Object Detection Engine for Automated Image Effect Selection
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Solution Overview
Problem
Selecting the most aesthetically appealing imaging effects for digital photos or videos can be challenging for novice users, as it is highly subjective and dependent on various elements such as subjects and composition, and existing technologies lack an automated solution for this.
Innovation Solution
A system and method that utilizes a database to store visual effects and employs a convolutional neural network-based object detection engine to recognize objects within an image stream, matching them with corresponding image effects through an image effects repository, applying filters, augmentations, or distortions based on detected optical labels and scene classifications.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Ease of operation
If automated object detection and effect matching is implemented, then ease of operation is improved, but device complexity increases
Solution Approach 1:
The system performs automated object detection, scene classification, and effect selection without requiring user intervention. The object detection engine automatically identifies objects in the image stream, the image effect matching algorithm automatically selects appropriate effects based on detected objects and optical labels, and the image processing engine automatically applies the selected effects, allowing the system to serve itself rather than requiring manual user configuration.
Solution Approach 2:
The patent introduces several intermediary components that bridge different system functions: the optical label correlator acts as an intermediary between optical label detection and effect selection by correlating detected labels with stored effect parameters; the image effect matching algorithm serves as an intermediary between object detection and effect application by matching detected objects with appropriate effects from the repository; these intermediaries simplify the overall system architecture by breaking down complex automated effect selection into manageable modular components.
2Adaptability or versatility
If multiple imaging effects are provided for selection, then adaptability is improved, but ease of operation worsens
Solution Approach 1:
Instead of requiring users to manually browse and select from multiple imaging effects, the system automatically detects objects and scenes in the captured image stream and self-selects the most appropriate effects from the repository. The image effect matching algorithm autonomously matches detected objects with corresponding effects, eliminating the need for users to navigate through multiple effect options and making the interface simple while maintaining access to a diverse library of effects.
Solution Approach 2:
The patent applies different imaging effects to different detected objects and regions within the image stream based on their specific characteristics. Rather than applying a single global effect to the entire image, the system identifies specific objects (such as pets, people, scenery) and applies locally optimized effects to each, allowing diverse effects to be available simultaneously applied where appropriate without overwhelming the user with selection choices.
3Productivity
If real-time effect application is implemented, then productivity is improved, but use of energy increases
Solution Approach 1:
The patent divides the image processing task into separate sequential stages: object detection identifies objects in the image stream, optical label detection identifies labeled objects, scene classification categorizes the overall scene, the image effect matching algorithm selects appropriate effects based on these inputs, and finally the image processing engine applies the selected effects. This segmentation allows the system to process only relevant portions of the image at each stage rather than applying heavy processing to the entire image continuously, reducing overall energy consumption while maintaining real-time capability.
Solution Approach 2:
The system applies imaging effects selectively only to detected objects and regions that require enhancement rather than processing the entire image stream uniformly. The image processing engine applies effects partially to specific detected objects based on the matching algorithm's selections, performing excessive action only where needed rather than throughout the entire image, thereby reducing computational load and energy consumption while maintaining real-time effect application for the most important visual elements.
Data Source
AI summary
A method of applying an image effect based on recognized objects involves capturing an imaging area comprising at least one object as an image stream through operation of an image sensor. The method recognizes the at least one object in the image stream through operation of an object detection engine. The method communicates at least one correlated image effect control to an image processing engine, in response to the at least one object comprising an optical label. The method communicates at least one matched image effect control to the image processing engine, in response to receiving at least a labeled image stream at an image effect matching algorithm from the object detection engine. The method generates a transformed image stream displayable through a display device by applying at least one image effect control to the image stream through operation of the image processing engine.