Classification of hardwood species using ANN classifier

Yadav, Arvind R.; Dewal, M. L.; Anand, R. S.; Gupta, Sangeeta

doi:10.1109/ncvpripg.2013.6776231

Cited by 27 publications

(6 citation statements)

References 7 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…This technique is preferred to normalize the brightness across an image and increase contrast in wood identification [58][59][60][61]. Homomorphic filtering also has the effect of sharpening the image [26,101] and Gabor filters have been used for sharpening [102]. Sharpening was used as a preprocessing to segment notable cells such as the vessel [59,61,103].…”

Section: Preprocessingmentioning

confidence: 99%

Computer vision-based wood identification and its expansion and contribution potentials in wood science: A review

Hwang

Sugiyama

2021

Plant Methods

View full text Add to dashboard Cite

The remarkable developments in computer vision and machine learning have changed the methodologies of many scientific disciplines. They have also created a new research field in wood science called computer vision-based wood identification, which is making steady progress towards the goal of building automated wood identification systems to meet the needs of the wood industry and market. Nevertheless, computer vision-based wood identification is still only a small area in wood science and is still unfamiliar to many wood anatomists. To familiarize wood scientists with the artificial intelligence-assisted wood anatomy and engineering methods, we have reviewed the published mainstream studies that used or developed machine learning procedures. This review could help researchers understand computer vision and machine learning techniques for wood identification and choose appropriate techniques or strategies for their study objectives in wood science.

show abstract

Section: Preprocessingmentioning

confidence: 99%

Computer vision-based wood identification and its expansion and contribution potentials in wood science: A review

Hwang

Sugiyama

2021

Plant Methods

View full text Add to dashboard Cite

show abstract

“…The fusion of GLCM and LPQ features reported a recognition accuracy of 93.20% using SVM classifier. The GLCM features obtained from Gabor wavelet images of hardwood species produced 92.60% classification accuracy with MLP-BP-NN classifier (Yadav et al 2013). Subsequently, Yadav et al (2014) acquired Coiflet DWT based features of the hardwood species.…”

Section: Literature Reviewmentioning

confidence: 99%

Hardwood species classification with DWT based hybrid texture feature extraction techniques

Yadav

Anand

Dewal

et al. 2015

Sadhana

Self Cite

View full text Add to dashboard Cite

In this work, discrete wavelet transform (DWT) based hybrid texture feature extraction techniques have been used to categorize the microscopic images of hardwood species into 75 different classes. Initially, the DWT has been employed to decompose the image up to 7 levels using Daubechies (db3) wavelet as decomposition filter. Further, first-order statistics (FOS) and four variants of local binary pattern (LBP) descriptors are used to acquire distinct features of these images at various levels. The linear support vector machine (SVM), radial basis function (RBF) kernel SVM and random forest classifiers have been employed for classification. The classification accuracy obtained with state-of-the-art and DWT based hybrid texture features using various classifiers are compared. The DWT based FOS-uniform local binary pattern (DWTFOSLBP u2) texture features at the 4 th level of image decomposition have produced best classification accuracy of 97.67 ± 0.79% and 98.40 ± 064% for grayscale and RGB images, respectively, using linear SVM classifier. Reduction in feature dataset by minimal redundancy maximal relevance (mRMR) feature selection method is achieved and the best classification accuracy of 99.00 ± 0.79% and 99.20 ± 0.42% have been obtained for DWT based FOS-LBP histogram Fourier features (DWTFOSLBP-HF) technique at the 5 th and 6 th levels of image decomposition for grayscale and RGB images, respectively, using linear SVM classifier. The DWTFOSLBP-HF features selected with mRMR method has also established superiority amongst the DWT based hybrid texture feature extraction techniques for randomly divided database into different proportions of training and test datasets.

show abstract

“…In the process of establishing features, the commonly used feature extraction methods for wood materials include the gray-level co-occurrence matrix [ 9 , 10 , 11 ], local binary pattern (LBP) [ 12 , 13 , 14 ], scale-invariant feature transform [ 15 , 16 ], and wavelet transform [ 17 , 18 ]. In addition, modified versions or other methods based on the above methods have emerged.…”

Section: Introductionmentioning

confidence: 99%

A Sawn Timber Tree Species Recognition Method Based on AM-SPPResNet

Ding

Liu

Zhuang

et al. 2021

Sensors

View full text Add to dashboard Cite

Sawn timber is an important component material in furniture manufacturing, decoration, construction and other industries. The mechanical properties, surface colors, textures, use and other properties of sawn timber possesed by different tree species are different. In order to meet the needs of reasonable timber use and product quality of sawn timber products, sawn timber must be identified according to tree species to ensure the best use of materials. In this study, an optimized convolution neural network was proposed to process sawn timber image data to identify the tree species of the sawn timber. The spatial pyramid pooling and attention mechanism were used to improve the convolution layer of ResNet101 to extract the feature vector of sawn timber images. The optimized ResNet (simply called “AM-SPPResNet”) was used to identify the sawn timber image, and the basic recognition model was obtained. Then, the weight parameters of the feature extraction layer of the basic model were frozen, the full connection layer was removed, and using support vector machine (SVM) and XGBoost classifier which were commonly used in machine learning to train and learn the 21 × 1024 dimension feature vectors extracted by feature extraction layer. Through a number of comparative experiments, it is found that the prediction model using linear function as the kernel function of support vector machine learning the feature vectors extracted from the improved convolution layer performed best, and the F1 score and overall accuracy of all kinds of samples were above 99%. Compared with the traditional methods, the accuracy was improved by up to 12%.

show abstract

Classification of hardwood species using ANN classifier

Cited by 27 publications

References 7 publications

Computer vision-based wood identification and its expansion and contribution potentials in wood science: A review

Computer vision-based wood identification and its expansion and contribution potentials in wood science: A review

Hardwood species classification with DWT based hybrid texture feature extraction techniques

A Sawn Timber Tree Species Recognition Method Based on AM-SPPResNet

Contact Info

Product

Resources

About