Overview of image segmentation with AI - DevConf.CZ 2024

Overview of Image Segmentation with AI - DevConf.CZ 2024

Introduction

Hello everyone. This article provides an overview of image segmentation with AI. It was presented by Jan Pedro (JP) at the DevConf.CZ 2024. Here's an in-depth look into the topic, accompanied by demos and practical examples.

Agenda

1. Introduction and Presenter Details
2. Definitions and Concepts of Image Segmentation
3. AI and Learning Types
4. AI Structures for Image Segmentation
5. Application of AI in Image Segmentation
6. Demos

Presenter Details

My name is Jan Pedro, often called JP. I am 29 years old from São Paulo, Brazil. I am an information engineer and currently a software engineer at "Headhead."

Definitions: What is Image Segmentation?

Image segmentation is a crucial process in computer vision where an image is divided into multiple segments or objects. It involves two significant steps:

1. Classification of objects in the image (like bus, car, building, etc.)
2. Finding the borders of these objects for detailed identification.

Human and Machine Segmentation

Humans typically identify objects and their borders in an image. Machines emulate this process to produce output images with segmented parts highlighted in different colors, such as streets, buses, buildings, etc.

AI Concepts

When we think about AI, science fiction often comes to mind. However, AI in reality is based on models, which are algorithms with defined properties used for learning. Here's an insight into the types of learning:

1. Supervised Learning: Data is provided with labels for each element.
2. Unsupervised Learning: Data is provided without labels, and the model identifies patterns to create clusters.
3. Reinforcement Learning: The model learns based on rewards from performing specific actions, suitable for tasks like navigating mazes.

AI Structures

1. Artificial Neural Networks (ANN): Suitable for structured data, divided into input layers, hidden layers, and output layers.
2. Convolutional Neural Networks (CNN): Ideal for image data, reduces dimensionality while preserving essential features.
3. Residual Networks (ResNet) and U-Net: Specialized for complex image segmentation tasks, utilizing multiple layers and stages for finer segmentation.

AI Application in Image Segmentation

ResNet and U-Net models are benchmarks in image segmentation research. They utilize multiple convolutional layers to extract and enhance essential features from images, eventually reconstructing segmented images.

Learning Types for Segmentation

Supervised learning is predominantly used for image segmentation, offering high precision with annotated data sets like COCO, KITTI, and medical tomography images.

Evaluation Metrics

1. Jaccard Index
2. Dice Score
3. Hausdorff Distance

Demos

Here are the practical applications demonstrated:

1. U-Net Example using TensorFlow and Keras: Demonstrated how to create and compile a U-Net model.
2. Hugging Face Pre-trained Models: Showed the utilization of a pre-trained model for image segmentation tasks.
3. YOLO (You Only Look Once): Demonstrated object detection and segmentation using a pre-trained YOLO model.

These demos illustrated both custom training and usage of pre-trained models, highlighting the efficiency and execution speed for image segmentation tasks.

Conclusion

Thank you for reading about the application and intricacies of image segmentation using AI. This article covered foundational concepts, learning types, AI structures, and real-world demos illustrating the potential of AI in image segmentation.

Keywords

• Image Segmentation
• AI
• Supervised Learning
• Convolutional Neural Networks (CNN)
• Residual Networks (ResNet)
• U-Net
• Jaccard Index
• Dice Score
• Hausdorff Distance
• TensorFlow
• Keras
• YOLO
• Hugging Face

FAQs

Q: What is image segmentation?
A: Image segmentation is the process of dividing an image into multiple segments or objects, often to simplify analysis or highlight specific areas.

Q: What types of learning are used in AI for image segmentation?
A: Supervised learning is primarily used, but unsupervised and reinforcement learning can also be applied.

Q: How do Convolutional Neural Networks (CNNs) work with image data?
A: CNNs utilize convolutional layers to reduce image dimensionality while preserving essential features, making them ideal for image data.

Q: What are the commonly used evaluation metrics for image segmentation models?
A: Jaccard Index, Dice Score, and Hausdorff Distance are commonly used for evaluating model performance in image segmentation.

Q: What are the practical applications of U-Net and YOLO in image segmentation?
A: U-Net is used for custom segmentation tasks, especially in medical imaging, while YOLO is effective for object detection and segmentation using pre-trained models.