Panoramic Image Stitching with Inertial Sensor Alignment
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Solution Overview
Problem
Conventional methods for generating panoramic images face challenges such as slow computation time, inaccurate frame alignment, distorted images due to inappropriate surface selection, and resource-intensive processing, particularly in sparsely distributed environments, leading to inefficient and low-quality panoramic image generation.
Innovation Solution
The method employs inertial sensor data to align frames, identify partial regions, and use advanced RANSAC techniques to calculate similarity matrices, adaptively select panoramic surfaces, and modulate frame capture rates, reducing computational resources and time while maintaining image quality.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If each of a plurality of image frames is processed wholly to generate the panoramic image, then the panoramic image can be generated with comprehensive coverage, but the computation time increases significantly
Solution Approach 1:
The patent divides each image frame into multiple partial regions (e.g., first partial region, second partial region, third partial region) and processes only the relevant partial regions for stitching, rather than processing the entire frame. This segmentation approach reduces computational load while maintaining the quality and completeness of the final panoramic image.
2Productivity
If conventional techniques are used for panoramic image generation, then the process can be completed, but the frame alignment is inaccurate resulting in distorted images
Solution Approach 1:
The patent performs preliminary actions by detecting features and identifying partial regions before the actual stitching process. By pre-processing and preparing the frames with feature detection and partial region identification, the system achieves accurate frame alignment and avoids distortions during the stitching operation.
3Measurement precision
If unsupervised deep image stitching technique is used, then image resolution can be enhanced, but a lot of frames are required and computational resources are consumed
Solution Approach 1:
The patent applies local quality by focusing feature detection and stitching operations on specific partial regions of interest rather than processing entire frames uniformly. This localized approach enhances image resolution in critical areas while reducing the total number of frames needed and computational resources consumed.
4Manufacturing precision
If direct stitching technique is used, then pixel intensity comparison can be performed, but very slow movement of imaging device is required to achieve high overlapping region
Solution Approach 1:
The patent segments the imaging process into partial region identification and feature detection steps, allowing the system to maintain stitching accuracy through intelligent feature matching rather than relying solely on slow device movement and high overlapping regions.
Data Source
AI summary
A method for panoramic image generation includes obtaining a plurality of frames corresponding to an environment using an imaging device; receiving inertial sensor data of the imaging device associated with the plurality of frames; obtaining first features associated with a first frame of the plurality of frames and second features associated with a second frame of the plurality of frames; obtaining a first partial region of the first frame and a second partial region of the second frame, based on a first comparison of the first features and the second features; generating a similarity between a respective frame of the plurality of frames and at least one adjacent frame to the respective frame based on obtained features associated with each frame; and generating a panoramic image by merging the plurality of frames based on the similarity between each frame of the plurality of frames.