Taoyuan, Taiwan

Shao-Hang Hsieh


Average Co-Inventor Count = 9.0

ph-index = 1


Company Filing History:


Years Active: 2025

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1 patent (USPTO):

Title: Innovations by Shao-Hang Hsieh in Computer Vision Technology.

Introduction

Shao-Hang Hsieh is a notable inventor based in Taoyuan, Taiwan. He has made significant contributions to the field of computer vision, particularly in enhancing object detection systems. His innovative work focuses on reducing false positive notifications, which is crucial for improving the reliability of automated detection systems.

Latest Patents

Shao-Hang Hsieh holds a patent titled "Filtering false positive computer-vision-based object detection events." This patent describes systems and techniques for suppressing false positive notifications for detected objects. In various examples, the invention involves receiving first bounding box data indicating a detection of a first class of object in a first frame of image data. It also includes determining second bounding box data indicating a prior detection of the same class of object in a second frame of image data. A first value representing the similarity between the two sets of bounding box data is calculated, allowing for the suppression of notifications associated with the detection of the first class of object based on this value.

Career Highlights

Shao-Hang Hsieh is currently employed at Amazon Technologies, Inc., where he continues to develop and refine technologies that enhance computer vision capabilities. His work is instrumental in advancing the efficiency and accuracy of object detection systems.

Collaborations

Some of his notable coworkers include Jhih-Yuan Lin and Chih-Ting Liu, who collaborate with him on various projects within the realm of computer vision technology.

Conclusion

Shao-Hang Hsieh's contributions to the field of computer vision through his innovative patent demonstrate his commitment to improving technology that impacts various industries. His work at Amazon Technologies, Inc. continues to pave the way for advancements in object detection systems.

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