This inventor holds 1 USPTO granted patent and 1 published patent application. Top assignee: Amgen Inc.. Active years: 2026.
Company Filing History:
Years Active: 2026
Title: Innovations of Kenneth E Hampshire in Image Augmentation Techniques
Introduction
Kenneth E Hampshire is a notable inventor based in Thousand Oaks, CA (US). He has made significant contributions to the field of automated visual inspection through his innovative patent. His work focuses on enhancing image processing techniques that are crucial for various applications in quality control and machine learning.
Latest Patents
Kenneth E Hampshire holds a patent for "Image augmentation techniques for automated visual inspection." This patent introduces various techniques that facilitate the development of an image library used to train and validate automated visual inspection (AVI) models, such as AVI neural networks for image classification. One of the key aspects of his invention is the arithmetic transposition algorithm, which generates synthetic images from original images by transposing features, such as defects, onto the original images with pixel-level realism. Additionally, digital inpainting techniques are employed to create realistic synthetic images, allowing for the addition, removal, or modification of defects or other depicted features. Quality control techniques are also integrated to assess the suitability of image libraries for training AVI models.
Career Highlights
Kenneth E Hampshire is currently associated with Amgen Inc., a leading biotechnology company. His work at Amgen involves applying his innovative techniques to improve automated visual inspection processes, thereby enhancing product quality and operational efficiency.
Collaborations
Throughout his career, Kenneth has collaborated with talented individuals such as Aik Jun Tan and Nishant Mukesh Gadhvi. These collaborations have contributed to the advancement of his research and the successful implementation of his patented techniques.
Conclusion
Kenneth E Hampshire's contributions to image augmentation techniques have paved the way for advancements in automated visual inspection. His innovative approaches are essential for improving the accuracy and efficiency of image classification in various industries.