Welcome back to Pattern Recognition! In our previous episode, we discussed Linear Discriminant Analysis (LDA) and its associated classification techniques. Today, we’ll delve into a few applications of LDA to demonstrate how this technique is not just theoretical, but is also being utilized in practical settings.
Application 1: Intelligent Shoe by Adidas
One fascinating example of LDA in action is the development of an intelligent shoe by Adidas. This groundbreaking shoe, created by my colleague Bjorn Escoffier, features embedded sensing technology in its sole. By adjusting the stiffness of the shoe, it can prevent injuries when running on different surfaces.
The shoe’s sensing and recognition system, developed at our lab, utilizes LDA classification. The system includes a cushioning element with a magnetic system for compression measurement, a microcontroller with a user interface, and a motor for adaptive cushioning. With limited processing capabilities, this embedded system relies on fast processing and simple methods, making LDA a perfect fit.
LDA maps the classification problem to a linear decision boundary, approximating a two-class problem with a first-order polynomial. By introducing weights and features, the classification decision is based on the sign of the projection onto the class boundary. In this shoe, 19 features were computed, but only three were selected for implementation. These features analyze the step signal, change in cushioning material, and surface steepness to control the shoe’s stiffness.
Application 2: Shape Modeling
Another application of LDA is shape modeling, particularly in the context of anatomical structures like organs. By sampling surface points on a shape, we can encode the surface into a high-dimensional vector. However, modeling high-dimensional spaces can be challenging. To address this, we use Principal Component Analysis (PCA) to capture the shape’s variations.
By computing the PCA of landmark configurations representing multiple shapes, we obtain eigenvectors and associated eigenvalues. These eigenvectors allow us to model any shape using a linear combination. By selecting the most significant modes of variation, we can capture the essential changes in shape with just a few parameters.
This approach, known as Active Shape Modeling, is particularly useful in medical applications. For instance, by estimating rotation, translation, and shape parameters, we can generate accurate segmentations of organs from medical images. PCA provides a powerful tool to describe the complex variations of anatomical changes in the body.
FAQs
Q: How does LDA handle the limited computing capabilities of an embedded system, like the intelligent shoe?
A: LDA’s simplicity and efficiency make it suitable for embedded systems. By utilizing fast processing and simple feature calculations, LDA can overcome the memory and compute limitations of such systems.
Q: Can PCA be applied to other fields beyond shape modeling?
A: Absolutely! PCA has widespread applications, including image compression, data analysis, and feature extraction in various domains. Its ability to reduce high-dimensional data to a lower-dimensional subspace makes it a versatile tool.
Conclusion
In this episode, we explored two real-world applications of Linear Discriminant Analysis. From the development of an intelligent shoe to shape modeling in medical imaging, LDA proves its effectiveness in solving classification and modeling problems. By leveraging LDA’s ability to map complex problems to linear decision boundaries, we can unlock exciting possibilities in various fields.
To learn more about these topics, we encourage you to attend our lectures on medical image processing. There, we delve deeper into the methods and algorithms used in active shape modeling and other related techniques. We look forward to welcoming you to our classes!
Thank you for joining us today, and we’ll see you in the next episode of Pattern Recognition. Stay tuned!
