Image Sensor Segmentation for Dead Zone Elimination
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Solution Overview
Problem
Current image sensors face challenges in capturing comprehensive images of scenes due to dead zones and limitations in spatial coverage, leading to incomplete or low-resolution images when multiple sensing areas are used.
Innovation Solution
The method involves positioning an image sensor with multiple physically separate sensing areas at strategic locations to capture partial images, which are then stitched together to form a combined image, with enhanced partial images used to replace lower-resolution images and equalize resolutions across regions.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Area of stationary object
If multiple sensing areas are used to capture images, then spatial coverage is improved, but dead zones and gaps between sensing areas cause incomplete coverage
Solution Approach 1:
The imaging system is divided into multiple physically separate sensing areas, each capable of capturing partial images of the scene. These segmented sensing areas are positioned at different locations to collectively cover the entire scene, with each area contributing a specific portion of the final composite image.
Solution Approach 2:
A processing system acts as an intermediary to stitch together the partial images captured by separate sensing areas. This intermediary process combines the discrete image segments into a complete composite image, filling the gaps that would otherwise exist between physically separate sensing areas.
2Area of stationary object
If multiple sensing areas are used to capture images, then spatial coverage is improved, but image resolution deteriorates due to splitting the scene across multiple areas
Solution Approach 1:
The system transitions from a single two-dimensional sensing plane to a multi-dimensional approach by positioning multiple sensing areas at different spatial locations. This dimensional expansion allows each sensing area to capture high-resolution details of its local field while the collective arrangement maintains overall scene coverage.
Solution Approach 2:
The partial images captured by multiple sensing areas are merged through a stitching process that combines them into a single composite image. This merging operation preserves the high resolution of individual sensing areas while achieving comprehensive spatial coverage that neither area could accomplish alone.
3Area of stationary object
If the image sensor is moved to multiple locations to capture partial images, then complete scene coverage is achieved, but the complexity of positioning and stitching increases
Solution Approach 1:
The system employs self-service mechanisms where the processing unit automatically performs positioning calculations and image stitching based on metadata embedded in the captured images. This self-service approach reduces the need for complex external positioning equipment and manual alignment procedures.
Solution Approach 2:
The system uses feedback from image overlap regions and metadata to automatically adjust positioning and alignment parameters during the stitching process. This feedback mechanism enables the system to compensate for positioning errors and achieve accurate image registration without requiring extremely precise manual positioning.
Data Source
AI summary
Disclosed herein is a method of using an image sensor comprising N sensing areas for capturing images of a scene, the N sensing areas being physically separate from each other, the method comprising: for i=1, . . . , P, and j=1, . . . , Q(i), positioning the image sensor at a location (i,j) and capturing a partial image (i,j) of the scene using the image sensor while the image sensor is at the location (i,j), thereby capturing in total R partial images, wherein R is the sum of Q(i), i=1, . . . , P, wherein P>1, wherein Q(i), i=1, . . . , P are positive integers and are not all 1, wherein for i=1, . . . , P, a location group (i) comprises the locations (i,j), j=1, . . . , Q(i), and wherein a minimum distance between 2 locations of 2 different location groups is substantially larger than a maximum distance between two locations of a same location group; and determining a combined image of the scene based on the R partial images.