Systems and methods for media projection surface selection

Patent metadata

Field	Value
Publication number	US-2020106996-A1
Application number	US-201816143728-A
Country	US
Kind code	A1
Filing date	Sep 27, 2018
Priority date	Sep 27, 2018
Publication date	Apr 2, 2020
Grant date	—

Abstract

Methods and systems are provided for automatically selecting a target area to project content thereon. For example, a projection device receives content to be projected and content attributes of the content. The projection device also captures images of candidate areas and determines candidate area characteristics based on the captured images. The projection device generates a respective quality-of-projection indicator based on the content attributes and the candidate area characteristics. The projection device selects the candidate area with the highest quality-of-projection indicator as the target area on which the content is to be projected.

First claim

1 . A method for selecting a target area on which content is projected, the method comprising:
receiving the content and content attributes of the content; capturing a respective image for each of a plurality of candidate areas; determining respective candidate area characteristics based on the captured respective image for each of the plurality of candidate areas; generating a respective quality-of-projection indicator based on the content attributes and the candidate area characteristics for each of the plurality of candidate areas; and selecting the candidate area with the highest quality-of-projection indicator as the target area to project the content thereon.

2 . The method of claim 1 , wherein determining respective candidate area characteristics based on the captured respective image for each of the plurality of candidate areas comprises:
analyzing the respective captured images to determine candidate area characteristics of each of the plurality of candidate areas, wherein the candidate area characteristics comprise one or more of the following: size, texture, color, reflectivity, intensity, and amount of ambient light; and determining respective candidate area characteristics for each of the plurality of candidate areas by assigning a respective score to each of the determined candidate area characteristics.

3 . The method of claim 2 , wherein the received content attributes comprises one or more of the following: size, texture, color, reflectivity, intensity, and amount of ambient light; and the method further comprises:
assigning a respective score to each of the content attributes.

4 . The method of claim 3 , wherein generating a respective quality-of-projection indicator for each of the candidate areas comprises:
multiplying the score assigned to each of the content attributes to the score assigned to the corresponding candidate area characteristics to calculate a respective combined characteristic score; and generating the respective quality-of-projection indicator by adding the respective combined characteristic scores for each of the plurality of candidate areas.

5 . The method of claim 2 , wherein determining respective candidate area characteristics further comprises:
extracting, from the received content attributes, a duration for which the content is scheduled to be projected; and predicting a change in the respective candidate area characteristics for each of the plurality of candidate areas for the duration for which the content is to be projected.

6 . The method of claim 1 , wherein generating a respective quality-of-projection indicator further comprises:
determining content attribute for acoustics of the content; assigning a first score to the content attribute for acoustics; determining respective candidate area acoustic characteristics corresponding to each of the plurality of candidate areas; assigning a second score to the respective candidate area acoustic characteristics corresponding to each of the plurality of candidate areas; calculating a respective quality-of-sound score by adding the first score to the respective second scores corresponding to each of the plurality of candidate areas; and adjusting the respective quality-of-projection indicator based on the respective quality-of-sound score for each of the plurality of candidate areas.

7 . The method of claim 1 , wherein the plurality of candidate areas is determined based on a field of view of a user viewing the content.

8 . The method of claim 7 , further comprising:
determining that the user has moved to a new area; in response to determining that the user has moved to the new area, automatically identifying a plurality of candidate areas within the new area; and selecting a target area from the plurality of candidate areas within the new area based on a respective quality-of-projection indicator of each of the plurality of candidate areas within the new area.

9 . The method of claim 1 , wherein capturing a respective image for each of a plurality of candidate areas comprises:
projecting a pre-determined pattern on each of the candidate areas, the pre-determined pattern including a plurality of intersection points; and capturing an image of each of the candidate areas having the pre-determined pattern projected thereon; and determining respective candidate area characteristics based on the captured respective image for each of the plurality of candidate areas comprises:
determining a respective curvature of each of the plurality of candidate areas by:
selecting, from each image, a subset of the intersection points of the pre-determined pattern projected on the respective candidate areas;
calculating, for each subset, a curvature value based on coordinates of the intersection points on the respective subset from the captured image; and
determining, for each image, a curvature of each of the plurality of candidate areas based on the corresponding curvature value.

10 . The method of claim 9 , wherein calculating, for each subset, the curvature value based on coordinates of the intersection points on the respective subset from the captured image comprises:
generating a cartesian plot mapping the coordinates of the intersection points of the respective subset; and applying a least means square regression model to the mapped intersection points on the cartesian plot of the respective subset to calculate the corresponding curvature value.

11 . A system for selecting a target area on which content is projected, the system comprising:
memory; and control circuitry configured to:
receive the content and content attributes of the content;
capture a respective image for each of a plurality of candidate areas;
determine respective candidate area characteristics based on the captured respective image for each of the plurality of candidate areas;
generate a respective quality-of-projection indicator based on the content attributes and the candidate area characteristics for each of the plurality of candidate areas; and
select the candidate area with the highest quality-of-projection indicator as the target area to project the content thereon.

12 . The system of claim 11 , wherein the control circuitry is further configured, when determining respective candidate area characteristics based on the captured respective image for each of the plurality of candidate areas, to:
analyze the respective captured images to determine candidate area characteristics of each of the plurality of candidate areas, wherein the candidate area characteristics comprise one or more of the following: size, texture, color, reflectivity, intensity, and amount of ambient light; and determine respective candidate area characteristics for each of the plurality of candidate areas by assigning a respective score to each of the determined candidate area characteristics.

13 . The system of claim 12 , wherein the received content attributes comprises one or more of the following: size, texture, color, reflectivity, intensity, and amount of ambient light; and the control circuitry is further configured to:
assign a respective score to each of the content attributes.

14 . The system of claim 13 , wherein the control circuitry is further configured, when generating a respective quality-of-projection indicator for each of the candidate areas, to:
multiply the score assigned to each of the content attributes to the score assigned to the corresponding candidate area characteristics to calculate a respective combined characteristic score; and generate the respective quality-of-projection indicator by adding the respective combi

Assignees

• Rovi Guides Inc

Inventors

• Singh Gyanveer
• Sen Susanto
• Ahamed Shakir Sharfraz Ashfaq
• Ponnuswamy Sriram

Classifications

• H04N9/3185

  Geometric adjustment, e.g. keystone or convergence (optical or mechanical adjustment of convergence H04N9/317; using scanning means H04N3/22; optical or mechanical adjustments of projectors not peculiar to the presence of an electronic spatial light modulator G03B21/14) · CPC title

• H04N9/3173

  wherein the projection device is specially adapted for enhanced portability · CPC title

• H04N9/3194

  including sensor feedback · CPC title

• G06T7/40

  Analysis of texture (depth or shape recovery from texture G06T7/529) · CPC title

• G06T2207/10024

  Color image · CPC title