Computer Vision Problems
Understanding the building blocks of visual AI: from pixels to perception, from low-level features to high-level understanding.
"Teaching machines to see is like teaching a baby to walk—one pixel at a time."
Introduction to Computer Vision
Computer vision sits at the intersection of artificial intelligence, image processing, and human perception. The field has evolved from classical hand-crafted methods to modern deep learning approaches, but understanding the classical problems remains crucial for building effective generative models.
We can broadly divide computer vision problems into low-level and high-level vision tasks. Low-level vision deals with basic feature extraction from raw pixels, while high-level vision tackles understanding, interpretation, and reasoning about visual scenes.
Low-Level Vision Problems
Definition: Low-level vision problems focus on extracting basic features and information from raw image data—the foundational building blocks for more complex visual tasks.
These tasks serve as the initial processing stages in visual perception, focusing on interpreting basic elements without delving into scene content or context. They're essential for both human vision and computer vision systems.
1 Edge Detection
🔲Identifies boundaries between different regions based on discontinuities in intensity or color. Edges are crucial for understanding object structure and geometry.
Key Applications: Visual saliency, segmentation, tracking, motion analysis, medical imaging, autonomous driving, structure-from-motion, and 3D reconstruction.
2 Image Enhancement
Techniques to improve image appearance or quality for further processing: contrast adjustment, noise reduction, sharpening.
3 Color Processing
Operations on color components: color correction, space conversion, and balancing for consistent representation.
4 Texture Analysis
Quantifying and identifying patterns or structures in object textures for classification, segmentation, and synthesis tasks.
5 Motion Detection
Identifying changes in object position between frames for tracking, surveillance, and dynamic scene analysis.
6 Stereo Vision
Deriving depth information from multiple viewpoints, mimicking human binocular vision for 3D reconstruction and robotics.
7 Optical Flow
Computing motion of objects from visual changes across image sequences to understand scene dynamics.
8 Image Segmentation
Partitioning images into regions or segments to isolate objects or boundaries using low-level cues like color and texture.
🧠 Quick Quiz 1 Test your knowledge
Which of the following is NOT a low-level vision problem?
High-Level Vision Problems
Definition: High-level vision involves interpretation, understanding, and reasoning about scenes and objects beyond mere detection—focusing on the "what" and "why" rather than just the "how."
These tasks require integrating contextual information, prior knowledge, and understanding complex relationships, mimicking the cognitive aspects of human vision.
Object Recognition
Identifying and classifying objects into predefined categories under varying conditions of lighting, pose, and occlusion.
Scene Understanding
Interpreting overall context: setting identification, object relationships, and semantic context inference.
Object Detection
Determining object presence and precise location with bounding boxes or segmentation masks.
Image Captioning
Generating textual descriptions of image content, understanding relationships and constructing coherent sentences.
Visual Question Answering
Answering natural language questions about images, combining vision and NLP.
Action Recognition
Identifying activities in videos by understanding motion patterns and behaviors.
Facial Recognition
Identity verification and emotion analysis from facial expressions.
Semantic Segmentation
Classifying each pixel into categories for detailed scene composition understanding.
3D Reconstruction
Creating 3D models from images, understanding depth, perspective, and spatial relationships.
Low-Level vs High-Level Vision
Low-Level Vision
- ✓ Works directly with pixels
- ✓ Extracts basic features
- ✓ No semantic understanding
- ✓ Building blocks for higher tasks
- ✓ Fast, local operations
High-Level Vision
- ✓ Understands content & context
- ✓ Recognizes objects & scenes
- ✓ Semantic interpretation
- ✓ Requires prior knowledge
- ✓ Complex, global reasoning
Classical Computer Vision Problems
🧩 Texture Synthesis
Goal: Algorithmically generate larger textures from a small sample while maintaining appearance and structural characteristics.
The synthesis should produce textures that are perceptually indistinguishable from the original, appearing seamless without visible repetitions or artifacts.
Pixel-Based Synthesis
Copy pixels one at a time based on neighborhood similarity (Efros & Leung, 1999)
Patch-Based Synthesis
Stitch patches together, minimizing seams between blocks
Parametric Methods
Model statistical properties like color distribution and spatial frequency
Deep Learning
CNNs and GANs learn complex representations for high-quality synthesis
Applications:
✨ Image Denoising
Removing noise from images while preserving original details and structures. Noise can arise from sensor imperfections, environmental conditions, or transmission errors.
Spatial Domain
Mean, median, Gaussian filters
Transform Domain
Fourier, wavelet thresholding, Wiener filtering
Patch-Based
Non-local Means, BM3D
Deep Learning
CNNs, GANs, autoencoders
🪡 Image Quilting
A texture synthesis technique by Efros & Freeman (2001) that generates seamless textures by stitching together blocks (patches) from a source texture.
Block Cutting
Divide source texture into overlapping blocks
Block Selection
Find blocks matching adjacent placed blocks
Minimum Error Boundary Cut
Find optimal seam path through overlap region
Stitching
Combine blocks along minimum error boundary
🔄 Image Analogies
Concept: "A is to B as C is to D" — Learn transformation from A→B, then apply it to C→D
Introduced by Hertzmann et al. (2001), this technique creates relationships between image pairs and applies those transformations to new images.
Texture Transfer
Transfer texture from B onto structure of A
Artistic Filters
Apply artistic styles to new photographs
Super-Resolution
Enhance low-res images to high-res
Impact: Image analogies paved the way for neural style transfer and modern deep learning image synthesis techniques!
🧠 Quick Quiz 2 Challenge yourself
What is the main goal of texture synthesis?
Neurological Vision: Learning from Biology
Classical vision methods relied on hand-crafted transforms, filters, and kernels. However, studying the visual cortex of animals has led to profound insights that influenced modern deep neural networks.
From Biological Vision to CNNs
Hubel & Wiesel's (1962) groundbreaking work on receptive fields in cat visual cortex revealed hierarchical processing of visual information—simple cells detect edges, complex cells detect patterns, and higher levels recognize objects.
This biological architecture directly inspired convolutional neural networks (CNNs), which use layers of convolution and pooling operations mimicking the visual cortex's hierarchical structure.
Key difference: Modern deep learning relies heavily on supervised learning with massive labeled datasets, while biological vision learns through unsupervised and self-supervised mechanisms.
Non-Photorealistic Rendering (NPR)
NPR focuses on capturing and reusing artistic styles from paintings, drawings, and other visual media to apply them to new digital content.
Style Analysis
Using image processing and ML to analyze art samples and extract quantifiable style features
Style Modeling
Developing computational models that encapsulate extracted style features
Style Application
Implementing algorithms to render new content in the analyzed style
User Interaction
Interfaces allowing artists to fine-tune style application
Vision Problems Mind Map
Low-Level Vision
- • Edge Detection
- • Enhancement
- • Color Processing
- • Texture Analysis
- • Motion Detection
- • Stereo Vision
- • Optical Flow
High-Level Vision
- • Object Recognition
- • Scene Understanding
- • Object Detection
- • Image Captioning
- • VQA
- • Action Recognition
- • 3D Reconstruction
Classical Methods
- • Texture Synthesis
- • Image Denoising
- • Image Quilting
- • Image Analogies
- • NPR
- • Neurological Insights
Remember...
"Computer vision isn't about making machines see—
it's about teaching them to understand what they're looking at."
Next up: How generative models revolutionize these classical vision problems!