Abstract: 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.

Abstract: 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.

Abstract: 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.

Abstract: 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.

Abstract: 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.

Abstract: 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.

Abstract: 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.

Abstract: 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.

Abstract: 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.

Abstract: 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.

Abstract: Devices, systems, and methods for classifying materials in a scene obtain spectral-BRDF material samples; learn feature-vector representations for the spectral-BRDF material samples based on the obtained spectral-BRDF material samples; train classifiers using the learned feature-vector representations; and generate a material classification using the trained classifiers and a new material sample.

Abstract: Feature vector representations are computed for BRDF image slices in a database of known materials captured under a relatively large number of incident illumination directions. Low-level features of each image slice are clustered into at least two clusters. An intermediate feature vector representation is computed for each image slice with entries that are weighted means of the clusters.

Abstract: 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.

Abstract: 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.

Abstract: 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.

Abstract: 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.

Abstract: Material classification using illumination by spectral light from multiple different incident angles, coupled with measurement of light reflected from the illuminated object of unknown material, wherein the incident angle and/or spectral content of each light source is selected based on a mathematical clustering analysis of training data, so as to select a subset of only a few light sources from a superset of many light sources.

Abstract: A system for detecting a vehicle occupancy violation includes an image capture module that acquires an image including a vehicle cabin from a camera positioned to view oncoming traffic. The system includes a violation determination device, which includes a feature extraction module that processes the image pixels for determining an image descriptor. The process is selected from a group consisting of a Successive Mean Quantization Transform; a Scale-Invariant Feature Transform; a Histogram of Gradients; a Bag-of-Visual-Words Representation; a Fisher Vector Representation; and, a combination of the above. The system further includes a classifier that determines a distance that the vehicle image descriptor/representation is positioned in the projected feature space relative to a hyper-plane. The classifier determines whether the distance meets a threshold and classifies the image when the threshold is met. A processor implements the modules. A graphic user interface outputs the classification.

Abstract: What is disclosed is a system and method for estimating color for pixels in an infrared image. In one embodiment, an infrared image is received which has been captured using a N-band infrared imaging system comprising a multi-spectral camera or a hyperspectral camera. The IR image is composed of an array of pixels with N intensity values having been collected for each pixel in the image. Then, for each pixel of interest, a search metric is used to search a database of vector samples to identify a visible-IR set which is closest to the intensity values of the IR band vector collected for the pixel. A visible vector representation is then estimated for the pixel based upon the visible portion corresponding to the closest visible-IR set. Thereafter, color coordinates for this pixel are computed from the visible vector. The method repeats for all pixels of interest in the IR image.

Abstract: A system and method for assisting a user in navigation of an image dataset are disclosed. The method includes receiving a user's text query, retrieving images responsive to the query from an image dataset, providing for receiving the user's selection of a first feature selected from a set of available first features via a graphical user interface, providing for receiving the user's selection of a second feature selected from a set of available second features different from the first features via the graphical user interface, and displaying at least some of the retrieved images on the graphical user interface. The displayed images are arranged, e.g., grouped, according to levels and/or combinations of levels of the user-selected first and second features.