Depth-Guided Crowd Removal and Automatic Focus in Images
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Solution Overview
Problem
Current methods for removing unwanted obstructions in images, such as crowds in tourist photos, are manual and time-consuming, especially when dealing with large volumes of images, making them impractical for commercial use.
Innovation Solution
An automated system that partitions images into segments, obtains depth information, identifies focal segments, and modifies the image to remove or replace obstructions based on depth, size, and position, using computer vision and machine learning techniques.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If manual methods are used to remove unwanted obstructions in images, then user control and precision in selecting objects for removal is improved, but time consumption and resource intensity increase significantly
Solution Approach 1:
The system automatically identifies and removes obstructions without requiring manual user input. The processor autonomously analyzes the image, identifies subjects and obstructions, and performs removal operations, making the system self-sufficient and eliminating time-consuming manual selection processes.
Solution Approach 2:
The system uses depth information as an additional parameter to distinguish between subjects and obstructions. By incorporating depth data alongside image analysis, the system can automatically differentiate foreground subjects from background obstructions, improving both automation and accuracy simultaneously.
2Measurement precision
If manual obstruction removal is performed on each image individually, then accuracy in obstruction removal is maintained, but productivity and scalability decrease
Solution Approach 1:
The processor divides the image into multiple segments and processes each segment independently to identify subjects and obstructions. This segmentation approach enables parallel processing of multiple images simultaneously, significantly improving throughput while maintaining accurate obstruction detection in each individual image.
Solution Approach 2:
The system performs preliminary analysis of image characteristics and depth information before actual obstruction removal. By pre-identifying subjects and planning removal operations in advance, the system optimizes processing efficiency for batch operations while ensuring accurate removal decisions for each image.
3Productivity
If automated systems are used to remove obstructions, then processing speed and scalability are improved, but complexity of the system increases
Solution Approach 1:
The system introduces depth information as an intermediary data layer between image capture and obstruction removal. This depth map serves as a mediator that simplifies the automated detection process by providing clear spatial separation between subjects and obstructions, reducing the computational complexity required for accurate identification.
4Measurement precision
If depth information is obtained for all image segments, then accuracy in identifying focal subjects is improved, but computational resources and processing time increase
Solution Approach 1:
The processor divides the image into segments and obtains depth information for each segment independently. This segmented approach to depth processing allows for optimized resource allocation, processing only the computational resources needed for each local region rather than uniformly processing the entire image, thereby reducing overall computational burden while maintaining identification accuracy.
Data Source
AI summary
A computer implemented method includes receiving, by a processor, an image including one or more subjects and one more obstructions. The method further includes partitioning, by the processor, the image into a plurality of image segments, where the one or more subjects and one or more obstructions are represented as separate image segments of the plurality of image segments. The method further includes obtaining, by the processor, depth information for the plurality of image segments. The method further includes identifying one or more focal image segments of the plurality of image segments based on the depth information of the plurality of image segments and modifying the image based on the one or more focal image segments to generate a modified image.