Initial Pattern Detectors CNN
Understanding the Role of Pattern Detectors (Filters)
CNN pattern detectors (kernels) are small matrices (like 3×3 or 5×5) that “slide” over the image and look for specific local features, such as:
- Edges
- Corners
- Textures
- Curves
These are the building blocks of higher-level patterns.
Step-by-Step Design Approach for Initial Pattern Detectors
| Step | Description | Example / Output |
|---|---|---|
| 1️ Understand the Problem Domain | What do we want to detect in the image? Edges? Corners? Specific shapes? | e.g., “I want to detect windows in a house image. Windows have edges, corners, and possibly vertical symmetry.” |
| 2️ Identify Primitive Features | Break down the object into simple patterns. | Window → horizontal edge, vertical edge, corners |
| 3️ Start with Known Kernels | Use standard filters: Sobel, Prewitt, Laplacian for edge detection. | Sobel X for vertical edges: [[1, 0, -1], [2, 0, -2], [1, 0, -1]] |
| 4️ Visualize the Effect | Apply these filters on sample images using convolution. | See how vertical lines get highlighted with Sobel-X |
| 5️ Manually Design Custom Kernels | You can design filters that focus on corners or curves by combining horizontal and vertical filters. | e.g., a diagonal filter: [[0, 1, 1], [-1, 0, 1], [-1, -1, 0]] |
| 6️ Simulate Convolution | Apply the kernel manually (or in code) on a small matrix and observe outputs. | Use NumPy to convolve on a 6×6 grayscale patch |
| 7️ Refine by Experimentation | Stack filters, try combinations, tweak weights | Try edge + corner or even directional filters |
| 8️ Let the CNN Learn from Here | After 1–2 handcrafted layers, let CNN learn filters via backpropagation | First few layers: hand-crafted Rest: trainable |
Common Initial CNN Kernels / Filters
| Kernel Name | Matrix (3×3) | Purpose / Effect | Visual Output Effect |
|---|---|---|---|
| Sobel X | [[ 1, 0, -1], [ 2, 0, -2], [ 1, 0, -1]] | Detect vertical edges | Highlights vertical lines (like poles) |
| Sobel Y | [[ 1, 2, 1], [ 0, 0, 0], [-1, -2, -1]] | Detect horizontal edges | Highlights horizontal lines (like steps) |
| Prewitt X | [[ 1, 0, -1], [ 1, 0, -1], [ 1, 0, -1]] | Simpler vertical edge detector | Crisper vertical edge lines |
| Prewitt Y | [[ 1, 1, 1], [ 0, 0, 0], [-1, -1, -1]] | Simpler horizontal edge detector | Clear horizontal cuts |
| Laplacian | [[ 0, -1, 0], [-1, 4, -1], [ 0, -1, 0]] | Detects edges in all directions | All-direction edge detection |
| Sharpen | [[ 0, -1, 0], [-1, 5, -1], [ 0, -1, 0]] | Enhances contrast & edges | Sharpens overall image |
| Box Blur | [[1, 1, 1], [1, 1, 1], [1, 1, 1]] / 9 | Simple average blurring | Smooths textures |
| Gaussian Blur | [[1, 2, 1], [2, 4, 2], [1, 2, 1]] / 16 | Smooths with Gaussian weighting | Reduces noise while preserving edges |
| Edge Detection 1 | [[-1, -1, -1], [-1, 8, -1], [-1, -1, -1]] | Highlights edges strongly | Very crisp outline of edges |
| Corner Detector | [[1, -1, 0], [-1, 1, 0], [0, 0, 0]] | Detects corners or changes in direction | Highlights corners and intersections |
| Emboss Filter | [[-2, -1, 0], [-1, 1, 1], [ 0, 1, 2]] | Gives a 3D raised/embossed effect | Image looks like pressed/embossed surface |
| Directional Filter (Diagonal) | [[0, 1, 1], [-1, 0, 1], [-1, -1, 0]] | Detects diagonal patterns | Catches tilted edges |
Use Cases Summary
Edge Detectors → First layer to find outlines
Blur/Sharpen Filters → Preprocessing or enhancing clarity
Corner/Diagonal Filters → Higher-detail detectors before deep layers
Initial Pattern Detectors CNN – Initial Pattern Detectors example with Simple Python