Computer Vision Pipeline: Steps Explained with Suitable Diagrams
A Computer Vision pipeline is a systematic set of steps that a system follows to interpret and extract meaningful information from images or videos. These steps allow a machine to understand visual data, just like a human would.
1. Image Acquisition
The first step in the pipeline is to capture the image or video data using various types of sensors or cameras. The quality and type of input data heavily influence the subsequent processing stages.
- Examples of image sources:
- CCD/CMOS camera sensors
- Medical imaging systems (e.g., X-ray, MRI)
- Satellite/drone imagery
- Depth sensors (e.g., LiDAR, Kinect)
This step may also involve converting raw sensor data into a usable image format and correcting lens distortions or sensor noise.
2. Preprocessing and Image Formation
Before any analysis, images are preprocessed to enhance their quality or to remove unwanted noise. This step may include:
- Geometric corrections: Removing lens distortion, image warping.
- Photometric corrections: Adjusting lighting, exposure.
- Filtering: Using Gaussian or median filters to remove noise.
- Color space conversion: RGB to grayscale or HSV as required.
Image formation covers how the 3D world projects into 2D images, considering:
- Geometry (camera models, perspective)
- Radiometry (light interaction with surfaces)
- Optics (lens behavior)
3. Feature Detection and Extraction
This step focuses on identifying key parts of the image that are informative and invariant to changes like scale or rotation. It includes:
- Edge detection (e.g., Canny)
- Corner/interest point detection (e.g., Harris, SIFT, SURF)
- Blob detection
- Line/curve detection
Features are often used for matching, tracking, or recognition tasks.
4. Feature Matching / Tracking
After detecting features, the next step is to match them across images (in case of stereo vision or motion) or track them in videos.
- Tracking: Lucas-Kanade optical flow, Kalman filter.
- Matching: Feature descriptors (e.g., SIFT, ORB) used to find correspondences.
Matching is critical for motion estimation, 3D reconstruction, and object recognition.
5. Model Fitting and Motion Estimation
This step involves computing camera motion or estimating transformations:
- Model fitting: RANSAC, Hough Transform used to fit geometric models (lines, planes).
- Motion estimation: Compute optical flow or estimate 3D structure (e.g., SLAM).
This allows understanding how the camera or objects in the scene have moved between frames.
6. Depth Estimation and 3D Reconstruction
Computer vision often attempts to recover the 3D structure of the scene using:
- Stereo vision: Estimate depth from disparity.
- Structure from motion (SfM): Estimate 3D from motion.
- Shape from X: (shading, texture, focus)
These steps convert 2D images into 3D models.
7. Object Recognition and Classification
The goal here is to identify and categorize objects in the scene:
- Object detection: Find and localize objects with bounding boxes.
- Semantic segmentation: Label each pixel with a class.
- Instance segmentation: Detect individual objects separately.
Modern methods use deep learning, especially convolutional neural networks (CNNs), to achieve high accuracy.
8. Scene Understanding and Reasoning
This is the final high-level step where the system tries to understand the overall context of the scene:
- Scene classification
- Object relationships
- Human activity recognition
- Event detection
This step may use contextual reasoning, temporal analysis, or language models for advanced tasks like image captioning or visual Q&A.
Summary Diagram
- It pictorially summarizes the chapter-wise breakdown of vision pipeline stages.
- Left side: Image-level (2D) tasks like processing, features, alignment.
- Right side: 3D tasks like structure from motion, depth, and rendering.
Conclusion
The Computer Vision pipeline transforms raw pixel data into a structured understanding of the visual world. Each stage contributes a critical part, whether it’s cleaning the data, extracting features, understanding motion, estimating 3D geometry, or recognizing objects. The pipeline may vary slightly depending on the specific task (e.g., face recognition vs. SLAM), but the core flow remains similar.