Local Search Area Research Articles

Research on textile-vamp punching based on punching point gradient phase and edge significance

A visual technology to identify the punching points of a textile vamp to replace manual punching by machinery is proposed in this paper. This could solve several problems relating to the manual punching of textile vamp, such as high manual-punching strength, low efficiency, and poor punching accuracy. Unsharp mask guided filtering was adopted, to enhance the details of the textile-vamp punching points, considering the edge point position, gradient phase, and edge significance of the punching points, in building the template matching similarity measure function. A partial Hausdorff distance was proposed to sum and average values, to improve the degree of matching of punching point shape defects. A local search area of punching points to improve identification efficiency and punching evaluation criteria was established to evaluate the punching effects. The results showed that the matching similarity of the complete boundary was above 0.9. Positioning accuracy was 0.43 mm in the x-direction, 0.38 mm in the y-direction, and repeat positioning accuracy was 0.09 mm. The center average relative error was 5.03% and the relative error of the radius was 7.81%. Identification timeliness increased with the rotation angle, template size, and the number of punching points. When the rotation angle was between –180° and 180° and the number of punching points was 24, identification timeliness was 830 ms, which met productivity requirements. Textile-vamp punching grades A to D qualified, however, grades E and F were unqualified for punching.

Robust Frontal Vehicle Pose Estimation Based on Structural Parameter Optimization Using Reliable Edge Point Sequences

In order to enhance the stability of vehicle pose estimation within driving videos, a novel methodology for optimizing vehicle structural parameters is introduced. This approach hinges on evaluating the reliability of edge point sequences. Firstly, a multi−task and iterative convolutional neural network (MI−CNN) is constructed, enabling the simultaneous execution of four critical tasks: vehicle detection, yaw angle prediction, edge point location, and visibility assessment. Secondly, an imperative aspect of the methodology involves establishing a local tracking search area. This region is determined by modeling the limitations of vehicle displacement between successive frames. Vehicles are matched using a maximization approach that leverages point similarity. Finally, a reliable edge point sequence plays a pivotal role in resolving structural parameters robustly. The Gaussian mixture distribution of vehicle distance change ratios, derived from two measurement models, is employed to ascertain the reliability of the edge point sequence. The experimental results showed that the mean Average Precision (mAP) achieved by the MI−CNN network stands at 89.9%. A noteworthy observation is that the proportion of estimated parameters whose errors fall below the threshold of 0.8 m consistently surpasses the 85% mark. When the error threshold is set at less than 0.12 m, the proportion of estimated parameters meeting this criterion consistently exceeds 90%. Therefore, the proposed method has better application status and estimation precision.

YUKI Algorithm and POD-RBF for Elastostatic and dynamic crack identification

This paper proposes a new metaheuristic algorithm with a search space reduction capability guided by simple formalism. The search population focuses partially on the inside the local search area while the rest explore globally, looking for better search areas. We call the new algorithm by YUKI Algoritm (YA) and employ it in a crack identification problem. With the aid of a set of measurements taken on the defected structure, we aim at identifying the crack parameters such as length and orientation. To this end, we use the so-called model reduction technique through Proper orthogonal Decomposition (POD) endorsed with Radial Basic Function (RBF), which helps in predicting (numerically) the measurement at new points (out of the set of sensors) via interpolation. This method is widely used in this context and was proven very effective computational-wise. In our study of the performance of YA, we deal with two cases; Firstly, in the case of the Elastostatic study. And secondly, in the case of dynamic analysis. We compare the performance of the suggested algorithm with the performance of well-known optimization methods, such as Teaching Learning Based Optimization (TLBO), Cuckoo Search (CS), and the Gray Wolf Optimizer (GWO). The results show that YA provides accurate and faster results compared to the mentioned algorithms.

Automatic Method for Extraction of Complex Road Intersection Points From High-Resolution Remote Sensing Images Based on Fuzzy Inference

Automatic extracting road intersection points is essential for applications such as data registration between vector data and remote sensing images, aircraft-assisted navigation. However, at a large scale, it is difficult to quickly and accurately extract road intersection points due to the problems caused by complex structures, geometric texture noise interference. In this context, taking OpenStreetMap (OSM) data as priori knowledge, we propose a method for automatic extraction of complex road intersection points based on fuzzy inference. First, OSM data are analyzed to obtain structural information of intersection points. Local search areas are built around the intersection points. Second, within the local search area, the candidate intersection point set are generated. Meanwhile the input image is segmented using multiresolution segmentation; then we establish a fuzzy rule to infer the road area from the segmentation result. The fuzzy indexes and rules are established for the candidate intersection point set to deduce the road intersection area. Finally, based on the results of the previous step, the road intersection points are extracted based on the line segment constraint, structure matching, and linkage equation. Three sets of high-resolution remote sensing images were used to verify the feasibility of the method. We demonstrate that the correctness and positioning accuracy of this method are superior to those of other methods through contrastive analysis.

Hybrid Genetic Algorithms and Simulated Annealing for Multi-trip Vehicle Routing Problem with Time Windows

Vehicle routing problem with time windows (VRPTW) is one of NP-hard problem. Multi-trip is approach to solve the VRPTW that looking trip scheduling for gets best result. Even though there are various algorithms for the problem, there is opportunity to improve the existing algorithms in order gaining a better result. In this research, genetic algoritm is hybridized with simulated annealing algoritm to solve the problem. Genetic algoritm is employed to explore global search area and simulated annealing is employed to exploit local search area. Four combination types of genetic algorithm and simulated annealing (GA-SA) are tested to get the best solution. The computational experiment shows that GA-SA1 and GA-SA4 can produced the most optimal fitness average values with each value was 1.0888 and 1.0887. However GA-SA4 can found the best fitness chromosome faster than GA-SA1.

Localization of Substation Fittings Based on a Stereo Vision Method

We propose a novel stereo vision method based on a fast template matching strategy to improve localization accuracy and efficiency of substation fittings. First, considering the salient features of the substation fittings that can be recognized easily, the method searches for features that are similar to the ones in the matching template related to the sub-image of the substation fittings from the global image. When the substation fittings are confirmed, the method repeatedly searches for the one of screw holes in the local region of the substation fittings. It then computes the centering coordinates of the template in the source images until the screw holes are matched. The experimental results show that the proposed template matching method increases the accuracy and efficiency of the substation fitting localization from the global to local search area. Correspondingly, the accuracy and efficiency of stereo vision localization of substation fittings is improved.

Missing Surface Estimation Based on Modified Tikhonov Regularization: Application for Destructed Dental Tissue.

Estimation of missing digital information is mostly addressed by one or two-dimensional signal processing methods; however, this problem can emerge in multi-dimensional data including 3D images. Examples of 3D images dealing with missing edge information are often found using dental micro-CT, where the natural contours of dental enamel and dentine are partially dissolved or lost by caries. In this paper, we present a novel sequential approach to estimate the missing surface of an object. First, an initial correct contour is determined interactively or automatically, for the starting slice. This contour information defines the local search area and provides the overall estimation pattern for the edge candidates in the next slice. The search for edge candidates in the next slice is performed in the perpendicular direction to the obtained initial edge in order to find and label the corrupted edge candidates. Subsequently, the location information of both initial and nominated edge candidates are transformed and segregated into two independent signals (X-coordinates and Y-coordinates) and the problem is changed into error concealment. In the next step, the missing samples of these signals are estimated using a modified Tikhonov regularization model with two new terms. One term contributes in the denoising of the corrupted signal by defining an estimation model for a group of mildly destructed samples, and the other term contributes in the estimation of the missing samples with the highest similarity to the samples of the obtained signals from the previous slice. Finally, the reconstructed signals are transformed inversely to edge pixel representation. The estimated edges in each slice are considered as initial edge information for the next slice and this procedure is repeated slice by slice until the entire contour of the destructed surface is estimated. The visual results as well as quantitative results (using both contour-based and area-based metrics) for seven image datasets of tooth samples with considerable destruction of the dentin-enamel junction (DEJ) demonstrates that the proposed method can accurately interpolate the shape and the position of the missing surfaces in computed tomography images in both two and three dimensions (e.g. 14.87 ±3.87 μ m of mean distance (MD) error for the proposed method versus 7.33 ±0.27 μm of MD error between human experts and 1.25 ±~0 % error rate (ER) of the proposed method versus 0.64 ±~0 % of ER between human experts (~1% difference)).

Alternative search techniques for face detection using location estimation and binary features

The sliding window approach is the most widely used technique to detect an object from an image. In the past few years, classifiers have been improved in many ways to increase the scanning speed. Apart from the classifier design (such as the cascade), the scanning speed also depends on a number of different factors (such as the grid spacing, and the scale at which the image is searched). When the scanning grid spacing is larger than the tolerance of the trained classifier it suffers from low detections. In this paper, we present a technique to reduce the number of missed detections when fewer subwindows are processed in the sliding window approach for face detection. This is achieved by using a small patch to predict the location of the face within a local search area. We use simple binary features and a decision tree for location estimation as it proved to be efficient for our application. We also show that by using a simple interest point detector based on quantized gradient orientation, as the front-end to the proposed location estimation technique, we can further improve the performance. Experimental evaluation on several face databases show better detection rate and speed with our proposed approach when fewer number of subwindows are processed compared to the standard scanning technique.

A fast VLSI architecture of a hierarchical block matching algorithm for motion estimation

In this paper, an efficient VLSI architecture of a hierarchical block matching algorithm has been proposed for motion estimation. At the lowest resolution level, two motion vector (MV) candidates are selected to get better performance. In the next search level, these two candidates provide the center points for local searches to get one MV candidate. Then, at next level and the finest level, one MV candidate is chosen from one local search area (LSA), defined by the MV candidate, obtained from lower resolution level. This architecture requires nine processing elements and data are processed in such a way that calculation to obtain frames of different resolution is overlapped with the MV calculation. Simulation results indicate that this architecture is more area-efficient and faster than many full-search, three-step-search and multiresolution architectures which makes it suitable for SD and HD video. To avoid the delay due to pipelining, the MVs of all the macro-blocks are calculated for one resolution level and stored in RAM to get LSA for next resolution level. This architecture with about 16 K gates is implemented for a search range of [?15, +15]. As this architecture requires only two-port memory, which is very common in most consumer electronics systems, it can be integrated easily in any existing system at the expense of a very small area.

The Parameterized Complexity of Local Search for TSP, More Refined

We extend previous work on the parameterized complexity of local search for the Traveling Salesperson Problem (TSP). So far, its parameterized complexity has been investigated with respect to the distance measures (defining the local search area) “Edge Exchange” and “Max-Shift”. We perform studies with respect to the distance measures “Swap” and “r-Swap”, “Reversal” and “r-Reversal”, and “Edit”, achieving both fixed-parameter tractability and W[1]-hardness results. In particular, from the parameterized reduction showing W[1]-hardness we infer running time lower bounds (based on the exponential time hypothesis) for all corresponding distance measures. Moreover, we provide non-existence results for polynomial-size problem kernels and we show that some in general W[1]-hard problems turn fixed-parameter tractable when restricted to planar graphs.