I'm relatively new in Computer Vision and I need some advice with respect to the general possibilities and shortcomings of images taken in the near infrared (NIR) range. I passed a great deal of time researching about that matter but I rather found specific papers in areas like pedestrian detection and face recognition which not completely answered my question.
The background is that I have to chose camera and lens for a stereo-vision system which will be used for object detection tasks.To make the system robust against variations in illumination I thought about the feasibility of employing active illumination with NIR-light and a NIR-bandpass filter. Thus, working exclusively in the NIR spectral range.
I found publications about stereo matching in the far-infrared range, which come to the conclusion that it is not possible to obtain a dense depth field. However, I could not find anything related to the NIR range. It may be because it is too obvious but unfortunately not to me at this point.
Thats why I would like to know in particular:
- if it is possible to employ standard stereo matching algorithms like block-matching to images taken in the NIR spectrum and if so would it be possible (of course depending on the conditions) to obtain a dense depth map.
- if standard feature detection and description techniques like SIFT, HOG,etc produces similar results in the NIR spectrum.
- if a neural network which was trained with visible-light images would be also able to detect an object in an NIR image. (I am aware that this is somehow connected to the prior question)
I would be grateful for any advice, personal experience, book/paper references you may could share with me.