Arca Detection and Matching Using Scale Invariant Feature Transform (SIFT) Method of Stereo Camera

Rahman, Aviv Yuniar; Sumpeno, Surya; Purnomo, Mauridhi Hery

doi:10.1109/icsiit.2017.45

Cited by 9 publications

(2 citation statements)

References 11 publications

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“…Tim pelaksana usaha kecil menengah adalah pihak internal yang bisa membuat langkah-langkah dengan pemerintah [3] [9]. Saat ini banyak usaha dengan berbagai macam olahan dan model bisnis yang beragam, salah satunya yaitu pengolahan sampah organik untuk pakan cacing [10].…”

Section: Pendahuluanunclassified

Model Pengolahan Kas UMKM Sampah Organik untuk Pakan Cacing menggunakan Finite State Automata

Rahman

Setyawan

Setiawan³

et al. 2019

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Financial processing is currently widely used for business purposes. The business includes small and medium businesses. With these small and medium businesses, the system used still uses financial processes manually in its processing. As a result, there are often errors in the calculation process as well as income and expenditure. And not the same as the results of the calculation. In this cash processing application system, it can help small and medium entrepreneurs to calculate large amounts of data. And will reduce the error rate. The application model is designed using Finite State Automata. With this model, it can be seen that the value of the design testing process shows that the first and second designs have smaller processing processes compared to the third design. It can be said that to conduct data processing in a large way the third design is more accurate than the first and second. It is hoped that this application can be applied by other small and medium micro-entrepreneurs, and can assist in carrying out a more efficient financial management process.

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Section: Pendahuluanunclassified

Model Pengolahan Kas UMKM Sampah Organik untuk Pakan Cacing menggunakan Finite State Automata

Rahman

Setyawan

Setiawan³

et al. 2019

FIFO

Self Cite

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“…Once these features are discovered, they can be used to compare new faces with a database of known faces to estimate the identity of those faces. SIFT has the ability to handle scaling and rotational changes in facial images, making it suitable for facial recognition under different conditions [8][15] [16]. The SIFT method on facial images consists of several stages [17][18]: a.…”

Section: Introductionmentioning

confidence: 99%

Comparison of Sift and Orb Methods in Identifying the Face of Buddha Statue

Marlinda

Budiman

Basuki

et al. 2023

jitk

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The statue is part of the heritage facial recognition process which is immobile and artistically stylized. Identifying the similarities between the statues can help provide an important reference for tourism in recognizing the faces of the statues which are different and have almost the same characteristics in every country, especially in Indonesia, among the facial recognition of the statues based on the condition, color, and shape of the face. The purpose of this study is to apply the original images that have characteristics, partially done manually to various types of transformations and calculate matching evaluation parameters such as the number of key points in the image, the level of matching, and the required execution time for each algorithm. To confirm the efficiency of the proposed method, experiments were carried out on private data sets obtained from statues under low light conditions and in different poses. The data was taken based on the image of the Buddha's face and matched with the facial image of the Buddha statue available in the database using comparisons resulting from data processing using the Sift and ORB methods with various types of transformations. The result will be seen in the image that is matched with the best algorithm for each type of distortion. The faces tested are images of the faces of the Buddha statues that are recognized, and photos of some of the original statues that were not saved due to unclear lighting and camera distance factors. The results show that the number of key points generated is the number of key points, the ORB method gives fewer results compared to the SIFT method and the average SIFT recognition and processing time shows better performance for an average of 100% at a SIFT matching rate of 2% with time 0.400285 and the ORB method is 1% for the time 0.400961

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Image Classification of Starlings Using Artificial Neural Network and Decision Tree

Rahman

2022

2022 IEEE International Conference on Cybernetics and Computational Intelligence (CyberneticsCom)

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Arca Detection and Matching Using Scale Invariant Feature Transform (SIFT) Method of Stereo Camera

Cited by 9 publications

References 11 publications

Model Pengolahan Kas UMKM Sampah Organik untuk Pakan Cacing menggunakan Finite State Automata

Model Pengolahan Kas UMKM Sampah Organik untuk Pakan Cacing menggunakan Finite State Automata

Comparison of Sift and Orb Methods in Identifying the Face of Buddha Statue

Image Classification of Starlings Using Artificial Neural Network and Decision Tree

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