Abstract: A method for stitching images by an electronic device is described. The method includes obtaining at least two images. The method also includes selecting a stitching scheme from a set of stitching schemes based on one or more content measures of the at least two images. The set of stitching schemes includes a first stitching scheme, a second stitching scheme, and a third stitching scheme. The method further includes stitching the at least two images based on a selected stitching scheme.

Abstract: Certain aspects of the present disclosure relate to a method for compressed sensing (CS). The CS is a signal processing concept wherein significantly fewer sensor measurements than that suggested by Shannon/Nyquist sampling theorem can be used to recover signals with arbitrarily fine resolution. In this disclosure, the CS framework is applied for sensor signal processing in order to support low power robust sensors and reliable communication in Body Area Networks (BANs) for healthcare and fitness applications.

Abstract: Techniques are described in which a device is configured to determine an overlap region between a first image and a second image, determine a first histogram based on color data included in the first image that corresponds to the overlap region, and determine a second histogram based on color data included in the second image that corresponds to the overlap region. The processor is further configured to determine, based on the first and second histograms, a mapping function that substantially maps the second histogram to the first histogram and apply the mapping function to the second image to generate a normalized second image with respect to the first image.

Abstract: Systems and methods are provided for merging multiple images to produce a single fused image having desirable image characteristics derived from the multiple images. For example, the system may determine image characteristics for first and second images. The image characteristics may be related to contrast, exposure, color saturation, and the like. Based on the image characteristics, the system may generate a combined luma weight map. The system may decompose the first and second images and the combined luma weight map. In an example, the first image, the second image, and the combined luma weight map may be represented as scale-space representations having multiple scales or levels. The system may merge the decomposed representations of the first and second images and the combined luma weight map to form a decomposed representation of the fused image. The system may generate the actual fused image from the decomposed representation of the fused image.

Abstract: 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.

Abstract: Certain aspects of the present disclosure relate to a method for quantizing signals and reconstructing signals, and/or encoding or decoding data for storage or transmission. Points of a signal may be determined as local extrema or points where an absolute rise of the signal is greater than a threshold. The tread and value of the points may be quantized, and certain of the quantizations may be discarded before the quantizations are transmitted. After being received, the signal may be reconstructed from the quantizations using an iterative process.

Abstract: Systems, apparatuses, and methods to relate images of words to a list of words are provided. A trellis based word decoder analyses a set of OCR characters and probabilities using a forward pass across a forward trellis and a reverse pass across a reverse trellis. Multiple paths may result, however, the most likely path from the trellises has the highest probability with valid links. A valid link is determined from the trellis by some dictionary word traversing the link. The most likely path is compared with a list of words to find the word closest to the most.

Abstract: A mobile platform efficiently processes image data, using distributed processing in which latency sensitive operations are performed on the mobile platform, while latency insensitive, but computationally intensive operations are performed on a remote server. The mobile platform acquires image data, and determines whether there is a trigger event to transmit the image data to the server. The trigger event may be a change in the image data relative to previously acquired image data, e.g., a scene change in an image. When a change is present, the image data may be transmitted to the server for processing. The server processes the image data and returns information related to the image data, such as identification of an object in an image or a reference image or model. The mobile platform may then perform reference based tracking using the identified object or reference image or model.

Abstract: Certain aspects of the present disclosure relate to a method for compressed sensing (CS). The CS is a signal processing concept wherein significantly fewer sensor measurements than that suggested by Shannon/Nyquist sampling theorem can be used to recover signals with arbitrarily fine resolution. In this disclosure, the CS framework is applied for sensor signal processing in order to support low power robust sensors and reliable communication in Body Area Networks (BANs) for healthcare and fitness applications.

Abstract: 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.

Abstract: A mobile platform efficiently processes sensor data, including image data, using distributed processing in which latency sensitive operations are performed on the mobile platform, while latency insensitive, but computationally intensive operations are performed on a remote server. The mobile platform acquires sensor data, such as image data, and determines whether there is a trigger event to transmit the sensor data to the server. The trigger event may be a change in the sensor data relative to previously acquired sensor data, e.g., a scene change in an image. When a change is present, the sensor data may be transmitted to the server for processing. The server processes the sensor data and returns information related to the sensor data, such as identification of an object in an image or a reference image or model. The mobile platform may then perform reference based tracking using the identified object or reference image or model.

Abstract: 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.

Abstract: 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.

Abstract: A mobile platform efficiently processes image data, using distributed processing in which latency sensitive operations are performed on the mobile platform, while latency insensitive, but computationally intensive operations are performed on a remote server. The mobile platform acquires image data, and determines whether there is a trigger event to transmit the image data to the server. The trigger event may be a change in the image data relative to previously acquired image data, e.g., a scene change in an image. When a change is present, the image data may be transmitted to the server for processing. The server processes the image data and returns information related to the image data, such as identification of an object in an image or a reference image or model. The mobile platform may then perform reference based tracking using the identified object or reference image or model.

Abstract: Certain aspects of the present disclosure relate to techniques for low-complexity encoding (compression) of broad class of signals, which are typically not well modeled as sparse signals in either time-domain or frequency-domain. First, the signal can be split in time-segments that may be either sparse in time domain or sparse in frequency domain, for example by using absolute second order differential operator on the input signal. Next, different encoding strategies can be applied for each of these time-segments depending in which domain the sparsity is present.

Abstract: An electronic device and method capture multiple images of a scene of real world at a several zoom levels, the scene of real world containing text of one or more sizes. Then the electronic device and method extract from each of the multiple images, one or more text regions, followed by analyzing an attribute that is relevant to OCR in one or more versions of a first text region as extracted from one or more of the multiple images. When an attribute has a value that meets a limit of optical character recognition (OCR) in a version of the first text region, the version of the first text region is provided as input to OCR.

Abstract: An electronic device and method identify a block of text in a portion of an image of real world captured by a camera of a mobile device, slice sub-blocks from the block and identify characters in the sub-blocks that form a first sequence to a predetermined set of sequences to identify a second sequence therein. The second sequence may be identified as recognized (as a modifier-absent word) when not associated with additional information. When the second sequence is associated with additional information, a check is made on pixels in the image, based on a test specified in the additional information. When the test is satisfied, a copy of the second sequence in combination with the modifier is identified as recognized (as a modifier-present word). Storage and use of modifier information in addition to a set of sequences of characters enables recognition of words with or without modifiers.

Abstract: 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.

Abstract: An attribute is computed based on pixel intensities in an image of the real world, and thereafter used to identify at least one input for processing the image to identify at least a first maximally stable extremal region (MSER) therein. The at least one input is one of (A) a parameter used in MSER processing or (B) a portion of the image to be subject to MSER processing. The attribute may be a variance of pixel intensities, or computed from a histogram of pixel intensities. The attribute may be used with a look-up table, to identify parameter(s) used in MSER processing. The attribute may be a stroke width of a second MSER of a subsampled version of the image. The attribute may be used in checking whether a portion of the image satisfies a predetermined test, and if so including the portion in a region to be subject to MSER processing.

Abstract: 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 ?max_theta to +max_theta, followed by determination of an angle ? of the identified lines, followed by use of the angle ? 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.