Image Orientation Detection Using Object Recognition
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Solution Overview
Problem
Smart devices often incorrectly determine the save orientation of digital photographs, leading to frustration when the images are not presented correctly later, and existing solutions do not adequately address this issue.
Innovation Solution
A device with a processor, camera, and display that uses optical character recognition, facial recognition, object recognition, and action recognition to determine the orientation of objects in an image and save it in a suitable orientation, with user input options if the confidence level is below a predefined threshold, allowing for learning of user preferences over time.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Ease of operation
If the device uses automatic orientation detection to save images, then the ease of operation is improved, but the reliability deteriorates due to incorrect orientation determination
Solution Approach 1:
The device automatically detects object orientation and determines save orientation without user intervention. The processor uses optical character recognition, facial recognition, object recognition, or action recognition to autonomously identify the correct orientation and save the image accordingly, eliminating the need for manual rotation or orientation input from the user.
Solution Approach 2:
The system provides feedback to the user by presenting proposed save orientations based on detected object orientation. When confidence is below the threshold, the device prompts the user to confirm or correct the detected orientation, creating a feedback loop that improves reliability while maintaining ease of operation.
2Reliability
If the device prompts user input for orientation selection, then the reliability is improved, but the ease of operation deteriorates due to additional user input required
Solution Approach 1:
The device presents proposed save orientations to the user for confirmation when confidence is below the threshold. This feedback mechanism allows the system to maintain high reliability by verifying orientation decisions while minimizing user input requirements through automated detection and proposal of correct orientations.
Solution Approach 2:
The system changes the confidence threshold parameter to control when user input is required. By adjusting this parameter, the device can optimize between automation (higher threshold) and user involvement (lower threshold), balancing reliability and ease of operation based on specific use cases.
3Reliability
If multiple recognition methods are used to determine orientation, then the reliability is improved, but the device complexity increases
Solution Approach 1:
The processor is designed to execute multiple recognition methods (optical character recognition, facial recognition, object recognition, action recognition) that can all serve the same function of determining object orientation. This multi-functionality allows the system to improve reliability through method diversity while managing complexity through unified processing architecture.
Solution Approach 2:
The system applies partial action by using multiple recognition methods only when necessary. The device can start with simpler detection methods and escalate to more complex methods only when needed, or use a subset of methods based on the image content, thereby improving reliability without consistently increasing device complexity.
Data Source
AI summary
In one aspect, a device includes at least one processor, a camera accessible to the at least one processor, a display accessible to the at least one processor, and storage accessible to the at least one processor. The storage includes instructions executable by the at least one processor to generate an image using the camera, to determine a first orientation of at least one object shown in the image, and to save the image in a second orientation determined based on the first orientation.