Abstract: A method, apparatus and system for image privacy protection and actionable response includes distorting an analog image captured using an image capture device in a residential, industrial or commercial environment using a transform filter, digitizing the distorted analog image, analyzing the distorted, digitized image using a trained machine learning process to identify at least one of an individual or an object in the distorted, digitized image, the machine learning process having been trained to identify individuals and objections in the distorted image, and upon identification of at least one of an individual or an object in the distorted image for which action is to be taken, communicating an indication to at least one device in the residential, commercial or industrial environment to cause the device to perform a predetermined action.

Abstract: Method and apparatus of processing a sequence of video frames comprising generating at least one video frame and using an analog neural network to select, within the at least one video frame, at least one patch of pixels and process the at least one pixel patch to produce a patch feature for each of the at least one pixel patches. The method digitizes the patch feature, identifies objects within the digitized patch feature, and tracks the objects to generate control information that is used by the analog neural network to select and process the pixel patches.

Abstract: An edge device comprising a feature extractor and a reconfigurator. The feature extractor comprises a first neural network for encoding input information into data vectors and extracting particular data vectors representing features within the input information, wherein the first neural network comprises at least one encoder layer and at least one adaptor layer. The reconfigurator is coupled to the feature extractor and comprises a second neural network for classifying the particular data vectors and wherein, upon requiring additional features to be extracted, the reconfigurator adapts at least one layer in the first neural network, second neural network or both by performing at least one of: (1) altering weights, (2) adding layers, (3) deleting layers, (4) reordering layers to improve classification of particular data vector. The first neural network, the second neural network or both are trained using gradient-free training.

Abstract: Embodiments of the disclosed technologies include a method of capturing, using a mobile device, a best-focused image of a skin surface of a subject, the method including: setting a camera of the mobile device to a fixed focal length; capturing, using the camera, a current image of a plurality of images of the skin surface, the plurality of images having a sequence and including a first previous image captured, using the camera, previously to the current image and a second previous image captured, using the camera, previously to the first previous image; producing a modified image from the current image; transforming the modified image, using a Laplacian pyramid, to produce a plurality of first luminance values from the modified image and a plurality of second luminance values from the plurality of first luminance values; averaging a plurality of first squared values, each including a square of a corresponding first luminance value of the plurality of first luminance values, to produce a first energy value; av

Abstract: Device logic in a mobile device configures a processor to capture a series of images, such as a video, using a consumer-grade camera, and to analyze the images to determine the best-focused image, of the series of images, that captures a region of interest. The images may be of a textured surface, such as facial skin of a mobile device user. The processor sets a focal length of the camera to a fixed position for collecting the images. The processor may guide the user to position the mobile device for capturing the images, using audible cues. For each image, the processor crops the image to the region of interest, extracts luminance information, and determines one or more energy levels of the luminance via a Laplacian pyramid. The energy levels may be filtered, and then are compared to energy levels of the other images to determine the best-focused image.

Abstract: Device logic in a mobile device configures a processor to capture a series of images, such as a video, using a consumer-grade camera, and to analyze the images to determine the best-focused image, of the series of images, that captures a region of interest. The images may be of a textured surface, such as facial skin of a mobile device user. The processor sets a focal length of the camera to a fixed position for collecting the images. The processor may guide the user to position the mobile device for capturing the images, using audible cues. For each image, the processor crops the image to the region of interest, extracts luminance information, and determines one or more energy levels of the luminance via a Laplacian pyramid. The energy levels may be filtered, and then are compared to energy levels of the other images to determine the best-focused image.

Abstract: A method and apparatus for optimizing image quality based on scene content comprising a sensor for generating a sequence of frames where each frame in the sequence of frames comprises content representing a scene and a digital processor, coupled to the sensor, for performing scene content analysis and for establishing a window defining a number of input frames from the sensor and processed output frames, and for aligning and combining the number of frames in the window to form an output frame, wherein sensor parameters and frame combination parameters are adjusted based on scene content.