Semantic recognition of workpiece using computer vision for shape feature extraction and classification based on learning databases

Ding, Wei; Gu, Jinan; Shang, Zhenyang; Tang, Shuang; Wu, Qian; Duodu, Enock A.; Yang, Zhi

doi:10.1016/j.ijleo.2016.11.155

Cited by 16 publications

(5 citation statements)

References 8 publications

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“…The first step above forms the basis of image recognition (Song et al, 2016 ). Choosing the appropriate features directly impacts the final segmentation and recognition accuracy (Ding et al, 2017 ). Hu et al proposed an image-feature-extraction method based on shape characteristics (Hu et al, 2016 ), and Yang et al introduced multi-structure feature fusion for face recognition based on multi-resolution exaction (Yang et al, 2011 ).…”

Section: Introductionmentioning

confidence: 99%

Wheat Ears Counting in Field Conditions Based on Multi-Feature Optimization and TWSVM

et al. 2018

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The number of wheat ears in the field is very important data for predicting crop growth and estimating crop yield and as such is receiving ever-increasing research attention. To obtain such data, we propose a novel algorithm that uses computer vision to accurately recognize wheat ears in a digital image. First, red-green-blue images acquired by a manned ground vehicle are selected based on light intensity to ensure that this method is robust with respect to light intensity. Next, the selected images are cut to ensure that the target can be identified in the remaining parts. The simple linear iterative clustering method, which is based on superpixel theory, is then used to generate a patch from the selected images. After manually labeling each patch, they are divided into two categories: wheat ears and background. The color feature “Color Coherence Vectors,” the texture feature “Gray Level Co-Occurrence Matrix,” and a special image feature “Edge Histogram Descriptor” are then exacted from these patches to generate a high-dimensional matrix called the “feature matrix.” Because each feature plays a different role in the classification process, a feature-weighting fusion based on kernel principal component analysis is used to redistribute the feature weights. Finally, a twin-support-vector-machine segmentation (TWSVM-Seg) model is trained to understand the differences between the two types of patches through the features, and the TWSVM-Seg model finally achieves the correct classification of each pixel from the testing sample and outputs the results in the form of binary image. This process thus segments the image. Next, we use a statistical function in Matlab to get the exact a precise number of ears. To verify these statistical numerical results, we compare them with field measurements of the wheat plots. The result of applying the proposed algorithm to ground-shooting image data sets correlates strongly (with a precision of 0.79–0.82) with the data obtained by manual counting. An average running time of 0.1 s is required to successfully extract the correct number of ears from the background, which shows that the proposed algorithm is computationally efficient. These results indicate that the proposed method provides accurate phenotypic data on wheat seedlings.

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Section: Introductionmentioning

confidence: 99%

Wheat Ears Counting in Field Conditions Based on Multi-Feature Optimization and TWSVM

et al. 2018

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“…The GRAPES equation matrix is three‐dimensional . When the resolution is 1, the matrix size is 360*180*38, and when the resolution is 0.5, the matrix size is 720*360*38.…”

Section: The Parallel Strategy For Solving Grapes Helmholtz Equation mentioning

confidence: 99%

Research on parallel solution of GRAPES Helmholtz equation

Tian

Zheng

et al. 2018

Concurrency and Computation

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GRAPES is a new generation of numerical weather prediction system developed and used by Chinese researchers. As the accuracy requirement of weather prediction system is increasing, the grid resolution of the global model is greatly increasing; therefore, the computing power becomes one of the important factors restricting the performance of the numerical weather prediction system. In order to solve the problem, the paper introduces the geometric multi-grid solution to solve the GRAPES Helmholtz equation. The core idea of multi-grid solution is to eliminate the swing component of the residual by iterative algorithm and eliminate the smooth components of the residuals by using the coarse grid interpolation to correct the fine grid solution. This paper designs a parallel solution for GRAPES multi-grid, including grid coarsening operation, smooth operation, and the coarsest grid accurate solution operation. Finally, the actual data with the resolution of 1 is tested, achieving the better acceleration effect. At the same time, the analysis and explanations of the test results have some useful conclusions. KEYWORDS GRAPES, multi-grid, parallel solution INTRODUCTIONIn daily life, the application of weather prediction is becoming more important, such as the disaster prevention and mitigation in agricultural production, the take-off and landing arrangement of civil aircraft, the rocket launching in space activities and the early warning of natural disasters, and so on; the weather prediction relates to all aspects of people's lives. GRAPES is a new generation of numerical weather prediction system developed by China Meteorological Bureau; the global numerical prediction model is its core component. 1-3 With the increasing prediction accuracy requirements, the resolution of GRAPES grid increases, so computing power becomes a bottleneck to constrain GRAPES.The core of global numerical prediction model is the solution of Helmholtz equation. 4 When the resolution increases, the dimension of the equation matrix increases sharply, and the overhead of computing resources increases. Multi-grid solving technique 5,6 is a matrix solution proposed by former Soviet scientists, which has been widely used in science and engineering. The multi-grid method is theoretically proved to be an optimal solution for linear elliptic problem at least; its computational complexity is proportional to the number of the grid nodes, and the convergence rate is independent of the grid size. 7 Therefore, the multi-grid solving technique can adapt to the increment of GRAPES grid resolution and the matrix equation size.This paper designs a parallel solution to solving the GRAPES Helmholtz equation by using the multi-grid method. It reduces the calculation time of multi-grid smooth operation and speeds up the convergence by using fine grid coarsening method, restriction operator, interpolation operator, and implementation strategy of parallel computation using MPI in GRAPES. Meanwhile, an optimization strategy of hybrid grid coarsening is designed aimed to...

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“…(Ding et al, 2017) propose not only to search for more elaborate algorithms to define robot manipulation patterns, but also to accompany the computer vision of a database of visual elements to facilitate the interpretation of the environment to improve the learning and recognition of figures and objects in order to establish more natural reactions in a actions performed by robots (Ding et al ,20 17).…”

Section: Interactionmentioning

confidence: 99%

Automatic Learning Improves Human-Robot Interaction in Productive Environments

Hernandez

Marin

García-Rodríguez

et al. 2017

International Journal of Computer Vision and Image Processing

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In the creation of new industries, products and services --all of which are advances of the Fourth Industrial Revolution --the human-robot interaction that includes automatic learning and computer vision are elements to consider since they promote collaborative environments between people and robots. The use of machine learning and computer vision provides the tools needed to increase productivity and minimizes delivery reaction times by assisting in the optimization of complex production planning processes. This review of the state of the art presents the main trends that seek to improve human-robot interaction in productive environments, and identifies challenges in research as well as in industrial -technological development in this topic. In addition, this review offers a proposal on the needs of use of artificial intelligence in all processes of industry 4.0 as a crucial linking element among humans, robots, intelligent and traditional machines; as well as a mechanism for quality control and occupational safety.

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Semantic recognition of workpiece using computer vision for shape feature extraction and classification based on learning databases

Cited by 16 publications

References 8 publications

Wheat Ears Counting in Field Conditions Based on Multi-Feature Optimization and TWSVM

Wheat Ears Counting in Field Conditions Based on Multi-Feature Optimization and TWSVM

Research on parallel solution of GRAPES Helmholtz equation

Automatic Learning Improves Human-Robot Interaction in Productive Environments

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