Xailient

Lars Oleson

Shivanthan Yohanandan

James J Meyer

Bernardo Rodeiro Croll

Ryan Mccrea

Yousef Rabi

Sabina Pokhrel

Deepa Lakshmi Chandrasekharan

Zhenhua Zhang

Priyadharshini Devanand

Herman Yau

Andy Atkinson

Mallika Patel

In an aspect, a system for image redaction is presented. The system includes an image recording device configured to generate image data. A computing device is in communication with the image recording device. The computing device is configured to detect a face in the image data as a function of a facial recognition process. The computing device is configured to modify the image data. Modifying the image data includes reversibly obscuring a face crop from a remaining portion of the image data. The computing device is configured to communicate the modified image data to another computing device.

Systems and methods for image encryption

The invention provides systems and method for generating device-specific artificial neural network (ANN) models for distribution across user devices. Sample datasets are collected from devices in a particular environment or use case and include predictions by device-specific ANN models executing the user devices. The received datasets are used with existing datasets and stored ANN models to generate updated device-specific ANN models from each of the stored instances of the device ANN models based on the training data.

Systems and methods for distributed data analytics

Techniques for identifying pixel groups representing objects in an image include using images having multiple groups of pixels, grouped such that each pixel group represents a zone of interest and determining a pixel value for pixels within each pixel group based on a comparison of pixel values for each individual pixel within the group. A probability heat map is derived from the pixel group values using a first neural network using the pixel group values as input and produces the heat map having a set of graded values indicative of the probability that the respective pixel group includes an object of interest. A zone of interest is identified based on whether the groups of graded values meet a determined probability threshold objects of interest are identified within the at least one zone of interest by way of a second neural network.

Systems and methods for object detection and recognition

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Bondi, Australia

Lars Oleson