Hello guys, I wanted to share this method with you, which came to my mind while I was in a radiation imaging class in graduate school. With radon transformation the signal data of the targeted object or image converted to readable text based sinogram dataset. Which means it gives the depth,wideness etc. all the properties which makes ai to understand. It can be used in many technologies.For example in robotics now ai can see in 3d and not just see it enables them to sense the real world. You can try a simple applied version here:
/huggingface.co
/spaces
/MehmetK/RAYSENSE
Technologies and Methods Used
Sinogram Analysis:
It extracts the basis of the 3D structure of an object by creating projections of visual data from different angles. This method is a technique used especially in tomography and medical imaging.
When extracting volumetric data of objects, it goes beyond the surface and provides detailed information about depth and internal structures.
Frequency-Based Analysis (Fourier Transform):
It obtains more meaningful data by converting the pixel-based representation of images into frequency components. This transformation makes the shape, surface detail and volumetric properties of an object understandable to artificial intelligence.
Optimized for noise reduction, detailed surface analysis and compressed data production.
Ramp and Special Filtering Algorithms:
Special mathematical filters such as the Ramp filter are used to accurately extract 3D models from sinogram data. Alternatively, better data accuracy can be achieved with advanced methods such as the Hann filter.
Machine Learning Integration:
Frequency data is processed with artificial neural networks (CNN, LSTM, etc.), creating a powerful artificial intelligence infrastructure for classifying, identifying, and even simulating objects.
Advanced Data Compression:
Sinogram and frequency-based representations work with smaller data sizes than traditional image processing methods. This enables fast processing and reduces energy costs.
Advantages
- True 3D Understanding
The algorithm makes all structural details of an object (volume, depth, indentations/protrusions) understandable to AI, not just at the surface level.
Unlike existing pixel-based methods such as base64, it provides 3D reconstruction of real-world objects.
- Higher Accuracy
Thanks to sinogram and frequency analysis, detailed surface and volume information of objects is obtained with high accuracy. This is a great advantage especially for objects with complex surfaces and internal structures.
- Faster Processing and Data Compression
Thanks to the compressed nature of frequency-based representations, processing times are reduced even when working with large data. This is a great advantage, especially in real-time applications.
- Sectoral Diversity
Medical Imaging: It can be used in disease diagnosis and treatment planning by analyzing data obtained from tomography devices more precisely.
Autonomous Vehicles: It provides safer navigation by understanding the structure and depth of objects in the environment.
Robotics and Industrial Automation: It offers more precise results in object recognition and quality control processes in production lines.
Entertainment and Simulation: It takes virtual reality (VR) applications to the next level with more realistic 3D modeling and simulations.
- Energy Efficiency
This method, which processes more information with less data, is especially advantageous in low-energy devices (e.g. IoT sensors).