Image Frame Detection via Edge Characteristic Scanning
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Solution Overview
Problem
Conventional methods for detecting images applicable to websites are inefficient in handling complex information and cannot satisfy the requirements for a large number of images, particularly in distinguishing qualified images based on frame existence and content area analysis.
Innovation Solution
A method and apparatus that automatically detect the existence of a frame and main content area in images by calculating edge characteristic values, scanning for edge lines, and assigning weights based on area proportions, allowing for the determination of image quality and applicability to specific website requirements.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If manual detecting method is used to analyze complex image information, then detection accuracy for complicated information is improved, but detection efficiency deteriorates when handling massive amounts of images
Solution Approach 1:
The patent segments the image detection process into multiple independent modules: edge detection module, frame detection module, main content area detection module, and background area detection module. Each module handles a specific aspect of image analysis, allowing complex detection tasks to be divided into manageable segments that can be processed efficiently in parallel or sequence.
Solution Approach 2:
The patent replaces manual mechanical detection with automated computer-based image processing algorithms. The system uses edge detection algorithms, gradient calculations, and automated thresholding methods to substitute human manual analysis, thereby maintaining high detection accuracy while dramatically improving processing efficiency for large volumes of images.
2Productivity
If automatic detecting method is used for basic image information, then detection efficiency is improved, but detection capability for complicated information deteriorates
Solution Approach 1:
The patent merges multiple detection capabilities into a single integrated system. It combines automatic edge detection, frame detection, main content area detection, and background area detection into one unified image processing system. This allows the system to maintain high efficiency while simultaneously handling both basic and complicated image information that would require manual analysis.
3Ease of manufacture
If conventional detection methods are used, then implementation simplicity is maintained, but ability to distinguish qualified images based on frame and content characteristics deteriorates
Solution Approach 1:
The patent performs preliminary detection actions by first identifying edge characteristics and locating frames before proceeding to main content area detection. This preliminary edge and frame detection establishes a structural framework that guides subsequent more complex analysis, improving the accuracy of image qualification determination while maintaining a systematic and relatively simple implementation approach.
Solution Approach 2:
The patent employs parameter changes in the form of adjustable detection thresholds, weight coefficients for different detection modules, and configurable area proportion criteria. These parameters can be tuned to optimize the balance between detection accuracy and computational complexity, allowing the system to accurately distinguish qualified images while maintaining implementation flexibility.
Data Source
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AI summary
Detecting a specific image applicable at a website includes detecting an existence of a frame of the specific image. The specific image applicable at the website is obtained. An edge characteristic value of each pixel of the image is calculated. A pixel whose edge characteristic value is a local maximum value is found along a slope direction of each pixel. When the edge characteristic value of the found pixel is larger than a preset characteristic value threshold, a preset color is used to fill the pixel. The specific image is scanned. An amount of pixels whose color is the preset color at each scanning line of the specific image is counted. When the amount of such pixels at a scanning line is larger than a preset number threshold, a line where the scanning line locates is determined as an edge line of the frame.