Qualcomm Incorporated

General Electric Company

12 Sigma Technologies

Digital Pathco, LLC

Dashan Gao

Xin Zhong

Peter Henry Tu

Yingyong Qi

Ting Yu

Yi Yao

Ning Bi

Kedar Anil Patwardhan

Steven Douglas Laver

Ananthapadmanabhan Arasanipalai Kandhadai

Kunter Seref Akbay

Yang Yang

Piero Patrone Bonissone

Christopher Donald Johnson

Naresh Sundaram Iyer

Scott David Beith

John Michael Lizzi

Corey Nicholas Bufi

Viswanath Avasarala

Lei Ma

Li Guan

Feng Guo

Sairam Sundaresan

Yu Sun

Yunqiang Chen

Jiao Wang

Diarmuid MacCormack

Ying Hai Chen

Lei Wang

Lei Zhang

Robert John Filkins

Shuxue Quan

Mainak Biswas

Gokce Dane

Shizhong Liu

Chinchuan Andrew Chiu

Thomas Baby Sebastian

Jianfeng Ren

Kevin Bernard Kenny

Dirk Ryan Padfield

Ruiduo Yang

Michael Christopher Montalto

Christopher Thomas Li

Jinglun Gao

Ming Yan

Donald Wagner Hamilton

Fan Deng

Shuai Zhang

David LaVan Henderson

Yunke Pan

Jiancheng Lyu

Kai Guo

Richard R McKay

Kuen-Han Lin

Shili Xu

Baozhong Zheng

Marc Bosch Ruiz

Nariaki Yamada

Ke Qi

Yunzhi Wang

Haichao Yu

Tianyi Zhao

Nagendra Kamath

Matteo Toti Mannino

Vipul A Baxi

Yuanpeng Wu

Seref Akbay Kunter

John Michael Jr Lizzi

Diarmuid Maccormack

Systems and techniques are provided for performing scene segmentation and object tracking. For example, a method for processing one or more frames. The method may include determining first one or more features from a first frame. The first frame includes a target object. The method may include obtaining a first mask associated with the first frame. The first mask includes an indication of the target object. The method may further include generating, based on the first mask and the first one or more features, a representation of a foreground and a background of the first frame. The method may include determining second one or more features from a second frame and determining, based on the representation of the foreground and the background of the first frame and the second one or more features, a location of the target object in the second frame.

Scene segmentation and object tracking

A method for picture processing is described. A first tracking area is obtained. A second tracking area is also obtained. The method includes beginning to track the first tracking area and the second tracking area. Picture processing is performed once a portion of the first tracking area overlapping the second tracking area passes a threshold.

Tracker assisted image capture

A method includes receiving a user input (e.g., a one-touch user input), performing segmentation to generate multiple candidate regions of interest (ROIs) in response to the user input, and performing ROI fusion to generate a final ROI (e.g., for a computer vision application). In some cases, the segmentation may include motion-based segmentation, color-based segmentation, or a combination thereof. Further, in some cases, the ROI fusion may include intraframe (or spatial) ROI fusion, temporal ROI fusion, or a combination thereof.

Object selection based on region of interest fusion

This disclosure is directed to a system and a method for providing enhanced real-time or near-real-time response to request for detached data analytics services. In one implementation, a system is disclosed for predicting a data analytics service that may be requested by a user based on real-time user interactive operations, and for pre-loading/pre-configuring a pipeline of data analytics components for performing the predicted data analytics service before an actual request is made. Additionally, at least some intermediate data may be calculated by the pre-configured pipeline and may be pre-cached in memory. Upon actual user request for the data analytics service, only data analytics that require additional input data concurrently provided with the request would need to be performed. In such a manner, user-perceived delay in completing the detached data analytics service is reduced.

Enhancement of real-time response to request for detached data analytics

To determine real-world information about objects moving in a scene, the camera capturing the scene is typically calibrated to the scene. Automatic scene calibration can be accomplished using people that are found moving about in the scene. During a calibration period, a video content analysis system processing video frames from a camera can identify blobs that are associated with people. Using an estimated height of a typical person, the video content analysis system can use the location of the person's head and feet to determine a mapping between the person's location in the 2-D video frame and the person's location in the 3-D real world. This mapping can be used to determine a cost for estimated extrinsic parameters for the camera. Using a hierarchical global estimation mechanism, the video content analysis system can determine the estimated extrinsic parameters with the lowest cost.

Automatic scene calibration method for video analytics

The system and method disclosed herein provides an integrated and automated workflow, sensor, and reasoning system that automatically detects breaches in protocols, appropriately alarms and records these breaches, facilitates staff adoption of protocol adherence, and ultimately enables the study of protocols for care comparative effectiveness. The system provides real-time alerts to medical personnel in the actual processes of care, thereby reducing the number of negative patient events and ultimately improving staff behavior with respect to protocol adherence.

System and method for protocol adherence

A method includes receiving information that identifies a reference position in a location space. The method also includes receiving data that identifies one among a plurality of candidate geometrical arrangements. The method also includes producing a representation that depicts a plurality of objects which are arranged, relative to the reference position in the location space, according to the identified candidate geometrical arrangement.

Systems, methods, and apparatus for digital composition and/or retrieval

Automatic adaptive zoom enables computing devices that receive video streams to use a higher resolution stream when the user enables zoom, so that the quality of the output video is preserved. In some examples, a tracking video stream and a target video stream are obtained and are processed. The tracking video stream has a first resolution, and the target video stream has a second resolution that is higher than the first resolution. The tracking video stream is processed to define regions of interest for frames of the tracking video stream. The target video stream is processed to generate zoomed-in regions of frames of the target video stream. A zoomed-in region of the target video stream corresponds to a region of interest defined using the tracking video stream. The zoomed-in regions of the frames of the target video stream are then provided for display on a client device.

Methods and systems for auto-zoom based adaptive video streaming

This disclosure relates to digital image segmentation, region of interest identification, and object recognition. This disclosure describes a method, a system, for image segmentation based on fully convolutional neural network including an expansion neural network and contraction neural network. The various convolutional and deconvolution layers of the neural networks are architected to include a coarse-to-fine residual learning module and learning paths, as well as a dense convolution module to extract auto context features and to facilitate fast, efficient, and accurate training of the neural networks capable of producing prediction masks of regions of interest. While the disclosed method and system are applicable for general image segmentation and object detection/identification, they are particularly suitable for organ, tissue, and lesion segmentation and detection in medical images.

Image segmentation and object detection using fully convolutional neural network

In accordance with one aspect of the present technique, a method includes receiving one or more videos from one or more image capture devices. The method further includes generating a video-loop of the person from the one or more videos. The video-loop depicts the person in the commercial site. The method also includes generating an action clip from the video-loop. The action clip includes a suspicious action performed by the person in the commercial site. The method further includes generating an activity summary of the person including the video-loop and the action clip.

System and method for generating an activity summary of a person

This disclosure relates to digital image segmentation and region of interest identification. A computer implemented image segmentation method and system are particularly disclosed, including a predictive model trained based on a deep fully convolutional neural network. The model is trained using a loss function in at least one intermediate layer in addition to a loss function at the final stage of the full convolutional neural network. The predictive segmentation model trained in such a manner requires less training parameters and facilitates quicker and more accurate identification of relevant local and global features in the input image. In one implementation, the fully convolutional neural network is further supplemented with a conditional adversarial neural networks iteratively trained with the fully convolutional neural network as a discriminator measuring the quality of the predictive model generated by the fully convolutional neural network.

Enhanced convolutional neural network for image segmentation

A method of generating a temporal saliency map is disclosed. In a particular embodiment, the method includes receiving an object bounding box from an object tracker. The method includes cropping a video frame based at least in part on the object bounding box to generate a cropped image. The method further includes performing spatial dual segmentation on the cropped image to generate an initial mask and performing temporal mask refinement on the initial mask to generate a refined mask. The method also includes generating a temporal saliency map based at least in part on the refined mask.

Temporal saliency map

A method of image retrieval includes obtaining information identifying a plurality of selected objects and selecting one among a plurality of candidate geometrical arrangements. This method also includes, by at least one processor, and in response to the selecting, identifying at least one digital image, among a plurality of digital images, that depicts the plurality of selected objects arranged according to the selected candidate geometrical arrangement.

Systems, methods, and apparatus for image retrieval

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San Diego, CA, United States of America

Dashan Gao