Company Filing History:
Years Active: 2022-2025
Title: Shitong Mao: Innovator in Automated Machine Vision
Introduction
Shitong Mao is a prominent inventor based in Pittsburgh, PA, known for his contributions to the field of automated machine vision. With a total of three patents to his name, he has made significant strides in developing technologies that enhance defect detection processes.
Latest Patents
One of Shitong Mao's latest patents focuses on automated machine vision-based defect detection. This innovative system utilizes various mechanisms and processes for automatic computer vision-based defect detection through a neural network. The system is designed to receive historical datasets that include training images corresponding to known defects. Each training image is converted into a matrix representation, allowing the neural network to adjust weighted parameters based on these known defects. Once the neural network is sufficiently trained, a test image of an object not included in the historical dataset is obtained. Portions of this test image are extracted as input patches for the neural network, which generates a probability score indicating the likelihood of defects in each input patch. An overall defect score for the test image is then produced based on these probability scores, providing a comprehensive assessment of the object's condition.
Career Highlights
Shitong Mao has established himself as a key figure in the field of machine vision technology. His work at Qeexo, Co. has been instrumental in advancing automated defect detection systems. His innovative approach and technical expertise have contributed to the development of cutting-edge solutions that improve quality control processes across various industries.
Collaborations
Shitong collaborates with talented professionals in his field, including Rajen Bhatt and Raviprakash Kandury. These collaborations enhance the innovative capabilities of their projects and foster a dynamic work environment.
Conclusion
Shitong Mao's contributions to automated machine vision and defect detection exemplify the impact of innovation in technology. His work continues to influence the industry and pave the way for future advancements in machine vision systems.