scholarly journals Uji Deteksi Objek Bentuk Bola Dengan Menerapkan Metode Circular Hough Transform

2021 ◽  
Vol 7 (1) ◽  
Author(s):  
Budi Cahyo Wibowo ◽  
Fajar Nugraha ◽  
Andy Prasetyo Utomo
Keyword(s):  
Image Processing ◽  
Hough Transform ◽  
Circular Hough Transform

Abstrak— Deteksi objek bentuk bola merupakan salah satu penerapan dari teknologi image processing yang saat ini banyak digunakan untuk teknologi robotika. Kemampuan dalam mengenali objek tertentu dalam berbagai kondisi lingkungan merupakan salah satu syarat teknologi image processing ini disebut handal. Untuk mengetahui kehandalannya maka perlu dilakukan pengujian. Uji deteksi objek berwarna bentuk bola dilakukan dengan melakukan pengujian terhadap perubahan kondisi lingkungan dimana objek tersebut berada, diantaranya dengan pengujian deteksi objek bentuk bola dengan variasi ukuran bola, pengujian deteksi objek bentuk bola dengan variasi perubahan intensitas cahaya dan pengujian deteksi objek bentuk bola dengan variasi perubahan jarak objek terhadap kamera. Dengan tiga pengujian yang telah dilakukan dengan metode hough transform yang diterapkan pada deteksi objek bentuk bola ini, diperoleh kesimpulan bahwa deteksi objek mampu mengenali variasi ukuran bola dengan diameter 16,9mm, 31mm, 63,7mm dan 95,8mm. Deteksi objek mampu mengenali bola dengan baik pada intensitas cahaya antara 80lux – 117lux. Dan deteksi objek mampu mengenali bola pada jarak 30cm – 140cm.

A Robust Generic Method for Grid Detection in White Light Microscopy Malassez Blade Images in the Context of Cell Counting

2014 ◽  
Vol 21 (1) ◽  
pp. 239-248 ◽  
Author(s):  
Ambroise Marin ◽  
Emmanuel Denimal ◽  
Stéphane Guyot ◽  
Ludovic Journaux ◽  
Paul Molin
Keyword(s):  
Image Processing ◽  
Fourier Transform ◽  
Light Microscopy ◽  
White Light ◽  
Hough Transform ◽  
High Variability ◽  
Cell Counting ◽  
Cell Segmentation ◽  
Primary Measurement

AbstractIn biology, cell counting is a primary measurement and it is usually performed manually using hemocytometers such as Malassez blades. This work is tedious and can be automated using image processing. An algorithm based on Fourier transform filtering and the Hough transform was developed for Malassez blade grid extraction. This facilitates cell segmentation and counting within the grid. For the present work, a set of 137 images with high variability was processed. Grids were accurately detected in 98% of these images.

Detection of Microbubbles Using the Hough Transform

2013 ◽  
Vol 378 ◽  
pp. 478-482
Author(s):  
Yoshihiro Mitani ◽  
Toshitaka Oki
Keyword(s):  
Image Processing ◽  
Error Rate ◽  
Hough Transform ◽  
Detection Method ◽  
Experimental Results ◽  
Average Error ◽  
Image Processing Techniques ◽  
Average Error Rate ◽  
Processing Techniques

The microbubble has been widely used and shown to be effective in various fields. Therefore, there is an importance of measuring accurately its size by image processing techniques. In this paper, we propose a detection method of microbubbles by the approach based on the Hough transform. Experimental results show only 4.49% of the average error rate of the undetected microbubbles and incorrectly detected ones. This low percentage of the error rate shows the effectiveness of the proposed method.

A RECONFIGURABLE ARCHITECTURE FOR IMAGE PROCESSING AND COMPUTER VISION

1995 ◽  
Vol 09 (02) ◽  
pp. 201-229 ◽  
Author(s):  
SUCHENDRA M. BHANDARKAR ◽  
HAMID R. ARABNIA ◽  
JEFFREY W. SMITH
Keyword(s):  
Image Processing ◽  
Computer Vision ◽  
Hough Transform ◽  
Broad Class ◽  
Building Blocks ◽  
Ring System ◽  
Reconfigurable Architecture ◽  
Vlsi Implementation ◽  
Ring Network ◽  
Linear Features

In this paper we describe a reconfigurable architecture for image processing and computer vision based on a multi-ring network which we call a Reconfigurable Multi-Ring System (RMRS). We describe the reconfiguration switch for the RMRS and also describe its VLSI implementation. The RMRS topology is shown to be regular and scalable and hence well-suited for VLSI implementation. We prove some important properties of the RMRS topology and show that a broad class of algorithms for the n-cube can be mapped to the RMRS in a simple and elegant manner. We design and analyze a class of procedural primitives for the SIMD RMRS and show how these primitives can be used as building blocks for more complex parallel operations. We demonstrate the usefulness of the RMRS for problems in image processing and computer vision by considering two important operations—the Fast Fourier Transform (FFT) and the Hough transform for detection of linear features in an image. Parallel algorithms for the FFT and the Hough transform on the SIMD RMRS are designed using the aforementioned procedural primitives. The analysis of the complexity of these algorithms shows that the SIMD RMRS is a viable architecture for problems in computer vision and image processing.

scholarly journals IDENTIFIKASI IRIS MENGGUNAKAN IMPROVED LEVENBERG-MARQUARDT

2015 ◽  
Vol 1 (1) ◽  
pp. 10
Author(s):  
Rocky Yefrenes Dillak
Keyword(s):  
Hough Transform ◽  
Recognition Rate ◽  
Correct Recognition ◽  
Gray Level ◽  
Maximum Probability ◽  
Circular Hough Transform ◽  
Marquardt Algorithm ◽  
Levenberg Marquardt ◽  
Correct Recognition Rate

Sistem biometrika adalah suatu sistem pengenalan diri menggunakan bagian tubuh atau perilaku manusia seperti sidik jari, telapak tangan, telinga, retina, iris mata, wajah, suhu tubuh, tanda tangan, dll. Iris mata merupakan salah satu biometrika yang sangat stabil, handal, akurat dan merupakan metode autentikasi biometrika tercepat  oleh karena itu merupakan suatu topik penelitian yang sangat diminati oleh banyak peneliti. Penelitian ini bertujuan untuk mengembangkan suatu metode yang dapat digunakan untuk mengidentifikasi secara otomatis seseorang berdasarkan citra iris mata miliknya menggunakan jaringan syaraf tiruan levenberg-marquardt. Penelitian ini menggunakan metode deteksi tepi cany dan circular hough transform untuk segmentasi wilayah iris yang terletak diantara pupil dan sclera serta metode ekstraksi ciri gray level cooccurence matrix (GLCM) yang digunakan untuk ekstraksi ciri. Ciri-ciri tersebut adalah maximum probability, correlation, contrast, energy, homogeneity, dan entropy. Ciri-ciri tersebut kemudian dilatih menggunakan jaringan syaraf tiruan dengan aturan pembelajaran levenberg–marquardt algorithm untuk mengidentifikasi seseorang berdasarkan citra irisnya. Penelitian ini menggunakan 150 data citra iris yang masing-masing terbagi atas 100 data citra iris untuk pelatihan dan 50 data citra iris  untuk pengujian. Berdasarkan hasil pengujian yang dilakukan diperoleh correct recognition rate (CRR) sebesar 99.98%  yang menunjukkan bahwa metode ini dapat digunakan untuk mengidentifikasi secara otomatis seseorang berdasarkan citra iris mata miliknya.

scholarly journals HOUGH TRANSFORM GENERATED STRONG IMAGE HASHING SCHEME FOR COPY DETECTION

2018 ◽  
Author(s):  
Mayank Srivastava ◽  
Jamshed M Siddiqui ◽  
Mohammad Athar Ali
Keyword(s):  
Image Processing ◽  
Wavelet Transform ◽  
Discrete Wavelet Transform ◽  
Hough Transform ◽  
Rapid Development ◽  
Discrimination Performance ◽  
Discrete Wavelet ◽  
Image Hashing ◽  
Copy Detection ◽  
Detection Scheme

The rapid development of image editing software has resulted in widespread unauthorized duplication of original images. This has given rise to the need to develop robust image hashing technique which can easily identify duplicate copies of the original images apart from differentiating it from different images. In this paper, we have proposed an image hashing technique based on discrete wavelet transform and Hough transform, which is robust to large number of image processing attacks including shifting and shearing. The input image is initially pre-processed to remove any kind of minor effects. Discrete wavelet transform is then applied to the pre-processed image to produce different wavelet coefficients from which different edges are detected by using a canny edge detector. Hough transform is finally applied to the edge-detected image to generate an image hash which is used for image identification. Different experiments were conducted to show that the proposed hashing technique has better robustness and discrimination performance as compared to the state-of-the-art techniques. Normalized average mean value difference is also calculated to show the performance of the proposed technique towards various image processing attacks. The proposed copy detection scheme can perform copy detection over large databases and can be considered to be a prototype for developing online real-time copy detection system.   

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