Wood identification based on histogram of oriented gradient (HOG) feature and support vector machine (SVM) classifier

Sugiarto, Bambang; Prakasa, Esa; Wardoyo, Riyo; Damayanti, Ratih; Krisdianto, Krisdianto; Dewi, Listya Mustika; Pardede, Hilman F.; Rianto, Yan

doi:10.1109/icitisee.2017.8285523

Cited by 31 publications

(13 citation statements)

References 12 publications

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“…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%

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: 99%

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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“…Each cell builds a histogram of the gradient direction for each pixel inside it. [23] At the same time, the important features are obtained by calculating the HOG of the image local area and then the HOG algorithm is used to identify the target object to be detected. So far, HOG descriptors have been widely used for various computer vision problems.…”

Section: Histogram Of Oriented Gradients (Hog)mentioning

confidence: 99%

Self-Interacting Proteins Prediction from PSSM Based on Evolutionary Information

Wang

et al. 2021

Scientific Programming

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Self-interacting proteins (SIPs) play an influential role in regulating cell structure and function. Thus, it is critically important to identify whether proteins themselves interact with each other. Although there are some existing experimental methods for self-interaction recognition, the limitations of these methods are both expensive and time-consuming. Therefore, it is very necessary to develop an efficient and stable computational method for predicting SIPs. In this study, we develop an effective computational method for predicting SIPs based on rotation forest (RF) classifier, combined with histogram of oriented gradients (HOG) and synthetic minority oversampling technique (SMOTE). When performing SIPs prediction on yeast and human datasets, the proposed method achieves superior accuracies of 97.28% and 89.41%, respectively. In addition, the proposed approach was compared with the state-of-the-art support vector machine (SVM) classifiers and other different methods on the same datasets. The experimental results demonstrate that our method has good robustness and effectiveness and can be regarded as a useful tool for SIPs prediction.

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“…Pengenalan jenis kayu menggunakan fitur citra makroskopik lebih mudah diterapkan, sederhana dan fleksibel [6]. Tantangan utama fitur citra makroskopik adalah kemiripan tekstur antara satu jenis kayu dengan jenis kayu lainnya [7]. Telah banyak penelitian untuk mengidentifikasi jenis kayu menggunakan citra makroskopis dan mikroskopis.…”

Section: Pendahuluanunclassified

“…Pendekatan mereka mampu mencapai akurasi 91,47%. Sugiarto dkk melakukan pengenalan jenis kayu dari citra makroskopik menggunakan Histogram of Oriented Gradient (HOG) dan SVM Classifier [7]. Akurasinya adalah 70,5% untuk pengujian citra positif dan 77,5% untuk pengujian citra negatif.…”

Section: Pendahuluanunclassified

Pengenalan Jenis Kayu Berdasarkan Citra Makroskopik Menggunakan Metode Convolutional Neural Network

Prastowo¹

2021

JuTISI

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Until now, wood has an irreplaceable function. Building materials, shipping, furniture, sports equipment, carvings and handicrafts using wood. Indonesia has more than 4,000 types of wood, so choosing the right wood is a challenge because choosing the wrong type of wood can make the quality of processed products decline and not as expected. In addition, proper identification of timber can also prevent illegal logging, especially on certain types of wood which are now increasingly scarce. Recognition to wood by looking directly is a difficult thing for ordinary people to do and can only be done by a wood expert, so it is necessary to find a method of recognizing wood that can be used by people independently. One method that can be used to identify type of wood is image processing based on characteristics of wood which include color, fiber direction and texture. This paper will describe recognition of wood-based image processing using Convolutional Neural Network (CNN) method. This method is derived from Neural Networks with addition of an extraction layer feature, which can reduce free parameters that are not needed by the system. Wood image data used in this study are four types of wood that are often used as raw materials for making houses and furniture, namely Glugu, Teak, Sengon and Waru. Results of this study were able to recognize four types of wood with an accuracy of 95% in 600 epochs/iteration, so that it can be used as a simple, easy and inexpensive wood recognition system.

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Wood identification based on histogram of oriented gradient (HOG) feature and support vector machine (SVM) classifier

Cited by 31 publications

References 12 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

Self-Interacting Proteins Prediction from PSSM Based on Evolutionary Information

Pengenalan Jenis Kayu Berdasarkan Citra Makroskopik Menggunakan Metode Convolutional Neural Network

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