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

Zhuang, Zilong; Liu, Ying; Ding, Fenglong; Wang, Zhengguang

doi:10.3390/s21020336

Cited by 22 publications

(14 citation statements)

References 19 publications

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“…e recognition of windows also plays an important role in the three-dimensional reconstruction of the floor plan. Lighting conditions are an important factor to consider in the subsequent home improvement design process [19].…”

Section: Frame Diagram Identification Processmentioning

confidence: 99%

Artificial Neural Network Model for Indoor Decoration Intelligence Calculation and Automation Design

2022

Computational Intelligence and Neuroscience

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With the continuous development of science and technology, the indoor decoration industry has gradually changed toward mechanization, specialization, and intelligent direction. Based on the predecessor research, this study proposes an artificial neural network model for indoor decoration intelligence calculation and automation design. Based on scales, walls, doors, windows, and other specific components, digital image processing technology implements automatic identification of the apartment graph and completes the preprocessing of the floor plan map. Combined with the indoor decoration data set, the automated design model based on an artificial neural network is established, and the network structure and training process of the model are analyzed. Finally, the bedroom and the living room were experimentally designed. The results showed that as the number of training increased to 30 times, the MAE and MSE assessment indicators gradually decreased, and the error of the model was very small and gradually stabilized. This shows that artificial neural network automation design is better; second, artificial neural network algorithms can generate multiple layout schemes within 1 minute. The design layout is efficient and the plan is reasonable. It meets all requests such as circulation, openness, lighting, and functionality, saving a lot of human and time and providing users with more choices.

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Section: Frame Diagram Identification Processmentioning

confidence: 99%

Artificial Neural Network Model for Indoor Decoration Intelligence Calculation and Automation Design

2022

Computational Intelligence and Neuroscience

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“…In this way, CNN manages to separate highly non-linear and correlated characteristics on different sources of information. At present, the range of applications involving CNN is vast and diverse, achieving the extraction of patterns on various problems and domains [Liu et al, 2020, Poterek et al, 2020, Renò et al, 2020, Wang and Deng, 2018, Westlake et al, 2016, Zhuang et al, 2021.…”

Section: Color and Emotion: From Computing To Deep Learningmentioning

confidence: 99%

Analysis of the use of color and its emotional relationship in visual creations based on experiences during the context of the COVID-19 pandemic

González-Martín¹,

Ortíz²,

Oviedo³

2022

Preprint

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Color is a complex communicative element that helps us understand and evaluate our environment. At the level of artistic creation, this component influences both the formal aspects of the composition and the symbolic weight, directly affecting the construction and transmission of the message that you want to communicate, creating a specific emotional reaction. During the COVID-19 pandemic, people generated countless images transmitting this event's subjective experiences. Using the repository of images created in the Instagram account CAM (The COVID Art Museum), we propose a methodology to understand the use of color and its emotional relationship in this context. The process considers two stages in parallel that are then combined. First, emotions are extracted and classified from the CAM dataset images through a convolutional neural network. Second, we extract the colors and their harmonies through a clustering process. Once both processes are completed, we combine the results generating an expanded discussion on the usage of color, harmonies, and emotion. The results indicate that warm colors are prevalent in the sample, with a preference for analog compositions over complementary ones. The relationship between emotions and these compositions shows a trend in positive emotions, reinforced by the results of the algorithm a priori and the emotional relationship analysis of the attributes of color (hue, chroma, and lighting).

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“…In recent years, machine vision technology [10,11] and machine learning algorithms [12,13] have become popular solutions for signal processing and have been applied to color image classification. At present, the main direction of wood color classification is to select the appropriate color features and to construct accurate classifiers [14]. Rong-Hui [15] extracted eight color features of images in HSV (hue, saturation, and value), HSL (hue, saturation, and luminance), and HSI (hue, saturation, and intensity) color spaces and used the k-NN algorithm to realize the classification of farmland images in different environments.…”

Section: Introductionmentioning

confidence: 99%

Color Classification and Texture Recognition System of Solid Wood Panels

Wang

Zhuang

Liu

et al. 2021

Forests

Self Cite

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Solid wood panels are widely used in the wood flooring and furniture industries, and paneling is an excellent material for indoor decoration. The classification of colors helps to improve the appearance of wood products assembled from multiple panels due to the differences in surface colors of solid wood panels. Traditional wood surface color classification mainly depends on workers’ visual observations, and manual color classification is prone to visual fatigue and quality instability. In order to reduce labor costs of sorting and to improve production efficiency, in this study, we introduced machine vision technology and an unsupervised learning technique. First-order color moments, second-order color moments, and color histogram peaks were selected to extract feature vectors and to realize data dimension reduction. The feature vector set was divided into different clusters by the K-means algorithm to achieve color classification and, thus, the solid wood panels with similar surface color were classified into one category. Furthermore, during twice clustering based on second-order color moment, texture recognition was realized on the basis of color classification. A sample of beech wood was selected as the research object, not only was color classification completed, but texture recognition was also realized. The experimental results verified the effectiveness of the technical proposal.

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

Cited by 22 publications

References 19 publications

Artificial Neural Network Model for Indoor Decoration Intelligence Calculation and Automation Design

Artificial Neural Network Model for Indoor Decoration Intelligence Calculation and Automation Design

Analysis of the use of color and its emotional relationship in visual creations based on experiences during the context of the COVID-19 pandemic

Color Classification and Texture Recognition System of Solid Wood Panels

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