Multi-Resolution-Based Contour Corner Extraction Algorithm for Computer Vision-Based Measurement

2018 ◽  
Author(s):  
Jianhua Li ◽  
Lin Liao
Keyword(s):  
Computer Vision ◽  
Experimental Results ◽  
Original Algorithm ◽  
Industry Standard ◽  
Extraction Algorithm ◽  
Corner Points ◽  
Multiple Resolutions ◽  
Key Steps

Corner-based registration of the industry standard contour and the actual product contour is one of the key steps in industrial computer vision-based measurement. However, existing corner extraction algorithms do not achieve satisfactory results in the extraction of the standard contour and the deformed contour of the actual product. This paper proposes a multi-resolution-based contour corner extraction algorithm for computer vision-based measurement. The algorithm first obtains different corners in multiple resolutions, then sums up the weighted corner values, and finally chooses the corner points with the appropriate corner values as the final contour corners. The experimental results show that the proposed algorithm, based on multi-resolution, outperforms the original algorithm in the aspect of the corner matching situation and helps in subsequent product measurements.

scholarly journals Random Forest with Adaptive Local Template for Pedestrian Detection

2015 ◽  
Vol 2015 ◽  
pp. 1-11 ◽  
Author(s):  
Tao Xiang ◽  
Tao Li ◽  
Mao Ye ◽  
Zijian Liu
Keyword(s):  
Computer Vision ◽  
Random Forest ◽  
Classification Accuracy ◽  
Template Matching ◽  
Detection Method ◽  
State Of The Art ◽  
Pedestrian Detection ◽  
Sliding Window ◽  
Experimental Results ◽  
Training Samples

Pedestrian detection with large intraclass variations is still a challenging task in computer vision. In this paper, we propose a novel pedestrian detection method based on Random Forest. Firstly, we generate a few local templates with different sizes and different locations in positive exemplars. Then, the Random Forest is built whose splitting functions are optimized by maximizing class purity of matching the local templates to the training samples, respectively. To improve the classification accuracy, we adopt a boosting-like algorithm to update the weights of the training samples in a layer-wise fashion. During detection, the trained Random Forest will vote the category when a sliding window is input. Our contributions are the splitting functions based on local template matching with adaptive size and location and iteratively weight updating method. We evaluate the proposed method on 2 well-known challenging datasets: TUD pedestrians and INRIA pedestrians. The experimental results demonstrate that our method achieves state-of-the-art or competitive performance.

Preparation and Properties of SiC/Phenolic Resin for the Heat of LED

2016 ◽  
Vol 848 ◽  
pp. 454-459
Author(s):  
Cong Wu ◽  
Kang Zhao ◽  
Yu Fei Tang ◽  
Ji Yuan Ma
Keyword(s):  
Mechanical Properties ◽  
Thermal Conductivity ◽  
Powder Metallurgy ◽  
Phenolic Resin ◽  
Bending Strength ◽  
Experimental Results ◽  
Low Thermal Conductivity ◽  
Industry Standard ◽  
Insulation Performance

In order to solve the problem that low thermal conductivity of the plastics for the heat of LED, SiC/Phenolic resin for the heat of LED were fabricated combining powder metallurgy. The effects of particles diameters, content and adding nanoparticles on thermal conductivity of the fabricated composites were investigated, the mechanical properties were also characterized. The experimental results showed that the materials were obtained, and the insulation performance of the fabricated SiC/Phenolic resin was higher than the industry standard one, the thermal conductivity reached 4.1W/(m·k)-1. And the bending strength of the fabricated composites was up to 68.11MPa. The problem of low thermal conductivity of the material is expected to be solved. In addition, it is meaningful for improving LED life.

scholarly journals Handwritten tifinagh character recognition using simple geometric shapes and graphs

2019 ◽  
Vol 13 (2) ◽  
pp. 598
Author(s):  
Youssef Ouadid ◽  
Abderrahmane Elbalaoui ◽  
Mehdi Boutaounte ◽  
Mohamed Fakir ◽  
Brahim Minaoui
Keyword(s):  
Character Recognition ◽  
Graph Matching ◽  
Recognition Rate ◽  
Recognition System ◽  
Features Extraction ◽  
Experimental Results ◽  
Feature Points ◽  
Matching Method ◽  
Extraction Algorithm ◽  
Novel Algorithm

<p>In this paper, a graph based handwritten Tifinagh character recognition system is presented. In preprocessing Zhang Suen algorithm is enhanced. In features extraction, a novel key point extraction algorithm is presented. Images are then represented by adjacency matrices defining graphs where nodes represent feature points extracted by a novel algorithm. These graphs are classified using a graph matching method. Experimental results are obtained using two databases to test the effectiveness. The system shows good results in terms of recognition rate.</p>

Obstacle negotiation analysis of track-legged robot based on terramechanics

2021 ◽  
Vol ahead-of-print (ahead-of-print) ◽  
Author(s):  
Zhu Hongbiao ◽  
Yueming Liu ◽  
Weidong Wang ◽  
Zhijiang Du
Keyword(s):  
Kinematic Model ◽  
Interaction Model ◽  
Experimental Results ◽  
Physical Parameters ◽  
Negotiation Analysis ◽  
Content Type ◽  
Practical Applications ◽  
Loose Soil ◽  
Key Steps ◽  
Drawbar Pull

Purpose This paper aims to present a new method to analyze the robot’s obstacle negotiation based on the terramechanics, where the terrain physical parameters, the sinkage and the slippage of the robot are taken into account, to enhance the robot’s trafficability. Design/methodology/approach In this paper, terramechanics is used in motion planning for all-terrain obstacle negotiation. First, wheel/track-terrain interaction models are established and used to analyze traction performances in different locomotion modes of the reconfigurable robot. Next, several key steps of obstacle-climbing are reanalyzed and the sinkage, the slippage and the drawbar pull are obtained by the models in these steps. In addition, an obstacle negotiation analysis method on loose soil is proposed. Finally, experiments in different locomotion modes are conducted and the results demonstrate that the model is more suitable for practical applications than the center of gravity (CoG) kinematic model. Findings Using the traction performance experimental platform, the relationships between the drawbar pull and the slippage in different locomotion modes are obtained, and then the traction performances are obtained. The experimental results show that the relationships obtained by the models are in good agreement with the measured. The obstacle-climbing experiments are carried out to confirm the availability of the method, and the experimental results demonstrate that the model is more suitable for practical applications than the CoG kinematic model. Originality/value Comparing with the results without considering Terramechanics, obstacle-negotiation analysis based on the proposed track-terrain interaction model considering Terramechanics is much more accurate than without considering Terramechanics.

scholarly journals Image Retrieval Based on Fractal Dictionary Parameters

2013 ◽  
Vol 2013 ◽  
pp. 1-8
Author(s):  
Yuanyuan Sun ◽  
Rudan Xu ◽  
Lina Chen ◽  
Xiaopeng Hu
Keyword(s):  
Computer Vision ◽  
Image Compression ◽  
Image Retrieval ◽  
Experimental Results ◽  
Content Based Image Retrieval ◽  
Efficient Management ◽  
Retrieval Rate ◽  
Statistical Indices ◽  
Visual Database

Content-based image retrieval is a branch of computer vision. It is important for efficient management of a visual database. In most cases, image retrieval is based on image compression. In this paper, we use a fractal dictionary to encode images. Based on this technique, we propose a set of statistical indices for efficient image retrieval. Experimental results on a database of 416 texture images indicate that the proposed method provides a competitive retrieval rate, compared to the existing methods.

scholarly journals NICE: Superpixel Segmentation Using Non-Iterative Clustering with Efficiency

2020 ◽  
Vol 10 (12) ◽  
pp. 4415 ◽  
Author(s):  
Cheng Li ◽  
Baolong Guo ◽  
Geng Wang ◽  
Yan Zheng ◽  
Yang Liu ◽  
...  
Keyword(s):  
Computer Vision ◽  
Computational Efficiency ◽  
Batch Processing ◽  
Experimental Results ◽  
Processing Strategy ◽  
Trade Off ◽  
Quantitative Metrics ◽  
Visual Tasks ◽  
Pixel Correlation ◽  
Region Description

Superpixels intuitively over-segment an image into small compact regions with homogeneity. Owing to its outstanding performance on region description, superpixels have been widely used in various computer vision tasks as the substitution for pixels. Therefore, efficient algorithms for generating superpixels are still important for advanced visual tasks. In this work, two strategies are presented on conventional simple non-iterative clustering (SNIC) framework, aiming to improve the computational efficiency as well as segmentation performance. Firstly, inter-pixel correlation is introduced to eliminate the redundant inspection of neighboring elements. In addition, it strengthens the color identity in complicated texture regions, thus providing a desirable trade-off between runtime and accuracy. As a result, superpixel centroids are evolved more efficiently and accurately. For further accelerating the framework, a recursive batch processing strategy is proposed to eliminate unnecessary sorting operations. Therefore, a large number of neighboring elements can be assigned directly. Finally, the two strategies result in a novel synergetic non-iterative clustering with efficiency (NICE) method based on SNIC. Experimental results verify that it works 40% faster than conventional framework, while generating comparable superpixels for several quantitative metrics—sometimes even better.

Study on Lane Detection Based on Computer Vision

2013 ◽  
Vol 765-767 ◽  
pp. 2229-2232
Author(s):  
Peng He ◽  
Feng Gao
Keyword(s):  
Computer Vision ◽  
Hough Transform ◽  
Driver Assistance System ◽  
Lane Detection ◽  
Experimental Results ◽  
Assistance System ◽  
Driver Assistance ◽  
Crucial Component ◽  
Straight Lines ◽  
The Way

Lane detection is a crucial component of automotive driver assistance system aiming to increase safety, convenience and efficiency of driving. This paper developed a vision based algorithm of detecting road lanes which is performed by extracting edges and finding straight lines using improved Hough transform. The experimental results indicate that this algorithm is effective and precise. Furthermore, this algorithm paves the way for the implementation of automotive driver assistance system.

Alphabet Recognition Based on Computer Vision

2014 ◽  
Vol 543-547 ◽  
pp. 2354-2357
Author(s):  
Hui Zhou
Keyword(s):  
Computer Vision ◽  
Experimental Results ◽  
Correct Match ◽  
Test Image ◽  
Recognition Method

In order to realize rapid alphabet recognition, the paper proposes an alphabet recognition method based on computer vision optimization technical which can also extract the classification features. Experimental results show that the obtained variance value of the test image and the standard image obtained by the proposed method is the minimum which indicating the method can achieve correct match, effective classification, and provide a great method of identification.

scholarly journals A Modified MinMaxk-Means Algorithm Based on PSO

2016 ◽  
Vol 2016 ◽  
pp. 1-13 ◽  
Author(s):  
Xiaoyan Wang ◽  
Yanping Bai
Keyword(s):  
Experimental Results ◽  
Data Sets ◽  
Clustering Method ◽  
Data Set ◽  
Original Algorithm ◽  
Executive Process ◽  
Proper Values ◽  
Two Parameters ◽  
Memory Parameter

The MinMaxk-means algorithm is widely used to tackle the effect of bad initialization by minimizing the maximum intraclustering errors. Two parameters, including the exponent parameter and memory parameter, are involved in the executive process. Since different parameters have different clustering errors, it is crucial to choose appropriate parameters. In the original algorithm, a practical framework is given. Such framework extends the MinMaxk-means to automatically adapt the exponent parameter to the data set. It has been believed that if the maximum exponent parameter has been set, then the programme can reach the lowest intraclustering errors. However, our experiments show that this is not always correct. In this paper, we modified the MinMaxk-means algorithm by PSO to determine the proper values of parameters which can subject the algorithm to attain the lowest clustering errors. The proposed clustering method is tested on some favorite data sets in several different initial situations and is compared to thek-means algorithm and the original MinMaxk-means algorithm. The experimental results indicate that our proposed algorithm can reach the lowest clustering errors automatically.

Posture Correction Technique Based on Visual Analysis

2014 ◽  
Vol 644-650 ◽  
pp. 2632-2635
Author(s):  
An Di
Keyword(s):  
Computer Vision ◽  
Visual Analysis ◽  
Experimental Results ◽  
Basketball Player ◽  
High Quality ◽  
Correction Technique ◽  
Definition Of

In basketball training, if the posture of basketball player is deviated, great impact will be brought to basketball training. Therefore, this paper presents a posture correction technology based on vision analysis. A lot of computer vision image are collected in basketball training, this images are enhanced to improve definition of image, with the high-quality images to identify wrong posture, and compare with standard posture to achieve posture correction in basketball training. Experimental results show that the proposed algorithm for posture correction in basketball training can improve the accuracy of correction, so as to meet the actual needs of basketball training.

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