Building Visual Intelligence: Convolutional Neural Networks in Image Processing

Photo by Igor Surkov on Unsplash

Convolutional Neural Networks (CNNs) are a class of deep neural networks that have proven highly effective in computer vision tasks, such as image classification, object detection, and image segmentation. Here’s an overview of CNNs:

I. Key Concepts of CNNs:

1. Convolutional Layers:

• CNNs use convolutional layers to learn hierarchical features from input images. Convolutional operations involve sliding small filters (kernels) over the input image to extract local patterns.

2. Pooling Layers:

• Pooling layers downsample the spatial dimensions of the input, reducing computational complexity and preserving important features.

3. Fully Connected Layers:

• After feature extraction, fully connected layers are typically used for classification or regression tasks.

II. CNN Architecture:

1. Convolutional Layers:

• These layers apply convolutional operations to the input, capturing local patterns and learning hierarchical representations.

2. Activation Functions: