Overview of image segmentation with AI - DevConf.CZ 2024
Science & Technology
Introduction
Introduction
In today's session, we will explore the fascinating world of image segmentation using artificial intelligence (AI). I will begin with a brief introduction about myself and subsequently dive into the fundamental concepts of image segmentation and AI. We will discuss how AI is applied to image segmentation, followed by two interactive demos showcasing the practical applications of these concepts.
About Me
My name is Jan Pedro, but you can call me JP. I’m a 29-year-old information engineer based in São Paulo, Brazil. Currently, I work as a software engineer at Headhead.
What is Image Segmentation?
Image segmentation is a crucial task in computer vision. The process involves identifying objects within an image and delineating their boundaries. To illustrate this, consider an image containing a bus, a car, a building, a street, a tree, and a sky.
- Object Classification: The first step involves recognizing what entities exist within the image.
- Boundary Detection: Next, we specify the exact locations—where the bus is, where the car is, and so on.
The goal is to convert a standard image into a segmented output, clearly defining the parameters of various objects using distinct colors.
In summary, image segmentation refers to the process of identifying objects in an image and determining their borders.
Understanding AI
When discussing AI, many of us think of science fiction robots from movies like 2001: A Space Odyssey or Interstellar. However, the real-world application of AI involves models that utilize algorithms for learning.
Learning Methods in AI
- Supervised Learning: In this method, we provide the AI with labeled data, allowing it to learn through examples.
- Unsupervised Learning: Here, the AI is fed unlabeled data, and it must identify patterns and separate information into categories.
- Reinforcement Learning: This approach involves an agent receiving feedback (rewards or penalties) based on its actions within an environment.
AI Structures
Two popular AI structures for image processing include:
- Artificial Neural Networks (ANN): Composed of interconnected neurons, ANNs are used for classification tasks.
- Convolutional Neural Networks (CNN): These are specifically designed to work with multi-dimensional data like images. They utilize convolutional layers to extract important features, making them particularly effective for image analysis.
Image Segmentation Techniques
Popular Architectures for Image Segmentation
- ResNet: A deep learning network designed to output segmented images while preserving the dimensions of the input.
- U-Net: This architecture features an encoder-decoder structure that extracts important features of the image and reconstructs it.
Learning Type for Image Segmentation
Supervised learning is most commonly used in image segmentation tasks, requiring pairs of input images and their corresponding segmented masks for the model to learn.
Datasets for Training Models
Some well-known datasets used for training image segmentation models include:
- COCO: Contains a variety of images with annotated segments across domains such as objects, people, and environments.
- KITTI: Focuses on street scenes, highlighting objects like cars and pedestrians.
Metrics for Evaluating Segmented Images
When evaluating the performance of image segmentation models, traditional metrics like accuracy and precision are often insufficient. Instead, we favor metrics like:
- Jaccard Index: Measures the intersection over the union of the predicted output and the actual mask.
- Dice Score: Similar to Jaccard but applies a constant for sensitivity.
- Hausdorff Distance: Measures the distance between predicted segments and ground truth segments.
Demonstrations
In this section, we explored two practical demos using Jupyter Notebook:
- U-Net Example: A simple implementation of U-Net utilizing TensorFlow and Keras for basic image segmentation.
- Hugging Face Model: A demonstration using a pre-trained model for segmenting images in a straightforward manner.
Both demonstrations illustrated how these AI models function in practice.
Conclusion
To wrap up our presentation, we have evaluated the processes and methodologies behind image segmentation using AI, discussed various architectures, learning types, datasets, and evaluation metrics, and showcased practical examples.
Keywords
Image segmentation, artificial intelligence, supervised learning, convolutional neural networks, U-Net, ResNet, Jaccard Index, Dice Score, Hausdorff Distance, COCO dataset, KITTI dataset.
FAQ
Q1: What is image segmentation?
A1: Image segmentation is the process of identifying and delineating distinct objects within an image.
Q2: What are the main learning types in AI?
A2: The main learning types are supervised learning, unsupervised learning, and reinforcement learning.
Q3: Why is supervised learning preferred for image segmentation?
A3: Supervised learning provides labeled data that helps the model learn how to segment images effectively.
Q4: What are Jaccard Index and Dice Score used for?
A4: They are metrics used to evaluate the performance of image segmentation models by measuring the overlap between predicted and actual segmented images.
Q5: What are some common datasets used for image segmentation training?
A5: Two prominent datasets are the COCO dataset, which offers a variety of images, and the KITTI dataset, which focuses on street scenes.
