ORGB

1. Problem

Non-uniform illumination confounds

• color space conversion
• image processing
• image analysis

To deal with this problem

• extend model
• improve algorithm
• ?

2. Basic Idea

Do not change models, algorithms, change images!

We proposed an approach to produce images more suitable for processing:

input image (RGB)  --- offset correction ---> output image (ORGB)

The output image, is more illumination-robust for color space conversion, image processing and analysis.

• (a) Raw images (RGB and ORGB).
• (b) Image segmentation using hue-saturation and k-means clustering.
• (c) Canny edge detection on saturation.
• (d) FAST feature match on saturation.

3. Advantages

• Existing techniques such as color space conversion using ORGB can perform well even in severe shadow cases.
• Using ORGB images instead of RGB images as input, the performance of existing methods may be improved in severe shadow cases.
• To test it, you only need to change the input images. No modification to the algorithms is needed.

4. Contributions

• Provide an explanation of why traditional techniques fail in severe shadow cases.
• Present a new aspect of dealing with severe shadow cases.
• Proposed an approach to produce images that do not cause trouble in severe shadow cases.