scholarly journals APLIKASI PENGOLAHAN CITRA UNTUK PROSES SORTASI BUAH MANGGA BERDASARKAN DIMENSI DAN BOBOT

2020 ◽  
Vol 8 (1) ◽  
pp. 12-19
Author(s):  
Setya Permana Sutisna ◽  
Roy Waluyo ◽  
Fahmi Aldiansyah ◽  
Mamat Rahmat
Keyword(s):  
Frame Grabber

Mangga memiliki ukuran dan bobot yang berbeda. Terkadang para petani masih menggunakan cara manual dalam membedakan mutu mangga. Cara manual sering kali tidak akurat dan konsisten dikarenakan setiap manusia memiliki persepsi masing-masing. Berdasarkan masalah tersebut telah dirancang sebuah mesin sortasi mangga menggunakan teknik pengolahan citra. Penelitian ini dimulai dari pengumpulan informasi parameter mutu buah mangga serta mempelajari konsep pengolahan citra dalam proses penentuan mutu dan sortasi buah. Penelitian ini hanya difokuskan pada parameter dimensi dan bobot. Pengelompokan buah mangga menggunakan bobot dilakukan berdasarkan statistik sampel buah mangga yang digunakan. Mangga dibagi ke dalam tiga kelompok yaitu A, B, dan C. Kelompok A dengan bobot mangga lebih besar dari 350 g, B dengan bobot mangga antara 200 g – 350 g, dan C dengan bobot kurang dari 200 g. Ruang pengambilan citra didesain tertutup dan dilengkapi sistem akuisisi citra menggunakan webcam dengan resolusi 3 Megapixel sehingga mampu menghasilkan citra sebesar 352 x  288 pixel. Citra dipetakan ke dalam koordinat dua dimensi yang setiap titiknya terdiri dari satu pixel menggunakan komponen frame grabber.  Pengujian menggunakan sampel mangga sejumlah 100 kg secara acak. Berdasarkan hasil pengujian hubungan jumlah pixel terhadap luas permukaan buah mangga diperoleh bahwa algoritma pengolahan citra yang dibuat telah mampu menduga luas permukaan mangga dengan R2 sebesar 0,99 dan nilai RSME sebesar 0,19 cm2. Hasil pengujian korelasi dimensi dan bobot mangga menunjukkan terdapat hubungan yang berbanding lurus. Meskipun demikian, pendugaan bobot dengan metode ini dapat dikatakan memiliki hasil  R2 hanya sebesar 0,76 dan nilai RSME sebesar 26,53 g.

scholarly journals DIGITISASI DAN PRINT-OUT CITRA RADIOGRAFI KARSINOMA NASOFARING

2016 ◽  
Vol 2 (1) ◽  
pp. 23-26
Author(s):  
Indri Purwanti ◽  
Gede Bayu Suparta ◽  
Maesadji Tjokronagoro
Keyword(s):  
Image Analyzer ◽  
Frame Grabber

Telah dilakukan penelitian tentang proses digitisasi dan print-out citra radiografi karsinoma nasofaring luaran suatu perangkat simulator di RS. DR.Sardjito. Proses digitisasi dilakukan menggunakan sistem frame grabber yang dikembangkan oleh Laboratorium Fisika Citra FMIPA UGM yang terhubung dengan sistem radiografi fluoroskopi. Setelah diperoleh 20 citra untuk sekali proses paparan radiasi, dilakukan proses normalisasi dan proses kalkulasi citra. Proses analisis terhadap citra hasil meliputi analisis visual, histogram, dan profil garis yang dilakukan menggunakan software New Image Analyzer 2007. Untuk kepentingan dokumentasi permanen, citra di-print-out. Berdasarkan penelitian ini, operasi add dapat memberikan kualitas visual citra terbaik, sebagaimana tampak di layar monitor maupun setelah di-print-out. Kualitas citra hasil print-out relatif tetap sepanjang spesifikasi printer yang digunakan sesuai.   Kata kunci: Digitisasi, Print-out, Frame Grabber, Radiografi Fluoroskopi, Karsinoma Nasofaring

scholarly journals Imaging Large Tissue Samples at High Resolution

2000 ◽  
Vol 8 (7) ◽  
pp. 31-31
Author(s):  
Robert Meyer
Keyword(s):  
High Resolution ◽  
Fundamental Problem ◽  
Digital Cameras ◽  
Image Capture ◽  
Tissue Samples ◽  
Video Cameras ◽  
Conventional Technology ◽  
High Resolution Images ◽  
Display Resolution ◽  
Frame Grabber

To those scientists and researchers who use microscopes with video or digital cameras for the purpose of capture and display, there has always existed a fundamental problem, namely, imaging large tissue samples at high resolution.For some time, conventional technology has allowed video cameras to be attached to microscopes. The signal generated by these cameras can be captured and saved to a computer's hard drive using a frame grabber. A typical image capture and display resolution is 640 by 480 (307,200) pixels. Although the image produced is ‘good’, it is by no means considered ‘high resolution’. For the sake of discussion, ‘high resolution’ is defined as greater than 1,000 x 1,000 (1,000,000) pixels. High resolution images are essential for medical applications. Today's modern digital cameras can meet or exceed this value. The human eye, if compared to these examples, has the equivalent resolution of greater than 5,000 x 5,000 (25,000,000) pixels.

Frame grabber circuit for IEEE1394 image transfer

2007 ◽  
Author(s):  
Dae Ro Lee ◽  
Sang-Won Lee ◽  
Jae Wook Jeon
Keyword(s):  
Image Transfer ◽  
Frame Grabber

scholarly journals An Inexpensive Frame Grabber for the Microscopist

1996 ◽  
Vol 4 (5) ◽  
pp. 16-17
Author(s):  
Richard S. Brown
Keyword(s):  
Digital Image ◽  
Digital Imaging ◽  
Imaging System ◽  
Image Acquisition ◽  
Black And White ◽  
Digital Imaging System ◽  
True Color ◽  
Frame Grabber

Having shopped for an inexpensive but powerful digital imaging system for the last four years, I have finally found a device that will satisfy even the most frugal budget. The Snappy Video Snapshot by Play, Incl, is a 24-bit true color frame grabber that plugs into your PC printer port. After plugging the Snappy device into your printer port (Figure 1) and spending approximately six minutes loading the Snappy software provided, you can connect a color or black and white camera, VCR, or television set and capture your first digital image. Because the device plugs into a printer port, it is completely portable. A switch box is needed if you will be changing between digital image acquisition and printing tasks frequently.

Ant search behaviour analysis with a video frame grabber

1995 ◽  
Vol 42 (3) ◽  
pp. 249-254 ◽  
Author(s):  
V. J. L. Fourcassi� ◽  
J. F. A. Traniello
Keyword(s):  
Video Frame ◽  
Search Behaviour ◽  
Behaviour Analysis ◽  
Frame Grabber

scholarly journals Control of a VanderLugt correlator using a single 8-bit frame grabber

1998 ◽  
Vol 153 (4-6) ◽  
pp. 331-338
Author(s):  
I Labastida ◽  
A Carnicer ◽  
E Martı́n-Badosa ◽  
S Vallmitjana ◽  
I Juvells
Keyword(s):  
Vanderlugt Correlator ◽  
Frame Grabber

The Application of Image Recognition Based on BP Neural Networks in Automatic Steel Rolling

2013 ◽  
Vol 764 ◽  
pp. 161-164
Author(s):  
Wei Jiang
Keyword(s):  
Neural Networks ◽  
Image Processing ◽  
Image Recognition ◽  
Character Recognition ◽  
Video Image ◽  
Bp Neural Networks ◽  
Steel Rolling ◽  
Image Processing Techniques ◽  
Processing Techniques ◽  
Frame Grabber

A BP neural networks is presented for billet character recognition. Firstly, by a series of image processing techniques, the character’feature in the billet character region of the video image gathered by frame grabber is abstracted. Secondly, the BP neural networks algorithm is employed for character recognition. Application results show that the image recognition based BP neural networks can performs well in billet character recognition, and the method presented is speedy, efficient and of high value in practice.

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