Mobile App Object Segmentation Style Transfer Depth
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Solution Overview
Problem
Current image processing technologies face challenges in efficiently segmenting objects from images using depth information and applying style transfer effects on mobile devices, particularly in real-time applications, due to limitations in processing power and data handling.
Innovation Solution
The development of a mobile application that employs machine learning models for object recognition, segmentation, and style transfer, utilizing both semantic and instance segmentation techniques, along with depth information captured by a camera, to segment objects and apply style transfer effects, enabling real-time image processing and synthesis.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If machine learning models are deployed for object segmentation and style transfer on mobile devices, then image processing accuracy and functionality are improved, but device processing power requirements and computational complexity increase
Solution Approach 1:
The patent segments the image processing task into distinct components: depth map generation, object segmentation, style transfer, and image synthesis. Each component is handled by specialized machine learning models that can be independently optimized and deployed, reducing the overall computational burden while maintaining high accuracy for each specific function.
Solution Approach 2:
The system performs preliminary depth map generation and object segmentation before applying style transfer effects. By pre-processing the image to identify objects and their boundaries using depth information, the system reduces the computational complexity of subsequent style transfer operations, as these operations can be applied more efficiently to already-segmented regions.
2Productivity
If real-time image processing is implemented on mobile devices, then processing speed is improved, but energy consumption and computational load increase
Solution Approach 1:
The system implements periodic processing where depth maps are generated at specific intervals or triggers rather than continuously. Machine learning models are executed in discrete processing cycles, allowing the mobile device to alternate between active computation and low-power states, thereby reducing overall energy consumption while maintaining real-time processing capability for user interactions.
Solution Approach 2:
The system utilizes depth information from the camera sensor to automatically guide the segmentation and style transfer processes without requiring extensive manual processing. The depth maps provide inherent structural information that simplifies object boundary detection, reducing the computational energy required for these operations compared to methods that rely solely on color and texture analysis.
3Measurement precision
If depth information is utilized for object segmentation, then segmentation accuracy is improved, but data handling requirements and processing complexity increase
Solution Approach 1:
The system extracts and utilizes only the essential depth information from depth maps for object segmentation, rather than processing the entire depth data set. By selectively extracting depth features that are most relevant for identifying object boundaries and structures, the system achieves high segmentation accuracy while minimizing the volume of data that needs to be handled and processed.
Solution Approach 2:
The system incorporates depth information as an additional dimension for object segmentation, moving beyond traditional two-dimensional color and texture analysis. This third dimension of depth data provides explicit spatial information that simplifies the segmentation process and improves accuracy, while the patent employs efficient algorithms to handle this additional dimensional data without proportionally increasing processing complexity.
Data Source
AI summary
Trained segmentation, classification, and style transformation models are used to apply style effects to image segments corresponding to objects (e.g., a portrait of a person) present in an image. When depth information is included with a source image it may be used to segment, classify, and/or apply style transformation to an image or image segments (as the case may be). One or more of the segmentation, classification, and style transformations may be performed at a computing apparatus or as a service for example, in the cloud. A model management tool implemented at a server(s) may continuously train models using supervised or unsupervised training techniques to improve the quality of segmentation, classification and style transformation as well as add new capabilities. Updated models may be pushed to services offering image processing. Additionally or alternatively, a mobile application can inquire about versions of models and request updated models, or in another case, the model management tool may push updates to the mobile application. Thus, the mobile application may perform on-board image processing including segmentation, classification, and style transformation.