Image gradients (Sobel, Laplacian) in Computer Vision - Model Metrics & Evaluation

Metrics & Evaluation - Image gradients (Sobel, Laplacian)

Which metric matters for Image Gradients and WHY

Image gradients like Sobel and Laplacian help find edges by showing where pixel brightness changes sharply.

We measure how well these gradients detect true edges versus noise. Key metrics include Edge Detection Accuracy, Precision, and Recall.

Precision tells us how many detected edges are real edges (avoiding false edges).

Recall tells us how many real edges were found (avoiding missed edges).

Good edge detection balances both, so we often use the F1 score to combine precision and recall.

Confusion Matrix for Edge Detection

      |                 | Predicted Edge | Predicted No Edge |
      |-----------------|----------------|-------------------|
      | True Edge       | TP             | FN                |
      | No Edge         | FP             | TN                |

      TP + FP + TN + FN = Total pixels

      Example:
      TP = 80 (correct edges found)
      FP = 20 (wrong edges detected)
      FN = 10 (edges missed)
      TN = 890 (correct non-edges)
    

Precision vs Recall Tradeoff in Edge Detection

If we set the gradient threshold low, we find more edges (high recall) but also more noise (low precision).

If we set the threshold high, we get fewer false edges (high precision) but miss some real edges (low recall).

For example, in medical imaging, missing an edge (low recall) could hide important details, so recall is more important.

In photo editing, avoiding false edges (high precision) is better to keep the image clean.

Good vs Bad Metric Values for Image Gradients

Good: Precision and recall both above 0.8 means most edges are correctly found and few false edges appear.

Bad: Precision below 0.5 means many false edges confuse the result.

Recall below 0.5 means many real edges are missed, losing important image details.

Accuracy alone can be misleading because most pixels are non-edges (TN), so high accuracy can happen even if edges are poorly detected.

Common Pitfalls in Evaluating Image Gradients

• Accuracy Paradox: High accuracy can occur by labeling most pixels as non-edge, ignoring edges.
• Data Leakage: Using test images similar to training images can inflate metrics.
• Overfitting: Tuning thresholds too much on one image set may fail on new images.
• Ignoring Context: Edges detected may not correspond to meaningful objects.

Self Check

Your edge detection model has 98% accuracy but only 12% recall on edges. Is it good?

Answer: No, because it misses most real edges (low recall). The high accuracy is due to many non-edge pixels correctly labeled, but the model fails its main job: finding edges.

Key Result

Precision and recall are key to evaluate image gradients; high accuracy alone can be misleading due to many non-edge pixels.