Digital Image Alignment Using Depth-Region Disparity Analysis
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Solution Overview
Problem
Panoramic imaging faces challenges with parallax-induced misalignments in three-dimensional scenes, leading to suboptimal quality in combined images, particularly affecting objects closer to the camera and complicating image processing tasks like object detection and analysis.
Innovation Solution
A method for aligning digital images involves receiving overlapping images, determining disparity values to identify misalignments, adjusting the transformation to compensate for these misalignments, and realigning the images to improve alignment accuracy, especially by locally adjusting the projection distance for identified blocks of pixels.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Manufacturing precision
If multiple images are combined into a panoramic image by minimizing parallax at a certain distance, then misalignments are reduced for the area of interest at that distance, but misalignments occur for features at other distances particularly affecting closer objects
Solution Approach 1:
The patent applies local quality by dividing the image into multiple depth regions (near, middle, far) and applying different transformation parameters to each region. Specifically, the system determines separate transformation parameters for each depth region based on disparity values, allowing each region to be aligned optimally for its specific distance range rather than using a single global transformation that would compromise alignment accuracy across all distances
Solution Approach 2:
The patent segments the panoramic image processing into distinct depth regions (near region, middle region, far region) with different alignment characteristics. By segmenting the scene based on disparity values and applying region-specific transformations, the system resolves the contradiction between achieving high alignment accuracy at specific distances while maintaining adaptability across the full depth range of the scene
2Adaptability or versatility
If a single camera is used to capture panoramic images, then the aim of single-camera panoramic imaging is achieved, but misalignments appear particularly for closer objects reducing image quality
Solution Approach 1:
The patent changes transformation parameters dynamically based on depth region and disparity values. Instead of using fixed transformation parameters for the entire image, the system adjusts transformation parameters (such as translation and rotation) separately for near, middle, and far regions. This parameter adaptation allows a single camera to achieve alignment accuracy comparable to multi-camera systems by compensating for parallax effects specific to each depth zone
3Extent of automation
If panoramic images are used for object detection and image-content analysis, then automatic image processing is enabled, but misalignments between images complicate these processing algorithms
Solution Approach 1:
The patent performs preliminary alignment by determining depth regions and calculating region-specific transformation parameters before executing object detection and image-content analysis algorithms. By pre-aligning the panoramic image with proper transformations applied to each depth region, the system eliminates misalignment issues that would otherwise complicate or prevent accurate automatic image processing and object detection
Data Source
AI summary
A digital camera and a method for aligning digital images comprising: receiving images including first and second images depicting a first and a second region of a scene, the regions being overlapping and displaced along a first direction; aligning the images using a transformation; determining disparity values for an overlap between the images; identifying misalignments by identifying blocks of pixels in the first image having a same position along a second direction and having disparity values exhibiting a variability lower than a first threshold and exhibiting an average higher than a second threshold; adjusting the transformation for the identified blocks of pixels in the first image and their matching blocks of pixels in the second image; and realigning the images using the adjusted transformation.