A Computational Introduction to Digital Image Processing

McAndrew, Alasdair

doi:10.1201/b19431

Cited by 66 publications

(72 citation statements)

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“…Here we present a method to analyze the quality of dispersion, using as a criterion the length and frequency of occurrence of agglomerates or networks formed by connected CNTs. From the proven observation that ∂C/∂d is the most robust channel for sub-surface imaging CNT networks, the network topology of ∂C/∂d maps are analyzed with image processing tools in Matlab [54][55][56], from which statistical distributions of connected cluster sizes are computed.…”

Section: Dispersion Analysis Of Cnt Network In Polymer Nanocompositesmentioning

confidence: 99%

Sub-surface imaging of carbon nanotube–polymer composites using dynamic AFM methods

et al. 2013

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High-resolution sub-surface imaging of carbon nanotube (CNT) networks within polymer nanocomposites is demonstrated through electrical characterization techniques based on dynamic atomic force microscopy (AFM). We compare three techniques implemented in the single-pass configuration: DC-biased amplitude modulated AFM (AM-AFM), electrostatic force microscopy (EFM) and Kelvin probe force microscopy (KPFM) in terms of the physics of sub-surface image formation and experimental robustness. The methods were applied to study the dispersion of sub-surface networks of single-walled nanotubes (SWNTs) in a polyimide (PI) matrix. We conclude that among these methods, the KPFM channel, which measures the capacitance gradient (∂C/∂d) at the second harmonic of electrical excitation, is the best channel to obtain high-contrast images of the CNT network embedded in the polymer matrix, without the influence of surface conditions. Additionally, we propose an analysis of the ∂C/∂d images as a tool to characterize the dispersion and connectivity of the CNTs. Through the analysis we demonstrate that these AFM-based sub-surface methods probe sufficiently deep within the SWNT composites, to resolve clustered networks that likely play a role in conductivity percolation. This opens up the possibility of dynamic AFM-based characterization of sub-surface dispersion and connectivity in nanostructured composites, two critical parameters for nanocomposite applications in sensors and energy storage devices.

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Section: Dispersion Analysis Of Cnt Network In Polymer Nanocompositesmentioning

confidence: 99%

Sub-surface imaging of carbon nanotube–polymer composites using dynamic AFM methods

et al. 2013

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“…Digital data in the form of images or images processed through several stages [6] so that computers can finally recognize the type of chrysanthemum flowers through digital data. The image is inputted through the preprocessing stage before the image is done crop stage and resizes [8]. The preprocessing stage is very important to do because the new image can be processed to the next stage after going through this stage.…”

Section: Methodsmentioning

confidence: 99%

“…Edge detection aims to get a border so that the image is easily recognizable during the identification process using a backpropagation neural network. The image that is processed using Sobel edge detection will have a matrix of 1 and 0, where 0 represents the background of the detected image and 1 represents the border of the detected object [2] [8]. The shape extraction stage using the detection of the Sobel edge is shown in Fig.…”

Section: B Sobel Edge Detectionmentioning

confidence: 99%

Identification of Chrysanthemum Flower Based on Color and Flower Form using Sobel Edge and Artificial Neural Network

2019

ijrte

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Chrysanthemum is a popular ornamental plant in Indonesia because it has a variety of shapes and colors that attract a lot of attention. In addition to the trimmer, this flower can be used as a mosquito repellent and also absorbs dirty air pollution. Chrysanthemum flowers can be distinguished by color and shape with two types of ways to grow Chrysanthemum flowers that grow in clusters are called spray and also flowers that grow with no cluster or a flower per stalk. In addition, Chrysanthemum flowers have various color variations including pink, purple, yellow, white, orange, and red. The purpose of this study is to identify the type of Chrysanthemum flowers using extraction of Red, Green, Blue (RGB) color features and characteristic extraction using a Sobel edge detection, for his identification method using Artificial Neural Networks. Chrysanthemum data are white puma, Vania, Iranian, purple aster, and pink standard used in this research consisted of 100 training data and 50 tests data. The test was done with four tests that is identification with form extraction (Sobel) yielded accuracy value equal to 59.52%, identification with color and shape extraction yield accuracy value equal to 65.36%, identification with color extraction on flower and base flower plate yield value accuracy of 69.44%, and identification with color extraction yields an accuracy value of 75.35%.

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“…Titik-titik ini disebut elemen gambar atau pixel. Dalam true color, setiap pixel memiliki warna tertentu, warna yang digambarkan oleh jumlah merah, hijau dan biru di dalamnya [7]. Setiap warna dihasilkan dengan penggabungan yang tepat ketiga warna primer atau RGB [8].…”

Section: Koordinat Gambar Digitalunclassified

“…Untuk RGB atau merah, hijau, biru memiliki jangkauan antara 0-255, ini memberikan total warna yang mungkin berbeda di dalam gambar [7]. Model warna RGB berdasarkan pada warna yang direspon mata manusia yang peka terhadap panjang gelombang sekitar 400 nm (biru) sampai 700 nm (merah) [9].…”

Section: Koordinat Gambar Digitalunclassified

Identifikasi Tingkat Kesehatan Karang Berdasarkan Coral Health Chart Menggunakan Pengolahan Citra Digital Dan Metode Kuadrat Terkecil

Mandagi¹,

Latumakulita²,

Rindengan³

2015

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Terumbu karang bermanfaat bagi kelangsungan hidup makhluk bawah air. Namun saat ini terancam keberadaannya, salah satu penyebab adalah peningkatan suhu Bumi sehingga banyak terumbu karang mengalami kerusakan ditandai dengan pemutihan warna karang. Pengamatan kesehatan karang dapat dilakukan dengan tabel kesehatan karang (Coral Health Chart). Penelitian ini dimulai dengan menghitung nilai rata-rata RGB (red, green, blue) dengan pengolahan citra digital terhadap Coral Health Chart, dan menerapkan metode kuadrat terkecil untuk memperoleh perumusan dalam rangka penentuan tingkat kesehatan dari terumbu karang menggunakan bantuan komputer. Langkah selanjutnya hitung nilai rata-rata RGB dari beberapa gambar karang dengan pengolahan citra digital, kemudian diterapkan pada perumusan yang telah diperoleh sebelumnya. Penelitian dilakukan pada 8 contoh gambar karang, hasilnya menunjukan tingkat kesehatan karang dan persentase tingkat kesehatan dari masing-masing karang yaitu untuk karang dengan simbol j=95.52%, k=65.02%, l=80.42%, m=81.11%, n=84.69%, o=52.87%, p=79.41% dan karang q=69.78%. Kata kunci : Tabel Kesehatan Karang, Pengolahan Citra Digital, Kesehatan Karang, Metode Kuadrat Terkecil

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A Computational Introduction to Digital Image Processing

Cited by 66 publications

References 0 publications

Sub-surface imaging of carbon nanotube–polymer composites using dynamic AFM methods

Sub-surface imaging of carbon nanotube–polymer composites using dynamic AFM methods

Identification of Chrysanthemum Flower Based on Color and Flower Form using Sobel Edge and Artificial Neural Network

Identifikasi Tingkat Kesehatan Karang Berdasarkan Coral Health Chart Menggunakan Pengolahan Citra Digital Dan Metode Kuadrat Terkecil

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