Two view NURBS reconstruction based on GACO model

The key job here in the presented work is to investigate the performance of Generalized Ant Colony Optimizer (GACO) model in order to evolve the shape of three dimensional free-form Non Uniform Rational B-Spline (NURBS) curve using stereo (two) views. GACO model is a blend of two well known meta-heuristic optimization algorithms known as Simple Ant Colony and Global Ant Colony Optimization algorithms. Basically, the work talks about the solution of NURBS-fitting based reconstruction process. Therefore, GACO model is used to optimize the NURBS parameters (control points and weights) by minimizing the weighted least-square errors between the data points and the fitted NURBS curve. The algorithm is applied by first assuming some pre-fixed values of NURBS parameters. The experiments clearly show that the optimization procedure is a better option in a case where good initial locations of parameters are selected. A detailed experimental analysis is given in support of our algorithm. The implemented error analysis shows that the proposed methodology perform better as compared to the conventional methods.

A Feedrate Planning Method for the NURBS Curve in CNC Machining Based on the Critical Constraint Curve

The curvature of the NURBS curve varies along its trajectory, therefore, the commonly used feedrate-planning method, which based on the acceleration/deceleration (Acc/Dec) model, is difficult to be directly applied in CNC machining of a NURBS curve. To address this problem, a feedrate-planning method based on the critical constraint curve of the feedrate (CCC) is proposed. Firstly, the problems of existing feedrate-planning methods and their causes are analyzed. Secondly, by considering both the curvature constraint and the kinematic constraint during the Acc/Dec process, the concept of CCC which represents the relationship between the critical feedrate-constraint value and the arc length is proposed. Then the CCC of a NURBS curve is constructed, and it has a concise expression conforming to the Acc/Dec model. Finally, a feedrate-planning method of a NURBS curve based on CCC and the Acc/Dec model is established. In the simulation, a comparison between the proposed method and the conventional feedrate-planning method is performed, and the results show that, the proposed method can reduce the Acc/Dec time by over 40%, while little computational burden being added. The machining experimental results validate the real-time performance and stability of the proposed method, and also the machining quality is verified. The proposed method offers an effective feedrate-planning strategy for a NURBS curve in CNC machining.

Traffic Flow Detection at Road Intersections Based on K-Means and NURBS Trajectory Clustering

In view of the variety and occlusion of vehicle target motion on the urban intersection, it is difficult to accurately detect the traffic flow parameters in all directions and categories of the intersection, so an improved k-means trajectory clustering method based on NURBS curve fitting is designed to obtain the traffic flow parameters. Firstly, the B-spline quadratic interpolation function is used to fit the smooth NURBS curve of vehicle trajectory; secondly, K-means clustering is used to measure the minimum distance, and the location of the first and last end points of the vehicle trajectory is used to realize the automatic division of the intersection area; finally, according to the intersection area where the start and end points of vehicle trajectory belong, respectively, the moving mode of a vehicle is determined, and the traffic flow parameters are classified and counted. Experiments show that the method has high accuracy and simple algorithm, which can meet the application requirements of intelligent transportation. It can provide effective data for traffic congestion analysis and lane occupancy estimation, and it is an important parameter for dynamic time setting of intersection information lights.