Single-Image Depth Mapping via Blur Segmentation
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Solution Overview
Problem
Existing distance evaluation methods, both active and passive, face challenges such as high cost, complexity, and inefficiency in real-time applications, particularly when measuring distances from a single image in scenes with multiple objects in motion.
Innovation Solution
A distance evaluation method and apparatus that captures a single image using a conventional camera, segments the image, computes a blur metric for each segment, and associates it with object distance, allowing for absolute or relative depth mapping without the need for multiple focus settings or complex computations.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If multiple images are captured using different camera parameters or focus settings to evaluate distances, then measurement precision is improved, but loss of time increases and productivity decreases
Solution Approach 1:
The captured image is segmented into multiple regions corresponding to different depth zones. By dividing the scene into foreground, midground, and background segments, the system can evaluate distances for each segment independently using blur metrics, achieving comprehensive depth information from a single image without requiring multiple captures.
Solution Approach 2:
The system changes the focus distance parameter of the camera to correspond to different limits of the chosen detection range. By capturing one image with a specific focus setting and analyzing blur metrics across segments, the system derives distance information that would otherwise require multiple images captured at different focus settings.
2Measurement precision
If active distance measuring methods such as LIDAR are used, then measurement precision is improved, but device complexity and cost increase
Solution Approach 1:
The patent replaces active mechanical/optical systems (LIDAR, ultrasonic range-finders) with a passive optical system using a conventional camera. Instead of emitting electromagnetic or acoustic waves and measuring their return, the system uses passive light reflection and analyzes image blur characteristics to determine distances, significantly reducing device complexity and cost.
Solution Approach 2:
The system uses the camera's own optical properties (depth of field, blur characteristics) to measure distances. By analyzing the natural blur in the captured image without external active sensors, the camera performs self-diagnosis of scene depth information, eliminating the need for separate active ranging devices.
3Measurement precision
If several images are captured to create a depth map, then measurement precision is improved, but productivity decreases and real-time application becomes difficult
Solution Approach 1:
The system performs preliminary action by capturing a single image with the focus distance set to correspond to a limit of the detection range. This preliminary capture contains all necessary information (blur variations across the image) to subsequently derive complete depth maps, eliminating the need for multiple preliminary captures and enabling real-time processing.
4Measurement precision
If passive distance measuring methods using multiple cameras are used, then measurement precision is improved, but device complexity and cost increase
Solution Approach 1:
The patent makes a single conventional camera perform multiple functions: capturing the scene image and simultaneously providing depth information through blur analysis. The same optical system used for imaging also serves as the depth sensing mechanism, eliminating the need for separate depth cameras or sensor arrays.
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
Enables efficient, cost-effective, and real-time distance evaluation from a single image, capable of handling multiple objects and providing both absolute and relative depth information, even in the presence of occlusions, using conventional camera equipment.
Implementation Method 1
Passive distance measuring methods gather surrounding relevant information, often in the form of light waves
Implementation Method 2
a focus distance is set to correspond to a lower or higher limit of a chosen detection range
Implementation Method 3
The optical parameters of the lens of the image acquisition system are obtained or known
Data Source
AI summary
A distance evaluation method for evaluating distances from an observation point to objects within an arbitrary detectable range in a scene is disclosed. The method includes the following steps. First, a focus distance is set to correspond to a lower or higher limit of a chosen detection range. Next, an image is then captured with an image acquisition system, wherein transfer function of the image acquisition system depends on the focus distance. The captured image of the scene is segmented. A blur metric is computed for each image segment of the captured image. The blur metric is associated with the distance of the objects from the observation point in each image segment.