Seiko Epson Corporation

Jennifer Jianing Sun

Syed Alimul Huda

Andrei Mark Rotenstein

Joseph Chitai Lam

Asheer Kasar Bachoo

Ariel Shlomo Kelman

Christina Melissa Sutanto

Systems and method for predicting performance of pipelines for object detection, pose estimation, and object tracking are provided. In one embodiment, a plurality of regressors are stored where each regressor may be trained for a specific object detection (OD), pose estimation (PE), and/or tracking pipeline, so that each regressor outputs a score equal to or greater than that of another regressor in response to a first feature vector as input, the first feature vector corresponding to a first object that is more suitable for the specific pipeline than other pipelines. A second feature vector is stored corresponding to a second object. The regressors are run with the second feature vector as input to derive respective scores, and a recommendation of at least one pipeline is indicated based on the derived scores with respect to the second object. OD/PE/tracking may then be performed using the at least one indicated pipeline.

Three-dimensional detection and tracking pipline recommendation using performance prediction

A method includes steps of: (a) causing a display device to display an image of a real object based on captured image data of a real object; (b) causing the display device to display a 2D model obtained from a projection of a 3D model corresponding to the real object based at least on a view; and (c) storing appearance data of the real object and data of the 2D model into a memory device while associating the appearance data and the data of the 2D model with each other after the real object and the 2D model are displayed so as to be aligned with or substantially aligned with each other by the display device.

Image processing method for generating training data

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Pasadena, CA, United States of America

Jennifer Jianing Sun