Object Detection via Internal Feature Projection
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Solution Overview
Problem
Mobile devices face challenges in processing and detecting objects within image and video data due to limited processing power, image resolution, and distortions introduced by camera optics, which hinder accurate object detection and reconstruction in three-dimensional space, especially when object edges are missing or obscured.
Innovation Solution
A computer-implemented method that detects objects by identifying internal features within the object and projects the location of edges based on these features, allowing for object detection and reconstruction in a three-dimensional coordinate space, independent of edge detection.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If conventional scanner-based processing algorithms are used on mobile devices, then object detection accuracy may be maintained, but processing time and computational cost become prohibitively high
Solution Approach 1:
The patent extracts and focuses only on the essential processing steps needed for object detection on mobile devices. Instead of implementing full scanner-based algorithms, it extracts the core functionality of detecting internal features and projecting edge locations, removing unnecessary computational overhead while maintaining detection accuracy.
Solution Approach 2:
The patent segments the object detection process into distinct stages: detecting internal features, determining object orientation, and projecting edge locations. This segmentation allows each stage to be optimized independently for mobile processing constraints, reducing overall computational burden while maintaining accuracy.
2Ease of operation
If camera capture is used instead of scanner, then portability and ease of use improve, but image distortions and resolution limitations increase
Solution Approach 1:
The patent converts the harmful effect of camera-induced distortions into a beneficial process. By detecting internal features and using them to determine object orientation and project edge locations, the system actively corrects for perspective distortion and optical aberrations, transforming the inherent limitations of camera capture into opportunities for enhanced measurement precision.
Solution Approach 2:
The patent changes the parameters used for object detection from edge-based methods to internal feature-based methods. This parameter change allows the system to work effectively with lower resolution images and distorted perspectives by focusing on invariant internal characteristics rather than relying on high-fidelity edge detection.
3Measurement precision
If edge detection methods are used, then object boundaries can be identified, but performance deteriorates when edges are missing or obscured
Solution Approach 1:
The patent introduces internal features as an intermediary between the captured image and the final object boundary determination. Instead of directly detecting edges, the system first detects internal features, uses them to infer object orientation and geometry, and then projects edge locations mathematically. This intermediary approach maintains edge location accuracy while significantly improving reliability when edges are missing or obscured.
4Productivity
If high processing power and memory are allocated, then algorithm performance improves, but device complexity and cost increase
Solution Approach 1:
The patent applies partial action by implementing only the essential components needed for effective object detection on mobile devices. Rather than allocating resources for full-featured scanner-based processing, it implements the minimum necessary: internal feature detection, orientation determination, and edge projection. This provides sufficient algorithmic performance while keeping device complexity and computational resource requirements manageable.
Data Source
AI summary
Systems, computer program products, and techniques for detecting objects depicted in digital image data are disclosed, according to various exemplary embodiments. The inventive concepts uniquely 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 thus 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, wherein the plurality of identifying features are 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.