View source for 18570913. INCLINE ESTIMATION SYSTEM, INCLINE ESTIMATION METHOD, INCLINE ESTIMATION PROGRAM, SEMICONDUCTOR INSPECTION SYSTEM, AND ORGANISM OBSERVATION SYSTEM simplified abstract (HAMAMATSU PHOTONICS K.K.)

=INCLINE ESTIMATION SYSTEM, INCLINE ESTIMATION METHOD, INCLINE ESTIMATION PROGRAM, SEMICONDUCTOR INSPECTION SYSTEM, AND ORGANISM OBSERVATION SYSTEM=


==Organization Name==

[[:Category:HAMAMATSU PHOTONICS K.K.|HAMAMATSU PHOTONICS K.K.]]


[[Category:HAMAMATSU PHOTONICS K.K.]]

==Inventor(s)==

[[:Category:Akari Ito of Hamamatsu-shi, Shizuoka (JP)|Akari Ito of Hamamatsu-shi, Shizuoka (JP)]][[Category:Akari Ito of Hamamatsu-shi, Shizuoka (JP)]]

[[:Category:Tomochika Takeshima of Hamamatsu-shi, Shizuoka (JP)|Tomochika Takeshima of Hamamatsu-shi, Shizuoka (JP)]][[Category:Tomochika Takeshima of Hamamatsu-shi, Shizuoka (JP)]]

==INCLINE ESTIMATION SYSTEM, INCLINE ESTIMATION METHOD, INCLINE ESTIMATION PROGRAM, SEMICONDUCTOR INSPECTION SYSTEM, AND ORGANISM OBSERVATION SYSTEM - A simplified explanation of the abstract==


This abstract first appeared for US patent application 18570913 titled 'INCLINE ESTIMATION SYSTEM, INCLINE ESTIMATION METHOD, INCLINE ESTIMATION PROGRAM, SEMICONDUCTOR INSPECTION SYSTEM, AND ORGANISM OBSERVATION SYSTEM

The inclination estimation system described in the patent application is designed to estimate the inclination of an imaging target captured in an image.

* The system includes an estimation target image acquisition unit, a focal position estimation unit, and an inclination estimation unit.
* The estimation target image acquisition unit acquires estimation target images from the image.
* The focal position estimation unit outputs feature quantities from the estimation target images using a feature quantity output model to estimate focal positions when in focus.
* The inclination estimation unit then estimates the inclination of the imaging target from the focal positions when in focus.
* The feature quantity output model is generated through machine learning from learning images associated with focal position information.

Potential Applications:
- Medical imaging for diagnosing conditions based on the inclination of internal structures.
- Robotics for determining the orientation of objects in a workspace.
- Surveillance systems for tracking the movement and orientation of individuals or objects.

Problems Solved:
- Accurately estimating the inclination of imaging targets in various applications.
- Enhancing the precision of focal position estimation in imaging systems.
- Improving the efficiency of machine learning models for feature quantity output.

Benefits:
- Increased accuracy in determining the inclination of imaging targets.
- Enhanced performance of imaging systems in various fields.
- Streamlined processes for focal position estimation and inclination estimation.

Commercial Applications:
Title: Advanced Inclination Estimation System for Imaging Applications
This technology can be utilized in medical imaging equipment, robotics, surveillance systems, and other industries requiring precise inclination estimation for imaging targets. The market implications include improved diagnostic capabilities, enhanced robotic functionalities, and increased security measures in surveillance applications.

Questions about Inclination Estimation System:
1. How does the machine learning process improve the accuracy of inclination estimation?
Machine learning allows the system to analyze and compare feature quantities from learning images to enhance the accuracy of estimating focal positions and inclinations.

2. What are the key factors that influence the performance of the inclination estimation unit?
The performance of the inclination estimation unit is influenced by the quality of the acquired estimation target images, the effectiveness of the feature quantity output model, and the accuracy of the focal position estimation process.


==Original Abstract Submitted==

An inclination estimation system is a system for estimating the inclination of an imaging target captured in an image, and includes: an estimation target image acquisition unit for acquiring estimation target images from the image; a focal position estimation unit for outputting feature quantities from estimation target images by using a feature quantity output model and estimating focal positions when in focus corresponding to the estimation target images; and an inclination estimation unit for estimating the inclination of the imaging target from the focal positions when in focus, wherein the feature quantity output model is generated by machine learning from a learning images associated with focal position information, and feature quantities of two different learning images are compared with each other according to focal position information associated with the two different learning images, and machine learning is performed based on a result of the comparison.



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