Module 4: Analyzing image content with computer vision


Lesson 4.1: Computer vision (CV) in social science

AI-aided content analysis of sustainability communication

nils.holmberg@iko.lu.se

Functions of Visuals in Sustainability Communication

  • Visuals enhance message clarity and audience engagement in sustainability topics.
  • Photos evoke emotional responses, encouraging empathy and action.
  • Infographics simplify complex data for easier public understanding.
  • Videos convey real-time impact, offering immersive storytelling.
  • Visual variety can reinforce key sustainability messages across contexts.

Computer Vision Areas and Content Challenges

  • Computer vision applications include object detection, facial recognition, and scene analysis.
  • Variations in lighting and angle present challenges in accurate image interpretation.
  • Environmental monitoring relies on detecting specific features in natural scenes.
  • Handling diverse image sources requires robust algorithms and large datasets.
  • Computer vision aids sustainability by tracking changes in environmental conditions.

Image Basics: Pixels, RGB, and Grayscale

  • Images are composed of pixels, each storing color or intensity values.
  • RGB channels (Red, Green, Blue) combine to create various colors in visuals.
  • Grayscale simplifies images by converting them into shades of gray.
  • Each color channel holds intensity values ranging from 0 to 255.
  • Understanding these basics is essential for manipulating and analyzing images.

Constructing and Manipulating Images with NumPy

  • Images can be represented as NumPy matrices, enabling pixel-based control.
  • Each matrix element corresponds to a pixel’s intensity or color value.
  • Adjusting matrix values allows for brightness or color changes in specific regions.
  • Image manipulations aid in highlighting areas of interest or removing noise.
  • Using NumPy enhances flexibility in preprocessing images for computer vision tasks.

Image Features: Colors, Histograms, and Edges

  • Image content features help models identify patterns within visuals.
  • Color histograms reveal dominant tones by analyzing pixel distributions.
  • Edge detection outlines shapes, essential for object and scene recognition.
  • Texture features assist in distinguishing smooth versus rough surfaces.
  • Features enable AI to interpret images more effectively by capturing key visual aspects.

Reading Images into Dataframes and Matrix Structures

  • Tools like OpenCV allow images to be imported as matrices or dataframes.
  • Dataframes enable organization of pixel data for efficient analysis.
  • Matrix structures store spatial information, which is crucial for vision models.
  • Image data in structured formats simplifies comparisons across visuals.
  • Accessing pixel-level data provides precision in analyzing image content.

Normalizing Image Content: Resize, Grayscale, and Consistency

  • Image normalization enhances model performance by standardizing inputs.
  • Resizing aligns images to a uniform size, aiding consistency in analysis.
  • Grayscale conversion reduces complexity and focuses on essential shapes.
  • Brightness adjustments help maintain consistency across varied image sources.
  • Normalizing inputs is vital for achieving accurate, reliable computer vision results.