Automatic Image Orientation Correction via Object Context Analysis
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Solution Overview
Problem
Existing image processing techniques are limited in their ability to automatically and accurately detect and correct the orientation of images, relying on the presence of orientation-sensitive objects or lines, which can be inefficient and imprecise.
Innovation Solution
A method and system that generate a set of images of an environment corresponding to an input image, using object detection and labelling models to determine regions and labels, and context generation models to assess the overall context, allowing for the estimation and correction of image orientation based on correlation between object regions and context.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Ease of operation
If manual rotation/transform techniques are used to correct image orientation, then the user can adjust image orientation, but the process becomes annoying and imprecise
Solution Approach 1:
The system performs automatic orientation detection and correction without requiring user intervention. The processor autonomously analyzes image features, determines the correct orientation, and applies the necessary transformation, making the system self-sufficient and eliminating manual operation requirements.
Solution Approach 2:
The system changes the orientation parameter of the image based on detected features. By identifying orientation-sensitive objects and their spatial relationships, the system determines the optimal orientation angle and transforms the image accordingly, achieving precise orientation correction through parameter adjustment.
2Extent of automation
If automatic identification techniques using image features are used, then orientation detection is automated, but the effectiveness is limited by dependency on orientation-sensitive objects
Solution Approach 1:
The system employs multiple detection mechanisms that work across different image types and scenarios. By combining detection of orientation-sensitive objects with analysis of spatial relationships between multiple objects, the system achieves universal applicability and maintains reliability regardless of the specific image content or orientation.
Solution Approach 2:
The system uses spatial relationships between objects as an intermediary to infer image orientation. Rather than relying solely on direct detection of orientation-sensitive objects, the system analyzes the relative positions and orientations of multiple objects to determine the correct image orientation, providing a more robust detection mechanism.
3Productivity
If existing automatic detection techniques are used, then orientation can be determined using extracted features, but the efficiency is reduced due to limited effectiveness
Solution Approach 1:
The system performs preliminary detection of multiple objects and their spatial relationships before final orientation determination. By pre-identifying orientation-sensitive objects and analyzing their configurations, the system prepares orientation candidates in advance, enabling faster and more accurate orientation correction.
Solution Approach 2:
The system uses feedback from the detected spatial relationships to refine orientation estimation. By analyzing the consistency of spatial patterns across multiple objects and comparing detected relationships with expected patterns, the system iteratively improves orientation accuracy while maintaining processing efficiency.
Data Source
AI summary
This disclosure relates to method and system for detecting and correcting an orientation of an image. The method may include generating a number of images of an environment corresponding to the input image, determining a region and a label corresponding to each of a number of objects in each of the images, determining a context for each of the objects in each of the images with respect to an overall context of the environment based on the label for each of the objects, estimating an orientation score for each of the images based on a correlation between the region and the label of each of the objects in each of the images and the context of each of the objects in each of the images, and correcting the orientation of the input image based on the estimated orientation score for each of the images.