PCD-ML Pengolahan Citra Menggunakan MATLAB

D10K-6C01 Pengolahan Citra
PCD-ML Pengolahan Citra
Menggunakan MATLAB
Program Studi S-1 Teknik Informatika
FMIPA Universitas Padjadjaran
Semester Genap 2015-2016
MATLAB 2015a
• Image Processing and Computer Vision
– Use graphical tools to visualize and manipulate images and
video. Connect to hardware and develop new ideas using
libraries of reference-standard algorithms.
• Products:
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MATLAB
Computer Vision System Toolbox
Image Acquisition Toolbox
Image Processing Toolbox
Parallel Computing Toolbox
Signal Processing Toolbox
Statistics and Machine Learning Toolbox
PCD-ML Pengolahan Citra Menggunakan MATLAB
Images in Matlab
• Matlab is optimised for operating on
matrices
• Images are matrices!
• Many useful built-in functions in the
Matlab Image Processing Toolbox
• Very easy to write your own image
processing functions
•3
PCD-ML Pengolahan Citra Menggunakan MATLAB
Loading and displaying images
>> I=imread('mandrill.bmp','bmp'); % load image
Matrix with
image data
image format as a string
image filename as a string
>> image(I) % display image
>> whos I
Name
I
Size
512x512x3
Bytes
Class
786432 uint8 array
Grand total is 786432 elements using 786432 bytes
Dimensions of I (red,
green and blue intensity
information)
Matlab can only
perform arithmetic
operations on data
with class double!
PCD-ML Pengolahan Citra Menggunakan MATLAB
Display
the
left half of the
mandrill
image
Ekstraksi Color Channel
%----------membaca dan isi variabel----------------¬
gambar=imread(‘lena.jpg’);
red=gambar;
green=gambar;
blue=gambar;
%----------memproses per channel ---------------¬
red(:,:,2)=0;
red(:,:,3)=0;
green=(:,:,1)=0;
green=(:,:,3)=0;
blue(:,:,1)=0;
blue(:,:,2)=0;
%----------menampilkan gambar----------------¬
imshow(gambar)
imshow(red)
imshow(green)
imshow(blue)
PCD-ML Pengolahan Citra Menggunakan MATLAB
Konversi RGB ke Gray
%------- Image Processing Toolbox -----gambar=imread(‘lena.jpg’);
gray=rgb2gray(gambar);
imshow(gray);
%------- Conventional -----gray2 = 0.2989 * rgb(:,:,1) +0.5870 *
rgb(:,:,2) + 0.1140 * rgb(:,:,3);
imshow(gray2);
PCD-ML Pengolahan Citra Menggunakan MATLAB
Tipe Image dalam Matlab
• Indexed Image
• Intensity Image
• RGB (truecolor) Image
PCD-ML Pengolahan Citra Menggunakan MATLAB
Image Complement
gambar=imread(‘lena.jpg’);
invert=imcomplement(gambar);
figure, imshow(invert);
Invert/
complement
PCD-ML Pengolahan Citra Menggunakan MATLAB
Histogram Citra
%------- Histogram Seluruh Channel -----gambar=imread(‘lena.jpg’);
gray=rgb2gray(gambar);
figure,imhist(gray);
%------- Histogram Per Channel -----figure, imhist(gambar(:,:,1)); %---Red --figure, imhist(gambar(:,:,2)); %---Green –
figure, imhist(gambar(:,:,3)); %---Blue –
%Bagaimana kalau imhist(gambar);???
Jelaskan !!!
PCD-ML Pengolahan Citra Menggunakan MATLAB
Histogram Equalization
%------- Histogram Citra -----gambar=imread(‘lena.jpg’);
gray=rgb2gray(gambar);
figure,imshow(gray),figure,imhist(gray);
%------- Histogram Equalization -----gambar=imread(‘lena.jpg’);
gray=rgb2gray(gambar);
grayeq=histeq(gray);
figure,imshow(grayeq),figure,imhist(grayeq
);
PCD-ML Pengolahan Citra Menggunakan MATLAB
Image Crop
%------- Image Crop-----gambar=imread(‘lena.jpg’);
crop=imcrop(gambar,[0 0 250 250]);
imshow(gambar), figure, imshow(crop);
% FORMAT x,y,w,h
PCD-ML Pengolahan Citra Menggunakan MATLAB
Region of Interest (ROI)
• Menandari bagian yang menjadi pusat
perhatian (region of interest)
• Bentuk area berupa polygon
• Format
roipoly(I,c,r);
• I adalah matriks gambar
• c adalah matriks dari kolom ROI
• r adalah matriks dari baris ROI
PCD-ML Pengolahan Citra Menggunakan MATLAB
Contoh Sederhana ROI
gambar=imread(‘lena.jpg’);
%------- ROI Segi Empat -----c=[0 100 100 0];
r=[0 0 100 100];
ROI=roipoly(gambar,c,r);
figure, imshow(ROI);
%------- ROI Poligon-----c=[0 100 150 100 0];
r=[0 0
50 100 100];
ROI=roipoly(gambar,c,r);
figure, imshow(ROI);
%------- Menggunakan fungsi roifill----g=gambar(:,:,1);
c=[0 100 150 100 0];
r=[0 0
50 100 100];
ROI=roifill(g,c,r);
figure, imshow(ROI);
PCD-ML Pengolahan Citra Menggunakan MATLAB
Region-based Processing
• Define and operate on regions of interest (ROI)
• Basic Functions
– roipoly
– poly2mask
mask
– regionfill
interpolation
– roicolor
– roifilt2
Specify polygonal region of interest (ROI)
Convert region of interest (ROI) polygon to region
Fill in specified regions in image using inward
Select region of interest (ROI) based on color
Filter region of interest (ROI) in image
• Interactive Functions
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imellipse
imfreehand
impoly
imrect
imroi
Create draggable ellipse
Create draggable freehand region
Create draggable, resizable polygon
Create draggable rectangle
Region-of-interest (ROI) base class
PCD-ML Pengolahan Citra Menggunakan MATLAB
Topik Lanjutan
• Pengolahan citra pada domain frekuensi
– FFT
– DCT
• Konversi ke Citra Biner
• Morphological Image Processing
– Dilasi
– Erosi
• Object counting
• Area Proses
– Konvolusi
– Filtering
– Edge Detection
• Image Reconstruction
• Proses Citra secara Live
– Koneksi Kamera
– Live histogram
PCD-ML Pengolahan Citra Menggunakan MATLAB
Image Processing Toolbox
• Perform image processing, analysis, and algorithm development
• Image Processing Toolbox™ provides a comprehensive set of referencestandard algorithms, functions, and apps for image processing, analysis,
visualization, and algorithm development. You can perform image
analysis, image segmentation, image enhancement, noise reduction,
geometric transformations, and image registration. Many toolbox
functions support multicore processors, GPUs, and C-code generation.
• Image Processing Toolbox supports a diverse set of image types, including
high dynamic range, gigapixel resolution, embedded ICC profile, and
tomographic. Visualization functions and apps let you explore images and
videos, examine a region of pixels, adjust color and contrast, create
contours or histograms, and manipulate regions of interest (ROIs). The
toolbox supports workflows for processing, displaying, and navigating
large images.
PCD-ML Pengolahan Citra Menggunakan MATLAB
Capabilities
PCD-ML Pengolahan Citra Menggunakan MATLAB
Computer Vision Toolbox
• Design and simulate computer vision and video processing systems
• Computer Vision System Toolbox™ provides algorithms, functions,
and apps for the design and simulation of computer vision and
video processing systems. You can perform object detection and
tracking, feature detection and extraction, feature matching, stereo
vision, camera calibration, and motion detection tasks. The system
toolbox also provides tools for video processing, including video file
I/O, video display, object annotation, drawing graphics, and
compositing. Algorithms are available as MATLAB® functions,
System objects™, and Simulink® blocks.
• For rapid prototyping and embedded system design, the system
toolbox supports fixed-point arithmetic and automatic C-code
generation.
PCD-ML Pengolahan Citra Menggunakan MATLAB
Computer Vision Toolbox
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Key Features
Feature Detection, Extraction, and Matching
Object Detection and Recognition
Object Tracking and Motion Estimation
Camera Calibration
Stereo Vision
Video Processing, Display, and Graphics
Fixed Point and Code Generation
PCD-ML Pengolahan Citra Menggunakan MATLAB
Key Features
• Object detection, including Viola-Jones and other pretrained
detectors
• Object tracking, including Kanade-Lucas-Tomasi (KLT) and Kalman
filters
• Feature detection, extraction, and matching, including FAST, BRISK,
MSER, and HOG
• Camera calibration for single and stereo cameras, including
automatic checkerboard detection and an app for workflow
automation
• Stereo vision, including rectification, disparity calculation, and 3D
reconstruction
• Support for C-code generation and fixed-point arithmetic with code
generation products
• Video processing, object annotation, video file I/O, video display,
graphic overlays, and compositing
PCD-ML Pengolahan Citra Menggunakan MATLAB
Feature Detection and Extraction
• A feature is an interesting part of an image, such as a corner, blob,
edge, or line. Feature extraction enables you to derive a set of
feature vectors, also called descriptors, from a set of detected
features. Computer Vision System Toolbox offers capabilities for
feature detection and extraction that include:
– Corner detection, including Shi & Tomasi, Harris, and FAST methods
– BRISK, MSER, and SURF detection for blobs and regions
– Extraction of BRISK, FREAK, SURF, and simple pixel neighborhood
descriptors
– Histogram of Oriented Gradients (HOG) feature extraction
– Visualization of feature location, scale, and orientation
PCD-ML Pengolahan Citra Menggunakan MATLAB
Feature Detection and Extraction
PCD-ML Pengolahan Citra Menggunakan MATLAB
Videos: Image Processing and
Computer Vision Products
• Overview
– Image Processing Toolbox
– Computer Vision Toolbox
– Image Acquisition Toolbox
– Statistics and Machine Learning Toolbox
– Parallel Computing Toolbox
PCD-ML Pengolahan Citra Menggunakan MATLAB