Computer Vision Fundamentals

Computer Vision (CV) is a field of artificial intelligence that enables computers to understand and process visual information from the world. This guide covers the fundamental concepts, techniques, and applications of computer vision.

Basic Concepts

Image Representation

Digital Images
- Pixels and color spaces
- Image resolution
- Color channels
- Bit depth
Image Properties
- Intensity
- Contrast
- Brightness
- Noise

Image Processing Basics

import cv2
import numpy as np

# Load and display an image
image = cv2.imread('image.jpg')
cv2.imshow('Original Image', image)

# Convert to grayscale
gray = cv2.cvtColor(image, cv2.COLOR_BGR2GRAY)

# Basic operations
blurred = cv2.GaussianBlur(gray, (5, 5), 0)
edges = cv2.Canny(blurred, 100, 200)

Core Operations

Image Preprocessing

Color Space Conversion

# RGB to different color spaces
hsv = cv2.cvtColor(image, cv2.COLOR_BGR2HSV)
lab = cv2.cvtColor(image, cv2.COLOR_BGR2LAB)

Filtering
- Gaussian blur
- Median filter
- Bilateral filter
- Custom kernels
Normalization

# Normalize image to 0-1 range
normalized = image.astype(float) / 255.0

# Standardization
mean = np.mean(image)
std = np.std(image)
standardized = (image - mean) / std

Feature Detection

Edge Detection

# Sobel edge detection
sobelx = cv2.Sobel(gray, cv2.CV_64F, 1, 0, ksize=5)
sobely = cv2.Sobel(gray, cv2.CV_64F, 0, 1, ksize=5)
magnitude = np.sqrt(sobelx**2 + sobely**2)

Corner Detection

# Harris corner detection
corners = cv2.cornerHarris(gray, 2, 3, 0.04)
corners = cv2.dilate(corners, None)

Feature Descriptors
- SIFT
- SURF
- ORB
- BRIEF

Advanced Techniques

Image Segmentation

Thresholding

# Simple thresholding
ret, thresh = cv2.threshold(gray, 127, 255, cv2.THRESH_BINARY)

# Adaptive thresholding
adaptive = cv2.adaptiveThreshold(
    gray, 255, cv2.ADAPTIVE_THRESH_GAUSSIAN_C,
    cv2.THRESH_BINARY, 11, 2
)

Contour Detection

contours, hierarchy = cv2.findContours(
    thresh, cv2.RETR_TREE, cv2.CHAIN_APPROX_SIMPLE
)

# Draw contours
cv2.drawContours(image, contours, -1, (0, 255, 0), 2)

Object Detection

Traditional Methods
- Haar Cascades
- HOG + SVM
- Template matching
Deep Learning Approaches
- R-CNN family
- YOLO
- SSD
- RetinaNet

Image Classification

Traditional Techniques
- Feature extraction
- Bag of visual words
- SVM classification
Deep Learning Models

import torch
import torchvision.models as models

# Load pre-trained ResNet
model = models.resnet50(pretrained=True)
model.eval()

# Prepare image for inference
transform = transforms.Compose([
    transforms.Resize(256),
    transforms.CenterCrop(224),
    transforms.ToTensor(),
    transforms.Normalize(
        mean=[0.485, 0.456, 0.406],
        std=[0.229, 0.224, 0.225]
    )
])

Practical Applications

Face Recognition

Face Detection

# Using haar cascades
face_cascade = cv2.CascadeClassifier(
    cv2.data.haarcascades + 'haarcascade_frontalface_default.xml'
)
faces = face_cascade.detectMultiScale(gray, 1.3, 5)

Face Recognition
- Eigenfaces
- Local Binary Patterns
- Deep face embeddings

Object Tracking

Basic Tracking

# Lucas-Kanade optical flow
prev_gray = cv2.cvtColor(prev_frame, cv2.COLOR_BGR2GRAY)
curr_gray = cv2.cvtColor(curr_frame, cv2.COLOR_BGR2GRAY)
flow = cv2.calcOpticalFlowFarneback(
    prev_gray, curr_gray, None, 0.5, 3, 15, 3, 5, 1.2, 0
)

Advanced Tracking
- Kalman filter
- Particle filter
- Deep learning trackers

Deep Learning in CV

Convolutional Neural Networks

Basic Architecture

import torch.nn as nn

class ConvNet(nn.Module):
    def __init__(self):
        super().__init__()
        self.conv1 = nn.Conv2d(3, 64, kernel_size=3)
        self.pool = nn.MaxPool2d(2, 2)
        self.fc1 = nn.Linear(64 * 30 * 30, 10)
        
    def forward(self, x):
        x = self.pool(F.relu(self.conv1(x)))
        x = x.view(-1, 64 * 30 * 30)
        x = self.fc1(x)
        return x

Common Architectures
- VGG
- ResNet
- Inception
- EfficientNet

Advanced CV Tasks

Semantic Segmentation
- U-Net
- FCN
- DeepLab
Instance Segmentation
- Mask R-CNN
- YOLACT
- PointRend

Best Practices

Image Processing Pipeline

Preprocessing
- Resize
- Normalize
- Augment
- Filter
Model Selection
- Task requirements
- Computational resources
- Accuracy needs
- Latency constraints

Performance Optimization

Hardware Acceleration
- GPU processing
- CPU optimization
- Edge devices
Code Optimization

# Efficient image processing
@numba.jit(nopython=True)
def process_image(image):
    # Optimized processing code
    pass

Tools and Libraries

Essential Libraries

OpenCV
- Image processing
- Video analysis
- Camera calibration
Deep Learning
- PyTorch
- TensorFlow
- Keras

Development Tools

Image Annotation
- LabelImg
- VGG Image Annotator
- CVAT
Visualization
- Matplotlib
- OpenCV
- TensorBoard

Future Trends

Emerging Technologies

3D Vision
- Depth estimation
- 3D reconstruction
- Point cloud processing
Multi-Modal Learning
- Vision + Language
- Vision + Audio
- Cross-modal understanding

Getting Started

Prerequisites

Mathematical Foundation
- Linear algebra
- Probability
- Statistics
- Calculus
Programming Skills
- Python
- OpenCV
- Deep learning frameworks

Learning Path

Basic Concepts
- Image processing
- Feature detection
- Classical algorithms
Advanced Topics
- Deep learning
- Object detection
- Semantic segmentation

Resources

Learning Materials

Books
- "Digital Image Processing" by Gonzalez & Woods
- "Computer Vision: Algorithms and Applications" by Szeliski
- "Deep Learning for Computer Vision" by Goodfellow et al.
Online Resources
- Course materials
- Tutorial series
- Research papers
- Blog posts

Remember that computer vision is a rapidly evolving field. Stay updated with the latest research and developments while building a strong foundation in the fundamentals.