Here Global B.v.

Nokia Technologies Oy

Percipient.ai Inc.

Timo Pekka Pylvänäinen

Radek Grzeszczuk

Ramakrishna Vedantham

Urban Velkavrh

Mridul Aanjaneya

Timo Juhani Ahonen

Vasudev Parameswaran

Jerome Berclaz

Balan Rama Ayyar

Ivan Kovtun

Rajendra Jayantilal Shah

Derek Young

Vidyadayini Talapady

Alison Higuera

Simon Chen

Atul Kanaujia

A computer vision system configured for detection and recognition of objects in video and still imagery in a live or historical setting uses a teacher-student object detector training approach to yield a merged student model capable of detecting all of the classes of objects any of the teacher models is trained to detect. Further, training is simplified by providing an iterative training process wherein a relatively small number of images is labeled manually as initial training data, after which an iterated model cooperates with a machine-assisted labeling process and an active learning process where detector model accuracy improves with each iteration, yielding improved computational efficiency. Further, synthetic data is generated by which an object of interest can be placed in a variety of setting sufficient to permit training of models. A user interface guides the operator in the construction of a custom model capable of detecting a new object.

Systems and methods for rapid development of object detector models

An approach is provided for providing perspective-based content placement. A content placement platform processes and/or facilitates a processing of one or more models of one or more objects associated with a geographical area to cause, at least in part, a decomposition of the one or more models into one or more simplified surfaces. The content placement platform further causes, at least in part, a selection of one or more portions of the one or more simplified surfaces as one or more content placement layers based, at least in part, on one or more viewpoints, with the one or more content placement layers supporting a perspective-based rendering of one or more content items associated with the one or more objects.

Geometrically and semanitcally aware proxy for content placement

Geometrically and semanitically aware proxy for content placement

Various methods are provided for automatically adjusting a zoom feature in accordance with a camera movement to perform a dolly zoom effect. One example method may include causing reception of a first image frame from video data, wherein the first image frame comprises two or more interest points, causing reception of a second image frame from the video data, wherein the second image frame comprises the two or more interest points in a different location, and tracking a difference in location of the two or more interest points from the first image frame to the second image frame. The method may also include calculating a scaling factor and applying the scaling factor to the second image frame.

Method and apparatus for automatically rendering dolly zoom effect

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Menlo Park, CA, United States of America

Timo Pekka Pylvänäinen