Abstract:
Systems and methods are provided that tune a convolutional neural network (CNN) to increase both its accuracy and computational efficiency. In some examples, a computing device storing the CNN includes a CNN tuner that is a hardware and/or software component that is configured to execute a tuning process on the CNN. When executing according to this configuration, the CNN tuner iteratively processes the CNN layer by layer to compress and prune selected layers. In so doing, the CNN tuner identifies and removes links and neurons that are superfluous or detrimental to the accuracy of the CNN.
Abstract:
Examples of systems and methods for augmented facial animation are generally described herein. A method for mapping facial expressions to an alternative avatar expression may include capturing a series of images of a face, and detecting a sequence of facial expressions of the face from the series of images. The method may include determining an alternative avatar expression mapped to the sequence of facial expressions, and animating an avatar using the alternative avatar expression.
Abstract:
Examples of systems and methods for augmented facial animation are generally described herein. A method for mapping facial expressions to an alternative avatar expression may include capturing a series of images of a face, and detecting a sequence of facial expressions of the face from the series of images. The method may include determining an alternative avatar expression mapped to the sequence of facial expressions, and animating an avatar using the alternative avatar expression.
Abstract:
Computer-readable storage media, computing devices and methods are discussed herein. In embodiments, a computing device may be configured to perform facial recognition based on gradient based feature extractions of images of faces. In embodiments, the computing device may be configured to determine directional matching patterns of the images from the gradient based feature extraction and may utilize these directional matching patterns in performing a facial recognition analysis of the images of faces. Other embodiments may be described and/or claimed.
Abstract:
Managing images in an image database is described, comprising: when a query image is input, performing a match to determine whether an image similar to the query image exists within the database by comparing the images stored in the database with the query image; and if the image similar to the query image is a recognized image, providing at least one image in an image group to which the recognized image belongs and information thereon as a result, and if the image similar to the query image is an unrecognized image, providing at least one image in an image group to which the unrecognized image belongs as a result. When at least one image of the image group including the image similar to the query image is a recognized image, information on the corresponding recognized image is assigned to the images in the image group and provided as a result.
Abstract:
Systems, apparatuses and methods may provide for technology that generates, by a full inference path of a neural network, a first detection result associated with one or more objects in a first video frame. The technology may also generate, by a partial inference path of the neural network, a second detection result based on the first detection result, wherein the second detection result corresponds to a second video frame that is subsequent to the first video frame.
Abstract:
Examples of systems and methods for augmented facial animation are generally described herein. A method for mapping facial expressions to an alternative avatar expression may include capturing a series of images of a face, and detecting a sequence of facial expressions of the face from the series of images. The method may include determining an alternative avatar expression mapped to the sequence of facial expressions, and animating an avatar using the alternative avatar expression.
Abstract:
Examples of systems and methods for augmented facial animation are generally described herein. A method for mapping facial expressions to an alternative avatar expression may include capturing a series of images of a face, and detecting a sequence of facial expressions of the face from the series of images. The method may include determining an alternative avatar expression mapped to the sequence of facial expressions, and animating an avatar using the alternative avatar expression.
Abstract:
An example computer-vision system to convert images includes an image converter (112) to convert a near infrared light first image (202) to form a visible light image (206), and to update a coefficient of the image converter (112) based on a difference (214), an object recognizer (102) to recognize an object (208) in the first visible light image (206), and an object recognition analyzer (210) to determine the difference (214) between the object (208) recognized in the first visible light image (206) and an object (212) associated with the near infrared light image (202).
Abstract:
Examples of systems and methods for augmented facial animation are generally described herein. A method for mapping facial expressions to an alternative avatar expression may include capturing a series of images of a face, and detecting a sequence of facial expressions of the face from the series of images. The method may include determining an alternative avatar expression mapped to the sequence of facial expressions, and animating an avatar using the alternative avatar expression.