Other

Columbia University

United States of America As Represented by the Secretary of the Air Force

Michael Grossberg

Shree Kumar Nayar

Peter Neil Belhumeur

Jong-Il Park

Moon Hyun Lee

Rahul Swaminathan

Leonid Roytman

Harish Peri

Irina Gladkova

A multispectral imaging method and system is provided. A multispectral imaging method of the present invention includes determining an on-off combination of a plurality of light sources illuminating a scene; illuminating the scene with the light sources according to the on-off combination selected on the basis of a first control signal generated by a microcontroller; capturing an image of the scene by operating a camera on the basis of a second control signal synchronized with the first control signal; determining a plurality of spectral basis functions and weights of the spectral basis functions; and acquiring a continuous spectral reflectance by summing values obtained by multiplying the spectral basis functions and respective weights. The multispectral imaging method of the present invention is practical and efficient in that a continuous spectral reflectance image can be acquired with a minimized number of measurements required for obtaining spectral reflectance. Accordingly, the multispectral imaging method of the present invention can be applied to various fields, such as image reproduction and medical imaging, while overcoming performance limits of the conventional RGB imaging techniques.

Method of multispectral imaging and an apparatus thereof

Lossless compression techniques provide efficient compression of hyperspectral satellite data. The present invention combines the advantages of a clustering with linear modeling. A number of visualizations are presented, which help clarify why the approach of the present invention is particularly effective on this dataset. At each stage, the algorithm achieves an efficient grouping of the data points around a relatively small number of lines in a very large dimensional data space. The parametrization of these lines is very efficient, which leads to efficient descriptions of data points. The method of the present invention yields compression ratios that compare favorably with what is currently achievable by other approaches.

Selectively lossy, lossless, and/or error robust data compression method

Methods and systems are provided for displaying images onto an arbitrary surface, using a projector, such that the quality of the images is preserved despite surface imperfections or color variations. Methods and systems are also provided for controlling the appearance of a projection surface. Various embodiments use a detailed radiometric model and a calibration method to determine the pixel values required to be projected by a projector in order for a camera to observe a desired image. Other embodiments use a compensation algorithm that uses a feedback approach to provide the desired image compensation. Geometric mapping may be used to establish a correspondence between points in the images to be displayed by the projector and the corresponding points in the images that are captured by the camera.

Methods and systems for compensating an image projected onto a surface having spatially varying photometric properties

An algorithm is provided for reducing distortion in an image captured by a camera or other imaging system having more than one optical viewpoint. The algorithm can use a captured image, statistical information regarding the depths of scene objects, and information regarding the geometrical characteristics of the imaging system to generate an approximation of a perspective projection representing the scene. The virtual viewpoint of the approximate perspective projection is selected so as to reduce distortion. The resulting image has less distortion than that of an image generated under an assumption that scene objects are infinitely distant. In addition, the parameters of any distortion reducing function can be optimized by minimizing an objective function representing the amount of image distortion.

Method and apparatus for reducing distortion in images

The quality (e.g., resolution) of image data, video data, and sound data representing a scene or signal is enhanced by a quality enhancement function trained on high quality and low quality representations of a portion of the same scene or signal. The enhancement function is thus optimized to the most relevant input. A training algorithm uses low quality image of a scene or scene portion, along with a high quality image of the same scene or scene portion, to optimize the parameters of a quality enhancement function. The optimized enhancement function is then used to enhance other low quality images of the scene or scene portion. Sound data is enhanced by using a low resolution sample of a portion of a signal, and a high resolution sample of the same signal portion, to train a quality enhancement function which is then used to enhance the remainder of the signal.

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New York, NY, United States of America

Michael Grossberg