Image Processing with Adaptive Superpixel Sizing for Boundary Accuracy
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Solution Overview
Problem
Existing image segmentation techniques using superpixels face challenges in achieving accurate boundary depiction while minimizing the workload of annotation, particularly when objects of varying sizes or complexities are present in an image.
Innovation Solution
An image processing apparatus and method that determines a segment size for superpixels based on the size of the object region and the division number, generating superpixels within a predetermined range to balance workload and segmentation accuracy.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If superpixels in an ROI are replaced with superpixels of a smaller scale based on user instruction, then boundary accuracy may be improved, but the workload of selecting superpixels increases
Solution Approach 1:
The system automatically determines the appropriate superpixel scale by analyzing object sizes and characteristics in the image, eliminating the need for manual user input. The determination unit autonomously selects optimal segmentation parameters based on image content, thereby reducing operational workload while maintaining boundary accuracy.
Solution Approach 2:
The system dynamically adjusts the superpixel scale parameter based on the actual object sizes detected in the image. By changing the segmentation parameter adaptively rather than using fixed user-defined values, the system achieves accurate boundaries for objects of varying sizes without requiring manual intervention for each case.
2Device complexity
If a fixed superpixel scale is used, then the segmentation process is simple, but accurate segmentation boundaries cannot be obtained for objects of different sizes
Solution Approach 1:
The system transitions from static fixed-scale superpixel segmentation to dynamic adaptive-scale segmentation. The determination unit adjusts superpixel scales dynamically based on detected object characteristics, enabling accurate segmentation of objects with varying sizes while maintaining reasonable process complexity through automated control.
Solution Approach 2:
Different regions of the image receive different superpixel scales according to the local object characteristics. Objects of different sizes are segmented using appropriately scaled superpixels, allowing each region to be processed with the optimal scale for its specific content rather than applying a uniform scale throughout.
3Ease of operation
If manual selection of superpixel scale is performed, then workload is reduced, but good segmentation boundaries are not always achieved
Solution Approach 1:
The system incorporates feedback mechanisms where the determination unit analyzes image content, object sizes, and segmentation results to automatically adjust and optimize superpixel scales. This closed-loop approach ensures high-quality segmentation boundaries by continuously adapting parameters based on actual image characteristics rather than relying on manual guesses.
Data Source
AI summary
An image processing apparatus comprises one or more processors and/or circuitry which function as: an input unit that inputs image data of an image; an acquisition unit that acquires a size of an object region including an object to be extracted that is included in the image; a setting unit that sets a division number into which the object region is divided; a determination unit that determines a segment size of a superpixel based on the size of the object region and the division number; and a generation unit that generates, using the image data, superpixels each having a size in a predetermined range that includes the segment size determined by the determination unit.