Neural Network Copy Space Detection in Images
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Solution Overview
Problem
Manual searching for suitable copy spaces in images is time-consuming and expensive, and existing automated systems are ineffective due to the varied shapes, sizes, and characteristics of copy spaces, making it challenging to design automated systems for identifying and indexing them.
Innovation Solution
A neural network learning technique using a regression convolutional neural network (CNN) to predict the presence, size, and type of copy spaces, combined with a segmentation CNN to generate pixel-level masks defining their locations, allowing for efficient processing and indexing of images to facilitate digital asset searching.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If manual searching is used to find copy spaces, then accuracy and suitability can be ensured, but time consumption and cost increase significantly
Solution Approach 1:
The patent replaces manual mechanical searching with an automated neural network-based system. The neural network models (including segmentation networks and regression networks) automatically detect, segment, and characterize copy spaces in images, substituting human visual search and evaluation with computational algorithms that operate at machine speed while maintaining identification accuracy.
Solution Approach 2:
The system enables self-service by automatically processing and indexing large databases of images to identify copy spaces without requiring designer intervention for each individual search. The automated system performs characterization, segmentation, and retrieval operations independently, allowing designers to simply query the pre-processed database rather than manually searching through collections.
2Productivity
If automated systems are designed to detect copy spaces with varied shapes and sizes, then search efficiency improves, but system complexity increases
Solution Approach 1:
The patent applies segmentation by dividing the complex task of copy space detection into separate specialized modules: segmentation networks that identify and separate copy space regions from backgrounds, regression networks that characterize detected spaces, and retrieval networks that match query requirements. This modular segmentation allows each component to handle specific aspects independently, improving overall efficiency while managing complexity through functional decomposition.
Solution Approach 2:
The system achieves universality by developing a multi-functional neural network architecture that can handle various copy space types, shapes, and sizes through a unified framework. The segmentation and regression networks are designed to generalize across different image categories and copy space configurations, allowing a single system to serve multiple detection and characterization functions without requiring separate specialized systems for each scenario.
3Measurement precision
If detailed characterization of copy spaces is performed, then search precision improves, but processing time increases
Solution Approach 1:
The patent implements preliminary action by pre-processing and pre-characterizing large databases of images using neural networks before actual search operations. The system performs segmentation, characterization, and indexing of copy spaces in advance, storing results in a structured database. During actual search queries, the system retrieves pre-processed information rather than performing analysis in real-time, significantly reducing query processing time while maintaining high characterization precision through the pre-computed detailed attributes.
Data Source
AI summary
Techniques are disclosed for characterizing and defining the location of a copy space in an image. A methodology implementing the techniques according to an embodiment includes applying a regression convolutional neural network (CNN) to an image. The regression CNN is configured to predict properties of the copy space such as size and type (natural or manufactured). The prediction is conditioned on a determination of the presence of the copy space in the image. The method further includes applying a segmentation CNN to the image. The segmentation CNN is configured to generate one or more pixel-level masks to define the location of copy spaces in the image, whether natural or manufactured, or to define the location of a background region of the image. The segmentation CNN may include a first stage comprising convolutional layers and a second stage comprising pairs of boundary refinement layers and bilinear up-sampling layers.