Image Region Localization Using Grad-CAM and Candidate Interpolation
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Solution Overview
Problem
Existing image processing technologies, such as those using convolutional neural networks (CNNs), suffer from insufficient accuracy in object location estimation during image recognition.
Innovation Solution
An image processing method and apparatus that utilizes a learned convolutional neural network (CNN) for object classification, generates heatmap images using Grad-CAM for region of interest visualization, performs region segmentation, and interpolates candidate regions to enhance object location estimation accuracy.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If a convolutional neural network (CNN) is used for object classification, then object recognition capability is improved, but object location estimation accuracy is insufficient
Solution Approach 1:
The patent divides the image processing into multiple stages: CNN-based object classification, heatmap generation for region identification, region segmentation, and candidate region interpolation. This segmentation allows each stage to specialize in one aspect (classification, localization, refinement) thereby improving overall location estimation accuracy while maintaining recognition capability
Solution Approach 2:
The patent introduces heatmap images as an intermediary between CNN classification and final object location determination. The heatmap visually represents the relationship between CNN output and spatial location, serving as a mediator that translates classification results into precise location estimates. This intermediary enables accurate location estimation without compromising the CNN's recognition strength
2Measurement precision
If region segmentation is performed to identify candidate regions, then object location precision is improved, but processing complexity increases
Solution Approach 1:
The patent performs preliminary region segmentation to identify candidate regions before final object location determination. By pre-identifying potential object regions through segmentation and heatmap analysis, the system reduces the search space for subsequent interpolation and refinement steps, making the overall complex process more manageable and efficient
Solution Approach 2:
The patent transforms the 2D image space into a 3D representation by incorporating heatmap intensity values as a third dimension. This dimensional transformation allows the system to analyze candidate regions not just by spatial coordinates but also by confidence intensity, enabling more precise location estimation while systematically managing processing complexity through multi-dimensional analysis
3Measurement precision
If candidate regions are interpolated to enhance location accuracy, then object location estimation accuracy is improved, but risk of partial object exclusion in cropped images increases
Solution Approach 1:
The patent performs interpolation on candidate regions to achieve sub-pixel location accuracy. By applying partial interpolation (only where needed based on heatmap intensity and region confidence) rather than exhaustive processing across the entire image, the system improves location precision while maintaining object completeness through selective, targeted refinement of only the most promising candidate regions
Data Source
Figure 1A
Figure 1B~1C
Figure 2
AI summary
An object region in image data is estimated based on region of interest data for indicating a targeted region in recognition processing for an object included in the image data, and information on an object candidate region, which is extracted from the image data.