Wood species recognition based on SIFT keypoint histogram

Hu, Shuaiqi; Li, Ke; Bao, Xudong

doi:10.1109/cisp.2015.7407968

Cited by 18 publications

(11 citation statements)

References 21 publications

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“…The BOF model trained with codewords converted from SIFT descriptors produced higher classification performance than the model trained with SIFT features intact [93]. Even for a macro image dataset, the SIFT-based BOF model outperformed the models trained with texture features [128].…”

Section: Local Featurementioning

confidence: 89%

“…In comparative studies of local features and textures (Table 4), SIFT and SURF had higher discriminative power than GLCM and LBP, whereas LPQ had similar discriminative power for local features [47,128]. Histograms of oriented gradients (HOG) [129] are descriptors that represent a local region of an image, and they have been used to classify macroscopic image datasets [130].…”

Section: Local Featurementioning

confidence: 99%

“…SVM requires less space than k-NN because it learns the training data and builds a classification model in advance. SVM has been shown to outperform k-NN for wood identification [68,73,128,147,148]. In most studies that compared the two classifiers in the same classification strategy, SVM outperformed k-NN (Table 5).…”

Section: Classificationmentioning

confidence: 99%

See 2 more Smart Citations

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

Hwang

Sugiyama

2021

Plant Methods

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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.

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Section: Local Featurementioning

confidence: 89%

Section: Local Featurementioning

confidence: 99%

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

Hwang

Sugiyama

2021

Plant Methods

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“…For example, both Souza et al (2020) and Zamri et al (2016) extracted texture features to classify wood with different classifiers, and good classification results were achieved. In addition, Hu et al (2015) and Rajagopal et al (2019) used a SIFT (scale invariant feature transform) algorithm to extract key points from wood cross-section images, and they used image deblurring and feature extraction methods to classify and recognize wood via a support vector machine.…”

Section: Introductionmentioning

confidence: 99%

Microstructure identification based on vessel pores feature extraction of high-value hardwood species

Yang

Zhao

Wang

et al. 2021

BioRes

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Because of the diversity of vessel pores in different hardwood species, they are important for wood species identification. In this paper, a Micro CT was used to collect wood images. The experiment was based on six wood types, Pterocarpus macrocarpus, Pterocarpus erinaceus, Dalbergia latifolia, Dalbergia frutescens var. tomentosa, Pterocarpus indicus, and Pterocarpus soyauxii. One-thousand cross-sectional images of 2042 px × 1640 px were collected for each species. One pixel represents 1.95 µm of the real physical dimension. The level set geometric active contour model was used to obtain the contour of the vessel pores. Combined with a variety of morphological processing methods, the binary images of the vessel pores were obtained. The features of the binary images were extracted for classification. Classifiers such as BP neural network and support vector machine were used, the number, roundness, area, perimeter, and other characteristic parameters of the vessel pores were classified, and the accuracy rate was more than 98.9%. The distribution and arrangement of the vessel pores of six kinds of hardwood were obtained through the level set geometric active contour model and image morphology. Then BP neural network and support vector machine were used for realizing the classification of hardwood species.

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“…While the information provided by this approach may differ from that produced by the currently established anatomy, the concept of pursuing species specificity in terms of features is fundamentally the same in both approaches. In recent years, some studies have reported that wood species can be recognized using BOF-based models [4,5]. However, because they used macroscopic images and focused on species classification, these approaches are difficult to interpret from the perspective of the anatomical and morphological characteristics of wood.…”

Section: Introductionmentioning

confidence: 99%

Detection and visualization of encoded local features as anatomical predictors in cross-sectional images of Lauraceae

2020

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This paper describes computer vision-based quantitative microscopy and its application toward better understanding species specificity. An image dataset of the Lauraceae family that consists of nine species across six genera was investigated, and structural features were quantified using encoded local features implemented in a bag-of-features framework. Of the algorithms used for feature detection, the scale-invariant feature transform (SIFT) achieved the best performance in species discrimination. In the bag-of-features framework with the SIFT features, each image is represented by a histogram of codewords. The codewords were further analyzed by mapping them to each image to visualize the corresponding anatomical elements. From this analysis, we were able to classify and quantify the modes of aggregation of different combinations of cell elements based on clustered codewords. An analysis of the term frequency-inverse document frequency weights revealed that blob-based codewords are generally shared by all species, whereas corner-based codewords are more species specific.

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Wood species recognition based on SIFT keypoint histogram

Cited by 18 publications

References 21 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

Microstructure identification based on vessel pores feature extraction of high-value hardwood species

Detection and visualization of encoded local features as anatomical predictors in cross-sectional images of Lauraceae

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