Foreground Detection Module for Adaptive Image Composition
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Solution Overview
Problem
Conventional image cropping techniques are limited in handling complex scenes with multiple objects and often result in poor visual images, as they focus on global saliency detection and fail to consider the overall composition, leading to tedious parameter adjustments and limited feature determination.
Innovation Solution
A foreground detection module that generates varying saliency thresholds from a saliency map using adaptive thresholding and multi-level segmentation, applying constraints to distinguish foreground regions and detect multiple foreground objects, while leveraging iterative saliency map estimation and Gaussian mixture models for robust composition analysis.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If conventional global saliency detection techniques are used, then the detection process is simple, but the ability to handle complex scenes with multiple objects is limited
Solution Approach 1:
The patent applies multi-level segmentation to divide the saliency map into multiple foreground regions with different saliency thresholds. This segmentation enables the system to handle complex scenes with multiple objects by creating hierarchical foreground regions (first foreground region, second foreground region, etc.) rather than treating the image as a single uniform region, thus improving adaptability while managing complexity through structured division.
Solution Approach 2:
The patent generates different saliency thresholds for different regions of the image, creating varying levels of foreground detection sensitivity across the image. This local quality approach allows the system to detect multiple foreground objects with different prominence levels, improving the ability to handle complex scenes while maintaining a systematic detection framework.
2Adaptability or versatility
If conventional single-level saliency thresholding is used, then the processing is fast, but multiple foreground regions cannot be detected
Solution Approach 1:
The patent segments the saliency map into multiple foreground regions by applying different saliency thresholds at different levels. This multi-level segmentation strategy enables the detection of multiple foreground regions (first foreground region, second foreground region, etc.) from a single saliency map, improving versatility without requiring multiple separate detection processes.
Solution Approach 2:
The patent introduces a hierarchical dimension to saliency thresholding by creating multiple foreground regions with different saliency levels. This dimensional approach transforms single-threshold detection into a multi-level detection system, enabling multiple foreground object detection while maintaining a unified detection framework rather than requiring multiple independent processes.
3Ease of operation
If manual parameter adjustments are used for cropping, then the cropping can be customized, but the process is tedious and time-consuming
Solution Approach 1:
The patent enables the system to automatically detect foreground regions and determine cropping parameters without requiring manual user input. The multi-level foreground detection and composition analysis perform self-service by autonomously identifying what should be kept and removed in the cropped image, eliminating tedious manual parameter adjustments while maintaining customization through intelligent automation.
Solution Approach 2:
The patent automatically adjusts cropping parameters based on the detected foreground regions and composition analysis. By dynamically changing cropping parameters (crop region, crop size, crop position) based on the multi-level foreground detection results, the system achieves automatic customized cropping without manual intervention, saving time while maintaining flexibility.
4Manufacturing precision
If conventional cropping techniques are used, then the implementation is simple, but the visual quality of cropped images is poor
Solution Approach 1:
The patent applies different composition rules and constraints to different foreground regions based on their saliency levels and spatial positions. This local quality approach allows the system to optimize cropping for each region's characteristics, improving cropping accuracy and visual quality by treating different parts of the image differently rather than applying uniform cropping rules.
Solution Approach 2:
The patent segments the image into multiple foreground regions with different saliency thresholds and applies composition analysis to each segment. This segmentation enables precise control over what content is retained or removed in the cropped image, improving cropping accuracy by considering the composition of each region separately rather than treating the entire image as a single unit.
Data Source
AI summary
In techniques for image foreground detection, a foreground detection module is implemented to generate varying levels of saliency thresholds from a saliency map of an image that includes foreground regions. The saliency thresholds can be generated based on an adaptive thresholding technique applied to the saliency map of the image and/or based on multi-level segmentation of the saliency map. The foreground detection module applies one or more constraints that distinguish the foreground regions in the image, and detects the foreground regions of the image based on the saliency thresholds and the constraints. Additionally, different ones of the constraints can be applied to detect different ones of the foreground regions, as well as to detect multi-level foreground regions based on the saliency thresholds and the constraints.