Stitched Image Reliability Scoring for Object Recognition
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Solution Overview
Problem
Existing image capture devices face challenges in determining the quality of stitched images for object recognition operations, leading to inefficient use of processor and memory resources and potential inaccuracies in identifying objects.
Innovation Solution
A device and method that calculate reliability scores for images based on differences in color parameters, using a predictive model to determine the likelihood of successful object recognition, and selectively perform the recognition operation only when the scores meet a threshold, conserving resources and improving accuracy.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If object recognition operation is performed on all stitched images, then object detection coverage is improved, but processor and memory resource consumption increases
Solution Approach 1:
The patent applies preliminary action by calculating reliability scores for images before performing object recognition. The system pre-assesses image quality using color parameter differences and predictive models, then selectively proceeds with object recognition only for images meeting quality thresholds. This prevents wasteful processing of low-quality images while ensuring comprehensive detection of viable candidates.
2Reliability
If object recognition operation is performed on all stitched images, then object detection completeness is improved, but processing time increases
Solution Approach 1:
The system performs preliminary quality assessment using reliability scores based on color parameter analysis before committing to time-consuming object recognition. By pre-filtering images that meet quality thresholds, the system eliminates unnecessary processing time for low-quality images while maintaining complete detection coverage for all viable images.
Solution Approach 2:
The patent changes parameters by using color parameter differences (hue, saturation, value) as the basis for reliability scoring. This parameter-based quality assessment enables rapid pre-filtering of images, allowing the system to quickly determine which images warrant full object recognition processing based on their color consistency characteristics.
3Measurement precision
If reliability scores are calculated for all images, then object recognition accuracy is improved, but computational overhead increases
Solution Approach 1:
The patent applies local quality assessment by calculating reliability scores based on local color parameter differences within overlapping regions of images. Rather than analyzing entire images globally, the system focuses computational effort on comparing corresponding local regions, reducing overall computational overhead while maintaining accurate quality assessment for object recognition purposes.
4Productivity
If selective object recognition is performed based on reliability scores, then resource efficiency is improved, but risk of missing objects in borderline images increases
Solution Approach 1:
The system implements feedback by using predictive models that learn from the relationship between reliability scores and actual object recognition outcomes. The feedback mechanism allows the system to refine its understanding of what reliability score thresholds correspond to successful object detection, dynamically adjusting selection criteria to minimize missed objects while maintaining resource efficiency.
Data Source
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AI summary
A device may receive a set of images for an object recognition operation to identify one or more objects in the set of images after the set of images are combined into a stitched image. The device may combine the set of images into the stitched image. The device may determine reliability scores for the set of images. The reliability scores may be determined based on a difference in images of the set of images, and the reliability scores may predict a quality of the stitched image that includes an image, of the set of images, to which the reliability scores correspond. The device may determine whether a result of the object recognition operation is likely to satisfy a threshold based on the reliability scores. The device may selectively perform the object recognition operation based on whether the result of the object recognition operation is likely to satisfy the threshold.