Image gradients (Sobel, Laplacian) in Computer Vision - Model Pipeline Trace

Model Pipeline - Image gradients (Sobel, Laplacian)

This pipeline shows how an image is processed to find edges using gradients. It uses Sobel and Laplacian filters to highlight where the image changes sharply, like edges of objects.

Data Flow - 4 Stages

1Input Image

1 image x 256 height x 256 width x 1 channel (grayscale)→Load grayscale image→1 image x 256 height x 256 width x 1 channel

A 256x256 pixel grayscale photo of a cat

↓

2Apply Sobel Filter

1 image x 256 x 256 x 1→Compute horizontal and vertical gradients using Sobel operator→1 image x 256 x 256 x 2 (gradient_x, gradient_y)

Edges detected horizontally and vertically in the cat image

↓

3Combine Sobel Gradients

1 image x 256 x 256 x 2→Calculate gradient magnitude from horizontal and vertical gradients→1 image x 256 x 256 x 1

Edge strength map showing where edges are strongest

↓

4Apply Laplacian Filter

1 image x 256 x 256 x 1→Compute second order derivative to find edges using Laplacian operator→1 image x 256 x 256 x 1

Edges detected with Laplacian highlighting sharp changes

Training Trace - Epoch by Epoch

Loss
0.5 |****
0.4 |***
0.3 |**
0.2 |**
0.1 |*
    +---------
     1 2 3 4 5 Epochs

Epoch	Loss ↓	Accuracy ↑	Observation
1	0.45	0.60	Initial edge detection with noisy gradients
2	0.30	0.75	Gradients become clearer, edges more defined
3	0.20	0.85	Edge maps sharpen, noise reduced
4	0.15	0.90	Stable edge detection with strong gradients
5	0.12	0.92	Final edge maps show clear object boundaries

Prediction Trace - 5 Layers

Layer 1: Input Image

Layer 2: Sobel Filter Horizontal Gradient

Layer 3: Sobel Filter Vertical Gradient

Layer 4: Gradient Magnitude Calculation

Layer 5: Laplacian Filter

Model Quiz - 3 Questions

What does the Sobel filter detect in an image?

A Color changes in the image

B Edges by measuring intensity changes horizontally and vertically

C Noise patterns

D Image brightness

Key Insight

Image gradients like Sobel and Laplacian help find edges by measuring how pixel brightness changes. Sobel uses first derivatives to find edges in horizontal and vertical directions, while Laplacian uses second derivatives to detect rapid changes in all directions. Combining these helps computers understand shapes and boundaries in images.