International Business Machines Corporation

University of Illinois

Dhiraj Joshi

Rogerio Schmidt Feris

Michele Merler

John Richard Smith

Stephen Carl Hammer

Nirmit V Desai

Quoc-Bao Nguyen

John Joseph Kent

Jenny S Li

Jinjun Xiong

Nancy Anne Greco

Tova Roth

Minh Do

Luis Angel Bathen

Wei-Han Lee

Gwilym Benjamin Lee Newton

Sebastien Gilbert

Raymond Alexander Yeh

Sujoy Kumar Roy Chowdhury

Aakash Praliya

Prakriti Pritmani

Satish Rajani

Shiva G

Kay Murphy

Khoi-Nguyen C Mac

Shilpa Shenai

Arun Yashwant Ingale

Apurv Gupta

Raghu Ramaswamy

Shyama Prosad Chowdhury

Chidansh Amitkumar Bhatt

Ajjay Ratnakar

Described are techniques for oblique image rectification. The techniques include receiving an original image depicting an oblique view of a circular object and pre-processing the original image into an edge image. The techniques further include generating, by a machine learning model based on the edge image, a heatmap including an ellipse formed by the oblique view of the circular object. The techniques further include computing ellipse parameters describing the ellipse of the heatmap. The techniques further include performing, using the ellipse parameters, an affine transformation on the original image to generate a rectified image, where the rectified image converts the ellipse to a circle.

Oblique image rectification

Dynamically adjusting, using artificial intelligence (AI), sensors and models of an autonomous roaming robotic device, which includes receiving data regarding an asset at a computer of a roaming robotic device from sensors on the robotic device. The robotic device identifies an asset at a location using the sensors, and the robotic device has instructions, received from a control system, to inspect the location or items at the location. The data is analyzed using the computer of the robotic device, and the analysis includes using historical data for the asset. An AI model is loaded using the computer of the robotic device, based on the identification of the asset. A sensor is selected using the computer of the robotic device, for conducting an inspection of the asset based on the analysis of the data and the AI model.

Dynamic use of artificial intelligence (AI) models on an autonomous AI enabled robotic device

A method and system for auto-curating a media are provided. Media content is received over the network interface. A set of markers is identified for the media content, each marker corresponding to one of a plurality of visible and audible cues in the media content. Segments in the media content are identified based on the identified set of markers. An excitement score is computed for each segment based on the identified markers that fall within the segment. A highlight clip is generated by identifying segments having excitement scores greater than a threshold.

Auto-curation and personalization of sports highlights

Techniques regarding action detection based on motion in receptive fields of a neural network model are provided. For example, one or more embodiments described herein can comprise a system, which can comprise a memory that can store computer executable components. The system can also comprise a processor, operably coupled to the memory, and that can execute the computer executable components stored in the memory. The computer executable components can comprise a motion component that can extract a motion vector from a plurality of adaptive receptive fields in a deformable convolution layer of a neural network model. The computer executable components can also comprise an action detection component that can generate a spatio-temporal feature by concatenating the motion vector with a spatial feature extracted from the deformable convolution layer.

Action detection by exploiting motion in receptive fields

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Yorktown Heights, NY, United States of America

Dhiraj Joshi