Fast Region of Interest detection for fetal genital organs in B-mode ultrasound images

Genital organ detection of fetus in B-mode ultrasound images has a considerable significance. It is useful to know any malformations present in the genital organs and also to determine the sex of the fetus. In this paper we propose a Feature from Accelerated Segment Test (FAST) technique for approximate detection of fetal genitals in ultrasound images. FAST algorithm is capable of producing the corner points at a higher speed which falls on the fetal genital organs. A window of size 60×60 pixels being corner point as a center is considered as Region of Interest (ROI), where genital organ of fetus is anticipated. The efficiency of the algorithm is calculated as the ratio of number of images where corner points are placed on the fetus genital organ to the total number of images tested. FAST algorithm is robust to speckles present in the image, machine independent, fast and also computationally less intensive to implement in real time with an efficiency of 96.7%

Corner Detection on hexagonal pixel based images

Corner detection is used in many computer vision applications that require fast and efficient feature matching. In addition, hexagonal pixel based images have been recently investigated for image capture and processing due to their ability to represent curved structures that are common in real images better than traditional rectangular pixel based images. Therefore, we present an approach to corner detection on hexagonal images and demonstrate that accuracy is comparable to well-known existing corner detectors applied to rectangular pixel based images

Model Addie Pada Augmented Reality Hewan Purba Bersayap Menggunakan Algoritma Fast Corner Detection Dan NFT

Pengenalan objek menggunakan Augmented Reality sudah menjadi trend di dunia media promosi kepada anak-anak usia dini hingga masyarakat umum. Objek yang digunakan berupa hewan, tumbuhan, huruf, angka dan lain lain. Penelitian ini menggunakan objek berupa hewan purbakala yang sudah punah sejak jutaan tahun yang lalu. Tujuan penelitian ini yaitu berfokus pada pengenalan hewan-hewan purbakala utnuk anak-anak bahwa terdapat hewan reptil yang berpostur raksasa telah hidup di zaman dahulu. Meskipun reptil ini telah punah, mereka akan menggunakan Augmented Reality pada penelitian ini sebagai media informasi yang menarik. Model ADDIE dikembangkan pada penelitian ini yang disusun oleh Natural Feature Tracking (NFT) menggunakan Algoritma FAST Corner Detection ke arah tingkat keberhasilan yang tinggi. Hasil pengujian pada beberapa versi android berupa objek gambar memiliki tingkat keakuratan yang tinggi melalui perhitungan FAST Corner Detection dan pengujian metode NFT. Semakin tinggi rating objek yang ditunjukkan pada vuforia, maka semakin tinggi ketelitian dalam mendeteksi objek pada marker

ALGORITMA BLOB dan FAST CORNER DETECTION PADA APLIKASI BANGUN RUANG MATEMATIKA BERBASIS MIXED REALITY

Materi pelajaran Bangun Ruang pada matematika tingkat Sekolah Dasar (SD) akan memperkenalkan bentuk, rumus perhitungan, dan pengaplikasiannya di kehidupan sehari-hari. Era digital saat ini sudah banyak teknologi yang bertujuan untuk mempermudah seseorang dalam  memberikan solusi pada akar permasalahan. Salah satu teknologi yang digunakan pada penelitian ini yaitu Augmented Reality (AR) dan Virtual Reality (VR) atau dikenal dengan Mixed Reality (MR). Kedua teknologi ini akan saling berintegrasi untuk memecahkan masalah dalam materi pelajaran matematika yaitu Bangun Ruang. Tujuan penelitian ini memperkenalkan visualisasi bangun ruang 2D hingga menjadi 3D serta audio yang mendukung untuk menambah daya tarik pengguna dengan menggunakan Algoritma Blob Detection dan FAST Corner Detection, yaitu dengan mendeteksi titik ataupun wilayah berupa warna, dan pendeteksi sudut akan dilakukan oleh FAST Corner Detection. Hasil pengujian didukung dengan rating marker yang dapat terbaca oleh beberapa perangkat android di dapatkan hasil uji terhadap waktu respon yaitu objek akan muncul dalam waktu 2 detik, hasil uji kemiringinan kamera menghasilkan nilai sudut pada 180maka marker tidak terbaca di semua perangkat dan akan memiliki nilai sempurna pada kemiringan sudut 600-900, dan hasil uji jarak kamera terhadap marker  di dapat hasil  + 30 – 120 cm agar marker terdeteksi oleh kamera

Pattern Compression of FAST Corner Detection for Efficient Hardware Implementation

This paper shows stream-oriented FPGA implementation of the machine-learned Features from Accelerated Segment Test (FAST) corner detection, which is used in the parallel tracking and mapping (PTAM) for augmented reality (AR). One of the difficulties of compact hardware implementation of the FAST corner detection is a matching process with a large number of corner patterns. We propose corner pattern compression methods focusing on discriminant division and pattern symmetry for rotation and inversion. This pattern compression enables implementation of the corner pattern matching with a combinational circuit. Our prototype implementation achieves real-time execution performance with 7∼9% of available slices of a Virtex-5 FPGA.2011 International Conference on Field Programmable Logic and Applications (FPL) : Chania, Greece, 2011.09.5-2011.09.

PENGENALAN KOMPONEN KOMPUTER BERBASIS AUGMENTED REALITY PADA ANDROID DENGAN METODE SINGLE MARKER

Computers are one of the most popular electronic devices today. With the development of PC games and Esports in the world, computers are becoming more and more popular among young people today. Computers have many components that are needed as a support so that the computer can be used. At the education level, students are taught about the components in a computer. However, the introduction still uses images because it is difficult to introduce the components one by one. One way to easily introduce components on a computer is to use Augmented Reality technology. Augmented Reality allows users to be able to combine the virtual world, both 2D and 3D, with the real environment in real time. This application is based on Android and is built using the Unity3D Game Engine. The method used in developing this computer component recognition application is the single marker method and uses the FAST Corner Detection algorithm. In addition, application development will use the Multimedia Development Life Cycle (MDLC) method. The reason for making this computer component recognition application is so that it can be a tool to introduce computer components to students more attractively and make it easier for students to absorb the existing material. Based on the test results, the single marker method and the FAST Corner Detection algorithm were successfully implemented into the application. The application can run well on Android 5.1 to Android 10 with a minimum of 2GB RAM. The results of the single marker method test that the marker can be detected by the camera. Marker detection can be done on markers with a slope of 0° - 45°. The closest distance for marker detection is 4cm and the farthest distance for marker detection is 92cm. The results of the questionnaire on questions about the functionality of the application with a total of 30 respondents who were students of Class 12 Bunda Mulia School answered "strongly agree" more than 50% and on questions about the use of applications as learning aids answered "strongly agree" more than 50%. These results mean that the application of the introduction of computer components has a good impact on the respondents.Keywords:  Augmented Reality, Unity 3D, Vuforia, Android, Single Marker, FAST Corner Detection, Computer