Multiscale Seam Carving for Image Resizing
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Solution Overview
Problem
Traditional image resizing methods, such as cropping or resampling, introduce visual distortion or remove important features, and existing seam carving techniques often misinterpret image energy, leading to distortion of main objects, especially in images with multiple objects and textures.
Innovation Solution
A computer-implemented method using a seam carving algorithm that measures pixel energy, applies filters iteratively to derive energy maps, combines them to form a composite image, and selectively deletes seams to resize images while preserving important objects, using filters like wavelet transforms or Gabor filters to improve texture sensitivity.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If traditional seam carving is applied to images with multiple objects and textures, then image resizing is achieved, but important features and main objects are misidentified and removed due to incorrect energy interpretation
Solution Approach 1:
The patent segments the energy analysis into multiple scales by applying filters at different resolution levels. Instead of analyzing the entire image at one scale, it divides the energy computation across multiple scales (coarse to fine), allowing important features to be identified at appropriate scales while ignoring distracting textures at other scales.
Solution Approach 2:
The patent adds a scale dimension to the traditional single-scale energy map by creating a pyramid of energy maps at different resolutions. This multi-scale dimensional approach allows the system to distinguish between important features (visible across scales) and distracting textures (visible only at specific scales), resolving the contradiction between resizing efficiency and feature preservation accuracy.
2Speed
If global resizing techniques are used instead of iterative seam carving, then processing speed improves, but scalability to arbitrary resolutions is reduced
Solution Approach 1:
The patent performs preliminary computation by building a complete multi-scale energy map pyramid in advance. This preliminary action stores energy information at multiple resolutions, enabling both fast processing (by querying pre-computed data) and arbitrary resolution adaptability (by selecting appropriate scales from the pre-built pyramid).
Solution Approach 2:
The multi-scale energy map pyramid serves multiple functions: it enables fast resizing at any resolution, provides adaptability to different image sizes, and maintains both processing speed and resolution versatility simultaneously, making the system universally applicable to various resizing scenarios.
3Device complexity
If single-scale energy maps are used in seam carving, then computational complexity is reduced, but accuracy in identifying important features decreases
Solution Approach 1:
The patent segments the energy measurement process across multiple scales, computing energy maps at different resolution levels. This segmentation allows accurate feature identification by analyzing energy distribution at appropriate scales while keeping individual scale computations manageable, balancing complexity and accuracy.
Solution Approach 2:
The patent creates a composite energy representation by combining information from multiple scale-level energy maps. This composite multi-scale energy map integrates features detected at different resolutions, achieving superior measurement accuracy compared to any single scale while maintaining computational feasibility through the hierarchical structure.
Data Source
AI summary
A computer-implemented method for resizing an image using a seam carving algorithm. The method may include measuring energy levels of pixels in an original image to derive an original energy map; applying a filter to an original energy map to derive a first energy map having a scale less than the original energy map; iteratively applying the filter N times, starting with the first energy map, to an energy map from an immediately preceding iteration; upsampling each of the energy maps to a resolution that matches the original energy map; combining the upsampled energy maps with the original energy map to form a composite image; identifying a seam by finding a path in the composite image having lowest energy quantities from one end of the composite image to an opposing end of the composite image; and selectively deleting the identified seam from the original image, thereby yielding a resized image.