Tomokazu Kaneko

Title: Innovations of Tomokazu Kaneko

Introduction

Tomokazu Kaneko is a prominent inventor based in Tokyo, Japan. He has made significant contributions to the field of learning data generation, holding 2 patents that showcase his innovative approach to technology.

Latest Patents

Kaneko's latest patents include a learning data generation device and a learning data generation method, along with a recording medium. The learning data generation device features an acquisition means that captures a first image corresponding to an arrangement of objects. It also includes an image deformation means that modifies a second image of a desired object. The image generation means then creates a fourth image that aligns with the predetermined arrangement state indicated by the first image. This process culminates in the generation of learning data that incorporates the fourth image and relevant information about the arrangement state of the object.

Another notable patent focuses on generating learning data from important, cut-out object regions. This invention involves an image acquisition unit that collects multiple images containing an object to be inferred. An image cut-out unit extracts the object region from each image, while an importance generation unit processes these regions to produce importance information. This information is crucial for developing an object inference model and is stored as part of the learning data.

Career Highlights

Tomokazu Kaneko is currently employed at NEC Corporation, where he continues to innovate and contribute to advancements in technology. His work has positioned him as a key figure in the field of learning data generation.

Collaborations

Kaneko collaborates with talented coworkers, including Soma Shiraishi and Ryosuke Sakai, who contribute to his projects and enhance the innovative environment at NEC Corporation.

Conclusion

Tomokazu Kaneko's contributions to learning data generation reflect his dedication to innovation and technology. His patents demonstrate a commitment to advancing the field and improving data generation methods.