Convolutional Neural Networks (CNNs) have revolutionized the field of computer vision. They have shown remarkable performance in various tasks such as image classification, object detection, and segmentation. In this tutorial, we will explore the basics of CNNs and their applications in vision.

CNN Architecture

A CNN typically consists of several layers:

  • Input Layer: The input to the network is an image.
  • Convolutional Layers: These layers apply various filters to the input image to extract features.
  • Pooling Layers: These layers reduce the spatial dimensions of the feature maps, reducing the computational complexity.
  • Fully Connected Layers: These layers perform classification based on the features extracted by the convolutional and pooling layers.
  • Output Layer: This layer provides the final prediction.

Convolutional Layers

Convolutional layers are the core building blocks of CNNs. They apply various filters to the input image to extract features. These filters are typically learned during the training process.

Example Filters

  • Gaussian Filter: Blurs the image.
  • Sobel Filter: Detects edges in the image.
  • Canny Edge Detector: Detects edges in the image with a higher level of accuracy.

Pooling Layers

Pooling layers reduce the spatial dimensions of the feature maps, which reduces the computational complexity. There are two main types of pooling:

  • Max Pooling: Keeps the maximum value in the region of the feature map.
  • Average Pooling: Keeps the average value in the region of the feature map.

Applications

CNNs have found applications in various computer vision tasks:

  • Image Classification: Classifying images into different categories.
  • Object Detection: Detecting and classifying objects in images.
  • Image Segmentation: Segmenting images into different regions.
  • Face Recognition: Recognizing faces in images.

Example

Let's take a look at a simple CNN architecture for image classification:

Input Layer
|
Convolutional Layer (Filter: 32x32)
|
Pooling Layer (Max Pooling)
|
Convolutional Layer (Filter: 64x64)
|
Pooling Layer (Max Pooling)
|
Fully Connected Layer (1024 neurons)
|
Output Layer (10 neurons)

For more information on CNN architectures and their applications, please refer to our Advanced CNN Architectures.

Convolutional Neural Network