Hewlett-Packard Development Company, L.p.

Hewlett-Packard Company

Oscar A Zuniga

Jason E Yost

Shane D Voss

Stephen Bernard Pollard

Kevin J Matherson

Shane D Vosa

Systems (), methods (), and machine-readable and executable instructions () are provided for depth mask assisted video stabilization. Depth mask assisted video stabilization can include creating a first depth mask from a first frame () and a second depth mask from a second frame () to obtain depth information () and defining a first list of feature points (---------) from the first frame () and a second list of feature points (---------) from the second frame () (). Depth mask assisted video stabilization can also include comparing feature points from the first list of feature points (------) with feature points from the second list of feature points (------) to find a number of common feature point pairs (--), (--), (--), (--), (--), (--), (--), (--), (--)) () and creating a number of motion matrices from the common feature point pairs ((--), (--), (--), (--), (--), (--), (--), (--), (--)) (). Depth mask assisted video stabilization can also include including the motion matrices in a correction matrix () and applying the inverse of the correction matrix to the second frame () to perform video stabilization ().

Depth mask assisted video stabilization

Example embodiments disclosed herein relate to feature based image registration. Feature based image registration determines correspondence between image features such as points, lines, and contours to align or register a reference or first image and a target or second image. The examples disclosed herein may be used in mobile devices such as cell phones, personal digital assistants, personal computers, cameras, and video recorders.

Feature based image registration

Example embodiments disclosed herein relate to image stabilization. Image stabilization includes techniques used to reduce jitter associated with the motion of a camera. Image stabilization can compensate for pan and tilt (angular movement, equivalent to yaw and pitch) of a camera or other imaging device. Image stabilization can be used in still and video cameras, including those found in mobile devices such as cell phones and personal digital assistants (PDAs).

Image stabilization

Methods and apparatuses for determining a skew angle of text relative to an axis are disclosed herein. One exemplary embodiment of the method comprises locating a first portion of the text that has a spatial frequency greater than a first preselected value. The method continues with locating a second portion of the text that has a spatial frequency greater than a second preselected value. A line is fitted between the first portion of the text and the second portion of the text. The angle between the line and the axis is measured, wherein the angle corresponding to the skew angle.

Method and device for determining skew angle of an image

A method of determining the orientation of text is disclosed herein. An embodiment of the method may comprise measuring the contrasts associated with an upper edge of the text relative to a background. A first length of the upper edge of text having contrasts that are at least a first preselected value is measured. In addition to measuring the contrasts associated with the upper edge of the text, contrasts associated with a lower edge of the text are measured relative to the background. A second length of the lower edge of text having contrasts that are at least the first preselected value is then measured. The first length is compared to the second length, wherein the text is improperly oriented if the first length is greater than the second length.

Method and device for determining orientation of text

In a digital image, for each pixel to be thresholded, a threshold is selected from a set of thresholds. Selection may be based on relative magnitudes of the thresholds. At least one of the thresholds may be dynamic, and one of the thresholds may be constant for at least a region of the image.

Dynamic bilevel thresholding of digital images

A method for classifying regions in a digital image as either photo regions or non-photo regions is presented. A digital image is divided into regions and each region is divided into a plurality of cells. Each cell comprises a group of pixels represented by one or more digital bits. Features of a cell such as its graylevel spread, or the moment of inertia of its co-occurrence matrix, are used to determine the likelihood that the cell is a photo cell or a non-photo cell. Based on this likelihood, the cell is classified as either a photo cell, a non-photo cell, or is unclassified. Classified cell is used in conjunction with unclassified cells to grow regions that can be characterized as photo regions or non-photo regions. Once a region is classified as a photo region or a non-photo region a decision is made regarding the number of bits per pixel needed to adequately represent that region.

Detection of photo regions in digital images

A system that converts a scanned image of a complex document into an image where text has been preserved and separated from the background. The system first subdivides the scanned image into blocks and then examines each block pixel by pixel to construct a histogram of the gray scale values of the pixels. The histogram is partitioned into a first, middle and last regions. If one or more peaks occur in the first and last regions, and a single histogram peak occurs within the middle region, the pixels are reexamined to determine the frequency of occurrence of pixels having a gray scale level of the middle peak nearby pixels which have a level of a first region peak. If this frequency is high, the middle peak is assumed to be background information. After determining the threshold, the system rescans the block applying the threshold to separate the text from background information within the block.

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Fort Collins, CO, United States of America

Oscar A Zuniga