Graphical Personalization Tool for Automated Video Object Insertion
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Solution Overview
Problem
Existing image and video personalization tools are cumbersome and require extensive user knowledge, imposing a significant ramp-up time for inexperienced users due to the need for manual discovery, manipulation, and adjustment of graphical objects, which limits their ability to create high-quality personalized content.
Innovation Solution
A graphical personalization tool that automatically identifies key regions or frames in image or video data and assists users in inserting personalized objects, performing tasks such as altering perspective, size, and color, to produce high-quality personalized outputs without requiring extensive background knowledge.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Manufacturing precision
If manual manipulation and adjustment of graphical objects is performed, then high-quality personalization effects can be achieved, but user expertise requirements and time investment increase significantly
Solution Approach 1:
The system performs automatic key region identification, motion estimation, and perspective transformation without requiring user intervention. The computer automatically analyzes video frames, identifies suitable regions for personalization, estimates motion vectors, and applies perspective transformations, enabling the system to serve itself rather than requiring expert user operation.
Solution Approach 2:
The system pre-processes video data by identifying key frames, detecting key regions within those frames, and estimating motion vectors before the actual personalization operation. This preliminary analysis prepares all necessary parameters and transformations in advance, so that when personalization objects are inserted, the system already has the information needed to apply them correctly without requiring user expertise.
2Ease of operation
If automatic key frame identification is implemented, then user expertise requirements decrease, but system complexity increases
Solution Approach 1:
The system replaces manual mechanical operations (user manually selecting frames and adjusting parameters) with automated computational processes. Computer algorithms automatically identify key frames based on motion analysis, detect key regions using image processing techniques, and calculate perspective transformations mathematically, substituting the need for user expertise with automated computational intelligence.
3Manufacturing precision
If comprehensive image adjustment features are provided, then personalization quality improves, but operation complexity increases
Solution Approach 1:
The system merges multiple complex operations into a unified automated process. Key region identification, motion estimation, perspective transformation, and personalization object insertion are combined into a single integrated workflow that operates automatically. This merging reduces operation complexity by presenting a simplified interface while maintaining high personalization quality through the coordinated execution of all processing steps.
Data Source
AI summary
Embodiments relate to systems and methods for image or video personalization with selectable effects. Image data, which can include video sequences or digital still images, can be received in a graphical personalization tool to perform various image processing and related operations to insert personalized objects into the image data. In aspects, the personalized object(s) can be or include graphical inputs such as, for instance, textual information, graphical information, and/or other visual objects. The graphical personalization tool can automatically perform one or more processing stages in the image path, such as identifying key regions in a still image and/or key frames in a video sequence, in which personalized objects will be generated and inserted. Personalized objects can be extended to additional regions of a still image, can be animated across multiple still images, and/or can be extended to additional frames of a video sequence, all on an automated or user-assisted basis.