Solid wood flooring has good esthetic properties and is an excellent material for interior decoration. To meet the artistic effects of specific interior decoration requirements, the color of solid wood flooring needs to be coordinated. Thus, the color of the produced solid wood flooring needs to be sorted to meet the individual needs of customers. In this work, machine vision, deep learning methods, and ensemble learning methods are introduced to reduce the cost of manual sorting and improve production efficiency. The color CCD camera was used to collect 108 solid wood floors of three color grades provided by the company and obtained 108 18,000 × 2048 pixel wood images. A total of 432 images were obtained after data expansion. Deep learning methods, such as VGG16, DenseNet121, and XGBoost, were compared. After using XGBoost to filter the features, the accuracy of solid wood flooring color classification was 97.22%, the training model time was 5.27 s, the average test time for each picture was 51 ms, and a good result was achieved.

https://www.mdpi.com/1424-8220/21/2/336/pdf

Online Color Classification System of Solid Wood Flooring Based on Characteristic Features

The work presented here describes the first results of an effective method of assessing the quality of electrostatic separation of mixtures of polymer materials. The motivation for the research was to find an effective method of mechanical separation of plastic materials and a quick assessment of the effectiveness of the method itself. The proposed method is based on the application of a dedicated vision system developed for needs of research on electrostatic separation. The effectiveness of the elaborated system has been demonstrated by evaluating the quality of the separation of mixtures of poly (methyl methacrylate) (PMMA) and polystyrene (PS). The obtained results show that the developed vision system can be successfully employed in the research on plastic separation, providing a fast and accurate method of assessing the purity and effectiveness of the separation process.

Assessment of the Electrostatic Separation Effectiveness of Plastic Waste Using a Vision System.

Solid wood floors are widely used as an interior decoration material, and the color of solid wood surfaces plays a decisive role in the final decoration effect. Therefore, the color classification of solid wood floors is the final and most important step before laying. However, research on floor classification usually focuses on recognizing complex and diverse features but ignores execution speed, which causes common methods to not meet the requirements of online classification in practical production. In this paper, a new online classification method of solid wood floors was proposed by combining probability theory and machine learning. Firstly, a probability-based feature extraction method (stochastic sampling feature extractor) was developed to obtain rapid key features regardless of the disturbance of wood grain. The stochastic features were determined by a genetic algorithm. Then, an extreme learning machine—as a fast classification neural network—was selected and trained with the selected stochastic features to classify solid wood floors. Several experiments were carried out to evaluate the performance of the proposed method, and the results showed that the proposed method achieved a classification accuracy of 97.78% and less than 1 ms for each solid wood floor. The proposed method has advantages including a high execution speed, great accuracy, and flexible adaptability. Overall, it is suitable for online industry production.

A Fast Online Classification Method of Solid Wood Floors Based on Stochastic Sampling and Machine Learning

People are increasingly using different kinds of plant products, such as wood, but there are many kinds of wood and it is difficult to analyze and identify them, so how to use auxiliary equipment to analyze wood and achieve the goal of accurate wood identification without damaging the product itself has become one of the important problems to be solved in the field of wood research. The axial thin-walled tissue has important wood grain information and it is one of the important features for wood identification. In this paper, we studied the microscopic images of broadleaf wood, and obtained the microstructure images of wood cross-section by photographing, and extracted the complete axial thin-walled tissue morphology of wood by using computer image processing technology and other ways about computer vision. Firstly, the axial thin-walled wood images were de-noised to eliminate some noise effects, so as to facilitate the separation of the axial thin-walled wood; then the images were processed by mathematical morphology to successfully extract the axial thin-walled wood and duct morphology from the cross-sectional images of broadleaf wood; finally, the axial thin-walled wood was separated from the duct by calculating the area of the closed area.

/pdf/extraction-of-plant-parenchyma-by-computer-image-processing-1221yrx8.pdf

Extraction of plant parenchyma by computer image processing technology

This study proposed an intelligent algorithm based on digital image processing and character recognition to address the current situation in which the National Medical Products Administration promulgated the relevant regulations on the complete prohibition of the use of mercury sphygmomanometers in 2020 and the currently widely used electronic sphygmomanometers need to undergo regular verification and quality testing. The intelligent algorithm made it possible to automatically acquire the electronic sphygmomanometer indication value during quality assurance or verification. The images of the electronic sphygmomanometer were captured using a Raspberry Pi-connected video camera head; in the software development, automatic detection of the electronic sphygmomanometer's indication value was achieved by running the computer vision-based OpenCV library on the Raspberry Pi and utilizing image preprocessing techniques like scale transformation, grayscale conversion, Gaussian smoothing and edge detection, and character segmentation. The development of intelligent devices for the automatic verification of electronic sphygmomanometers has a technical foundation in the research and design of the digital recognition algorithm, and it has a certain reference value for character recognition of electronic instruments or the creation of automatic instrument indication value recording devices.

https://oajmist.com/index.php/12/article/download/198/115

Studying electronic blood pressure monitor digital recognition algorithm based on computer vision and design

Color classification of wooden boards is helpful to improve the appearance of wooden furniture that is spliced from multiple wooden boards. Due to the similarity of colors among wooden boards, manual color classification is inaccurate and unstable. Thus, supervised learning algorithms can hardly be used in this scenario. Moreover, wooden boards are long, and their images have a high resolution, which may lead to the growth of computational complexity. To overcome these challenges, in this paper, we propose a new mechanism for color classification of wooden boards based on machine vision. The image of the wooden board is preprocessed to subtract irrelevant colors, and the feature vector is extracted based on 3D color histogram to reduce the computational complexity. In the offline clustering, the feature vector sets are partitioned into different clusters through the K-means algorithm. Then, the clustering result can be used in the online classification to classify the new wood image. Furthermore, to process the abnormal images of wooden boards, we propose an improved algorithm with centroid improvement and image filtering. The experimental results verify the effectiveness of the proposed mechanism.

Color Classification of Wooden Boards Based on Machine Vision and the Clustering Algorithm

Wood color classification is used to classify the set of wooden boards with similar colors. It is helpful to improve the appearance of the wooden furniture which is spliced by multiple wooden boards. Due to the similarity of colors among wooden boards, the manual color classification is inaccurate and unstable, thus the supervised learning algorithms can hardly be used in this scenario. Moreover, the wooden board is long and its image has high resolution that may lead to the growth of computation complexity. To overcome these challenges, in this paper, we propose a new mechanism for wood color classification with the support of cloud and edge computing. The wood image is preprocessed to subtract irrelevant colors. Then the feature vector is extracted based on 3-D color histogram to reduce the computation complexity. In the offline clustering, the feature vector sets are partitioned into different clusters through K-means algorithm in the cloud server. The clustering result can be used in the online classification to classify the new wood image by the edge server. The experimental results verify the effectiveness of the proposed mechanism.

Wood Color Classification Based on Color Spatial Features and K-means Algorithm

In the wooden furniture industry, the increasing demand of mass customized production generates the diversity of customized wooden components that brings a challenge to the design of the cutting strategy. In this paper, we propose a framework for the cross cutting of wooden boards based on industrial internet. The list of customized wooden components are updated on demand by the production management server, and then transmitted to the industrial computer through industrial internet. Based on the information of the components and the scanned wooden board, the computer executes the cutting strategy to obtain the cutting list in real time. Then it is sent to the saw for the cross cutting. The proposed cutting strategy contains three algorithms: optimization of fixed-length components with uniform quality, matching component to wooden board in intervals, and processing continuous-length components. The experimental results verify the effectiveness of the proposed cutting strategy.

Liang Shijia

Papers

Color Classification of Wooden Boards Based on Machine Vision and the Clustering Algorithm

Wood Color Classification Based on Color Spatial Features and K-means Algorithm

A Cutting Strategy for Customized Production of Wooden Components Based on Industrial Internet