Document Detection via Internal Feature Projection
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Solution Overview
Problem
Existing technologies face challenges in capturing and processing documents using mobile devices due to limited processing power, image resolution, and the inability to accurately detect objects with missing or obscured edges in image and video data.
Innovation Solution
A computer-implemented method and system for detecting documents in digital images using mobile devices, which involves detecting internal features of the object and projecting the location of object edges based on these features, allowing for accurate detection and reconstruction of objects in a three-dimensional coordinate space.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If conventional scanner-based processing algorithms are used on mobile devices, then document detection accuracy is improved, but processing time and computational cost become prohibitively expensive
Solution Approach 1:
The patent extracts and focuses only on the most critical processing steps needed for document detection, removing unnecessary computational overhead from conventional scanner-based algorithms. This allows the system to achieve adequate detection accuracy with significantly reduced processing time suitable for mobile devices.
Solution Approach 2:
The patent modifies processing parameters and thresholds to optimize performance for mobile device constraints. By adjusting detection sensitivity, processing resolution, and algorithmic parameters, the system achieves acceptable document detection accuracy while maintaining fast processing speeds on mobile platforms.
2Measurement precision
If conventional scanner-based processing algorithms are used on mobile devices, then document detection accuracy is improved, but device complexity and processing power requirements increase
Solution Approach 1:
The patent employs simpler, more lightweight processing algorithms that can be executed on mobile devices with limited resources. Rather than implementing complex conventional algorithms, the system uses optimized, simplified versions that reduce computational complexity while maintaining adequate detection accuracy for mobile applications.
Solution Approach 2:
The patent divides the document detection process into distinct segments or stages, each handling specific aspects of detection. This segmentation allows the system to process images in manageable steps, reducing overall computational complexity and making the algorithm more suitable for mobile device constraints.
3Ease of operation
If mobile capture components are used, then portability and ease of operation are improved, but image quality and consistency deteriorate due to projective effects and distortions
Solution Approach 1:
The patent converts the projective effects and distortions introduced by mobile camera capture into beneficial information. By detecting and analyzing these distortions, the system can calculate the three-dimensional orientation and position of the document, then apply corrective transformations to produce a rectified, high-quality image suitable for further processing.
Solution Approach 2:
The patent addresses two-dimensional image distortions by introducing three-dimensional geometric reconstruction. By modeling the document in 3D space and considering camera orientation, position, and focal length, the system can computationally rectify the captured image to correct perspective distortions and produce a consistent, high-quality output.
4Measurement precision
If edge-based detection methods are used, then object boundary detection is improved, but detection reliability deteriorates when edges are missing or obscured
Solution Approach 1:
The patent uses internal features rather than requiring complete edge detection. By focusing on detecting sufficient internal features (such as text lines, shapes, or patterns) rather than attempting to detect all edges, the system achieves reliable document detection even when edges are partially missing or obscured in the captured image.
Data Source
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AI summary
Systems, computer program products, and techniques for detecting objects depicted in digital image data are disclosed, according to various exemplary inventive concepts. The inventive concepts utilize internal features to accomplish object detection, thereby avoiding reliance on detecting object edges and/or transitions between the object and other portions of the digital image data, e.g. background textures or other objects. The inventive concepts provide an improvement over conventional object detection since objects may be detected even when edges are obscured or not depicted in the digital image data. In one aspect, a computer-implemented method of detecting an object depicted in a digital image includes: detecting a plurality of identifying features of the object, the plurality of identifying features being located internally with respect to the object; and projecting a location of one or more edges of the object based at least in part on the plurality of identifying features.