Image Feature Detection and Intent-Based Field of View Adjustment
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Solution Overview
Problem
In video systems, manually adjusting the field of view to display a specific feature within an image can be difficult and impractical for users, especially during procedures like medical operations, where constant adjustments are needed to maintain a clear and focused view.
Innovation Solution
An apparatus comprising a camera, processor, and memory that uses a convolutional neural network to detect and place a feature within the image, determine user intent, and modify the image accordingly, such as zooming in or out, to present the desired view automatically.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Ease of operation
If manual adjustment of field of view is used, then user control over image display is maintained, but operation complexity and time consumption increase
Solution Approach 1:
The system automatically detects features in the image and determines user intent without requiring manual input. The processor autonomously identifies what the user wants to view and adjusts the field of view accordingly, making the system self-sufficient in maintaining the desired view during procedures
Solution Approach 2:
The patent replaces manual mechanical adjustment of the camera field of view with an automated computational system. Instead of physically moving camera components, the system uses image processing, feature detection algorithms, and digital transformation to achieve the same effect of maintaining focus on the region of interest
2Ease of operation
If automated feature detection is implemented, then ease of operation improves, but device complexity increases
Solution Approach 1:
The processor performs multiple functions including feature detection, intent determination, and field of view adjustment using a unified automated system. The same computational hardware handles diverse tasks such as identifying surgical instruments, detecting anatomical structures, and adjusting camera parameters, reducing the need for separate specialized components
Data Source
AI summary
For modifying an image, a processor detects a feature in the image using a convolutional neural network trained on a feature training set. The processor further places the feature within the displayed image. The processor determines an intent for the image. In addition, the processor modifies the image based on the intent.