Image classification with a five band camera

Introduction

Image classification is a central topic in computer vision in which images are lumped into categories via comparison and prediction. Many state-of-the-art classification systems follow a somewhat ubiquitous procedure of feature extraction via dense grid descriptor sampling, coding into higher dimensions, pooling, and finally classification. Integrated computer vision techniques like object and scene recognition are also gaining traction in consumer-grade electronics. Two now common examples of this in digital cameras are (1) scene recognition, in which the appropriate aperture, shutter speed, and nonlinear gamma settings are selected and (2) face detection, which is used to determine the appropriate autofocus parameters and locations for redeye removal.

Aside from pure software-based developments, data acquisition is central to the heart of this problem. Most silicon based digital camera sensors use CCD or CMOS sensor chips that are inherently sensitive to a broad spectrum of light, starting at short visible wavelengths, and reaching into the near-infrared. Many of these sensors have a series of filters that discretize or remove light, ending with three channels of digital data, each corresponding to an RGB color channel: red, green or blue. Such cameras are often termed "three band". It is argued here that a camera with more sensitivity over a broader spectrum of wavelengths could be helpful to many computer vision techniques.

To test this hypothesis, a set of 72 images of four different pieces of fruit were taken on a prototype Olympus brand five band camera donated to Stanford's Center for Image Systems Engineering (SCIEN) lab, sampled using SIFT feature descriptors, and compared using RANSAC.

Background

- What is known from the literature.

Results

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Conclusions

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References

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Appendix I

Presentation

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