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: Modules and control units cooperate to simultaneously and independently control and adjust pixel parameters non-uniformly at regional increments across an entire image captured in an image frame by pixels in a pixel array. Pixel parameter changes for pixels in a given region occur, based on i) a contextual understanding of what contextually was happening in the one or more prior image frames and ii) whether salient items are located within that region. Additionally, guidance is sent to the sensor control unit to i) increase or decrease pixel parameters within those regions with salient items and then either to i) maintain, ii) increase or iii) decrease pixel parameters within regions without salient items in order to stay within any i) bandwidth limitations ii) memory storage, and/or iii) power consumptions limitations imposed by 1) one or more image sensors or 2) the communication loop between the sensor control unit and the image processing unit.

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: Modules and control units cooperate to simultaneously and independently control and adjust pixel parameters non-uniformly at regional increments across an entire image captured in an image frame by pixels in a pixel array. Pixel parameter changes for pixels in a given region occur, based on i) a contextual understanding of what contextually was happening in the one or more prior image frames and ii) whether salient items are located within that region. Additionally, guidance is sent to the sensor control unit to i) increase or decrease pixel parameters within those regions with salient items and then either to i) maintain, ii) increase or iii) decrease pixel parameters within regions without salient items in order to stay within any i) bandwidth limitations ii) memory storage, and/or iii) power consumptions limitations imposed by 1) one or more image sensors or 2) the communication loop between the sensor control unit and the image processing unit.

Abstract: A microscopy system comprising a microscope including a stage, at least one magnifying lens, and a lens controller, a video capture device coupled to the microscope capturing a plurality of images of an object on the stage of the microscope and a processing subsystem receiving the plurality of images from the video capture device, in which the processing subsystem generates at least one resultant image as a function of the plurality of images. The at least one resultant image generated by the processing subsystem may include a mosaic, submosaic or a sequence of mosaics.