IMAGE PROCESSING Ferda Ernawan Ph D Postgraduate Program

IMAGE PROCESSING Ferda Ernawan, Ph. D Postgraduate Program of Dian Nuswantoro University Email: ferda. ernawan@yahoo. com

Administrative � Course Implementation � Lecture: 2 hrs per week for 10 weeks (Total=20 hrs) � Course Evaluation: Course Works Marks Assignments 1 (Critique a conference article) 20% Case Study 20% Research Proposal (5 pages) 30% Examination Marks Final Examination 30% Total 100%

Lecture 1 Introduction

References “Digital Image Processing”, Rafael C. Gonzalez & Richard E. Woods, Addison-Wesley, 2002 ◦ Bahan materi yang di ambil dari buku tersebut

Contents � Sub pokok pembahasan meliputi: ◦ ◦ ◦ Pengertian digital image? Pengertian digital image processing? Contoh digital image processing dalam kehidupan sehari-hari Aspect of Image Processing Fundamental step in digital image processing Matlab as a research tools for image processing and applications ◦ Characteristic of a good paper in term of image processing and applications ◦ Penjelasan assignment 1

Pengertian Digital Image � Sebuah gambar digital adalah representasi dari gambar dua dimensi sebagai himpunan terhingga dari nilai digital, atau biasa disebut elemen gambar atau pixel.

Digital Image � Nilai sebuah pixel biasanya merupakan tingkat abu-abu, warna, tingkat kecerahan atau kekeruhan, dll. � Digitalisasi sebuah image adalah menggambarkan sebuah kejadian nyata. 1 pixel

Digital Image Nilai suatu pixel merupakan single number dari suatu intensitas gambar atau warna gambar. Digital Image merupakan multidimensional array dari intensitas gambar atau warna gambar.

Digital Image ◦ 1 sample per point (Black & White or Grayscale) ◦ 3 samples per point (Red, Green, and Blue) ◦ 4 samples per point (Red, Green, Blue, and “Alpha”)

Image Format � GIF (Graphic Interchange Format) � PNG (Portable Network Graphics) � JPEG (Joint Photographic Experts Group) � TIFF (Tagged Image File Format) � PGM (Portable Gray Map) � FITS (Flexible Image Transport System) � BMP (Bitmap)

Digital Image Processing � Digital Image Processing fokus pada dua tugas utama: ◦ Peningkatan informasi gambar untuk interpretasi manusia ◦ Pengolahan data image untuk penyimpanan, transmisi, dan representasi untuk autonomous machine perception.

Digital Image Processing � Penggunaan teknik pengolahan citra digital telah meningkat dan image processing application sekarang telah digunakan untuk semua jenis pekerjaan pada semua jenis bidang, misalnya: ◦ Image enhancement dan restoration ◦ Artistic effects ◦ Medical visualisation ◦ Industrial inspection ◦ Law enforcement ◦ Human computer interfaces

Contoh Image Enhancement � Salah satu penggunaan yang paling umum dari teknik Digital Image Processing yaitu meningkatkan kualitas, remove noise, dan sebagainya.

Contoh Image Restoration Image yang terdegradasi The result of image restoration

Contoh Efek Artistik � Efek Artistik digunakan untuk membuat gambar lebih menarik dan untuk menambahkan suatu efek khusus.

Contoh: Medicine � Ambil irisan dari scan hati, dan menentukan batas antara jenis jaringan ◦ Gambar dengan abu-abu mewakili tingkat kepadatan jaringan. ◦ Gunakan filter yang cocok untuk menyorot tepi. Original Image of a Heart Edge Detection Image

Contoh Industrial Inspection � Manusia sebagai operator memerlukan biaya mahal. � Mesin dapat melakukan pekerjaan lebih cepat, dan sistem industri seperti itu sudah digunakan dalam semua jenis industri.

Contoh Law enforcement � Teknik pemrosesan gambar digunakan secara luas oleh penegak hukum ◦ Monitoring nomor pelat mobil dalam memonitor kecepatan / sistem tol otomatis ◦ Pengenalan sidik jari ◦ Peningkatan CCTV gambar

Contoh GIS � Sistem Informasi Geografis ◦ Digital image processing digunakan secara ekstensif untuk memanipulasi citra satelit.

Fundamental step in digital image processing Color image processing Wavelets and multi resolution processing Image Compression Morphological Processing Image Restoration Segmentation Image Enhancement Representation & Description Image Acquisition Problem Domain Knowledge base Object Recognition

Fundamental step in digital image processing Color image processing Wavelets and multi resolution processing Image Compression Morphological Processing Image Restoration Segmentation Image Enhancement Representation & Description Image Acquisition Object Recognition Problem Domain

Fundamental step in digital image processing Color image processing Wavelets and multi resolution processing Image Compression Morphological Processing Image Restoration Segmentation Image Enhancement Representation & Description Image Acquisition Object Recognition Problem Domain

Fundamental step in digital image processing Color image processing Wavelets and multi resolution processing Image Compression Morphological Processing Image Restoration Segmentation Image Enhancement Representation & Description Image Acquisition Object Recognition Problem Domain

Fundamental step in digital image processing Colour image processing Wavelets and multi resolution processing Image Compression Morphological Processing Image Restoration Segmentation Image Enhancement Representation & Description Image Acquisition Object Recognition Problem Domain

Fundamental step in digital image processing Color image processing Wavelets and multi resolution processing Image Compression Morphological Processing Image Restoration Segmentation Image Enhancement Representation & Description Image Acquisition Object Recognition Problem Domain

Fundamental step in digital image processing Color image processing Wavelets and multi resolution processing Image Compression Morphological Processing Image Restoration Segmentation Image Enhancement Representation & Description Image Acquisition Object Recognition Problem Domain

Fundamental step in digital image processing Color image processing Wavelets and multi resolution processing Image Compression Morphological Processing Image Restoration Segmentation Image Enhancement Representation & Description Image Acquisition Object Recognition Problem Domain

Fundamental step in digital image processing Color image processing Wavelets and multi resolution processing Image Compression Morphological Processing Image Restoration Segmentation Image Enhancement Representation & Description Image Acquisition Object Recognition Problem Domain

Fundamental step in digital image processing Color image processing Wavelets and multi resolution processing Image Compression Morphological Processing Image Restoration Segmentation Image Enhancement Representation & Description Image Acquisition Object Recognition Problem Domain

MATLAB as research tool for Image Processing and Application

Matlab � The basic data structure in Matlab is the array, and the operation is sequence, like C programming. � Matlab stores image as two dimensional array (matrices), each element of the matrix represents a single pixel in the displayed image. � By default, Matlab stores the data in array of class double.

What is Image Processing Toolbox? � Image processing toolbox is collection of function of Matlab numeric computing environment. � toolbox functions implement specialized image processing algorithm.

Image Processing Toolbox � Geometric operations � Neighborhood and block operation � Linier filtering and filter design � Transformation domain � Image analysis and enhancement � Binary image operations � Region of interest operation.

Image Type in the toolbox � The Image Processing toolbox supports basic type of image, for example: ◦ Indexed images ◦ Binary images ◦ RGB images

RGB Image � In Matlab , the red, green, and blue component of RGB image reside in single m-by-n-by-3 array. � m and n are the number of rows and columns of pixels in the image, and the third dimension consists of red, green, and blue intensity values. � Each pixel in the image, the red, green, and blue elements are combine to create the pixel’s actual color.

RGB Image An RGB array can be: � Class double, in this case it contains values in the range [0, 1] � Class uint 8, in this case the data range is [0, 255]

What should you do with image data? � Reading in image data from files, and writing image data out to file. � Converting images to other image types. � Working with uint 8 arrays in Matlab and the Image Processing toolbox.

Reading images You can use the Matlab function to read image data from files. Imread(‘filename’) Imread function can read these graphics file formats: � TIFF (Tagged Image File Format) � JPEG (Join Photographics Expert’s Group) � HDF (Hierarchical Data Format) � BMP (Windows Bitmap) � XWD (X-Window Dump)

Writing Images � To write image data from Matlab to a file, you can use imwrite function. Imwrite can write the same file formats that imreads. � See the references entire for imread and imwrite for more information about these function. � In addition, you can use imfinfo function to return information about the image data in a file.

Converting Image Function Purpose dither Binary from grayscale indexed from RGB Gray 2 ind Indexed from grayscale grayslice Indexed from grayscale by thresholding Im 2 bw Binary from intensity, indexed or RGB image by luminance threshold. Ind 2 gray Indexed to grayscale

Converting Image Ind 2 rgb Indexed to RGB Mat 2 gray Create a grayscale intensity image from data in a matrix, Rgb 2 gray RGB to intensity Rgb 2 ind RGB to indexed