Mobile Visual Media Identification via Quadrilateral Detection
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Solution Overview
Problem
Current technologies face challenges in efficiently identifying and capturing visual media content using camera-enabled mobile devices, particularly due to complications in orientation and extraneous images within the field of view, limiting convenient access and retrieval of media content on the go.
Innovation Solution
A method and system that utilize a camera-enabled mobile device to detect a quadrilateral representing an external display, capture and analyze visual media content, and identify it against a database, allowing for seamless retrieval and continuation of media consumption on the mobile device, employing techniques like histogram analysis, corner detection, and Scale Invariant Feature Transform (SIFT) for accurate recognition.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Ease of operation
If automated vision recognition is performed on media content using a mobile device camera, then users can capture and identify visual media content on the go, but the process becomes complicated by orientation issues and extraneous images within the field of view
Solution Approach 1:
The patent segments the captured image into multiple regions of interest (ROIs) based on detected features such as text, logos, or visual elements. By dividing the complex image processing task into smaller regional analyses, the system simplifies the overall complexity while maintaining ease of operation for content identification.
Solution Approach 2:
The patent extracts specific regions of interest from the captured image that contain the media content, separating them from extraneous background elements. This extraction process removes distracting elements and focuses processing on relevant content, reducing complexity while improving operational ease.
2Measurement precision
If high fidelity sampling and analysis of video content is performed, then accurate identification of media content is achieved, but the processing requires high processing capacity and is not suitable for mobile devices
Solution Approach 1:
The patent applies local quality by performing high-precision analysis only on specific regions of interest within the captured image, rather than processing the entire image at high fidelity. This allows accurate identification of media content while reducing overall processing requirements to levels suitable for mobile devices.
Solution Approach 2:
The patent performs partial action by selectively processing only the portions of the image that contain media content indicators (such as text regions, logos, or key visual elements) rather than analyzing the entire image. This partial processing maintains identification accuracy while significantly reducing power requirements.
3Reliability
If the entire captured image is analyzed for media content identification, then comprehensive recognition is achieved, but extraneous images and orientation issues complicate the process
Solution Approach 1:
The patent performs preliminary action by pre-processing the captured image to detect and mark regions of interest before the main identification process. This preliminary step organizes the image data, identifies potential media content areas, and prepares the image for more efficient analysis, reducing the difficulty of detecting relevant content among extraneous elements.
Solution Approach 2:
The patent introduces an intermediary processing step that acts as a mediator between the captured image and the final identification process. This intermediary layer detects and extracts regions of interest, effectively filtering out extraneous images and orienting relevant content, thereby simplifying the detection process while maintaining comprehensive recognition.
Data Source
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AI summary
Automatic identification of media content is at least partially based upon visually capturing a still or video image of media content being presented to a user via another device. The media content can be further refined by determining location of the user, capturing an audio portion of the media content, date and time of the capture, or profile/behavioral characteristics of the user. Identifying the media content can require (1) distinguishing a rectangular illumination the corresponds to a video display; (2) decoding a watermark presented within the displayed image/video; (3) characterizing the presentation sufficiently for determining a particular time stamp or portion of a program; and (4) determining user setting preferences for viewing the program (e.g., close captioning, aspect ratio, language). Thus identified, the media content appropriately formatted can be received for continued presentation on a user interface of the mobile device.