Input Device Object Detection via Peak Labeling
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Solution Overview
Problem
Existing object detection methods, such as template matching and binarization, are inadequate for recognizing objects like fingers due to variations in thickness, length, and tilt, and are sensitive to threshold values, leading to poor detection accuracy, especially when multiple objects with different heights and shapes are present.
Innovation Solution
An input device with a sensor system that detects approaching objects and specifies adjacent regions based on peak detection positions and label assignment, using a peak position specification part to identify maximum peak detection positions and a label assigning part to assign labels to surrounding detection positions, improving object separation and recognition accuracy.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If template matching is used to recognize objects, then objects with fixed patterns can be identified, but objects with varying characteristics (thickness, length, tilt) cannot be accurately recognized
Solution Approach 1:
The patent segments the detection area into multiple detection positions arranged in rows and columns, and further segments objects into multiple regions by specifying adjacent regions for each detected object. This segmentation allows independent analysis of different parts of objects with varying characteristics, enabling accurate recognition without requiring fixed templates.
Solution Approach 2:
The patent applies local quality by using peak detection at specific detection positions and assigning labels to adjacent regions based on local detection data. Each detection position evaluates its own peak condition independently, and objects are recognized based on local characteristics rather than global template matching, accommodating variations in object thickness, length, and tilt.
2Measurement precision
If binarization with labeling is used to identify objects, then multiple objects can be separated, but detection accuracy deteriorates when objects have different heights and shapes due to threshold sensitivity
Solution Approach 1:
The patent changes the detection parameter from fixed threshold binarization to peak condition evaluation. Instead of using a fixed threshold value that requires careful tuning, the system detects peaks based on relative comparison at each detection position, making the detection reliable across objects with different heights and shapes without sacrificing separation accuracy.
3Device complexity
If a single threshold is used for binarization, then the recognition process is simple, but multiple objects with different characteristics cannot be accurately detected
Solution Approach 1:
The patent segments both the detection area into grid positions and the objects into adjacent regions, allowing each region to be independently labeled. This segmentation enables accurate multi-object detection without requiring complex global threshold tuning, as each local region can be evaluated independently based on peak conditions.
Applied Scientific Principles
This section explains which scientific principles are used to turn an abstract innovation direction into a practical engineering solution.
Function Achieved in This Case
The device achieves high-precision recognition of multiple objects with different shapes and distances by accurately specifying adjacent regions and reducing noise, thereby enhancing object separation and recognition accuracy.
Implementation Method 1
a sensor that detects an approach or contact of the object by a change of an electrostatic capacitance has been widely used
Data Source
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AI summary
Provided is an input device including a sensor part to detect approaching states of objects at detection positions and an adjacent region specification part to specify adjacent regions of the objects based on detection data from the sensor part. The adjacent region specification part specifies a peak detection position, of which a value of the detection data satisfies a certain peak condition among the detection positions, and conducts a label assigning process that assigns a label applied at the peak detection position to one or more detection positions among surrounding detection positions of the peak detection position. The label is not assigned to the one or more detection positions, and the detection data of the one or more detection positions indicate greater than or equal to a first threshold defined based on the detection data of the peak detection position being specified.