Object-Selective Synthetic Images for Privacy-Safe Lighting Design
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Solution Overview
Problem
Consumers are hesitant to share images of their spaces with lighting professionals due to privacy concerns, hindering personalized lighting design services.
Innovation Solution
A system that generates a synthetic image for lighting design by parsing an input image to detect and classify objects relevant to lighting design and privacy, excluding privacy-sensitive objects while including relevant design elements, using neural networks for object detection and image generation.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If consumers share real images of their spaces with lighting professionals, then lighting design accuracy is improved, but privacy security deteriorates
Solution Approach 1:
The system creates a synthetic copy of the input image that preserves lighting design information while removing privacy-sensitive content. The synthetic image is generated by parsing the original image, detecting objects relevant to lighting design, and reconstructing the image without privacy concerns, thus enabling sharing while protecting privacy.
Solution Approach 2:
The system extracts only the necessary information from the input image for lighting design purposes. By parsing the image and identifying objects relevant to lighting design, the system separates the useful lighting information from privacy-sensitive elements, allowing selective sharing of only the necessary data.
2Adaptability or versatility
If consumers provide detailed images of their spaces, then lighting design customization is improved, but privacy exposure increases
Solution Approach 1:
The system applies different levels of detail to different regions of the image. Objects relevant to lighting design maintain their detailed information for customization, while privacy-sensitive objects are either removed or anonymized. This local differentiation allows customized lighting design without unnecessary privacy exposure.
Solution Approach 2:
The system segments the input image into multiple objects and regions based on parsing. By classifying each detected object as relevant to lighting design or privacy-sensitive, the system can selectively include or exclude specific segments, enabling customized lighting design information sharing while controlling privacy exposure.
3Object-affected harmful factors
If the system removes all objects from input images to protect privacy, then privacy security is improved, but lighting design capability deteriorates
Solution Approach 1:
The system applies selective removal based on object relevance. Objects relevant to lighting design maintain their presence and detail, while only privacy-sensitive objects are removed or anonymized. This localized approach preserves lighting design capability while protecting privacy where necessary.
Solution Approach 2:
The system changes the presence parameter of objects in the generated image based on their classification. Objects relevant to lighting design have their presence parameter set to true (included in output), while privacy-sensitive objects have their presence parameter set to false (removed from output), thus balancing privacy security with lighting design capability.
Data Source
AI summary
A computer-implemented method of generating a synthetic image of a space for lighting design includes obtaining an input image of the space and performing parsing of the input image of the space to detect objects in the input image. The method further includes classifying the objects detected in the input image at least based on relevance to lighting design of the space and relevance to privacy. The method also includes generating a synthetic image of the space from the input image of the space. A first object of the objects in the input image is included in the synthetic image, and a second object of the objects in the input image is left out from the synthetic image. The first object is relevant to the lighting design, and the second object is relevant to privacy.