Image Processor Edge Correction for Alpha-Numeric Segmentation
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Solution Overview
Problem
Display devices face challenges in accurately detecting and segmenting alpha-numeric characters, leading to suboptimal image display quality due to inaccuracies in image segmentation.
Innovation Solution
An image processor is introduced, comprising a pre-processor for spatial filtering, a segmentation processor for classifying image signals, and a correction processor that detects edges and corrects segmentation signals based on class boundaries, improving the reliability of character detection and display.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Difficulty of detecting and measuring
If image segmentation is performed to detect alpha-numeric characters, then character detection capability is improved, but segmentation accuracy deteriorates due to inaccurate pixel classification
Solution Approach 1:
An edge detection module is introduced as an intermediary between the segmentation processor and the display output. This module detects edges in the original image signal and uses them to correct inaccurate segmentation boundaries, thereby improving segmentation accuracy without compromising character detection capability
Solution Approach 2:
The system implements feedback by comparing the segmentation signal with the original image signal through edge detection. When segmentation inaccuracies are detected at boundaries, the edge information from the original signal is used to correct the segmentation, creating a closed-loop improvement mechanism
2Object-affected harmful factors
If spatial filtering is applied to process image signals, then noise reduction is improved, but processing complexity increases
Solution Approach 1:
Spatial filtering is performed as a preliminary action on the image signal before segmentation processing. By pre-processing the image to reduce noise and enhance edges, the subsequent segmentation operates on cleaner data, improving overall accuracy while the filtering itself is a well-established operation that adds manageable complexity
3Measurement precision
If edge detection is performed to correct segmentation signals, then segmentation accuracy is improved, but processing time increases
Solution Approach 1:
Edge detection and correction operations are applied locally only at segmentation boundaries rather than across the entire image. This localized approach focuses computational resources on critical areas where segmentation accuracy matters most, minimizing overall processing time while maintaining high segmentation accuracy
Data Source
AI summary
The present disclosure describes an image processor of a display device. The image processing includes a pre-processor, a segmentation processor, and a correction processor. The pre-processor performs spatial filtering on an input image signal and output a line image signal. The segmentation processor classifies a class of the line image signal and outputting a segmentation signal representing the class. The correction processor corrects the segmentation signal based on the line image signal. Additionally, the correction processor detects a class boundary of the segmentation signal, detects an edge within an edge region of the line image signal corresponding to the class boundary, and corrects the segmentation signal based on the detected edge.