Charles J Snider is a notable inventor based in Albuquerque, NM (US). He has made significant contributions to the field of digital image segmentation, holding a total of 3 patents. His work focuses on refining image segmentation techniques, particularly in the context of domain shifts.

Latest Patents

One of his latest patents is titled "Uncertainty-refined image segmentation under domain shift." This innovative method involves training a neural network for image segmentation using a labeled training dataset from a first domain. During training, a subset of nodes in the neural network is dropped out, allowing the network to receive image data from a second, different domain. For each image element, a vector of N values that sum to 1 is calculated, with each value representing an image segmentation class. A label is assigned to each image element based on the class with the highest value in the vector. The method also includes performing multiple inferences with active dropout layers for each image element, generating an uncertainty value for each. If an image element's uncertainty exceeds a predefined threshold, its label is replaced with a new label corresponding to the class with the next highest value.

Career Highlights

Charles J Snider is currently employed at National Technology & Engineering Solutions of Sandia, LLC. His work at this organization has allowed him to explore advanced techniques in image processing and machine learning.

Collaborations

Some of his notable coworkers include John P Korbin and Matthew David Smith, who contribute to the innovative environment at Sandia.

Conclusion

Charles J Snider's contributions to digital image segmentation demonstrate his expertise and commitment to advancing technology in this field. His innovative methods continue to influence the way image data is processed and analyzed.