Abstract: Stereoscopic image capture is provided. A blur value expected for multiple pixels in left and right images is predicted. The blur value is predicted based on designated capture settings. A disparity value expected for multiple pixels in the left and right images is predicted. The disparity value is predicted based on the designated capture settings. Stressed pixels are identified by comparing the predicted disparity value to a lower bound of disparity value determined from the predicted blur value using a predetermined model. A pixel with predicted disparity value less than the lower bound is identified as a stressed pixel. The predicted disparity is adjusted by modifying the designated capture settings to reduce the number of stressed pixels, or an alert to the presence of stressed pixels is given to the user.

Abstract: Systems, devices, and methods for classifying a sample by material type measure a reflection of a sample at an illumination angle and an observation angle in at least two spectral bands, calculate a feature value for the sample based on an estimated ratio value of the at least two spectral bands of the measured reflection, and classify the sample based on the feature value.

Abstract: Material classification for a sample fabricated from an unknown material, particularly a specular sample such as a metallic sample fabricated from an unknown metal. A measurement is obtained of a specular reflection of the sample at at least one observation angle and in at least two spectral bands including first and second spectral bands; a feature vector is calculated using a homogeneous function that combines the measured specular reflections in the first and second spectral bands; and the sample is classified based on the feature vector. The homogeneous function may include a ratio of values of the first and second spectral bands of the measured specular reflections, and the feature vector may be based on a histogram of such ratio values. Classification may include determining a distance between the feature vector of the sample and a feature vector of a reference sample in a predetermined database of labeled samples.

Abstract: A surface roughness value and a sub-surface scattering property of a material are estimated. The material is illuminated with a light beam with controlled coherence properties at multiple incident angles. Multiple speckle patterns are recorded, each speckle pattern being recorded for a respective one of the multiple incident angles. Both of a surface roughness value and a sub-surface scattering property of the material are estimated by calculations using the multiple speckle patterns and the incident angle for each such speckle pattern.

Abstract: Colors in a color image are transformed by a destination device. The color image comprises pixels with color information. A depth map corresponding to the color image is accessed. The depth map comprises depth information for the color image and indicates the relative position of objects in the color image from a reference point of view. A collection of plural different color transforms is accessed. In addition, a depth value for a target pixel in the color image is determined by using the depth map. There is a selection of a color transform for the target pixel from the collection of plural different color transforms, based on the depth value determined for the target pixel. The selected color transform is applied to the color information of the target pixel by the destination device.

Abstract: An unknown material is classified using texture. A database of predetermined images is accessed, each of a known material and each captured at a magnification factor that is relatively high. A query image of the unknown material is received. The query image is captured at a second magnification factor that is relatively lower than the magnification factors of the predetermined images. A collection of images of the known materials is dynamically generated at the second magnification factor. The received query image is matched against the dynamically generated collection of images, and the unknown material is classified in correspondence to a match between the received image and the dynamically generated collection of images.

Abstract: A material is illuminated with one or more light sources including at least one light source which emits light of controlled coherence properties. Both of a spectral characteristic and a speckle statistic are derived using light reflected from the illuminated material. The spectral characteristic and the speckle statistic are compared against plural entries in a database. Each entry in the database correlates the identity of a material against a corresponding spectral characteristic and a corresponding speckle statistic for the material. At least one candidate for the identity of the illuminated material is determined based at least in part on the comparison.

Abstract: Encoding image data and mask information to be used for matte images and for image and video matting. Image data and mask information for pixels of the image data in a first representation domain are accessed. The mask information defines background pixels and foreground pixels. The image data in the first representation domain is transformed to a second representation domain. Mask information in the second representation domain is determined by using the mask information in the first representation domain. The image data in the second representation domain is masked by setting image data to zero for background pixels as defined by the determined mask information in the second representation domain. The masked image data in the second representation domain is encoded.

Abstract: A material is illuminated with one or more light sources including at least one light source which emits light of controlled coherence properties. Both of a spectral characteristic and a speckle statistic are derived using light reflected from the illuminated material. The spectral characteristic and the speckle statistic are compared against plural entries in a database. Each entry in the database correlates the identity of a material against a corresponding spectral characteristic and a corresponding speckle statistic for the material. At least one candidate for the identity of the illuminated material is determined based at least in part on the comparison.

Abstract: A surface roughness value and a sub-surface scattering property of a material are estimated. The material is illuminated with a light beam with controlled coherence properties at multiple incident angles. Multiple speckle patterns are recorded, each speckle pattern being recorded for a respective one of the multiple incident angles. Both of a surface roughness value and a sub-surface scattering property of the material are estimated by calculations using the multiple speckle patterns and the incident angle for each such speckle pattern.

Abstract: A property of a material is determined. The material is illuminated with a light beam of controlled spectral and coherence properties. A stack of speckle field images is recorded from speckle fields reflected from the illuminated material in multiple spectral channels. The stack of speckle field images includes multiple speckle field images each being recorded in a respectively different spectral channel. Statistical properties of the speckle field images in the stack of speckle field images are analyzed to determine at least one property of the illuminated material.

Abstract: Illumination of an object with spectral structured light, and spectral measurement of light reflected therefrom, for purposes which include derivation of a three-dimensional (3D) measurement of the object, such as depth and/or contours of the object, and/or for purposes which include measurement of a material property of the object, such as by differentiating between different types of materials.

Abstract: Illumination of an object with spectral structured light, and spectral measurement of light reflected therefrom, for purposes which include derivation of a three-dimensional (3D) measurement of the object, such as depth and/or contours of the object, and/or for purposes which include measurement of a material property of the object, such as by differentiating between different types of materials.

Abstract: Compression of image data is provided. Image data is accessed, along with depth information for pixels of an image. A distance from a region of focus for pixels of the image is determined, by calculations that use the depth information. A bit rate for compression of the image data is controlled in accordance with the distance from the region of focus, such that more bits are used for pixels closer to the region of focus and fewer bits are used for pixels farther from the region of focus.

Abstract: Color management in which a spectral gamut is determined for spectral gamut mapping by constructing subdivisions of a set of samples spanning the spectral gamut. The samples are designated by specifying device values and are subdivided based on their lightness, chroma and hue under a reference illuminant such that the number of samples in each subdivision is limited by a predetermined number. A color value to be spectrally gamut mapped is accepted, and converted into a colorimetric value under the reference illuminant. The converted colorimetric value includes a lightness, a chroma and a hue. Subdivisions within a tolerance of the converted colorimetric value are identified using its lightness, chroma and hue. Samples within the identified subdivisions are searched to find a sample that matches acceptably to the color value relative to an objective function.

Abstract: Color correction of an autostereoscopic color display capable of displaying multiple views of one scene. Multiple viewing regions of the autostereoscopic color display are identified. The multiple viewing regions together comprise the whole of an operating viewing zone for the autostereoscopic color display. A respective plurality of color correction LUTs are constructed. At least one color correction LUT is constructed for each different viewing region. Color correction LUTs corresponding to current viewing regions are selected based on information regarding viewer location. The selected color correction LUTs are applied to the autostereoscopic color display.

Abstract: Stereoscopic image capture is provided. A blur value expected for multiple pixels in left and right images is predicted. The blur value is predicted based on designated capture settings. A disparity value expected for multiple pixels in the left and right images is predicted. The disparity value is predicted based on the designated capture settings. Stressed pixels are identified by comparing the predicted disparity value to a lower bound of disparity value determined from the predicted blur value using a predetermined model. A pixel with predicted disparity value less than the lower bound is identified as a stressed pixel. The predicted disparity is adjusted by modifying the designated capture settings to reduce the number of stressed pixels, or an alert to the presence of stressed pixels is given to the user.

Abstract: Determining a spectral gamut of a device by designating a spanning set of samples which span the spectral gamut. A first crude spanning set of samples is established by specifying one or more corresponding device values in a device color space. The first crude spanning set is refined by processing a plurality of new samples in a predetermined order. The processing includes, for each new sample, determining if the new sample differs in an objective function value by more than a predetermined threshold from all samples in the first crude spanning set and adding the new sample to the first crude spanning set if the new sample differs in the objective function value by more than the predetermined threshold. The resulting first crude spanning set is designated as the spanning set of samples.

Abstract: Systems and methods for capturing light field information including spatial and angular information using an image pickup device that includes an image sensor and at least one spatial light modulator (SLM) take multiple captures of a scene using the at least one SLM to obtain coded projections of a light field of the scene, wherein each capture is taken using at least one pattern on the at least one SLM, and recover light field data using a reconstruction process on the obtained coded projections of the light field.

Abstract: Stereoscopic color management of images with plural views. Image data for each view is defined in a component input device color space. Image data in the component input device color spaces is converted to a nominal source color space using plural input transforms each corresponding to one of the plural views. A rendering transform is used to convert image data for each view in the nominal source color space to a nominal destination color space. The nominal source color space, nominal destination color space and rendering transform are the same for all views. The image data for each view in the nominal destination color space is ultimately converted to a component output device color space associated with a stereoscopic output device respective of the view using a respective output transform.