An automatic identification of wood materials from color images

Stevek, Juraj; Katuščák, Svetozár; Dubinyova, Lenka; Fikar, Miroslav

doi:10.1109/cyberi.2016.7438597

Cited by 5 publications

(4 citation statements)

References 11 publications

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“…Camera and infrared based systems. Thermal infrared sensor or visible light camera is widely used for target imaging [48] and material identification [4,20,27]. Specifically, they acquire a target's millions of reflectance and/or fluorescence spectra for material identification [4].…”

Section: Related Workmentioning

confidence: 99%

TagScan

Wang

Xiong

Chen

et al. 2017

Proceedings of the 23rd Annual International Conference on Mobile Computing and Networking

167

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Target imaging and material identification play an important role in many real-life applications. This paper introduces TagScan, a system that can identify the material type and image the horizontal cut of a target simultaneously with cheap commercial offthe-shelf (COTS) RFID devices. The key intuition is that different materials and target sizes cause different amounts of phase and RSS (Received Signal Strength) changes when radio frequency (RF) signal penetrates through the target. Multiple challenges need to be addressed before we can turn the idea into a functional system including (i) indoor environments exhibit rich multipath which breaks the linear relationship between the phase change and the propagation distance inside a target; (ii) without knowing either material type or target size, trying to obtain these two information simultaneously is challenging; and (iii) stitching pieces of the propagation distances inside a target for an image estimate is non-trivial. We propose solutions to all the challenges and evaluate the system's performance in three different environments. TagScan is able to achieve higher than 94% material identification accuracies for 10 liquids and differentiate even very similar objects such as Coke and Pepsi. TagScan can accurately estimate the horizontal cut images of more than one target behind a wall.

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Section: Related Workmentioning

confidence: 99%

TagScan

Wang

Xiong

Chen

et al. 2017

Proceedings of the 23rd Annual International Conference on Mobile Computing and Networking

167

View full text Add to dashboard Cite

show abstract

“…Acoustic signals play a pivotal role in the current intelligent perception, finding extensive applications [ 11 , 12 , 13 ]. Unlike previous approaches to model construction [ 14 , 15 , 16 , 17 , 18 ], our design uses the built-in microphone and speaker of a mobile phone as the receiver (RX) and transmitter (TX), making it portable and commercially off-the-shelf (COTS). The underlying intuition of our design is that the multipath effects vary when acoustic signals propagate in different materials, forming the basis for the acoustic signal-based material identification that can be used for identifying different materials.…”

Section: Introductionmentioning

confidence: 99%

Identification of Solid and Liquid Materials Using Acoustic Signals and Frequency-Graph Features

Zhang,

Zhou

2023

Entropy

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Material identification is playing an increasingly important role in various sectors such as industry, petrochemical, mining, and in our daily lives. In recent years, material identification has been utilized for security checks, waste sorting, etc. However, current methods for identifying materials require direct contact with the target and specialized equipment that can be costly, bulky, and not easily portable. Past proposals for addressing this limitation relied on non-contact material identification methods, such as Wi-Fi-based and radar-based material identification methods, which can identify materials with high accuracy without physical contact; however, they are not easily integrated into portable devices. This paper introduces a novel non-contact material identification based on acoustic signals. Different from previous work, our design leverages the built-in microphone and speaker of smartphones as the transceiver to identify target materials. The fundamental idea of our design is that acoustic signals, when propagated through different materials, reach the receiver via multiple paths, producing distinct multipath profiles. These profiles can serve as fingerprints for material identification. We captured and extracted them using acoustic signals, calculated channel impulse response (CIR) measurements, and then extracted image features from the time–frequency domain feature graphs, including histogram of oriented gradient (HOG) and gray-level co-occurrence matrix (GLCM) image features. Furthermore, we adopted the error-correcting output code (ECOC) learning method combined with the majority voting method to identify target materials. We built a prototype for this paper using three mobile phones based on the Android platform. The results from three different solid and liquid materials in varied multipath environments reveal that our design can achieve average identification accuracies of 90% and 97%.

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“…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). Some studies used more accurate classification algorithms for classification [ 9 , 10 ]. Nasir [ 11 ] compared and evaluated the performance of artificial neural networks (ANN), SVM, and naive Bayes (NB) classifiers for thermowood classification and obtained the conclusion that the SVM and NB models can be used for online quality control.…”

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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An automatic identification of wood materials from color images

Cited by 5 publications

References 11 publications

TagScan

TagScan

Identification of Solid and Liquid Materials Using Acoustic Signals and Frequency-Graph Features

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

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