Semantic Segmentation Architectures
Semantic segmentation refers to the process of partitioning an image into various parts, each representing a different class of objects, where all instances of a particular class are considered as a single entity. Here are some key models in semantic segmentation:
UNet, developed for biomedical image segmentation, features a symmetric architecture that consists of a contracting path to capture context and a symmetric expanding path that enables precise localization. This model is particularly known for its effectiveness in medical image analysis where fine detail is crucial.
Feature Pyramid Networks (FPN)
FPNs are used to build high-level semantic feature maps at all scales, enhancing the performance of various tasks in both detection and segmentation. The architecture uses a top-down approach with lateral connections to combine low-resolution, semantically strong features with high-resolution, semantically weak features, creating rich multi-scale feature pyramids.
PSPNet addresses complex scene understanding by aggregating context information through different-region-based context aggregation. It uses a pyramid pooling module at different scales to achieve effective global context prior representation, significantly boosting performance in various scene parsing benchmarks.
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)