Canon Kabushiki Kaisha

Microsoft Technology Licensing, LLC

Microsoft Corporation

Siu-Kei Tin

Francisco H Imai

Jinwei Ye

Todd D Newman

Mahdi Nezamabadi

Ankit Mohan

Sandra Skaff

Cameron Altenhof-Long

Eric W Tramel

The shape of a specular object is measured by illumination of the object by a light field generated by two or more spaced apart layers controllable to display multiple patterns that are predetermined relative to a bounding volume within which the object is positioned. The patterns code a sparse subset of the multitude of light rays that can be generated by the layers to those that can actually reach the bounding volume. A process is described by which a sparse coding of the light rays can be derived.

Measuring shape of specular objects by local projection of coded patterns

A method of measuring a depth map for an object or a material property of an object involves positioning multiple projectors at respectively multiple angular positions relative to an object. Each of the projectors comprises color channels whose colors differ from that of other projectors. An image capture device is positioned relative to the multiple projectors. A combined color pattern is projected onto the object using the multiple projectors. Each projector projects a color striped pattern whose resolution differs from that of others of the projectors and the resolution of the color striped pattern is related to the angular position of the projector. An image of the object is captured with the combined color pattern using the image capture device and a depth map or a material property of the object is recovered by calculations using the captured image.

Multispectral binary coded projection using multiple projectors

A depth value of an object is measured. The object is illuminated with a luminaire comprising at least three or more pixel-layers including a first pixel-layer, a second pixel-layer and a third pixel-layer, each pixel-layer including a rectangular array of pixels. One or more images are captured of the object illuminated by the pixel-layers of the luminaire. The depth value of a point on the surface of the object is determined based on the one or more captured images. The spaced-apart pixel-layers of the luminaire are grouped into at least a front group and a back group, and the front group is separated from the back group by a distance that is relatively large as compared to a distance by which the spaced-apart pixel-layers within any one group are separated.

Depth value measurement

A depth value is measured. An image is captured of an object illuminated by one or more light patterns effected by multiple pixel-layers. Each pixel-layer includes an array of pixels. A direction of incoming illumination is determined for a specular reflection at a point on the surface of the object. The depth value of the point on the surface of the object is determined based on the captured image and the direction of incoming illumination.

Depth value measurement using illumination by pixels

Multiple pixels are selected from one or more images of an object fabricated from an unknown material captured from one or more viewing directions and one or more trained classification engines are applied to the selected pixels so as to obtain initial estimates of the material at each selected pixel. The one or more trained classification engines are each trained at a training direction and are each characterized by performance characteristics that vary based at least in part on directional deviation from that training direction. A posterior probability for the collection of selected multiple pixels is calculated, wherein the posterior probability is calculated based at least in part on the performance characteristics and directional deviation of a surface normal of the object at a selected pixel from the one or more training directions. The material of the object is classified based on the calculated posterior probability.

Material classification using multiview capture

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.

Devices, systems, and methods for classifying materials based on a bidirectional reflectance distribution function

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.

Depth measurement using multispectral binary coded projection and multispectral image capture

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.

Feature vector for classifying specular objects based on material type

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.

Classifying materials using texture

The present disclosure relates to classification of a material type of an unknown material. The material type is classified into a probability distribution of multiple predetermined material types by using a collection of plural predetermined material classifiers. Each material type of the multiple predetermined material types is associated with a corresponding best performing classifier from the collection of plural predetermined material classifiers by a predesignated stored association. The plural material classifiers are applied to the unknown material to obtain a list of candidate material types. A list of potential best performing classifiers is looked up using the list of candidate material types as a reference into the predesignated stored association. A respective probability that the unknown material belongs to a material type is assigned based on the list of potential best performing classifiers.

Material classification

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.

Systems and methods for compressive light sensing using multiple spatial light modulators

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.

Determining material properties using speckle statistics

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.

Stereoscopic image capture

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.

Estimating material properties using speckle statistics

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.

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Milpitas, CA, United States of America

Siu-Kei Tin