Gaze-Guided Image Capture for Efficient Object Segmentation
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Solution Overview
Problem
Existing object segmentation technologies face challenges in accurately segmenting objects of interest due to various features like color, shape, movement, and texture, while also requiring significant computational resources.
Innovation Solution
An electronic device that selects a target object based on a user's gaze direction, divides the image into areas, calculates feature points, and determines an optimized capture area to generate an image of the target object, thereby reducing computational requirements.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If object segmentation technology is implemented using traditional methods, then object identification can be achieved, but the computational resources and processing time required are excessive
Solution Approach 1:
The image is divided into multiple regions of interest (ROIs) based on eye gaze direction and feature point distribution. By segmenting the image into relevant areas rather than processing the entire image, the computational load is reduced while maintaining segmentation accuracy in the focused regions.
Solution Approach 2:
Different processing strategies are applied to different regions of the image based on their importance. Regions with higher feature point density and those aligned with eye gaze receive more intensive processing, while less important regions are processed with reduced complexity, optimizing the balance between accuracy and computational efficiency.
2Measurement precision
If object segmentation technology is implemented using traditional methods, then object identification can be achieved, but the computational complexity and resource consumption are excessive
Solution Approach 1:
The image processing is segmented into multiple stages: eye gaze detection, feature point detection, ROI determination, and object segmentation. This staged approach reduces computational complexity by only applying full segmentation algorithms to relevant regions identified in earlier stages.
Solution Approach 2:
Eye gaze direction and feature points are detected beforehand to pre-identify regions of interest before performing the computationally intensive object segmentation. This preliminary action reduces the search space and computational requirements for the main segmentation task.
3Reliability
If the entire image is processed for object segmentation, then comprehensive object identification is achieved, but the processing efficiency is reduced
Solution Approach 1:
The image is divided into multiple ROIs based on eye gaze and feature point distribution. Object segmentation is performed independently on each ROI, which increases processing efficiency by parallelizing the computation while maintaining reliability through focused analysis of relevant regions.
Solution Approach 2:
Instead of processing the entire image uniformly, the system applies partial processing to specific ROIs that are most likely to contain the target object. This partial action approach maintains high reliability for the target object while improving overall processing efficiency.
Data Source
AI summary
An example electronic device is configured to divide an image into a plurality of areas, calculate a number of feature points included in each of the plurality of divided areas by detecting the feature points from the image, select at least one candidate area among the plurality of divided areas based on a result of the calculating, and generate an image of the target object based on a partial image captured from the capture area determined in at least one selected candidate area.