illuminationinvariance

Illumination invariance refers to the property of an image or a feature extracted from an image that remains unchanged despite variations in lighting conditions. In computer vision, achieving illumination invariance is a significant challenge because lighting directly affects the pixel intensities of an image. For instance, the same object captured under bright sunlight will appear very different from when it's photographed under dim indoor lighting.

Methods to achieve illumination invariance aim to either remove or normalize the effects of illumination before or during image analysis. One common approach is to transform the image into a different color space or use a specific mathematical transformation that is less sensitive to lighting changes. For example, converting an image to grayscale can reduce the impact of color variations caused by different light sources, but it doesn't fully address intensity differences. More advanced techniques involve analyzing the intrinsic properties of surfaces, such as their reflectance, which are theoretically independent of illumination.

Another strategy is to normalize image intensities. This can involve techniques like histogram equalization, which redistributes pixel intensities to cover a wider range, or more complex methods that attempt to estimate and remove the lighting component from the image. Feature extraction methods that are inherently robust to illumination changes are also a key area of research. These features, such as edges, corners, or texture descriptors, are designed to capture structural information that is less affected by how brightly the scene is lit. Achieving true illumination invariance is often an ideal goal, and practical systems typically aim for robustness to a reasonable range of lighting variations.