Sri International

Sarnoff Corporation

L-3 Communications Corporation

Imax Corporation

Harpreet Singh Sawhney

Rakesh Kumar

Ajay Divakaran

Hui Ting Cheng

Supun Samarasekera

Keith James Hanna

Jayakrishnan Kumar Eledath

Manoj Aggarwal

James Russell Bergen

Mayank Bansal

Bogdan C Matei

Qian Yu

Ying Shan

Jeffrey Lubin

Bogdan Calin Mihai Matei

Zhiwei Zhu

Feng Han

Sang-Hack Jung

Yanlin Guo

Jingen Liu

Stephen Charles Hsu

Omar Javed

Vincent Paragano

Amir Tamrakar

Aydin Arpa

Jiangjian Xiao

Xutao Lv

Hai Tao

Taragay Oskiper

Oleg Naroditsky

Timothy J Shields

Saad Masood Khan

Raia Hadsell

Saad Ali

Tao Zhao

Peter Jeffrey Burt

Steven S Weiner

Ryan Villamil

Chaitanya Desai

Manika Puri

Dennis Lee Matthies

David Kalokitis

David R Patterson

Jayan Eledath

Jiejie Zhu

Douglas Fremont Dixon

Gary A Gendel

Gajinder Singh

Behjat Siddiquie

Jane Asmuth

Thomas Germano

Steve Hsu

Chris Broaddus

Weiyu Zhang

David Nister

Elizabeth E Shriberg

Girish Acharya

Michael John Wolverton

Curtis R Carlson

Michael Henry Brill

Lambert Ernest Wixson

John Benjamin Southall

Mikhail Sizintsev

Subhodev Das

Philip Miller

Robert Mandelbaum

Shmuel Peleg

Ishani Chakraborty

Michael Raymond Piacentino

Aaron Spaulding

Doug Dixon

Janet Kim

Charles Fielding Finch Karney

Ron Krakower

Christopher P Broaddus

Nicholas Vandervalk

Shunguang Wu

Ryan Cekander

Vivek Verma

Supun Samarasakera

Mohamed Elhoseiny

Norman Winarsky

Garbis Salgian

Vince Paragano

Joshua Randy Herman, Deceased

Xiyang Dai

Changjiang Yan

Nhon Hoc Trinh

Jayakrishan Eledath

Nicholas John Vander Valk

Tom Pschar

David Chisolm

Sujit Kuthirummal

A computing system includes a vision-based user interface platform to, among other things, analyze multi-modal user interactions, semantically correlate stored knowledge with visual features of a scene depicted in a video, determine relationships between different features of the scene, and selectively display virtual elements on the video depiction of the scene. The analysis of user interactions can be used to filter the information retrieval and correlating of the visual features with the stored knowledge.

Real-time human-machine collaboration using big data driven augmented reality technologies

A computing system and method for identifying related visual content of a collection of visual media files comprising one or more image files and video files includes monitoring inputs to the computing system, the inputs associated with a user interaction with electronic content using the computer system, identifying a visual media file in the collection of visual media files relevant to the electronic content based on a semantic label assigned to the visual media file by the computing system, creating a representative image of the identified visual media file, and displaying the representative image for selection. The computing system enables a selection of the displayed representative image for association of the identified visual media file with the electronic content.

Multi-dimensional realization of visual content of an image collection

Zero-shot content detection includes building/training a semantic space by embedding word-based document descriptions of a plurality of documents into a multi-dimensional space using a semantic embedding technique; detecting a plurality of features in the multimodal content by applying feature detection algorithms to the multimodal content; determining respective word-based concept descriptions for concepts identified in the multimodal content using the detected features; embedding the respective word-based concept descriptions into the semantic space; and in response to a content detection action, (i) embedding/mapping words representative of the content detection action into the semantic space, (ii) automatically determining, without the use of training examples, concepts in the semantic space relevant to the content detection action based on the embedded words, and (iii) identifying portions of the multimodal content responsive to the content detection action based on the concepts in the semantic space determined to be relevant to the content detection action.

Zero-shot event detection using semantic embedding

Techniques are disclosed for identifying discriminative, fine-grained features of an object in an image. In one example, an input device receives an image. A machine learning system includes a model comprising a first set, a second set, and a third set of filters. The machine learning system applies the first set of filters to the received image to generate an intermediate representation of the received image. The machine learning system applies the second set of filters to the intermediate representation to generate part localization data identifying sub-parts of an object and one or more regions of the image in which the sub-parts are located. The machine learning system applies the third set of filters to the intermediate representation to generate classification data identifying a subordinate category to which the object belongs. The system uses the part localization and classification data to perform fine-grained classification of the object.

Machine learning system for generating classification data and part localization data for objects depicted in images

A computing system for realizing visual content of an image collection executes feature detection algorithms and semantic reasoning techniques on the images in the collection to elicit a number of different types of visual features of the images. The computing system indexes the visual features and provides technologies for multi-dimensional content-based clustering, searching, and iterative exploration of the image collection using the visual features and/or the visual feature indices.

A computing system for recognizing salient events depicted in a video utilizes learning algorithms to detect audio and visual features of the video. The computing system identifies one or more salient events depicted in the video based on the audio and visual features.

Recognizing salient video events through learning-based multimodal analysis of visual features and audio-based analytics

A system for object detection and tracking includes technologies to, among other things, detect and track moving objects, such as pedestrians and/or vehicles, in a real-world environment, handle static and dynamic occlusions, and continue tracking moving objects across the fields of view of multiple different cameras.

Real-time detection, tracking and occlusion reasoning

A complex video event classification, search and retrieval system can generate a semantic representation of a video or of segments within the video, based on one or more complex events that are depicted in the video, without the need for manual tagging. The system can use the semantic representations to, among other things, provide enhanced video search and retrieval capabilities.

Classification, search and retrieval of complex video events

Methods and apparatuses of the present invention generally relate to generating actionable data based on multimodal data from unsynchronized data sources. In an exemplary embodiment, the method comprises receiving multimodal data from one or more unsynchronized data sources, extracting concepts from the multimodal data, the concepts comprising at least one of objects, actions, scenes and emotions, indexing the concepts for searchability; and generating actionable data based on the concepts.

Method and apparatus for correlating and viewing disparate data

A computer implemented method for determining a vehicle type of a vehicle detected in an image is disclosed. An image having a detected vehicle is received. A number of vehicle models having salient feature points is projected on the detected vehicle. A first set of features derived from each of the salient feature locations of the vehicle models is compared to a second set of features derived from corresponding salient feature locations of the detected vehicle to form a set of positive match scores (p-scores) and a set of negative match scores (n-scores). The detected vehicle is classified as one of the vehicle models based at least in part on the set of p-scores and the set of n-scores.

3-D model based method for detecting and classifying vehicles in aerial imagery

A food recognition assistant system includes technologies to recognize foods and combinations of foods depicted in a digital picture of food. Some embodiments include technologies to estimate portion size and calories, and to estimate nutritional value of the foods. In some embodiments, data identifying recognized foods and related information are generated in an automated fashion without relying on human assistance to identify the foods. In some embodiments, the system includes technologies for achieving automatic food detection and recognition in a real-life setting with a cluttered background, without the images being taken in a controlled lab setting, and without requiring additional user input (such as user-defined bounding boxes). Some embodiments of the system include technologies for personalizing the food classification based on user-specific habits, location and/or other criteria.

Automated food recognition and nutritional estimation with a personal mobile electronic device

A multi-sensor, multi-modal data collection, analysis, recognition, and visualization platform can be embodied in a navigation capable vehicle. The platform provides an automated tool that can integrate multi-modal sensor data including two-dimensional image data, three-dimensional image data, and motion, location, or orientation data, and create a visual representation of the integrated sensor data, in a live operational environment. An illustrative platform architecture incorporates modular domain-specific business analytics 'plug ins' to provide real-time annotation of the visual representation with domain-specific markups.

Real-time system for multi-modal 3D geospatial mapping, object recognition, scene annotation and analytics

Real-time object detection, tracking and occlusion reasoning

Growing community of inventors

Princeton Junction, NJ, United States of America

Harpreet Singh Sawhney