scale invariant feature transform
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Holzforschung ◽  
2021 ◽  
Vol 0 (0) ◽  
Author(s):  
Sung-Wook Hwang ◽  
Taekyeong Lee ◽  
Hyunbin Kim ◽  
Hyunwoo Chung ◽  
Jong Gyu Choi ◽  
...  

Abstract This paper describes feature-based techniques for wood knot classification. For automated classification of macroscopic wood knot images, models were established using artificial neural networks with texture and local feature descriptors, and the performances of feature extraction algorithms were compared. Classification models trained with texture descriptors, gray-level co-occurrence matrix and local binary pattern, achieved better performance than those trained with local feature descriptors, scale-invariant feature transform and dense scale-invariant feature transform. Hence, it was confirmed that wood knot classification was more appropriate for texture classification rather than an approach based on morphological classification. The gray-level co-occurrence matrix produced the highest F1 score despite representing images with relatively low-dimensional feature vectors. The scale-invariant feature transform algorithm could not detect a sufficient number of features from the knot images; hence, the histogram of oriented gradients and dense scale-invariant feature transform algorithms that describe the entire image were better for wood knot classification. The artificial neural network model provided better classification performance than the support vector machine and k-nearest neighbor models, which suggests the suitability of the nonlinear classification model for wood knot classification.


Author(s):  
Marziye Shahrokhi ◽  
Alireza Akoushideh ◽  
Asadollah Shahbahrami

Today, manipulating, storing, and sending digital images are simple and easy because of the development of digital imaging devices from hardware and software points of view. Digital images are used in different contexts of people’s lives such as news, forensics, and so on. Therefore, the reliability of received images is a question that often occupies the viewer’s mind and the authenticity of digital images is increasingly important. Detecting a forged image as a genuine one as well as detecting a genuine image as a forged one can sometimes have irreparable consequences. For example, an image that is available from the scene of a crime can lead to a wrong decision if it is detected incorrectly. In this paper, we propose a combination method to improve the accuracy of copy–move forgery detection (CMFD) reducing the false positive rate (FPR) based on texture attributes. The proposed method uses a combination of the scale-invariant feature transform (SIFT) and local binary pattern (LBP). Consideration of texture features around the keypoints detected by the SIFT algorithm can be effective to reduce the incorrect matches and improve the accuracy of CMFD. In addition, to find more and better keypoints some pre-processing methods have been proposed. This study was evaluated on the COVERAGE, GRIP, and MICC-F220 databases. Experimental results show that the proposed method without clustering or segmentation and only with simple matching operations, has been able to earn the true positive rates of 98.75%, 95.45%, and 87% on the GRIP, MICC-F220, and COVERAGE datasets, respectively. Also, the proposed method, with FPRs from 17.75% to 3.75% on the GRIP dataset, has been able to achieve the best results.


Author(s):  
Yohannes Yohannes ◽  
Siska Devella ◽  
William Hadisaputra

White blood cells are cells that makeup blood components that function to fight various diseases from the body (immune system). White blood cells are divided into five types, namely basophils, eosinophils, neutrophils, lymphocytes, and monocytes. Detection of white blood cell types is done in a laboratory which requires more effort and time. One solution that can be done is to use machine learning such as Support Vector Machine (SVM) with Scale Invariant Feature Transform (SIFT) feature extraction. This study uses a dataset of white blood cell images that previously carried out a pre-processing stage consisting of cropping, resizing, and saliency. The saliency method can take a significant part in image data and. The SIFT feature extraction method can provide the location of the keypoint points that SVM can use in studying and recognizing white blood cell objects. The use of region-contrast saliency with kernel radial basis function (RBF) yields the best accuracy, precision, and recall results. Based on the test results obtained in this study, saliency can improve the accuracy, precision, and recall of SVM on the white blood cell image dataset compared to without saliency.


2021 ◽  
Vol 15 (2) ◽  
pp. 106-118
Author(s):  
Deden Nurudin ◽  
Tito Sugiharto ◽  
Rio Priantama

AbstrakMata pelajaran Biologi merupakan salah satu mata pelajaran yang diberikan kepada siswa/i di SMA NEGERI 1 Kuningan. Pengenalan animalia coelenterta merupakan salah satu materi mata pelajaran biologi yang dipelajari oleh siswa/i dengan menggunakan media pembelajaran konvensional yang digunakan oleh guru berupa buku yang menampilkan gambaran animalia coelenterata 2D, sehingga  siswa/i terbatas dalam mendapatkan gambaran lengkap Animalia coelenterata. Proses pembelajaran yang berlangsung belum efektif dan belum dapat meningkatkan antusiasme siswa/i pada saat pembelajaran dikelas. Maka dari itu dengan memanfaatkan teknologi Augmented Reality dapat menjadi salah satu langkah penyelesaian dalam mengatasi hal tersebut. Augmented Reality merupakan teknologi yang menggabungkan benda antara dunia nyata dengan dunia maya berupa objek 3D yang dapat dimanfaatkan dalam pembelajaran pengenalan animalia coelenterata oleh siswa/i, agar pembelajaran pengenalan animalia coelenterata lebih menarik dan tidak monoton karena menampilkan gambaran  animasi 3D. Untuk membangun aplikasi pengenalan animalia coelenterata, perancangan menggunakan UML (Unified Modelling Language) serta penerapan algoritma SIFT(Scale Invariant Feature Transform) untuk pendeteksian titik keypoint pada marker. Pendeteksian titik keypoint dilakukan pada marker yang ada didalam program. Aplikasi ini dapat membantu siswa/i dalam mempelajari pengenalan animalia coelenterata karena gambaran animalia coelenterata terlihat lebih nyata dengan Animasi 3D, gerakan animalia coelenterata dan deskripsi animalia coelenterata. Kata Kunci:SIFT, Augmented Reality, Animalia Coelenterata, UML


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