DETERMINING PITCH FOR PROXIMITY SENSITIVE INTERACTIONS

• US 20170024892A1
• Filed: 06/30/2016
• Published: 01/26/2017
• Est. Priority Date: 07/01/2015
• Status: Active Grant

• Current Assignee: Qeexo Co. (TDK Corporation)
• Original Assignee: Qeexo Co. (TDK Corporation)
• Inventors: HARRISON, CHRISTOPHER, SCHWARZ, JULIA, XIAO, ROBERT BO
• Granted Patent: US 10,564,761 B2
• Time in Patent Office: Days
• US Class Current: 1/1
• CPC Class Codes: G06F 18/00 Pattern recognition
• CPC Class Codes: G06F 2203/04108 Touchless 2D- digitiser, i.e. digitiser detecting the X/Y position of the input means, finger or stylus, also when it does not touch, but is proximate to the digitiser's interaction surface without distance measurement in the Z direction
• CPC Class Codes: G06F 3/042 by opto-electronic means
• CPC Class Codes: G06F 3/0421 by interrupting or reflecting a light beam, e.g. optical touch-screen
• CPC Class Codes: G06F 3/0428 by sensing at the edges of the touch surface the interruption of optical paths, e.g. an illumination plane, parallel to the touch surface which may be virtual sensing beam interruptions in a planar beam grid of an optical touch-screen G06F3/0421
• CPC Class Codes: G06F 3/044 by capacitive means
• CPC Class Codes: G06F 3/0488 using a touch-screen or digitiser, e.g. input of commands through traced gestures
• CPC Class Codes: G06F 3/04883 for inputting data by handwriting, e.g. gesture or text
• CPC Class Codes: G06T 7/73 using feature-based methods
• CPC Class Codes: G06V 10/28 Quantising the image, e.g. histogram thresholding for discrimination between background and foreground patterns
• CPC Class Codes: G06V 10/30 Noise filtering
• CPC Class Codes: G06V 10/457 by analysing connectivity, e.g. edge linking, connected component analysis or slices

Abstract

Methods and apparatuses are provided for determining a pitch and yaw of an elongated interface object relative to a proximity sensitive surface. In one aspect, a proximity image is received having proximity image data from which it can be determined which areas of the proximity sensitive surface sensed the elongated interface object during a period of time. A proximity blob is identified in the proximity image and the proximity image is transformed using a plurality of different transformations to obtain a plurality of differently transformed proximity images. A plurality of features is determined for the identified blob in the transformed proximity images and the pitch of the elongated interface object relative to the proximity sensitive surface is determined based upon the determined features and a multi-dimensional heuristic regression model of the proximity sensitive surface; and a yaw is determined based upon the pitch.

Claims

1. A method for determining the pitch of an elongated interface object relative to a proximity sensitive surface, the method comprising:

receiving proximity image data from which it can be determined which areas of the proximity sensitive surface sensed the elongated interface object during a predetermined period of time;

identifying a proximity blob in the proximity image data;

transforming proximity image data associated with the proximity blob using a plurality of different transformations to obtain a plurality of differently transformed blob proximity image data;

determining a plurality of features of the transformed proximity image data associated with the blob;

determining the pitch of the elongated interface object relative to the proximity sensitive surface based upon the determined features and a multi-dimensional heuristic regression model of the proximity sensitive surface; and

determining a yaw based upon the determined features and the determined pitch data.

2. The method of claim 1, wherein the multi-dimensional heuristic regression model is generated based upon heuristically determined responses of the proximity sensitive surface.

3. The method of claim 1, wherein the multi-dimensional heuristic regression model is generated based upon heuristic responses obtained from another proximity sensitive surface similar to the proximity sensitive surface.

4. The method of claim 1, wherein the multi-dimensional heuristic regression model is one of a mathematical function, an algorithm, or a logical expression.

5. The method of claim 1, wherein an algorithm determines a pitch based upon the features of the transformed blobs and the multi-dimensional heuristic regression model.

6. The method of claim 1, further comprising calculating initial yaw data, determining a first yaw based upon the initial yaw when the pitch is within a first range and determining a second yaw based upon the initial yaw when the pitch is within a second range wherein the first pitch range is different than the second pitch range and the first yaw is different than the second yaw.

7. The method of claim 1, further comprising calculating the yaw based upon the features, the pitch and a model correlating a yaw estimate and the pitch with a yaw.

8. A method for operating a device driver for a proximity sensitive surface, the method comprising:

receiving proximity image data from the proximity sensitive surface;

generating multiple different transformed proximity image data based upon the proximity image data;

identifying blob areas in the transformed proximity image data; and

determining features of the transformed proximity image data associated with the blob areas; and

determining a pitch of the elongated interface object relative to the touch screen device based upon the determined features and a multi-dimensional heuristic regression model of the proximity sensitive surface; and

determining a yaw based upon the determined pitch.

9. An apparatus comprising:

a proximity sensitive surface generating proximity image data it can be determined which areas of the proximity sensitive surface sensed the elongated interface object during a predetermined period of time; and

a processor that is adapted to identify a proximity blob in the proximity image data, transform proximity image data associated with the proximity blob using a plurality of different transformations to obtain a plurality of transformed blob proximity image data;

determine a plurality of features of the transformed blob proximity image data, to determine the pitch of the elongated interface object relative to the proximity sensitive surface based upon the determined features and a multi-dimensional heuristic regression model of the proximity sensitive surface; and

to determine a yaw based upon the determined pitch.

10. The apparatus of claim 9, wherein the multi-dimensional heuristic regression model is generated based upon heuristically determined responses of the proximity sensitive surface.

11. The apparatus of claim 9, wherein the multi-dimensional heuristic regression model is generated based upon heuristically determined responses a proximity sensitive surface similar to the proximity sensitive surface.

12. The apparatus of claim 9, wherein the multi-dimensional heuristic regression model is one of a mathematical function, an algorithm, or a logical expression.

13. The apparatus of claim 9, wherein an algorithm determines a pitch based upon the features of the transformed blobs and the multi-dimensional heuristic regression model.

14. The apparatus of claim 9, further comprising calculating initial yaw data, determining a first yaw based upon the initial yaw when the pitch data is within a first range and determining a second yaw based upon the initial yaw when the pitch data is within a second range wherein the first pitch range is different than the second pitch range and the first yaw is different than the second yaw.

15. The apparatus of claim 9, further comprising calculating the yaw based upon features of the proximity image blobs, the pitch data and model correlating an initial yaw data and the pitch data.

16. A proximity sensing surface driver comprising:

an interface adapted to interact with a proximity sensitive surface to obtain proximity image data from which it can be determined which areas of the proximity sensitive surface sensed the elongated interface object during a predetermined period of time; and

a processor adapted to;

identify a proximity blob in the proximity image data;

transform the proximity image data associated with the proximity blob using a plurality of different transformations to obtain a plurality of transformed blob proximity image data;

determine a plurality of features of the transformed blob proximity image data;

determine the pitch of the elongated interface object relative to the proximity sensitive surface based upon the determined features and a multi-dimensional heuristic regression model of the proximity sensitive surface; and

determine a yaw based upon the determined features of the proximity image blobs and the determined pitch data.