Canon Kabushiki Kaisha

Sandra Skaff

Jie Yu

Francisco H Imai

Chao Liu

Siu-Kei Tin

Junjie Cai

Iro Armeni

Devices, systems, and methods obtain a region of an image; generate known-object scores for the region using known-object detectors, wherein each known-object detector of the known-object detectors detects objects in a respective object class; determine a likelihood that the region includes a complete object; and determine a likelihood that the region includes an unknown object based on the likelihood that the region includes a complete object and on the known-object scores.

Devices, systems, and methods for detecting unknown objects

Devices, systems, and methods obtain an object model, add the object model to a synthetic scene, add a texture to the object model, add a background plane to the synthetic scene, add a support plane to the synthetic scene, add a background image to one or both of the background plane and the support plane, and generate a pair of images based on the synthetic scene, wherein a first image in the pair of images is a depth image of the synthetic scene, and wherein a second image in the pair of images is a color image of the synthetic scene.

Devices, systems, and methods for generating multi-modal images of a synthetic scene

Devices, systems, and methods for computer recognition of action in video obtain frame-level feature sets of visual features that were extracted from respective frames of a video, wherein the respective frame-level feature set of a frame includes the respective visual features that were extracted from the frame; generate first-level feature sets, wherein each first-level feature set is generated by pooling the visual features from two or more frame-level feature sets, and wherein each first-level feature set includes pooled features; and generate second-level feature sets, wherein each second-level feature set is generated by pooling the pooled features in two or more first-level feature sets, wherein each second-level feature set includes pooled features.

Devices, systems, and methods for generating a temporal-adaptive representation for video-event classification

Material classification of an object is provided. Parameters for classification are accessed. The parameters include a selection to select a subset of angles for classification, a selection to select a subset of spectral bands for classification, a selection to capture texture features, and a selection to compute image-level features. The object is illuminated and a feature vector is computed based on the parameters. The material from which the object is fabricated is classified using the feature vector.

Material classification

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

Material classification using multiplexed illumination by broadband spectral light from multiple different incident angles, coupled with multi-spectral narrow band spectral measurement of light reflected from the illuminated object of unknown material, wherein selection of spectral bands for illumination or for narrow-band capture may comprise analysis of a database of labeled training material samples within a multi-class classification framework, captured using a relatively large number of spectral bands (such as 32 spectral bands), so as to select a subset of a relatively fewer number of spectral bands (such as 5 spectral bands), wherein the selected spectral bands in the subset retain a significant aptitude for distinguishing between different classifications of materials.

Selection of spectral bands or filters for material classification under multiplexed illumination

The present disclosure relates to classification of a material type of an object. A first phase applies an object classifier and a material classifier to obtain first object and material probabilities of the object. A second phase applies an interdependent object/material classifier to the first object and material probabilities to obtain further object and material class probabilities. The interdependent object/material classifier performs multiple iterations of calculating the further object and material class probabilities, and utilizes feedback in which an immediately preceding calculated prior further object class probability is included in a next iteration of calculating a further material class probability, and an immediately preceding calculated prior further material class probability is included in a next iteration of calculating a further object class probability.

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Mountain View, CA, United States of America

Sandra Skaff