Wood Colorimetry for the Characterization of Amazonian Tree Species: A Subsidy for a More Efficient Classification

Sousa, Wilza Carla Santos e; Barbosa, Lucas de Jesus; Soares, Álvaro Augusto Vieira; Goulart, Selma Lopes; Protásio, Thiago de Paula

doi:10.1590/01047760201925042650

Cited by 15 publications

(11 citation statements)

References 42 publications

(56 reference statements)

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“…In [7], the authors found that the three parameters (L*, a*, and b*) are adequate for the classification of thermally-modified ash and beech hardwood according to the analysis of the wood color with the CIE L*a*b* color space (L* for the lightness from black to white, a* from green to red, and b* from blue to yellow). In [8], the authors used the CIE L*a*b* system to discriminate the variability of the wood color and determined the correlation with the wood's basic density for ten Amazonian tree species.…”

Section: Related Workmentioning

confidence: 99%

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

et al. 2020

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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.

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Section: Related Workmentioning

confidence: 99%

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

et al. 2020

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“…Apart from the unequivocal theoretical interest, these efforts have been also driven for the enormous applications of accurate colour representations for quality control in the industry (e.g. textiles [2], foods [3]- [5], woodworking [6], automotive [7], etc. ).…”

Section: Introductionmentioning

confidence: 99%

Multilayer Perceptron to Boost Colorimetric Capacities of 2D RGB and Hyperspectral Snapshot Cameras

Lopez-Lopez

Méndez-Rial

2022

2022 10th European Workshop on Visual Information Processing (EUVIP)

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The ability to accurately measure the surface colour has enormous applications in the quality control of a wide range of manufacturing processes. Currently, the industry still relies on traditional colourimeters to implement these tasks.In this work, we compare the behaviour of two types of snapshot cameras (RGB and hyperspectral) in the frame of accurate colour measuring. We use two approaches for colour and spectral reconstruction, one based on linear modelling and another novel one based on Multilayer Perceptron (MLP). Experiments use charts with a large number of different colours (165 for training and 165 for testing). The results showed promising properties of neural networks in this task. We reach an average 1.6 CIEDE 2000 colour difference between estimations and ground truth.

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“…At present, the color classification of wood usually begins from two directions: one is to extract better feature values, and the other is to select a more accurate classifier. Some studies have designed better color characteristics to describe the wood color [ 3 , 4 , 5 , 6 ]. Barmpoutis [ 7 ] proposed a method to enable the representation of wood images as concatenated histograms of higher-order linear dynamical systems produced by vertical and horizontal image patches; Hiremath [ 8 ] proposed an efficient multiresolution method for texture classification based on anisotropic diffusion and local directional binary patterns (LDBP).…”

Section: Introductionmentioning

confidence: 99%

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

Zhuang

Liu

Ding

et al. 2021

Sensors

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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.

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Wood Colorimetry for the Characterization of Amazonian Tree Species: A Subsidy for a More Efficient Classification

Cited by 15 publications

References 42 publications

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

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

Multilayer Perceptron to Boost Colorimetric Capacities of 2D RGB and Hyperspectral Snapshot Cameras

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

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