Image classification with a five band camera: Difference between revisions
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== Introduction == | == 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 correction are selected and (2) face detection, which is used to determine the appropriate autofocus parameters and locations for redeye removal. | ||
== Background== | == Background== | ||
Revision as of 21:48, 17 March 2014
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 correction are selected and (2) face detection, which is used to determine the appropriate autofocus parameters and locations for redeye removal.
Background
- What is known from the literature.
Methods
- Describe techniques you used to measure and analyze. Describe the instruments, and experimental procedures in enough detail so that someone could repeat your analysis. What software did you use? What was the idea of the algorithms and data analysis?
Results
- Organize your results in a good logical order (not necessarily historical order). Include relevant graphs and/or images. Make sure graph axes are labeled. Make sure you draw the reader's attention to the key element of the figure. The key aspect should be the most visible element of the figure or graph. Help the reader by writing a clear figure caption.
Conclusions
- Describe what you learned. What worked? What didn't? Why? What should someone next year try?
References
- List references. Include links to papers that are online.
Appendix I
Source Code
Test Images
Presentation
- Upload source code, test images, etc, and give a description of each link. In some cases, your acquired data may be too large to store practically. In this case, use your judgement (or consult one of us) and only link the most relevant data. Be sure to describe the purpose of your code and to edit the code for clarity. The purpose of placing the code online is to allow others to verify your methods and to learn from your ideas.