Scene Reconstruction
Scene reconstruction process in computer vision helps in creating a 3D model of a real-world scene. It is like creating a virtual replica of a room using multiple images taken of the room. Scene reconstruction process is very useful for capturing, analysing and manipulation the physical world in a digital format.
One of the real-world applications would be Crime Scene reconstruction which helps to understand how the crime unfolded and to identify the potential suspects. Other use cases include Virtual Reality, Augmented Reality, Autonomous Navigation and Film & Video Production.
There are two main reconstruction techniques used as below:
- Traditional Techniques: The traditional techniques generally rely on geometric principles and computer vision algorithms. The Structure from Motion (SfM) technique is the most reliable one in traditional method. The SfM is often combined with triangulation to compute 3D points from corresponding image features.
- Deep Learning Techniques: With the popular use of deep learning methods, Convolutional Neural Networks (CNNs) play a key role in image reconstruction tasks. The CNNs can learn to directly predict and capture complext patterns and structures from single images.
Computer Vision Tasks
Computer vision is a branch of artificial intelligence that helps computers understand and analyze visual data from digital images, videos, and similar visual inputs. Using digital visual data obtained from various sources, we can teach computers to detect and interpret visual objects. It also plays a critical role in areas such as image recognition and object detection. There are many different tasks that computer vision can perform. In this article, we will discuss computer vision tasks in detail.
Table of Content
- What are computer vision tasks?
- Image Classification
- Object Detection
- Image Segmentation
- Face and Person Recognition
- Edge Detection
- Image Restoration
- Feature Matching
- Scene Reconstruction
- Video Motion Analysis
- Conclusion: