INTRODUCTION TO IMAGE ANALYSIS AND
COMPUTER VISION
Feature Extraction
CSTRACKA1
Group 1: Kahlille, Frederico, Sofia, Mely, Sarah, Edcel
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Foundations
02
Feature Extraction
03
Applications & Conclusion
Image Analysis and Computer Vision basics
PRESENTATION
Overview
Features, techniques, and their role in CV
Real-world impact and key takeaways
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FOUNDATIONS
01 FOUNDATIONS
02 FEATURE EXTRACTION
03 APPLICATIONS
What is Image Analysis?
Image Analysis is the process of extracting meaningful, quantifiable
information from digital images using computational methods.
It transforms raw pixel data into measurable knowledge — numbers,
patterns, or decisions that humans can interpret.
Key Goal: Convert pixels into knowledge.
Example: A medical scan showing a tumor is just pixels; image analysis
measures the tumor's size, shape, and density to assist diagnosis.
Think of it as reading an image carefully to find what mathematically
Medical image analysis assists radiologists in diagnosis
matters.
Examine
Measure
Report
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01 FOUNDATIONS
02 FEATURE EXTRACTION
03 APPLICATIONS
What is Computer Vision?
Computer Vision (CV) is a field of Artificial Intelligence that enables
machines to "see" and interpret visual information from the world.
Goal: Teach computers to understand images and videos the way
humans do — through recognition, understanding, and decisionmaking.
Example: A self-driving car uses CV to identify pedestrians, traffic lights,
and road signs in real time.
Key idea: If image analysis is reading the image, computer vision is
Computer vision powers autonomous vehicle perception
understanding what it reads.
Human Vision vs. Computer Vision
Humans: Instinctive, contextual, emotion-aware
Machines: Precise, tireless, scalable, consistent
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01 FOUNDATIONS
02 FEATURE EXTRACTION
03 APPLICATIONS
Image Analysis vs. Computer Vision
Image Analysis
Computer Vision
Focus: Extracting data and measurements from images
Focus: Interpreting and understanding visual data
Question: "What is in this image?" (quantitative level)
Question: "What is happening in this scene?"
Output: Numbers, statistics, measurements
Output: Decisions, actions, classifications
Analogy: Reading words from a page
Analogy: Understanding the story
Example: Measuring tumor size in a medical scan
Example: Autonomous driving decisions
Scope: Narrow and focused
Scope: Broad and comprehensive
Relationship: Image Analysis is a building block of Computer Vision. CV uses analysis results to perform higher-level tasks.
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01 FOUNDATIONS
02 FEATURE EXTRACTION
03 APPLICATIONS
Why These Fields Are Important
90% of information transmitted to the human brain is visual. We live in an inherently visual world.
Faster Medical Diagnosis
Safer Autonomous Vehicles
AI detects diseases in scans earlier and more accurately
Real-time perception prevents accidents and saves lives
Smarter Security Systems
Improved Accessibility
Automated surveillance detects threats 24/7 without fatigue
Helping visually impaired navigate the world through AI
The global computer vision market is projected to reach $41 billion by 2030, creating massive career opportunities.
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01 FOUNDATIONS
02 FEATURE EXTRACTION
03 APPLICATIONS
Basic Steps in Image Analysis
1. Acquisition
Capture or load image
2. Preprocessing
Enhance quality
3. Segmentation
Divide into regions
4. Feature Extraction
Identify key traits
5. Classify
1. Image Acquisition
2. Preprocessing
3. Segmentation
Capturing or loading the digital image from
Enhancing image quality through noise
Dividing the image into meaningful regions or
cameras, scanners, or databases. The starting
reduction, contrast adjustment, resizing, and
objects to isolate areas of interest for further
point of all analysis.
normalization.
analysis.
4. Feature Extraction
5. Classification / Decision
Identifying and extracting key characteristics like edges, textures, and
Using extracted features to classify objects, detect patterns, or make
shapes. This is the critical bridge between raw data and understanding.
automated decisions based on the analysis.
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FEATURE EXTRACTION
01 FOUNDATIONS
02 FEATURE EXTRACTION
03 APPLICATIONS
Introduction to Feature Extraction
Feature Extraction is the process of identifying and isolating the most
The Feature Extraction Pipeline
important and distinctive characteristics from an image.
A "feature" is a piece of information relevant for solving a computational
task — it's what makes an object recognizable.
Raw Image
3,000,000 pixels
Raw pixel data is too large and noisy to process directly. A 1000×1000
image has 3 million pixel values!
Feature Extraction
Find key characteristics
Feature extraction reduces this massive data into a compact, meaningful
representation.
Analogy: Recognizing a Friend
When you recognize a friend, you don't memorize every pixel of their face — you
Feature Vector
~500 features
Dimensionality reduced by 99.98%
while preserving what matters
remember their eyes, nose shape, and smile. Those are "features."
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02 FEATURE EXTRACTION
01 FOUNDATIONS
03 APPLICATIONS
What Are Features in an Image?
Features are measurable properties or patterns that help distinguish one object from another. They act like a "fingerprint" for
objects in an image.
Qualities of Good Features
Distinctive
Repeatable
Informative
Compact
Unique to the object
Found consistently
Useful for the task
Small in size
Everyday Feature Examples
Edge of a building — defines its outline
Striped texture of a zebra — unique identifier
Round shape of a wheel — distinguishes it from a square
Red color of a stop sign — immediate recognition
Without features, every image is just a grid of numbers. Features give those numbers meaning.
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01 FOUNDATIONS
02 FEATURE EXTRACTION
03 APPLICATIONS
Types of Features
Edge Features
Corner Features
Texture Features
Boundaries where intensity changes sharply.
Points where two or more edges meet. Corners are
Patterns describing surface quality — smooth,
Define object outlines.
highly distinctive and easy to track.
rough, striped, or spotted.
Example: Canny edge detector
Example: Harris corner detector
Example: Zebra stripes, fabric weave
Color Features
Shape Features
Based on pixel color values (RGB, HSV). Highly useful for identifying objects
Describe geometric structure — area, perimeter, circularity, aspect ratio, and
with distinct colors.
contour.
Example: Red stop sign, green traffic light, blue sky
Example: Circle vs. square, compact vs. elongated
Each type captures a different aspect of visual information. Real systems often combine multiple feature types for best results.
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02 FEATURE EXTRACTION
01 FOUNDATIONS
03 APPLICATIONS
Common Feature Extraction Techniques
Technique
What It Does
Strengths
Best For
SIFT
Detects key points stable across scale, rotation,
and lighting changes
Highly robust and accurate
Object matching, panorama stitching
SURF
Similar to SIFT but uses faster box filters and
integral images
Fast, good for real-time
Real-time tracking, mobile apps
HOG
Counts gradient directions in local regions of the
image
Excellent for rigid shapes
Pedestrian detection, human figures
ORB
Fast alternative combining FAST keypoints and
BRIEF descriptors
Free, fast, efficient
Mobile, embedded, real-time systems
Speed vs. Robustness Comparison
SIFT
SURF
HOG
ORB
Slow but very robust
Medium speed
Moderate speed
Very fast
Gold standard accuracy
Good balance
Best for humans
Modern standard
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01 FOUNDATIONS
02 FEATURE EXTRACTION
03 APPLICATIONS
How Feature Extraction Helps Computer Vision
Feature extraction is the foundation of almost every computer vision task. Without it, CV would be impossibly slow and inaccurate.
Object Recognition
Image Matching
Features allow machines to identify objects regardless of
Features help stitch photos together for panoramas or find similar
position, size, or viewing angle.
images in databases.
Motion Tracking
3D Reconstruction
By tracking features across video frames, systems can follow
Features from multiple camera angles help build 3D models of
moving objects smoothly.
objects and scenes.
Machine Learning Input
Extracted features become the input data for AI models — neural networks, SVMs, and decision trees.
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APPLICATIONS & CONCLUSION
01 FOUNDATIONS
03 APPLICATIONS
02 FEATURE EXTRACTION
Real-Life Applications
Face Recognition
Object Detection
Medical Imaging
Unlocking phones, airport security, social media tagging.
Self-driving cars detect pedestrians, cyclists, and traffic
AI detects tumors in X-rays, MRIs, and CT scans.
Features like eye distance and jawline are extracted and
signs. Drones identify targets. Retail systems track
Features like shape irregularity and texture density
matched.
inventory.
indicate cancer.
Autonomous Vehicles
Security Systems
CV systems process camera feeds to navigate roads, read signs, and
Surveillance cameras use CV to detect intruders, recognize license plates,
avoid collisions — all in real time.
and monitor crowd behavior automatically.
Companies like Tesla and Waymo rely on feature extraction to make split-
Modern systems can identify suspicious activity and alert authorities in
second driving decisions.
real time.
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01 FOUNDATIONS
03 APPLICATIONS
02 FEATURE EXTRACTION
Advantages and Challenges
Advantages
Automation
Processes millions of images without human fatigue or boredom.
Challenges
Lighting Changes
Shadows and glare can confuse feature detectors and reduce
accuracy.
Speed
Occlusion
Real-time analysis for critical applications like driving and surgery.
When objects are partially hidden, key features may be lost.
Variability
Accuracy
Same objects look different under rotation, scale, or viewpoint
Consistent and objective; not affected by human bias or tiredness.
changes.
Scalability
Can handle massive datasets impossible for humans to review.
Privacy Concerns
Face recognition and surveillance raise ethical and legal questions.
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01 FOUNDATIONS
03 APPLICATIONS
02 FEATURE EXTRACTION
Summary & Conclusion
1
Image Analysis transforms raw pixels into
measurable, meaningful data.
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2
Computer Vision enables machines to
understand and act upon visual
information.
Types of features include edges, corners, texture, color, and shape —
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each capturing a different visual aspect.
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3
Feature Extraction is the critical bridge
that makes objects recognizable.
Techniques like SIFT, SURF, HOG, and ORB provide robust ways to
extract these features efficiently.
Applications span healthcare, transportation, security, and everyday technology — transforming how we live and work.
Key Takeaway
Understanding features is understanding how machines see the world. The field continues to grow, combining classical
methods with modern deep learning for ever more powerful visual AI.
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01 FOUNDATIONS
02 FEATURE EXTRACTION
03 APPLICATIONS
References
[1] Bradski, G., & Kaehler, A. (2008). Learning OpenCV: Computer Vision with the OpenCV Library. O'Reilly Media.
[2] Lowe, D. G. (2004). Distinctive Image Features from Scale-Invariant Keypoints. International Journal of Computer Vision, 60(2), 91-110.
[3] Bay, H., Ess, A., Tuytelaars, T., & Van Gool, L. (2008). SURF: Speeded Up Robust Features. Computer Vision and Image Understanding, 110(3), 346-359.
[4] Dalal, N., & Triggs, B. (2005). Histograms of Oriented Gradients for Human Detection. IEEE Conference on CVPR.
[5] Rublee, E., Rabaud, V., Konolige, K., & Bradski, G. (2011). ORB: An Efficient Alternative to SIFT or SURF. IEEE ICCV.
[6] Gonzalez, R. C., & Woods, R. E. (2018). Digital Image Processing (4th ed.). Pearson.
[7] Szeliski, R. (2022). Computer Vision: Algorithms and Applications (2nd ed.). Springer.
Tip: OpenCV is free and open-source. Students can download it and start experimenting with feature extraction algorithms today.
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THANK YOU