Qualcomm Incorporated

Hemanth Acharya

Pawan Kumar Baheti

Kishor Kumar Barman

Ajit Deepak Gupte

Naveen Srinivasamurthy

Ajit Venkat Rao

Sanjeev Kumar

Padmapriya Jagannathan

A method includes receiving, at a device, a plurality of image frames corresponding to a video stream. The plurality of image frames include a first image frame having a first resolution and a second image frame having a second resolution that is lower than the first resolution. The method also includes detecting, at the device, a trigger by analyzing the second image frame. The method further includes designating, at the device, the first image frame as an action frame based on the trigger.

Detection of action frames of a video stream

Generally described, aspects of the present disclosure relate to generation of an image representing a panned shot of an object by an image capture device. In one embodiment, a panned shot may be performed on a series of images of a scene. The series of images may include at least subject object moving within the scene. Motion data of the subject object may be captured by comparing the subject object in a second image of the series of images to the subject object in a first image of the series of images. A background image is generated by implementing a blur process using the first image and the second image based on the motion data. A final image is generated by including the image of the subject object in the background image.

Automated generation of panning shots

Embodiments disclosed facilitate robust, accurate, and reliable recovery of words and/or characters in the presence of non-uniform lighting and/or shadows. In some embodiments, a method to recover text from image may comprise: expanding a Maximally Stable Extremal Region (MSER) in an image, the neighborhood comprising a plurality of sub-blocks; thresholding a subset of the plurality of sub-blocks in the neighborhood, the subset comprising sub-blocks with text, wherein each sub-block in the subset is thresholded using a corresponding threshold associated with the sub-block; and obtaining a thresholded neighborhood.

Processing text images with shadows

An electronic device and method receive (for example, from a memory), a grayscale image of a scene of real world captured by a camera of a mobile device. The electronic device and method also receive a color image from which the grayscale image is generated, wherein each color pixel is stored as a tuple of multiple components. The electronic device and method determine a new intensity for at least one grayscale pixel in the grayscale image, based on at least one component of a tuple of a color pixel located in correspondence to the at least one grayscale pixel. The determination may be done conditionally, by checking whether a local variance of intensities is below a predetermined threshold in a subset of grayscale pixels located adjacent to the at least one grayscale pixel, and selecting the component to provide most local variance of intensities.

Method of improving contrast for text extraction and recognition applications

An electronic device and method identify regions that are likely to be text in a natural image or video frame, followed by processing as follows: lines that are nearly vertical are automatically identified in a selected text region, oriented relative to the vertical axis within a predetermined range &#x2212;max_theta to +max_theta, followed by determination of an angle &#x3b8; of the identified lines, followed by use of the angle &#x3b8; to perform perspective correction by warping the selected text region. After perspective correction in this manner, each text region is processed further, to recognize text therein, by performing OCR on each block among a sequence of blocks obtained by slicing the potential text region. Thereafter, the result of text recognition is used to display to the user, either the recognized text or any other information obtained by use of the recognized text.

Method of perspective correction for devanagari text

An electronic device and method use a camera to capture an image of an environment outside followed by identification of regions therein. A subset of the regions is selected, based on attributes of the regions, such as aspect ratio, height, and variance in stroke width. Next, a number of angles that are candidates for use as skew of the image are determined (e.g. one angle is selected for each region. based on peakiness of a histogram of the region, evaluated at different angles). Then, an angle that is most common among these candidates is identified as the angle of skew of the image. The just-described identification of skew angle is performed prior to classification of any region as text or non-text. After skew identification, at least all regions in the subset are rotated by negative of the skew angle, to obtain skew-corrected regions for use in optical character recognition.

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Bengaluru, India

Hemanth Acharya