Stereo Image Positioning via Segmented Depth Computation
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Solution Overview
Problem
Existing stereo image techniques require high-resolution images and sophisticated computation for accurate positional information acquisition, leading to increased processing load and a trade-off between accuracy and speed, especially when updating positional information at frame rates for motion-based processing.
Innovation Solution
The system divides positional information generation into two blocks: a first block for approximate position detection and a second block for detailed position acquisition, restricting search ranges and using high-resolution images and sophisticated algorithms only where necessary, allowing for efficient and accurate positional information processing.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If high-resolution stereo images and sophisticated computation techniques are used to accurately acquire positional information, then measurement precision is improved, but processing load and computation time increase significantly
Solution Approach 1:
The positional information acquisition process is divided into two distinct blocks: a first block that performs approximate position detection using simplified methods, and a second block that performs detailed position acquisition using high-resolution images and sophisticated algorithms. This segmentation allows the system to achieve high measurement precision for critical regions while maintaining overall processing speed by avoiding expensive computations across the entire image space.
Solution Approach 2:
The system applies different processing qualities to different regions of the image based on their importance. The second positional information acquisition block focuses computational resources on specific regions of interest where high precision is required, while using simpler methods in the first block for general approximate positioning. This local quality approach optimizes the balance between measurement precision and processing efficiency.
2Measurement precision
If sophisticated computation techniques are applied to detect corresponding points in high-resolution stereo images, then measurement precision is improved, but processing time increases
Solution Approach 1:
The corresponding point detection process is segmented into two stages: first, approximate corresponding points are identified using simplified computation on lower-resolution images; second, only these approximate points serve as seeds for refined detection in high-resolution images. This segmentation dramatically reduces the time required for sophisticated computation by limiting its application to specific regions rather than the entire image.
Solution Approach 2:
The first positional information acquisition block performs preliminary corresponding point detection using simplified methods before the second block applies sophisticated algorithms. This preliminary action establishes initial estimates that guide the more computationally intensive processing, reducing the search space and time required for high-precision corresponding point detection.
3Measurement precision
If detailed stereo matching is performed across the entire image, then measurement precision is improved, but device complexity and processing load increase
Solution Approach 1:
The stereo matching process is divided into two functional blocks with different complexity levels. The first block uses simplified stereo matching algorithms suitable for rapid processing, while the second block applies detailed stereo matching only where necessary. This segmentation reduces overall device complexity by avoiding the deployment of maximum-complexity algorithms throughout the entire processing pipeline.
Solution Approach 2:
Instead of applying detailed stereo matching to the entire image (excessive action), the system applies it only to specific regions or points where high precision is required (partial action). This partial application of the complex algorithm reduces processing load and system complexity while maintaining measurement precision for critical tasks.
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
This approach enables high-speed and high-accuracy information processing of object positions and motions, reducing processing load while maintaining accuracy, by using simplified methods for initial approximation and detailed processing only where needed.
Implementation Method 1
an imaging device for imaging an object
Implementation Method 2
corresponding points are detected from stereo images of a same space simultaneously taken with two cameras horizontally separated from each other by a known interval and, on the basis of a resultant parallax between the detected points, a distance from an imaged surface of an object is computed by use of the principle of triangulation
Implementation Method 3
a distance from an imaged surface of an object is computed by use of the principle of triangulation
Data Source
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AI summary
An image acquisition block 42 of an information processing apparatus 10 acquires stereo images taken by a first camera 13a and a second camera 13b that make up an imaging apparatus 12. An input information acquisition block 44 accepts a user manipulation. A first positional information acquisition block 52 of a positional information generation block 46 identifies an approximate position of an object to be imaged through predetermined means. A second positional information acquisition block 56 determines an estimated distance range of a target on the basis of the identified approximate position of the object to be imaged, detects corresponding points by executing block matching on stereo images thorough only a search range corresponding to the determined estimated distance range, and obtains a position of the target with high resolution and accuracy. An output information generation block 50 generates output data on the basis of the position of that target and outputs the generated output data.