Instance Segmentation Architectures
Instance segmentation not only labels every pixel of an object with a class, but also distinguishes between different instances of the same class. Below are some pioneering models:
Mask R-CNN
Mask R-CNN enhances Faster R-CNN by incorporating an additional branch that predicts segmentation masks for each Region of Interest (RoI) alongside the existing branches for classification and bounding box regression. The key innovation of Mask R-CNN is its use of RoIAlign, which accurately extracts features from non-aligned objects, significantly improving the accuracy of instance segmentation.
YOLACT (You Only Look At CoefficienTs)
YOLACT is a real-time instance segmentation model that separates the task into two parallel processes: generating a set of prototype masks and predicting per-instance mask coefficients. At inference, it combines these to form the final instance masks dynamically. This separation allows for the real-time operation, making YOLACT suitable for applications requiring high frame rates.
Computer Vision Algorithms
Computer vision seeks to mimic the human visual system, enabling computers to see, observe, and understand the world through digital images and videos. This capability is not just about capturing visual data. Still, it involves interpreting and making decisions based on that data, opening up myriad applications that span from autonomous driving and facial recognition to medical imaging and beyond.
This article delves into the foundational techniques and cutting-edge models that power computer vision, exploring how these technologies are applied to solve real-world problems. From the basics of edge and feature detection to sophisticated architectures for object detection, image segmentation, and image generation, we unravel the layers of complexity in these algorithms.
Table of Content
- Edge Detection Algorithms in Computer Vision
- Canny Edge Detector
- Gradient-Based Edge Detectors
- Laplacian of Gaussian (LoG)
- Feature Detection Algorithms in Computer Vision
- SIFT (Scale-Invariant Feature Transform)
- Harris Corner Detector
- SURF (Speeded Up Robust Features)
- Feature Matching Algorithms
- Brute-Force Matching
- FLANN (Fast Library for Approximate Nearest Neighbors)
- RANSAC (Random Sample Consensus)
- Deep Learning Based Computer Vision Architectures
- Convolutional Neural Networks (CNN)
- CNN Based Architectures
- Object Detection Models
- RCNN (Regions with CNN features)
- Fast R-CNN
- Faster R-CNN
- Cascade R-CNN
- YOLO (You Only Look Once)
- SSD (Single Shot MultiBox Detector)
- Semantic Segmentation Architectures
- UNet Architecture
- Feature Pyramid Networks (FPN)
- PSPNet (Pyramid Scene Parsing Network)
- Instance Segmentation Architectures
- Mask R-CNN
- YOLACT (You Only Look At CoefficienTs)
- Image Generation Architectures
- Variational Autoencoders (VAEs)
- Generative Adversarial Networks (GANs)
- Diffusion Models
- Vision Transformers (ViTs)