Contour Histogram Descriptor for Efficient Image Classification
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Solution Overview
Problem
Conventional image contour detection methods are computationally intensive, making them impractical for many image analysis applications due to high processing time and costs, despite their accuracy.
Innovation Solution
The development of techniques to create contour images, calculate histogram descriptors, and classify images based on these descriptors, allowing for efficient orientation determination and comparison of images by representing objects as left- or right-facing, facilitating accurate image comparisons.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If conventional image contour detection methods are used, then measurement precision is improved, but processing time and computational cost increase significantly
Solution Approach 1:
The patent extracts only the essential contour information from images by detecting edges and representing objects as simplified contour images with histogram descriptors. This extraction approach retains the critical shape information needed for classification while discarding unnecessary pixel data, thereby reducing processing time and computational cost while maintaining detection accuracy.
Solution Approach 2:
The patent transforms complex pixel-based contour data into parameterized histogram descriptors that capture shape characteristics in a compressed format. By changing the representation from raw pixel data to statistical parameters (histogram bins), the system achieves faster processing while preserving the essential geometric information for accurate image classification.
2Measurement precision
If conventional image contour detection methods are used, then measurement precision is improved, but device complexity and processing cost increase
Solution Approach 1:
The patent extracts only the essential contour information from images by detecting edges and representing objects as simplified contour images with histogram descriptors. This extraction approach retains the critical shape information needed for classification while discarding unnecessary pixel data, thereby reducing processing time and computational cost while maintaining detection accuracy.
Solution Approach 2:
The patent transforms complex pixel-based contour data into parameterized histogram descriptors that capture shape characteristics in a compressed format. By changing the representation from raw pixel data to statistical parameters (histogram bins), the system achieves faster processing while preserving the essential geometric information for accurate image classification.
3Measurement precision
If high precision contour detection is applied to large image collections, then classification accuracy is improved, but productivity decreases
Solution Approach 1:
The patent segments the image processing task into distinct stages: edge detection, contour extraction, and histogram descriptor computation. This segmentation allows each stage to be optimized independently and enables parallel processing, significantly improving throughput when analyzing large image collections while maintaining classification accuracy through the cumulative effect of these segmented processing steps.
Solution Approach 2:
The patent transforms complex pixel-based contour data into parameterized histogram descriptors that capture shape characteristics in a compressed format. By changing the representation from raw pixel data to statistical parameters (histogram bins), the system achieves faster processing while preserving the essential geometric information for accurate image classification.
Data Source
AI summary
Systems and methods are provided for creating contour images that represent the contour of objects reflected in images, calculating contour histogram descriptors of the contour images, and classifying images based in part on the histogram descriptors of the contour images. For example, a contour image of an image is created. A radial-polar grid having a plurality of radial-polar bins is then positioned on the contour image. A contour histogram descriptor is created to include a number of bins that correspond to the radial-polar bins of the radial-polar grid, where the contents of the bins of the contour histogram descriptor represent the number of pixels of the contour image that are located in the corresponding radial-polar bins of the radial-polar grid. Images are classified at least based in part on comparisons between contour histogram descriptors of the images and contour histogram descriptors of training images.