Abstract: A method of removing unwanted camera motion from a video sequence is provided. The method matches a group of feature points between each pair of consecutive video frames in the video sequence. The method calculates the motion of each matched feature point between the corresponding pair of consecutive video frames. The method calculates a set of historical metrics for each feature point. The method, for each pair of consecutive video frames, identifies a homography that defines a dominant motion between the pair of consecutive frames. The homography is identified by performing a geometrically biased historically weighted RANSAC on the calculated motion of the feature points. The geometrically biased historically weighted RANSAC gives a weight to the calculated motion of each feature point based on the historical metrics calculated for the feature point. The method removes the unwanted camera motion from the video sequence by using the identified homographies.

Abstract: A method of activating a first device is disclosed. The method includes: the first device pairing with a second device; receiving a connection request from a second device; connecting to the second device; opening a communication channel to the second device; transmitting an activation package to the second device; receiving an activation payload from the second device; and performing an activation using information from the activation payload.

Abstract: A method of removing unwanted camera motion from a video sequence is provided. The method matches a group of feature points between each pair of consecutive video frames in the video sequence. The method calculates the motion of each matched feature point between the corresponding pair of consecutive video frames. The method calculates a set of historical metrics for each feature point. The method, for each pair of consecutive video frames, identifies a homography that defines a dominant motion between the pair of consecutive frames. The homography is identified by performing a geometrically biased historically weighted RANSAC on the calculated motion of the feature points. The geometrically biased historically weighted RANSAC gives a weight to the calculated motion of each feature point based on the historical metrics calculated for the feature point. The method removes the unwanted camera motion from the video sequence using the identified homographies.

Abstract: Systems, methods and computer program products are disclosed for resampling a digital image. According to an implementation, a source image can be presharpened and upsampled to a first upsampled image having a specified image size and a first level of presharpening. The source image is also presharpened and upsampled to a second upsampled image having the specified image size and second level of presharpening that is less than the first level of presharpening. The first and second upsampled images are deblurred. A binary edge mask image is generated from the deblurred, upsampled images. The binary edge mask image is dilated and blurred to generate a deep mask image. The first and second, deblurred upsampled images are blended together using the deep mask image.

Abstract: Methods and apparatus for a roof analysis tool for constructing a parameter set, where the parameter set is derived from mapping data for a map region, and where the parameter set describes the roofs for the buildings within the map region. In some cases, the parameter set includes a list of roof type identification values and the respective buildings in the map region for which a given roof type identification value corresponds. The roof analysis tool may operate on a server and work in conjunction with a mobile device, where the mobile device may display map views of a map region such that the map view is based on a three-dimensional model of the map region, and where a portion of the three-dimensional model is based on data generated on the mobile device and a portion of the three-dimensional model is based on data generated on the server.

Abstract: A method to improve the efficiency of the detection and tracking of machine-readable objects is disclosed. The properties of image frames may be pre-evaluated to determine whether a machine-readable object, even if present in the image frames, would be likely to be detected. After it is determined that one or more image frames have properties that may enable the detection of a machine-readable object, image data may be evaluated to detect the machine-readable object. When a machine-readable object is detected, the location of the machine-readable object in a subsequent frame may be determined based on a translation metric between the image frame in which the object was identified and the subsequent frame rather than a detection of the object in the subsequent frame. The translation metric may be identified based on an evaluation of image data and/or motion sensor data associated with the image frames.

Abstract: Techniques to permit a digital image capture device to stabilize a video stream in real-time (during video capture operations) are presented. In general, techniques are disclosed for stabilizing video images using an overscan region and a look-ahead technique enabled by buffering a number of video input frames before generating a first stabilized video output frame. (Capturing a larger image than is displayed creates a buffer of pixels around the edge of an image; overscan is the term given to this buffer of pixels.) More particularly, techniques are disclosed for buffering an initial number of input frames so that a “current” frame can use motion data from both “past” and “future” frames to adjust the strength of a stabilization metric value so as to keep the current frame within its overscan. This look-ahead and look-behind capability permits a smoother stabilizing regime with fewer abrupt adjustments.

Abstract: Techniques to permit a digital image capture device to stabilize a video stream in real-time (during video capture operations) are presented. In general, techniques are disclosed for stabilizing video images using an overscan region and a look-ahead technique enabled by buffering a number of video input frames before generating a first stabilized video output frame. (Capturing a larger image than is displayed creates a buffer of pixels around the edge of an image; overscan is the term given to this buffer of pixels.) More particularly, techniques are disclosed for buffering an initial number of input frames so that a “current” frame can use motion data from both “past” and “future” frames to adjust the strength of a stabilization metric value so as to keep the current frame within its overscan. This look-ahead and look-behind capability permits a smoother stabilizing regime with fewer abrupt adjustments.

Abstract: A barcode decoding system and method are disclosed that use a data-driven classifier for transforming a potentially degraded barcode signal into a digit sequence. The disclosed implementations are robust to signal degradation through incorporation of a noise model into the classifier construction phase. The run-time computational cost is low, allowing for efficient implementations on portable devices. Implementations are disclosed for intelligent preview scaling, barcode-aware autofocus augmentation and multi-scale signal feature extraction.

Abstract: A method of activating a first device is disclosed. The method includes: the first device pairing with a second device; receiving a connection request from a second device; connecting to the second device; opening a communication channel to the second device; transmitting an activation package to the second device; receiving an activation payload from the second device; and performing an activation using information from the activation payload.

Abstract: A method of removing unwanted camera motion from a video sequence is provided. The method matches a group of feature points between each pair of consecutive video frames in the video sequence. The method calculates the motion of each matched feature point between the corresponding pair of consecutive video frames. The method calculates a set of historical metrics for each feature point. The method, for each pair of consecutive video frames, identifies a homography that defines a dominant motion between the pair of consecutive frames. The homography is identified by performing a geometrically biased historically weighted RANSAC on the calculated motion of the feature points. The geometrically biased historically weighted RANSAC gives a weight to the calculated motion of each feature point based on the historical metrics calculated for the feature point. The method removes the unwanted camera motion from the video sequence using the identified homographies.

Abstract: Embodiments of the present disclosure provide a method and system for sharing information between a first computing device and a second computing device. In the described embodiments, an optical label, such as, for example a QR code, is generated on the first computing device. In embodiments, the optical label is color encoded and displayed in such a way that the optical label is not perceptible to a user. The second computing device may capture the encoded optical label and subject the captured images to a processing technique that decodes the encoded optical label.

Abstract: Embodiments of the present disclosure provide a method and system for sharing information between a first computing device and a second computing device. In the described embodiments, an optical label, such as, for example a QR code, is generated on the first computing device. In embodiments, the optical label is color encoded and displayed in such a way that the optical label is not perceptible to a user. The second computing device may capture the encoded optical label and subject the captured images to a processing technique that decodes the encoded optical label.

Abstract: Techniques to permit a digital image capture device to stabilize a video stream in real-time (during video capture operations) are presented. In general, techniques are disclosed for stabilizing video images using an overscan region and a look-ahead technique enabled by buffering a number of video input frames before generating a first stabilized video output frame. (Capturing a larger image than is displayed creates a buffer of pixels around the edge of an image; overscan is the term given to this buffer of pixels.) More particularly, techniques are disclosed for buffering an initial number of input frames so that a “current” frame can use motion data from both “past” and “future” frames to adjust the strength of a stabilization metric value so as to keep the current frame within its overscan. This look-ahead and look-behind capability permits a smoother stabilizing regime with fewer abrupt adjustments.

Abstract: Embodiments of the present disclosure provide a method and system for sharing information between a first computing device and a second computing device. In the described embodiments, an optical label, such as, for example a QR code, is generated on the first computing device. In embodiments, the optical label is color encoded and displayed in such a way that the optical label is not perceptible to a user. The second computing device may capture the encoded optical label and subject the captured images to a processing technique that decodes the encoded optical label.

Abstract: Embodiments of the present disclosure provide a method and system for sharing information between a first computing device and a second computing device. In the described embodiments, an optical label, such as, for example a QR code, is generated on the first computing device. In embodiments, the optical label is color encoded and displayed in such a way that the optical label is not perceptible to a user. The second computing device may capture the encoded optical label and subject the captured images to a processing technique that decodes the encoded optical label.

Abstract: A barcode decoding system and method are disclosed that use a data-driven classifier for transforming a potentially degraded barcode signal into a digit sequence. The disclosed implementations are robust to signal degradation through incorporation of a noise model into the classifier construction phase. The run-time computational cost is low, allowing for efficient implementations on portable devices. Implementations are disclosed for intelligent preview scaling, barcode-aware autofocus augmentation and multi-scale signal feature extraction.

Abstract: Methods, systems, and apparatus, including computer program products, for identifying regions of interest in an image and identifying a barcode in a degraded image are provided. A region of interest is identified by pre-processing an image, generating a binary image based on a metric calculated on the pre-processed image, and analyzing regions of the image identified using connected components and other analysis. A barcode is identified by searching a population of barcodes, degrading ideal image intensity profiles of candidate barcodes, and comparing the degraded ideal image intensity profiles to an image intensity profile of the degraded image.

Abstract: Methods, systems, and apparatus, including computer program products, for identifying regions of interest in an image and identifying a barcode in a degraded image are provided. A region of interest is identified by pre-processing an image, generating a binary image based on a metric calculated on the pre-processed image, and analyzing regions of the image identified using connected components and other analysis. A barcode is identified by searching a population of barcodes, degrading ideal image intensity profiles of candidate barcodes, and comparing the degraded ideal image intensity profiles to an image intensity profile of the degraded image.

Abstract: A method of removing unwanted camera motion from a video sequence is provided. The method matches a group of feature points between each pair of consecutive video frames in the video sequence. The method calculates the motion of each matched feature point between the corresponding pair of consecutive video frames. The method calculates a set of historical metrics for each feature point. The method, for each pair of consecutive video frames, identifies a homography that defines a dominant motion between the pair of consecutive frames. The homography is identified by performing a geometrically biased historically weighted RANSAC on the calculated motion of the feature points. The geometrically biased historically weighted RANSAC gives a weight to the calculated motion of each feature point based on the historical metrics calculated for the feature point. The method removes he unwanted camera motion from the video sequence using the identified homographies.