Computer Vision
Subject
Materi
e-Book | Linda Shapiro - The University Of Washington | Download
How Computer Vision is Changing the Way We Interact with Technology | Link
How do you test and improve computer vision algorithms? | Link
SAP | Download
GBPP | Download
Chapter 1 .
Introduction to computer vision
Chapter 2 .
What is computer vision,
Why computer vision,
Manfaat dan guna computer science.
History of computer vision
Chapter 3 .
Image gradient and edge
Edge detection, convulotion, what cause edge,gausian, dan laplacian
Chapter 4 .
Proyeksi
Proyeksi Perspektif
Proyeksi Affine
Proyeksi Spherica
Chapter 5 .
Sensor Kamera
Kamera CCD
Model Sensor
Warna
Kuantitas Spektral
Persepsi Mata
Representasi Warna
Chapter 6 .
Mengkaji berbagai algoritma tracking
Mendemokan dan mensimulasikan berbagai algoritma tracking
contoh image traking dan filtering dalam kehidupan sehari-hari.
Chapter 7 .
Linier filtering, image formation, digital images, image noise, image filtering.
Edge and binary image analysis
Edge detection, canny detector, sobel detector, thresholding
Chapter 8 .
Texture
Chamfer matching system,
analysis and syntetis,
texture analysis.
Chapter 9 .
Color
Color and light,
what is color,
color matching,
measuring color,
distance color.
Chapter 10 : transformation dan geomatric transformation
Image transformations
Grey level transformations.
Histogram equalization.
Geometric transformations.
Affine transformations.
Polynomial warps
Chapter 11 :
Face detection and Recognition
Chapter 12 : image morphology
Morphological operation
Erode and dilate as max and min operators on binary images.
Open, close, thinning and other transforms.
Medial axis transform.
Introduction to grey-level morphology.
Chapter 13 : Filtering
Image filtering
Fourier descriptors.
Linear and non-linear filtering operations.
Image convolutions.
Separable convolutions.
Sub-sampling and interpolation as convolution operations
Chapter 14 : Region of interest dan image boundary
Feature characterisation
Calculation of region properties.
Moment features.
Boundary coding line descriptors from boundary coding and from moments.
Image search and multi-resolution algorithms.
Chapter 15 : Edge detection
Edge and corner detection
Edge enhancement by differentiation.
Effect of noise, edge detection and Canny implementation.
Edge detector performance evaluation.
Image structure tensor.
Relationship to image auto-correlation.
Characterisation and Harris corner detector
Chapter 16 : image colopr representation
Colour images : IGI Global | Article |
Representations of colour in digital images.
Colour metrics.
Pixel-wise (point) operations.
Colour invariants and Finlayson colour constancy algorithm
Chapter 16 : Content based image rerieval
Template matching and advanced topics
Similarity and dissimilarity matching metrics.
L2 metric and relationship to cross-correlation2D object detection, recognition, location.
Practice : Membuat script dengan Matlab untuk CBIR
Journal :
1. Content Based Image Retrieval Berdasarkan Fitur Bentuk Menggunakan Metode Gradien Vector Slow Snake | Download
2. Aplikasi Content-based Image Retrieval (CBIR) dengan Octave | Youtube
Pattern recognition
Pengenalan pola merupakan bidang dalam pembelajaran mesin (Machine Learning) dan dapat diartikan sebagai "tindakan mengambil data mentah dan bertindak berdasarkan klasifikasi data"
Journal - Reference
Indonesian Association for Pattern Recognition - inar.org | Link
Peran Pattern Recognition dalam Pengembangan Sistem Cerdas | Download
Identifikasi Kain Tapis Lampung Menggunakan Ekstrasi Fitur Edge Detection (CANNY) Dan Klasifikasi Probability Neural Network - Admi Syarif | Download
Prediksi struktur sekunder protein menggunakan Deep Learning | Made Windu Antara Kesiman | Link
Jenis Kain Tapis Lampung | Link