Yilan County, Taiwan

Yi-An Chen


Average Co-Inventor Count = 9.0

ph-index = 1


Company Filing History:


Years Active: 2025

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1 patent (USPTO):

Title: Yi-An Chen: Innovator in Computer Vision Technology

Introduction

Yi-An Chen is a notable inventor based in Yilan County, Taiwan. He has made significant contributions to the field of computer vision, particularly in enhancing object detection systems. His innovative approach aims to improve the accuracy and reliability of these technologies.

Latest Patents

Yi-An Chen holds a patent for a system titled "Filtering false positive computer-vision-based object detection events." This patent describes systems and techniques designed to suppress false positive notifications for detected objects. In various examples, the system receives first bounding box data indicating a detection of a first class of object in a first frame of image data. It then determines 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 first and second bounding box data is calculated. Based on this value, notifications associated with the detection of the first class of object may be suppressed, thereby enhancing the system's efficiency.

Career Highlights

Yi-An Chen is currently employed at Amazon Technologies, Inc., where he continues to develop and refine innovative technologies in computer vision. His work is instrumental in advancing the capabilities of object detection systems, making them more reliable and effective.

Collaborations

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

Conclusion

Yi-An Chen's contributions to computer vision technology, particularly through his patent on filtering false positives in object detection, highlight his role as an innovator in this rapidly evolving field. His work at Amazon Technologies, Inc. continues to push the boundaries of what is possible in computer vision.

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