Introduction to Computer Vision

Computer Vision

• Computer vision is a subfield of AI enabling computers to interpret images, mimicking human vision.
• The objective is automating tasks achievable by the human visual system.
• The process includes image acquisition, processing, analysis, and comprehension.
• Image recognition, object detection, and scene understanding stand as the primary goals.
• Machine learning and deep learning algorithms are leveraged to extract image insights.
• Applications span various domains, including:
  • Detecting objects in images and videos
  • Image classification and recognition
  • Image segmentation
  • Facial recognition
  • Enabling autonomous driving
  • Analyzing medical images
  • Creating augmented reality experiences
  • Enhancing robotics
• Core concepts are:
  • Feature extraction identifies key image elements like edges, corners, and textures
  • Image classification assigns labels based on content
  • Object detection locates specific objects
  • Image segmentation divides images into meaningful regions
  • Pattern recognition identifies image regularities
• Convolutional neural networks (CNNs) are frequently employed in computer vision for image analysis
• Datasets such as ImageNet are critical in the training, evaluation, and benchmarking of computer vision models.

Image Processing

• Image processing involves image manipulation and analysis for quality enhancement or information extraction.
• Relative to computer vision, it is a lower-level process.
• The emphasis is on transforming images, accentuating features, and readying them for more in-depth analysis.
• Common techniques include:
  • Image enhancement refines visual quality using methods like contrast stretching and histogram equalization
  • Image restoration minimizes noise and artifacts
  • Image segmentation partitions images into regions, often at the pixel level based on color or intensity
  • Image compression reduces the storage size required for images
  • Image filtering modifies or enhances image features such as blurring or sharpening
  • Morphological operations change object shapes and structures using techniques like erosion and dilation
• Image processing modifies images at the pixel level.
• Image processing serves as a pre-processing stage for computer vision, aiming to improve input image quality.
• OpenCV provides many image processing utilities.
• Applications for image processing include:
  • Enhancing medical images like X-rays and MRIs
  • Correcting distortions in satellite images
  • Adjusting brightness, contrast, and color in photography
  • Improving surveillance footage in security contexts
• Key concepts encompass:
  • Spatial domain methods directly manipulate image pixels
  • Frequency domain methods transform images using Fourier transforms for filtering

Differences and Relationships

• Image processing is a subset of computer vision.
• Tools to manipulate and enhance images are provided, while computer vision interprets image content.
• The focus of image processing is manipulating images while computer vision focuses on extracting meaning.
• Image processing is a pre-processing step to improve the quality of images.
• Computer vision systems usually need more complex algorithms and computational resources than image processing.
• Examples of:
  • Image processing: Noise reduction
  • Computer vision: Identifying objects
  • Image processing: Enhancing image contrast
  • Computer vision: Recognizing faces
• Mathematical and statistical techniques for image analysis are used in both fields.
• Feature extraction bridges image processing and computer vision by using processed image data to identify features for vision tasks.