Panoramic Camera Image Stitching for Distorted Fisheye Views
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Solution Overview
Problem
Existing imaging systems fail to effectively stitch multiple images into panoramic scenes under conditions of noise, non-standard lenses, dynamic camera positions, and limited computational resources, which are common in emergency response scenarios.
Innovation Solution
A throwable panoramic camera system with wide-angle lenses, near-infrared LEDs, and a processor that stitches images in real-time, using a sensor unit and receiver unit to create omnidirectional scenes, even in low-light and dynamic conditions, and supports various sensors for environmental data.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Area of moving object
If fisheye lenses with significant distortion are used to capture panoramic images, then the field of view is increased, but the image distortion increases
Solution Approach 1:
The panoramic image capture is divided into multiple separate fisheye lens images taken from different positions. Instead of attempting to correct distortion in a single image, the system segments the scene into multiple overlapping views that are later stitched together to form the complete panorama, allowing each individual fisheye image to be processed with appropriate distortion correction algorithms.
Solution Approach 2:
A computational stitching and blending process acts as an intermediary between the distorted fisheye images and the final panoramic output. This intermediary processing stage includes distortion correction, feature matching, perspective transformation, and seamless blending operations that convert the distorted individual images into a coherent undistorted panoramic view.
2Productivity
If multiple images are captured simultaneously to create panoramic views, then the processing speed is improved, but the computational complexity increases
Solution Approach 1:
The system performs preliminary actions by capturing all necessary images simultaneously in parallel using multiple fisheye lenses before processing begins. This parallel capture approach eliminates sequential shooting time and allows the computational workload to be focused on processing already-acquired data, improving overall productivity despite the complexity of processing multiple images at once.
Solution Approach 2:
The computational processing pipeline is designed to be dynamic and adaptive, automatically adjusting the stitching and blending parameters based on the specific characteristics of the captured images. The system dynamically selects feature matching algorithms, determines optimal blending regions, and adjusts transformation parameters to balance computational complexity with processing speed for different scene conditions.
3Loss of time
If images are processed and stitched in real-time, then the response time is reduced, but the computational load increases
Solution Approach 1:
The system implements periodic action by processing images in discrete frames or batches rather than continuously processing every pixel in real-time. This allows for periodic updates of the panoramic view at optimized intervals, reducing the instantaneous computational load while maintaining acceptable response times for most applications. The processing can be synchronized with camera frame rates or user interaction patterns.
Solution Approach 2:
The system applies partial processing actions by focusing computational resources on key regions of interest or by processing only the essential stitching and blending operations initially, with optional post-processing enhancements. This partial action approach reduces the immediate computational load while still delivering functional real-time panoramic views, allowing excessive or full processing to be applied selectively based on performance requirements.
4Ease of operation
If wireless transmission is used to send images to mobile devices, then the system portability is improved, but the transmission reliability in harsh environments deteriorates
Solution Approach 1:
The wireless transmission system implements feedback mechanisms including automatic retry logic, data packet acknowledgment, and error detection/correction codes. When transmission reliability is compromised in harsh environments, the system receives feedback about transmission status and automatically retransmits corrupted or lost data packets, ensuring reliable delivery of panoramic images to mobile devices despite environmental challenges.
Solution Approach 2:
The system applies beforehand cushioning by implementing forward error correction and redundant data transmission protocols before transmission begins. Extra error correction bits and alternative data paths are prepared in advance to cushion against potential transmission failures in harsh environments, allowing the portable wireless system to maintain reliability without requiring complex real-time error handling.
Applied Scientific Principles
This section explains which scientific principles are used to turn an abstract innovation direction into a practical engineering solution.
Function Achieved in This Case
Enables rapid creation of stable, omnidirectional scenes with integrated environmental data, suitable for emergency response, despite challenging conditions, by using fisheye lenses, near-infrared LEDs, and efficient image processing.
Implementation Method 1
near-infrared LEDs
Implementation Method 2
near-infrared LEDs
Implementation Method 3
fisheye lenses
Implementation Method 4
wide-angle lenses
Data Source
AI summary
At least one combined image may be created from a plurality of images captured by a plurality of cameras. A sensor unit may receive the plurality of images from the plurality of cameras. At least one processor in communication with the sensor unit may correlate each received image with calibration data for the camera from which the image was received. The calibration data may comprise camera position data and characteristic data. The processor may combine at least two of the received images from at least two of the cameras into the at least one combined image by orienting the at least two images relative to one another based on the calibration data for the at least two cameras from which the images were received and merging the at least two aligned images into the at least one combined image.