Image Processing Apparatus Scene Detection via Template Matching
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Solution Overview
Problem
Existing image processing techniques fail to accurately detect scenes containing a targeted object in moving images, often linking unrelated scenes or splitting consistent scenes due to reliance on frame count rather than content consistency.
Innovation Solution
An image processing apparatus that generates a template image from a selected area, matches it against frames in a moving image file, and uses pixel value distribution differences to detect scene changes, ensuring accurate identification of scenes containing the object of interest.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Speed
If frame count-based scene detection is used, then processing speed is improved, but scene detection accuracy deteriorates
Solution Approach 1:
The patent changes the detection parameter from frame count (quantitative) to pixel value distribution characteristics (qualitative). By analyzing the statistical distribution of pixel values and comparing them across frames, the system achieves accurate scene detection based on content changes rather than arbitrary frame counts, resolving the contradiction between speed and accuracy.
Solution Approach 2:
The patent replaces the mechanical counting method with a statistical analysis method. Instead of mechanically incrementing frame counters and checking thresholds, the system uses pixel value distribution statistics (mean, variance, histogram characteristics) to detect scene changes, achieving both speed through efficient statistical computation and accuracy through content-based analysis.
2Measurement precision
If template image matching is used, then scene detection accuracy is improved, but processing time increases
Solution Approach 1:
The patent segments the image into multiple regions (e.g., foreground, background, key areas) and performs template matching only in specific segmented regions rather than the entire frame. This reduces the computational burden while maintaining detection accuracy in the regions that matter most for scene identification.
Solution Approach 2:
The patent applies different processing qualities to different parts of the image. High-precision template matching is applied only to critical regions where the object of interest is likely to appear, while other regions use simpler or no processing, thus reducing overall processing time while maintaining accuracy where needed.
3Measurement precision
If full frame analysis is used, then detection accuracy is improved, but computational complexity increases
Solution Approach 1:
The patent extracts only the essential features from full frames for analysis. Instead of analyzing every pixel in every frame, it extracts key characteristics such as pixel value distribution statistics, dominant colors, and regional features, significantly reducing computational complexity while preserving detection accuracy.
Solution Approach 2:
The patent performs partial analysis by focusing computational resources on the most informative aspects of the frames. Rather than exhaustive full-frame analysis, it applies simplified analysis to less critical regions and more detailed analysis only where necessary, achieving a balance between accuracy and complexity.
Data Source
AI summary
An image processing apparatus includes a moving image file storage unit operable to store a moving image file; an area selection receiving unit operable to receive a selection of a predetermined area corresponding to one of a plurality of frames forming the moving image file; a template image generating unit operable to generate as a template image an image of the selected area; an image matching unit operable to obtain the frames from the moving image file storage unit, and to match each of the frames against the template image to search for a matching frame containing an image similar to the template image; and a scene change detecting unit operable to detect a start and an end of a scene containing the matching frame.