Vincennes, France

Frederic Jahard

USPTO Granted Patents = 1 

Average Co-Inventor Count = 6.0

ph-index = 1

Forward Citations = 1(Granted Patents)


Company Filing History:


Years Active: 2016

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1 patent (USPTO):Explore Patents

Title: Frederic Jahard: Innovator in Image Processing

Introduction

Frederic Jahard is a notable inventor based in Vincennes, France. He has made significant contributions to the field of image processing, particularly through his innovative patent that addresses symmetry in visual descriptors. His work is essential for advancements in computer vision and image analysis.

Latest Patents

Frederic Jahard holds a patent titled "Method and apparatus for obtaining a symmetry invariant descriptor from a visual patch of an image." This invention focuses on deriving a descriptor that remains invariant under arbitrary mirror symmetries. The method involves determining a direction for a point of interest and mirroring the local image patch to achieve a normalized intrinsic orientation. This approach enhances the reliability of image descriptors, making it particularly useful in applications like object recognition.

Career Highlights

Frederic Jahard has been associated with Jastec Co., Ltd., where he has applied his expertise in image processing. His innovative approach has led to the development of advanced methods that improve the accuracy of visual data interpretation. His contributions are recognized within the industry, showcasing his commitment to pushing the boundaries of technology.

Collaborations

Frederic has collaborated with talented individuals such as Ayoub Massoudi and Simon Dolle. These partnerships have fostered a creative environment that encourages the exchange of ideas and the development of cutting-edge solutions in image processing.

Conclusion

Frederic Jahard's work exemplifies the impact of innovation in the field of image processing. His patent and collaborations highlight his dedication to advancing technology and improving image analysis techniques. His contributions are invaluable to the ongoing evolution of computer vision.

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