Northwestern University

Motorola Corporation

Future Wei Technologies, Inc.

Baxter International Inc.

Baxter Healthcare Sa

Motorola Mobility LLC

The University of Chicago

Uchicago Argonne, LLC

Peking University

Huawei Technologies Co., Limited

Google Technology Holdings LLC

Aggelos K Katsaggelos

James C Brailean

Zhu Li

Taner Ozcelik

Oliver Strider Cossairt

Xin Xin

Faisal Ishtiaq

Stephen N Levine

Mark R Banham

Georgios Andrea Bertos

Neal Zupec

Guido M Schuster

Zihao Wang

Armin Kappeler

Kirk Andrew Ashline

Bhavan Gandhi

Tiejun Huang

Cheung Auyeung

Boxin Shi

Shih-Ta Hsiang

Sevket Derin Babacan

Nabil I Alshurafa

Norbert F Scherer

Srutarshi Banerjee

Todd B Parrish

Mark Hereld

Juan Gabriel Serra Pérez

Henry H Chopp

Virginia Boyce Hill

Michael Alexander Iorga

Reza Borhani

Kuan He

Mark R Trandel

Peiqi Duan

Reuven Hammer

James R Booth

Ozan Erdogan

Sevket D Babacan

A system for automatically labeling massive quantities of image report data includes a memory configured to store image data and metadata corresponding to the image data. The system also includes a processor operatively coupled to the memory and configured to apply a natural language processing algorithm to identify one or more characteristics of the image report data and the metadata. The processor is also configured to label the image report data based at least in part on the one or more identified characteristics to form a labeled dataset.

Method and system for labeling medical images

Systems and methods for automated detection and classification of objects in a fluid of a receptacle such as, for example, a soft-sided receptacle such as a flexible container. The automated detection may include initiating movement of the receptacle to move objects in the fluid contained by the receptacle. Sequential frames of image data may be recorded and processed to identify moving objects in the image data. In turn, at least one motion parameter of the objects may be determined and utilized to classify the object into at least one of a predetermined plurality of object classes. For example, the object classes may at least include a predetermined class corresponding to bubbles and a predetermined class corresponding to particles.

Visual inspection system for automated detection of particulate matter in flexible medical containers

A system to process imaging data includes an imaging system configured to capture image data and event data of a scene, compress the image data and the event data, and transmit the compressed image data and compressed event data to a host. The host is operatively coupled to the imaging system, and includes a processor configured to perform object detection on the compressed image data and the compressed event data to identify one or more objects. The processor is also configured to perform object tracking on the one or more objects. The processor is also configured to predict one or more regions of interest for subsequent data capture based on the object detection and the object tracking. The processor is further configured to provide the one or more regions of interest to the imaging system to control capture of additional image data and additional event data by the imaging system.

Bandwidth limited context based adaptive acquisition of video frames and events for user defined tasks

A system to generate image representations includes a first objective that receives a first light beam emitted from a sample and a second objective that receives a second light beam emitted from the sample, where the first light beam and the second light beam have conjugate phase. The system also includes a first diffractive element to receive the first light beam and separate it into a first plurality of diffractive light beams that are spatially distinct, and a second diffractive element to receive the second light beam and separate it into a second plurality of diffractive light beams that are spatially distinct. The system further includes a detector that receives the first and second plurality of diffractive light beams. The first plurality of diffractive light beams and the second plurality of diffractive light beams are simultaneously directed and focused onto different portions of an image plane of the detector.

Systems and methods for interferometric multifocus microscopy

An imaging system includes an event camera configured to capture a first image of a scene. The system also includes a shutter camera collocated with the event camera, where the shutter camera is configured to capture a second image of the scene. The system also includes a processor operatively coupled to the event camera and the shutter camera. The processor is configured to apply guided event filtering (GEF) on the first image and the second image. The processor is also configured to generate a third image based on filtering of the first image and the second image.

System and method for high-resolution, high-speed, and noise-robust imaging

Systems, methods, and computer readable media for hyperspectral imaging are provided. An example hyperspectral imaging sensor system to identify item composition includes an imager to capture hyperspectral imaging data of one or more items with respect to a target. The example includes a sensor to be positioned with respect to the target to trigger capture of the image data by the imager based on a characteristic of the target. The example includes a processor to prepare the captured imaging data for analysis to at least: identify the one or more items; determine composition of the one or more items; calculate an energy intake associated with the one or more items; and classify the target based on the energy intake.

Hyperspectral imaging sensor

Using a plurality of distinct behavioral tasks conducted in a functional magnetic resonance imaging (fMRI) scanner, fMRI data acquired from one or more subjects performing working memory tasks can be used for diagnosing psychiatrics and neurological disorders. A classification algorithm can be used to determine a classification model, tune the model, and apply the model. An output indicative of a subject's clinical condition can then be provided and used to diagnose new cases.

Pattern analysis based on fMRI data collected while subjects perform working memory tasks allowing high-precision diagnosis of ADHD

Object recognition can be improved by verifying a geometric consistency between matching interesting points pairs of two images, as objects with the images are more likely to match when the matching interesting points pairs are geometrically consistent. The geometric consistency between matching interesting points pairs can be verified in accordance with a topology code distance vector (D). The topology code distance vector (D) may be evaluated in accordance with a decision tree classifier, which may be trained in accordance with previous or historic topology code distance vectors. The topology code distance vector (D) may be computed from a subset of matching interesting points pairs having the shortest matching distance.

System and method for randomized point set geometry verification for image identification

A system and a method for progressive queries, which are a sequence of queries for visual search of an image, sent from a sender to a receiver of a communication network, are disclosed. Queries based on a set of feature points and their associated locations of an image may be selected in sequence. A sender sends a first query to a receiver, wherein the first query comprises a number of feature points which is a subset of the set of feature points of the image invariant to rotation and scaling in creating the image. The sender receives a feedback from the receiver, wherein the feedback is generated by the receiver based on searching an image repository using information from the first query. The sender then decides whether there is a match of the image in the image repository based on the received feedback.

Scalable query for visual search

A method and an apparatus for processing an image generates, using a linear embedding operator, a second set of feature points from a first set of feature points of the image, so that a pair-wise nearest neighbor (NN) topological relationship among the first set of feature points is preserved in the second set of feature points. The linear embedding operator is determined by an affinity model comprising a first affinity parameter, a second affinity parameter, and an affinity matrix, wherein a sparsity of the affinity matrix is controlled by the first affinity parameter and the second affinity parameter.

System and method for compact descriptor for visual search

A method and an apparatus for processing an image generate a first vector of a first number dimension for the image from a first number of points of the image based on topological information of the first number of points, and the first vector for the image is invariant to rotation and scaling in creating the image. The first number of points may be locations of a set of rotation and scaling invariant feature points for the image, and the generated first vector may be a graph spectrum of a pair-wise distance matrix generated from the first number of points of the image.

Image topological coding for visual search

A scalable video compression system () having an encoder (), bit extractor (), and decoder () for efficiently encoding and decoding a scalable embedded bitstream () at different video resolution, framerate, and video quality levels is provided. Bits can be extracted in order of refinement layer (), followed by temporal level (), followed by spatial layer (), wherein each bit extracted provides an incremental improvement in video decoding quality. Bit extraction can be truncated at a position in the embedded bitstream corresponding to a maximum refinement layer, a maximum temporal level, and a maximum spatial layer. For a given refinement layer, bits are extracted from all spatial layers in a lower temporal level prior to extracting bits from spatial layers in a higher temporal level for prioritizing coding gain to increase video decoding quality, and prior to moving to a next refinement layer.

Method and system for scalable bitstream extraction

A scalability type selection method and structure for hybrid SNR-temporal scalability that employs a decision mechanism capable of selecting between SNR and temporal scalability based upon desired criteria is disclosed. This method and structure utilizes models of the desired criteria such as the extent of motion between two encoded frames, the temporal distance between two encoded frames, the gain in visual quality achieved by using SNR scalability over temporal scalability, and the bandwidth available to the scaleable layer in deciding which form of scalability to use. Furthermore, the method and structure allows for control over the extent to which a certain type of scalability is used. By the selection of parameters used in the models, a certain type of scalability can be emphasized while another type of scalability can be given less preference. This invention not only allows the type of scalability to be selected but also to the degree to which the scalability will be used.

Method and structure for scalability type selection in digital video

At least one visual significance metric is determined () for at least some frames belonging to an original series of frames (). Key frames are identified () as a function, at least in part, of the visual significance metric. Cumulative visual significance values are then determined () for at least some of the frames that intervene between each pair of key frames. These cumulative visual significance values are then used to identify () frames of additional content interest. Various frames are then selected () for use in a visual summary. In one embodiment, all of the key frames and frames of additional content interest are selected for inclusion in the visual summary.

Method and apparatus for reduction of visual content

Growing community of inventors

Chicago, IL, United States of America

Aggelos K Katsaggelos