Wearable Image Cropping via Cloud Mediator and Segmentation
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Solution Overview
Problem
Current wearable multimedia devices face challenges in efficiently displaying and manipulating images, particularly in cropping images to focus on noteworthy features while reducing unwanted elements, which affects image quality and storage requirements.
Innovation Solution
The system employs a network-connected infrastructure with a processor and engines (cropping and target feature engines) to identify and crop images based on user-selected or automated reference images, using machine learning models to generate high-quality images by selecting portions with features similar to the reference images, thereby reducing storage and processing needs.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If images are displayed using wearable multimedia devices with laser projected VIs, then image manipulation and interaction capabilities are improved, but device complexity and processing requirements increase
Solution Approach 1:
The patent introduces a cloud-based processing system as an intermediary between the wearable device and image processing tasks. The wearable device captures images and transmits them to the cloud, where sophisticated cropping and manipulation algorithms process the images. This mediator approach allows the wearable device to provide advanced image manipulation capabilities without incorporating complex processing hardware, thus resolving the contradiction between adaptability and device complexity.
Solution Approach 2:
The system divides image processing functions into separate components: the wearable device handles image capture and basic processing, while the cloud-based system handles complex cropping and manipulation tasks. This segmentation allows each component to be optimized independently, enabling advanced image manipulation capabilities without proportionally increasing the complexity of the wearable device itself.
2Manufacturing precision
If automatic image cropping is implemented to retain only relevant content, then image quality is improved, but processing time and computational resources increase
Solution Approach 1:
The system performs preliminary actions by pre-defining crop regions of interest based on scene analysis and object detection before final image processing. The cloud-based system analyzes the captured image, identifies relevant objects and regions, and pre-calculates optimal crop boundaries. This preliminary action approach enables high-quality image cropping while minimizing processing time, as the complex analysis is performed once during the cropping phase rather than requiring iterative processing.
Solution Approach 2:
The patent uses copying by creating a virtual copy of the image data in the cloud-based system for processing. Instead of performing complex processing operations directly on the original image data in the wearable device, the system creates a copy and processes this copy in the cloud environment. This copying approach allows sophisticated image quality enhancement through cropping while reducing the computational burden and processing time on the wearable device.
3Quantity of substance
If images are cropped to focus on noteworthy features, then storage requirements are reduced, but information loss occurs
Solution Approach 1:
The patent applies local quality by differentiating between regions of interest and background areas. The cloud-based system analyzes the image to identify which regions contain noteworthy features and which regions can be cropped out. By applying selective quality preservation only to local regions of interest rather than uniformly across the entire image, the system reduces storage requirements while minimizing information loss in the cropped regions that contain meaningful content.
Solution Approach 2:
The system implements feedback mechanisms where the wearable device can receive cropped images from the cloud and provide feedback about which regions were most important. This feedback loop allows the system to learn from user interactions and refine its cropping algorithms, ensuring that information loss is minimized by focusing crop regions on the most significant features. The feedback mechanism enables continuous improvement of the balance between storage efficiency and information preservation.
Applied Scientific Principles
This section explains which scientific principles are used to turn an abstract innovation direction into a practical engineering solution.
Function Achieved in This Case
This approach enhances image quality by automatically cropping images to retain only relevant content, reduces storage and processing requirements, and saves time by eliminating the need for manual adjustments, while maintaining high-quality images.
Implementation Method 1
the projector subsystem can project light onto a surface (e.g., a surface of a user's hand, such as the user's palm) according to a particular spatial and/or temporal pattern
Data Source
AI summary
Systems, methods, devices and non-transitory, computer-readable storage mediums are disclosed for a wearable multimedia device and cloud computing platform with an application ecosystem for processing data captured by the wearable multimedia device. In an embodiment, operations performed by the wearable multimedia device or cloud computing platform include obtaining an image captured using a camera coupled to a wearable multimedia device; comparing the image to one or more known images; identifying one or more target features in the image; generating a new image comprising a portion of the image that includes the one or more target features; and providing the new image to the wearable multimedia device for presentation.