Microsoft Technology Licensing, LLC

University of Florida Research Foundation, Incorporated

Mukhil Azhagan Mallaiyan Sathiaseelan

Sergio Ortiz Egea

Mark M Tehranipoor

Navid Asadi-Zanjani

One example provides a computing device including a logic machine and a storage machine holding instructions executable by the logic machine to implement a depth image processing pipeline including a neural network, the neural network including an edge detecting layer. The neural network is configured to receive input of an active brightness image and receive input of one or more of real data or imaginary data of a complex depth image, the complex depth image corresponding to the active brightness image. The neural network is further configured to, at the edge detecting layer, apply one or more convolutional processes to the active brightness image to identify one or more edge pixels in the active brightness image, and at a second layer, denoise one or more of the real data or the imaginary data of the complex depth image based on the one or more edge pixels identified.

Denoising depth image data using neural networks

There is a need for more effective and efficient printed circuit board (PCB) design. This need can be addressed by, for example, solutions for performing automated PCB component estimation. In one example, a method includes identifying a PCB image of a PCB; performing chromaticity-based background subtraction on the PCB image to generate a background-subtracted PCB image; performing morphological noise removal on the background-subtracted PCB image to generate a noise-removed PCB image; and performing object localization on the noise-removed PCB image to identify one or more PCB component estimations within the noise-removed PCB image.

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Gainesville, FL, United States of America

Mukhil Azhagan Mallaiyan Sathiaseelan