Image View Adjustment Using Object Position Feedback
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Solution Overview
Problem
Existing image recognition systems are often open-looped and may fail to capture all relevant information about an object due to suboptimal viewing parameters, leading to incomplete data acquisition.
Innovation Solution
A method that adjusts viewing parameters such as heading, field of view, and pitch to obtain a second image that includes specific types of information about an object by analyzing the relative position of the object within the first image using machine learning algorithms and positional relationships.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Loss of information
If viewing parameters are kept fixed for simple image capture, then device complexity is reduced, but completeness of object information is insufficient
Solution Approach 1:
The system analyzes the relative position of the object within the first image and uses this feedback to dynamically adjust viewing parameters. The machine learning system provides object location information, which feeds back to the viewing parameter adjustment mechanism, creating a closed-loop system that optimizes information capture while managing complexity through intelligent control.
Solution Approach 2:
The system changes viewing parameters (heading, field of view, pitch) based on the calculated relative position of the object. By dynamically adjusting these parameters rather than keeping them fixed, the system captures comprehensive object information while using algorithmic control to manage the complexity of parameter adjustment.
2Loss of information
If viewing parameters are adjusted to capture complete object information, then information completeness is improved, but processing time increases
Solution Approach 1:
The system calculates the relative position of the object and determines the optimal viewing parameters in advance before capturing the second image. This preliminary analysis of object position and computation of required parameter adjustments allows the system to efficiently capture complete information in a targeted manner, reducing the need for multiple trial acquisitions.
Solution Approach 2:
The system replaces brute-force mechanical approaches (capturing multiple images with fixed parameter sets) with an intelligent computational approach using machine learning to predict optimal viewing parameters. This substitution of mechanical trial-and-error with algorithmic optimization reduces processing time while ensuring information completeness.
3Measurement precision
If machine learning analysis is performed on every image to determine view adjustments, then object detection accuracy is improved, but computational complexity increases
Solution Approach 1:
The system extracts only the essential information needed for view adjustment - the relative position of the object within the first image - rather than performing comprehensive analysis of all image features. This extraction of critical positional data maintains detection accuracy while significantly reducing computational complexity compared to full-image analysis.
Solution Approach 2:
The system focuses computational resources on analyzing the local region where the object is detected, rather than uniformly processing the entire image. By concentrating analysis on the relevant local area to determine relative position, the system achieves accurate object detection with reduced overall computational complexity.
Data Source
AI summary
A method may include obtaining a first image of a first view. The first illustrated by the first image may be defined by one or more viewing parameters. The method may also include acquiring, from a machine learning system, a location of an object within the first image, calculating a relative position of the object within the first image using the location of the object within the first image, and determining whether the relative position of the object satisfies a condition. In response to the relative position of the object not satisfying the condition, at least one of the viewing parameters may be adjusted based on the relative position of the object and the condition. The method may further include obtaining a second image of a second view. The second view illustrated by the second image may be defined by the adjusted at least one of the viewing parameters.