Maisons Alfort, France

Ni Zhenjiang


 

Average Co-Inventor Count = 4.6

ph-index = 1

Forward Citations = 8(Granted Patents)


Company Filing History:


Years Active: 2018-2019

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3 patents (USPTO):Explore Patents

Title: Ni Zhenjiang: Innovator in Visual Tracking Technologies

Introduction

Ni Zhenjiang is a notable inventor based in Maisons Alfort, France. He has made significant contributions to the field of visual tracking, holding a total of 3 patents. His work focuses on advanced methods for tracking objects using innovative technologies.

Latest Patents

One of Ni Zhenjiang's latest patents is a method for visual tracking of an object represented by a cluster of points. This method includes steps to receive data representing a set of space-time events and determine the probability that an event belongs to the cluster of points representing the object. It also involves updating information associated with the cluster of points and calculating the position, size, and orientation of the object based on this updated information. Another significant patent is a method of tracking shape in a scene observed by an asynchronous light sensor. This method utilizes a matrix of pixels to provide asynchronous information, allowing for the updating of a model representing the tracked shape of an object after detecting events attributed to it.

Career Highlights

Throughout his career, Ni Zhenjiang has worked with prestigious institutions such as the Centre National de la Recherche Scientifique and Université Pierre et Marie Curie (Paris 6). His research has significantly advanced the understanding and application of visual tracking technologies.

Collaborations

Ni Zhenjiang has collaborated with notable individuals in his field, including Ryad Benosman and Stéphane Regnier. These collaborations have contributed to the development of innovative solutions in visual tracking.

Conclusion

Ni Zhenjiang's work in visual tracking technologies showcases his innovative spirit and dedication to advancing the field. His patents reflect a deep understanding of complex systems and a commitment to improving object tracking methods.

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