Snapping Guidelines for Raster Image Alignment
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Solution Overview
Problem
Graphic designers face challenges in accurately aligning text and image objects with raster-based digital images, as existing techniques lack effective methods to determine object alignment guidelines, leading to increased development time and effort, especially when object edges are not parallel or perpendicular.
Innovation Solution
A computing device implements a guidelines segmentation system that includes modules for image pre-processing, object segmentation, edge detection, and snapping guideline generation, which determines image content snapping guidelines based on object external and feature edges, allowing for accurate alignment of text and objects with raster-based digital images.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If conventional techniques are used for aligning text with objects in raster-based graphics, then the alignment process becomes time-consuming and labor-intensive, but there are no automated techniques available to determine text and object alignment guidelines
Solution Approach 1:
The patent introduces an intermediary system (guidelines segmentation system) that acts as a mediator between the raster-based digital image and the text/objects to be aligned. This system processes the image to extract edge information and generate alignment guidelines, which then serve as intermediaries to guide the placement and alignment of text and objects, eliminating the need for manual trial-and-error alignment processes
Solution Approach 2:
The patent replaces the manual mechanical alignment process with an automated computational system. Instead of designers manually positioning text and objects, the system automatically analyzes the raster image, detects edges, and generates alignment guidelines that can be programmatically applied, substituting manual mechanical operations with automated image processing and computational geometry
2Loss of time
If manual alignment techniques are used for non-parallel or perpendicular object edges, then significant development time and effort are required, but automated methods do not exist for raster-based graphics
Solution Approach 1:
The patent performs preliminary action by pre-processing the raster-based digital image to extract edge information before the actual alignment task. The system identifies and stores edge data from the image, which is then reused during the alignment process, eliminating the need to re-analyze the image repeatedly and significantly reducing development time for multiple alignment operations
Solution Approach 2:
The patent introduces edge maps and alignment guidelines as intermediary representations that simplify the complex task of aligning with arbitrary object edges. Instead of directly comparing text/objects with complex raster image data, the system creates simplified intermediary structures (edge maps containing line segments, angles, and positions) that make detection and measurement straightforward
3Manufacturing precision
If no automated guideline system is implemented for raster-based graphics, then alignment precision remains low, but implementing such a system requires complex image processing
Solution Approach 1:
The patent applies segmentation by dividing the complex image processing task into distinct modular components: edge detection module, edge map generation module, and alignment guideline generation module. Each module handles a specific aspect of the processing, making the overall complex system manageable and maintainable while achieving high alignment precision through coordinated operation of these segmented functions
Solution Approach 2:
The patent extracts only the essential alignment-relevant information from the complex raster image, rather than processing the entire image data. The edge detection and edge map generation processes extract specifically the boundary and feature edge information needed for alignment, filtering out irrelevant image data and reducing processing complexity while maintaining alignment precision
Data Source
AI summary
In implementations of image content snapping guidelines, a guidelines segmentation system includes modules, such as an image pre-processing module to reduce the image size of a digital image if the image size exceeds an image size threshold. An object segmentation module segments objects depicted in the digital image and identifies each object by a bounding border that delineates an object region boundary. An edge detection module receives a segmented object and determines object external edges and object feature edges from the segmented object, and identifies object corners of the object. A snapping guidelines module determines image content snapping guidelines of an object depicted in the digital image, the image content snapping guidelines for an object determined based on the bounding border of the object region boundary, the object external edges, the object feature edges, and projected snapping guidelines that extend from the object corners of the object.