since it does not need a priori knowledge about the object shapes. In the contour tracking, energy-minimizing elastic contour models are utilized, which is newly presented in this paper. The contour tracking is formulated as an optimization problem to find the position that minimizes both the elastic energy of its model and the potential energy derived from the edge potential image that includes a target object contour. We also present an algorithm which efficiently solves energy minimization problems within a dynamic programming framework. The algorithm enables us to obtain optimal solution even when the variables to be optimized are not ordered. We show the validity and usefulness of the proposed method with some experimental results.

MISSION-ADAPTABLE ROUTE PLANNING IN UNCERTAIN AND ADVERSARIAL ENVIRONMENT

2006 ◽  
Vol 15 (05) ◽  
pp. 803-821 ◽  
Author(s):  
PING YAN ◽  
MINGYUE DING ◽  
CHANGWEN ZHENG
Keyword(s):  
Real Time ◽  
A Priori ◽  
Route Planning ◽  
Optimal Solution ◽  
Dijkstra Algorithm ◽  
Solution Quality ◽  
Model Complex ◽  
Local Methods ◽  
Domain Specific Knowledge ◽  
Planning Algorithm

In this paper, the route-planning problems of Unmanned Aerial Vehicle (UAV) in uncertain and adversarial environment are addressed, including not only single-mission route planning in known a priori environment, but also the route replanning in partially known and mission-changeable environments. A mission-adaptable hybrid route-planning algorithm based on flight roadmap is proposed, which combines existing global and local methods (Dijkstra algorithm, SAS and D*) into a two-level framework. The environment information and constraints for UAV are integrated into the procedure of building flight roadmap and searching for routes. The route-planning algorithm utilizes domain-specific knowledge and operates in real time with near-optimal solution quality, which is important to uncertain and adversarial environment. Other planners do not provide all of the functionality, namely real-time planning and replanning, near-optimal solution quality, and the ability to model complex 3D constraints.

A Novel Hybrid Level Set Model for Non-Rigid Object Contour Tracking

2022 ◽  
Vol 31 ◽  
pp. 15-29
Author(s):  
Qing Cai ◽  
Huiying Liu ◽  
Yiming Qian ◽  
Sanping Zhou ◽  
Jinjun Wang ◽  
...  
Keyword(s):  
Level Set ◽  
Contour Tracking ◽  
Rigid Object ◽  
Object Contour ◽  
Object Contour Tracking ◽  
Level Set Model

scholarly journals Object contour tracking via adaptive data-driven kernel

2020 ◽  
Vol 2020 (1) ◽  
Author(s):  
Xin Sun ◽  
Wei Wang ◽  
Dong Li ◽  
Bin Zou ◽  
Hongxun Yao
Keyword(s):  
Data Driven ◽  
Contour Tracking ◽  
Object Contour ◽  
Object Contour Tracking

scholarly journals Video object extraction using optimized smoothed dirichlet process multi-view learning with improved adaptive modified Markova random field

2018 ◽  
Vol 7 (4) ◽  
pp. 2598
Author(s):  
G S. Gowri ◽  
Dr. P. Ponmuthuramalingam
Keyword(s):  
Random Field ◽  
Dirichlet Process ◽  
Video Segmentation ◽  
Target Object ◽  
Object Extraction ◽  
Contour Tracking ◽  
Video Object ◽  
Object Boundary ◽  
Current Frame ◽  
Film Making

Video object extraction (VOE) using segmentation from a video sequence is a very important task in editing and multimedia analysis for film making. Most of the VOE approaches required prior knowledge about background and foreground to extract target objects. In this paper, an Optimized smoothed Dirichlet Process Multi-view learning with improved adaptive Modified Markov Random Field which is enhanced by adaptive shape prior modified graph cut (OsDPMVL-IASMMRF) model has been extended for video-based object extraction. The contour tracking has been additionally included OsDPMVL-IASMMRF for VOE. The Teh–Chin algorithm has been used with OsDPMVL-IASMMRF for predicting the contour in the current frame by matching the extracted object contour from the previous segmented frame. The contour tracking propagates the shape of the target object, whereas the OsDPMVL-IASMMRF segmentation refined the object boundary and the shape for enhancing the accuracy of video segmentation. The experimental outcomes show that the proposed approach provides better segmentation results in terms of accuracy, precision and recall.  

Convergence to Uniformity in a Cellular Automaton via Local Coevolution

1997 ◽  
Vol 08 (05) ◽  
pp. 1013-1024 ◽  
Author(s):  
Moshe Sipper ◽  
Marco Tomassini
Keyword(s):  
Parallel Machines ◽  
Parallel Implementation ◽  
A Priori ◽  
Practical Interest ◽  
Optimal Solution ◽  
Cellular Systems ◽  
Evolving System ◽  
Uniform Solution ◽  
Specialized Hardware ◽  
Density Problem

Cellular programming is a coevolutionary algorithm by which parallel cellular systems evolve to solve computational tasks. The evolving system is a massively parallel, locally interconnected grid of cells, where each cell operates according to a local interaction rule. If this rule is identical for all cells, the system is referred to as uniform, otherwise, it is non-uniform. This paper describes an experiment that addresses the following question: Employing a local coevolutionary process to solve a hard problem, known as density classification, can an optimal uniform solution be found? Since our approach involves the evolution of non-uniform CAs, where cellular rules are initially assigned at random, such convergence to uniformity cannot be a priori expected to easily emerge. The question is of both theoretical and practical interest. As for the latter, one major advantage of local evolutionary processes is their amenability to parallel implementation, using commercially available parallel machines or specialized hardware. Our experiment shows that when such local evolution is applied to the density problem, the optimal solution can be found.

Single Beacon Indoor Localization System Based on Counter-Synchronized Compass and RSSI

2014 ◽  
Vol 627 ◽  
pp. 217-222
Author(s):  
Kok Seng Eu ◽  
Kian Meng Yap ◽  
Tiam Hee Tee
Keyword(s):  
Indoor Localization ◽  
Target Item ◽  
Optimal Solution ◽  
Research Area ◽  
Target Object ◽  
Localization Algorithm ◽  
Distance Information ◽  
Localization Method ◽  
Localization System ◽  
Lost And Found

Indoor localization system has been a popular research area in recent decades and many of them are based on multiple beacons localization method. However, there are some special applications to which the multiple beacons method is not an optimal solution due to its overdesign and cost of redundancy. Multiple beacons method uses at least three transducers and each transducer’s location must be known to find the location of a target object by using either Triangulation or Trilateration calculation. When the multiple beacons method is applied in an items lost and found system, the precise Cartesian coordinates of a target item can be found, but it is definitely overdesign and incurring redundant cost. It is due to the fact that the target item requires only two simple information i.e. Clock orientation and distance information; therefore, single beacon is enough for the task. In this paper, we propose a single beacon localization method to optimize the solution in the items lost and found system by utilizing clock orientation and estimated distance information. The proposed single beacon localization algorithm has been demonstrated and proven that it can be one of the optimal solutions for items lost and found system.

Human–robot shared control for humanoid manipulator trajectory planning

2020 ◽  
Vol 47 (3) ◽  
pp. 395-407 ◽  
Author(s):  
Yuanchao Zhu ◽  
Canjun Yang ◽  
Qianxiao Wei ◽  
Xin Wu ◽  
Wei Yang
Keyword(s):  
Control Process ◽  
Optimal Solution ◽  
Target Object ◽  
Mapping Method ◽  
Shared Control ◽  
Control Mode ◽  
Content Type ◽  
Working Space ◽  
Single Target ◽  
Human Robot Collaboration

Purpose This paper aims to propose an intuitive shared control strategy to control a humanoid manipulator that can fully combine the advantages of humans and machines to produce a stronger intelligent form. Design/methodology/approach The working space of an operator’s arm and that of a manipulator are matched, and a genetic algorithm that limits the position of the manipulator’s elbow joint is used to find the optimal solution. Then, the mapping of the operator’s action to that of manipulators is realized. The controls of the human and robot are integrated. First, the current action of the operator is input. Second, the target object is predicted according to the maximum entropy hypothesis. Third, the joint angle of the manipulator is interpolated based on time. Finally, the confidence and weight of the current moment are calculated. Findings The modified weight adjustment method is the optimal way to adjust the weight during the task. In terms of time and accuracy, the experimental results of single target obstacle avoidance grabbing and multi-target predictive grabbing show that the shared control mode can provide full play to the advantages of humans and robots to accomplish the target task faster and more accurately than the control merely by a human or robot on its own. Originality/value A flexible and highly anthropomorphic human–robot action mapping method is proposed, which provides operator decisions in the shared control process. The shared control between human and the robot is realized, and it enhances the rapidity and intelligence, paving a new way for a novel human–robot collaboration.

Multi-View 3D Object Reconstruction Using Coordinate Transformation

2013 ◽  
Vol 284-287 ◽  
pp. 2167-2170 ◽  
Author(s):  
Ji Hun Park
Keyword(s):  
Coordinate Transformation ◽  
Digital Camera ◽  
Optimal Solution ◽  
Calibration Method ◽  
Target Object ◽  
Feature Points ◽  
Geometric Information ◽  
Local Coordinates ◽  
Camera Parameters ◽  
Parameter Values

The algorithm presented in this paper takes at least two images taken by an ordinary digital camera, computes camera parameters and outputs scene geometric information. The method takes at least one right-angle triangle in the scene, install a local coordinate at each triangle, and compute camera parameters by using the local coordinate transformation. The algorithm computes orientations and displacement relationships among the triangles in the scene while correcting the previously computed camera parameter values. In order to find optimal solution, we set optimization variables as camera calibration parameters and coordinate transformation values between local coordinates. The background eliminated images and calculated camera parameters allow us to reconstruct a 3D target object in VRML. The merit of this algorithm is in handling varying focal camera, in easy initial guessing value computation and in using fewer feature points compared to Zhang’s calibration method.

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