CNN_Image_Filtering_Presentation_Updated.pptx

CNN_Image_Filtering_Presentation_Updated.pptx

• 1.
  CNN-based Image Filteringand Visualization Understanding Edge Detection with Sobel Filters
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  Overview • This presentationdemonstrates how a CNN with a Sobel filter processes images: • - Applies Sobel filter to original and blurred images • - Visualizes edge detection behavior • - Uses a single Conv2D layer without activation
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  Step 1: InstallDependencies • !pip install tensorflow opencv-python matplotlib --quiet
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  Step 2: DownloadImage • Download a sample image from Wikimedia: • - URL: Golden retriever image • - Saved as 'dog.jpg'
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  Step 3: Load& Preprocess Image • - Convert to grayscale • - Resize to 128x128 • - Normalize pixel values • - Reshape to (1, 128, 128, 1)
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  Step 4: BuildCNN Model • - Single Conv2D layer • - 1 filter, 3x3 kernel • - No activation function • - No bias
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  Step 5: SetSobel Filter • Manually set the Conv2D weights to a Sobel X filter: • [[-1, 0, 1], [-2, 0, 2], [-1, 0, 1]] • Detects vertical edges
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  Step 6: ApplyFilter to Original • - Use model.predict on the original image • - Get 2D filtered output • - Shows detected edges
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  Step 7: Blurand Filter Again • - Apply Gaussian blur to original • - Filter blurred image using same CNN • - Shows reduced edge intensity
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  Step 8: Visualization •Show 4 subplots: • - Original • - CNN Filtered • - Blurred • - CNN Filtered on Blurred
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  Conclusion • This exampledemonstrates: • - How CNNs use filters for feature detection • - Sobel filter behavior • - Effect of blurring on edge detection
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  What is aCNN Model? • A Convolutional Neural Network (CNN) is a deep learning model used primarily for image processing tasks. • Key Components: • 1. **Convolutional Layers** – Extract local features from the input image using filters. • 2. **Activation Function (ReLU)** – Adds non- linearity to the network.