Identification of wood defect using pattern recognition technique

Chun, Teo Hong; Hashim, Ummi Rabaah; Ahmad, Sabrina; Salahuddin, Lizawati; Choon, Ngo Hea; Kanchymalay, Kasturi; Ismail, Nur Haslinda

doi:10.26555/ijain.v7i2.588

Cited by 4 publications

(3 citation statements)

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“…The recall is a function of the true-positive and false-negative images in the retrieval system [27]. The F-Measure in each class is a composite measure of precision and recall, as shown in (8). The experiment aims to improve experiment accuracy, typically measured using classification accuracy [28].…”

Section: Evaluation Of Detection Performancementioning

confidence: 99%

“…The Automated Visual Inspection (AVI) method includes automated image capture, enhancement, segmentation, feature extraction, and categorization. AVI is a completely automated extraction and categorization procedure that would enhance the inspection process and lower labour expenditures [7][8][9]. This research aims to classify timber faults using a pattern recognition technique contributing to AVI, emphasizing the feature extraction stage.…”

Section: Introductionmentioning

confidence: 99%

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Local Texture Representation for Timber Defect Recognition based on Variation of LBP

Rahiddin¹,

Hashim²,

Salahuddin³

et al. 2022

IJACSA

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This paper evaluates timber defect classification performance across four various Local Binary Patterns (LBP). The light and heavy timber used in the study are Rubberwood, KSK, Merbau, and Meranti, and eight natural timber defects involved; bark pocket, blue stain, borer holes, brown stain, knot, rot, split, and wane. A series of LBP feature sets were created by employing the Basic LBP, Rotation Invariant LBP, Uniform LBP, and Rotation Invariant Uniform LBP in a phase of feature extraction procedures. Several common classifiers were used to further separate the timber defect classes, which are Artificial Neural Network (ANN), J48 Decision Tree (J48), and K-Nearest Neighbor (KNN). Uniform LBP with ANN classifier provides the best performance at 63.4%, superior to all other LBP types. Features from Merbau provide the greatest F-measure when comparing the performance of the ANN classifier with Uniform LBP across timber fault classes and clean wood, surpassing other feature sets.

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Section: Evaluation Of Detection Performancementioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Local Texture Representation for Timber Defect Recognition based on Variation of LBP

Rahiddin¹,

Hashim²,

Salahuddin³

et al. 2022

IJACSA

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“…We propose using Radon transformation as edges detection method due to its lesser computation budget unlikely other methods e.g. [20], [21].…”

Section: Features Extractionmentioning

confidence: 99%

Personal Identification Using Palm Features Recognition

Mahmood,

Ismail Ibrahim,

Tharwat Saeed

2022

IJETS

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Personal recognition is meant for finding a way for establishment of connections between the person and his/her biometrical features. Such system is depending various data types such as facial images, voice and limbs. In this paper, palm print recognition is made using deep learning paradigms such as feed forward neural network (FFNN). The palm features are extracted by tracking the principal lines of palm skin. This involves performing of pixel to pixel analysis by comparing pixel value with its four sides neighbors. FFNN model is tuned up using ABC-KNN algorithm then used for classification. The proposed system has yielded good recognition accuracy score of 98.66%.

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