Depth Map Based Image Feature Detection Territory
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Solution Overview
Problem
Current image searching technologies face challenges in efficiently detecting and classifying features within images, particularly in defining and confining the search territory based on depth values, which affects the accuracy and efficiency of feature detection.
Innovation Solution
The method involves using a depth map to identify image pixels with specific depth values and setting a territory for feature detection, limiting the search to this defined area, allowing for focused feature extraction and classification within the processing system.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If feature detection is performed on the entire image, then comprehensive feature detection is achieved, but processing time and computational resources increase
Solution Approach 1:
The image is segmented into multiple territories based on depth map analysis. Each territory represents a specific depth range containing potential features of interest. The feature detection process is then applied separately to each territory rather than the entire image, reducing overall processing time while maintaining detection accuracy within each segmented region.
Solution Approach 2:
Different processing strategies are applied to different territories based on their depth characteristics. Territories containing relevant depth ranges receive focused feature detection, while other areas are processed differently or excluded. This localizes the high-quality feature detection to only where it is most beneficial.
2Reliability
If feature detection is performed on the entire image, then all potential features are detected, but computational resources and processing complexity increase
Solution Approach 1:
The image processing is divided into multiple independent territory processing tasks. Each territory is processed separately with its own feature detection parameters, allowing for optimized and simplified processing in each region rather than one complex global processing approach.
Solution Approach 2:
The depth map analysis and territory definition are performed as preliminary steps before feature detection. This pre-processing organizes the image into meaningful regions based on depth information, so that subsequent feature detection operates on pre-defined, manageable territories rather than the entire image at once.
3Adaptability or versatility
If the search territory is expanded to cover the entire image, then more features may be found, but processing efficiency decreases
Solution Approach 1:
The search space is segmented into multiple territories based on depth map analysis. Each territory covers a specific depth range and spatial region. This segmentation allows the system to adaptively search only relevant portions of the image for specific feature types, maintaining versatility while improving processing efficiency.
Solution Approach 2:
The territory boundaries and search regions are dynamically determined based on depth map characteristics and the specific feature detection task. Rather than using fixed or static search regions, the system adapts the search territories to match the actual depth distribution and feature locations in the image.
Data Source
AI summary
Among other things, the present disclosure relates to image searching. With a depth map, image pixels of an image having depth values intersecting a desired optical field can be identified. A territory can be set based on locations of the identified image pixels. Feature detection can be performed on the image within the set territory. The feature detection can be limited to the set territory.