Darmstadt, Germany

Stefan Walk

USPTO Granted Patents = 1 

Average Co-Inventor Count = 6.0

ph-index = 1

Forward Citations = 2(Granted Patents)


Company Filing History:


Years Active: 2017

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1 patent (USPTO):Explore Patents

Title: The Innovative Mind of Stefan Walk

Introduction

Stefan Walk is a notable inventor based in Darmstadt, Germany. He has made significant contributions to the field of image processing, particularly in the detection of objects within images. His innovative approach utilizes self-similarity to enhance classification accuracy.

Latest Patents

Stefan Walk holds a patent for the invention titled "Detection of objects in an image using self similarities." This patent describes an image processor that includes a window selector for choosing a detection window within an image. It features a self-similarity computation part that determines self-similarity information for a group of pixels in any part of the detection window. This process generates a global self-similarity descriptor, which is then used by a classifier to determine the presence of an object. By focusing on global self-similarity rather than local similarities, Walk's invention captures more information, leading to improved classification and recognition of self-similarities at various scales.

Career Highlights

Throughout his career, Stefan Walk has worked with prominent organizations, including Toyota Motor Europe and Technische Universität Darmstadt. His experience in these institutions has allowed him to refine his skills and contribute to advancements in technology.

Collaborations

Stefan has collaborated with notable individuals in his field, including Gabriel Othmezouri and Ichiro Sakata. These partnerships have fostered innovation and the exchange of ideas, further enhancing his contributions to image processing.

Conclusion

Stefan Walk's work in image processing and object detection showcases his innovative spirit and dedication to advancing technology. His patent reflects a significant step forward in the field, demonstrating the importance of self-similarity in improving classification accuracy.

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