Ann Arbor, MI, United States of America

Thomas R Crimmins


Average Co-Inventor Count = 1.2

ph-index = 3

Forward Citations = 54(Granted Patents)


Company Filing History:


Years Active: 1986-1988

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3 patents (USPTO):Explore Patents

Title: Thomas R Crimmins: Innovator in Image Processing Technologies

Introduction

Thomas R Crimmins is a notable inventor based in Ann Arbor, MI (US). He has made significant contributions to the field of image processing, particularly in reducing noise and recognizing shapes in digital images. With a total of 3 patents, his work has advanced the capabilities of computing machines in handling complex visual data.

Latest Patents

One of Crimmins' latest patents is a "System for reducing speckle noise." This invention focuses on a method of operating a computing machine to reduce speckle noise in video images, especially radar images. The technique employs a complementary hulling method on vertical pixel grids, allowing for the decay of small image features at a faster rate than larger ones.

Another significant patent is the "Method and apparatus for automatic shape recognition." This invention outlines a process for determining the existence of a shape or pattern within an image represented by a matrix of digital data signals. The method involves using a general-purpose computer to store and manipulate image matrices, ultimately identifying the desired shapes or patterns effectively.

Career Highlights

Throughout his career, Thomas R Crimmins has worked with the Environmental Research Institute of Michigan, where he has contributed to various innovative projects. His expertise in image processing has positioned him as a valuable asset in the field.

Collaborations

Crimmins has collaborated with notable individuals, including his coworker William M Brown. Their joint efforts have likely contributed to advancements in the technologies they have developed.

Conclusion

Thomas R Crimmins stands out as an influential inventor in the realm of image processing technologies. His patents reflect a commitment to enhancing the capabilities of computing machines in visual data analysis.

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