Image Processor Depth-Based Template Matching
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Solution Overview
Problem
In environments with numerous objects, accurately identifying and tracking a desired target in a three-dimensional space is challenging due to factors like changing light sources and high processing loads, leading to delayed response times.
Innovation Solution
An image processor that acquires depth images to determine object distances and adjusts template image sizes based on these distances, enabling efficient matching and position detection of targets within the three-dimensional space.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If template matching is performed at high spatial resolution for accurate target detection, then detection precision improves, but processing load increases and response time deteriorates
Solution Approach 1:
The patent divides the image processing into multiple stages: first performing template matching at low resolution to identify candidate regions, then refining detection at high resolution only in those specific regions. This segmentation allows accurate target detection while reducing overall processing load by avoiding full high-resolution processing.
Solution Approach 2:
The patent introduces depth information as an additional dimension to create a three-dimensional search space. By using depth maps and performing matching in 3D space rather than 2D, the system can more accurately identify targets and reduce false positives, improving detection precision without proportionally increasing processing complexity.
2Measurement precision
If template matching is performed across the entire image at high resolution, then target detection accuracy improves, but processing time increases
Solution Approach 1:
The patent performs preliminary template matching at low resolution before conducting high-resolution matching. This preliminary action identifies candidate regions where targets are likely to be located, allowing the system to skip high-resolution processing in regions where no targets exist, thereby reducing total processing time while maintaining detection accuracy.
Solution Approach 2:
The patent applies different processing qualities to different regions of the image: low resolution for most areas and high resolution only for candidate regions identified by the preliminary low-resolution matching. This local quality approach ensures accurate detection where needed while minimizing processing time in other areas.
3Measurement precision
If depth information is utilized for three-dimensional target detection, then detection accuracy in complex environments improves, but device complexity increases
Solution Approach 1:
The patent uses depth maps as an intermediary data structure that simplifies three-dimensional target detection. By representing depth information as a 2D map with depth values, the system can perform 3D matching operations without requiring complex three-dimensional processing hardware, thus improving detection accuracy while managing device complexity.
Data Source
AI summary
An image acquisition section of an information processor acquires stereo images from an imaging device. An input information acquisition section acquires an instruction input from a user. A depth image acquisition portion of a position information generation section generates a depth image representing a position distribution of subjects existing in the field of view of the imaging device in the depth direction using stereo images. A matching portion adjusts the size of a reference template image in accordance with the position of each of the subjects in the depth direction represented by the depth image first, then performs template matching on the depth image, thus identifying the position of a target having a given shape and size in the three-dimensional space. An output information generation section generates output information by performing necessary processes based on the target position.