Image Alignment Block Density Control for Panoramic Stitching
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Solution Overview
Problem
Existing methods for generating panoramic images face challenges in accurately detecting misalignment between images due to high processing time and reduced accuracy when the overlap area between image slits is small, leading to unnatural image combinations.
Innovation Solution
An image processing apparatus that sets a misalignment detection area in a second captured image to be combined with a first captured image, increases the density of alignment blocks within this area, and reduces the detection area using information from the reduced area to enhance detection accuracy without increasing processing load.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Loss of time
If the detection area is reduced to improve processing speed, then processing time is reduced, but detection accuracy is degraded
Solution Approach 1:
The patent applies local quality by differentiating the treatment of different regions within the detection area. Specifically, the overlapping region between first and second captured images is identified and processed with higher block density compared to non-overlapping regions. This localized enhancement ensures that detection accuracy is improved where it matters most (in the overlapping area that contributes to panoramic stitching) while maintaining faster processing in non-critical areas, thus resolving the contradiction between processing speed and detection accuracy.
2Measurement precision
If the block density is increased to improve detection accuracy, then detection accuracy is improved, but processing load is increased
Solution Approach 1:
The patent implements local quality by selectively increasing block density only in the overlapping region between captured images rather than uniformly across the entire image. This localized approach concentrates computational resources where they are most needed for accurate panoramic stitching, while reducing the overall processing load by avoiding unnecessary high-density block processing in non-overlapping areas.
Solution Approach 2:
The patent applies segmentation by dividing the detection area into distinct regions: overlapping regions and non-overlapping regions. Each region is then processed with appropriate block density levels. This segmentation strategy allows the system to achieve high detection accuracy in critical overlapping areas while maintaining lower processing loads in non-critical areas, effectively resolving the contradiction between detection accuracy and processing load.
Data Source
AI summary
A system control unit sets at least one of a non-overlapped area in which an nth image and an (n+1)st image do not overlap and a low contrast area of the (n+1)st image as an in valid area that is not used for misalignment detection of the (n+1)st image. Then, the system control unit sets alignment small blocks for a motion vector search in only an area excluding the invalid area in the overlapped area in which two combining target images overlap.