Introduction to Computer Vision and Digital Image Processing
Computer Vision is the field of artificial intelligence that enables computers to derive meaningful information from digital images and videos, effectively attempting to bridge the semantic gap between raw pixel data and human-level understanding. Digital Image Processing serves as the foundational layer for Computer Vision, focusing on the manipulation and enhancement of image signals through pixel-to-pixel transformations to prepare data for higher-level interpretative tasks.
Key Principles
- Data Representation: At the machine level, an image is a numerical tensor rather than a holistic picture. Grayscale images are 2D matrices of intensity values, whereas color images are 3D tensors representing Red, Green, and Blue (RGB) channels with dimensions $H \times W \times 3$.
- Transformation vs. Interpretation: Digital Image Processing is primarily concerned with image-to-image operations such as noise reduction, sharpening, or histogram equalization. Computer Vision focuses on image-to-knowledge operations such as object classification, localization, and segmentation.
- The Inverse Graphics Paradigm: Computer Vision can be viewed as the inverse of Computer Graphics. While graphics seeks to generate a visual world from mathematical models, vision seeks to recover 3D structures and semantic labels from 2D projections.
The Core Challenge
The primary challenge in this field is the Semantic Gap, which is the disconnect between the low-level pixel values processed by machines and the high-level concepts perceived by humans.
Python Implementation
Question 1
Which process is categorized as an image-to-knowledge operation?
Question 2
At the machine level, what is the data structure of a standard color image?
Case Study: Medical Diagnostic System
Read the scenario below and answer the questions.
A hospital is developing a new automated medical diagnostic system designed to analyze X-ray scans for potential bone fractures. The system processes raw sensor data from the X-ray machine and outputs a diagnostic report for the radiologist.
Q
1. If the system applies contrast enhancement to make bone structures clearer, is this Digital Image Processing (DIP) or Computer Vision (CV)?
Answer:
Digital Image Processing. Contrast enhancement is an image-to-image transformation that improves the visual quality of the signal without extracting semantic meaning.
Digital Image Processing. Contrast enhancement is an image-to-image transformation that improves the visual quality of the signal without extracting semantic meaning.
Q
2. If the system automatically flags a specific area as a potential fracture, what task is it performing?
Answer:
Computer Vision / Object Detection. The system is interpreting the image content to extract high-level knowledge (locating a fracture).
Computer Vision / Object Detection. The system is interpreting the image content to extract high-level knowledge (locating a fracture).
Q
3. Why is noise reduction necessary before running a detection algorithm?
Answer:
To improve signal quality and reduce false positives in the semantic interpretation phase. Noise can be misinterpreted by CV algorithms as actual features or edges.
To improve signal quality and reduce false positives in the semantic interpretation phase. Noise can be misinterpreted by CV algorithms as actual features or edges.