Image Contour Orientation Detection Using Adaptive Wavelet Segmentation
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Solution Overview
Problem
Existing image processing techniques fail to accurately detect the orientation of contours in images due to issues with resolution choice and direction selection, leading to inefficiencies in denoising, compression, and interpolation processes.
Innovation Solution
Combining wavelet multi-resolution approaches with a directional analysis procedure that uses adaptive segmentation and non-iterative methods to determine the optimal resolution and predominant direction of contours in image blocks, avoiding favored directions and improving calculation efficiency.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If iterative segmentation procedures are used for direction detection, then comprehensive analysis of all directions is achieved, but calculation time and computational space increase significantly
Solution Approach 1:
The patent applies preliminary action by performing a non-decimated multi-resolution transformation of the image before direction detection. This pre-processing step decomposes the image into multiple resolution levels, allowing subsequent direction detection to operate on pre-organized data structures rather than raw pixel information, thereby reducing computational complexity and processing time while maintaining detection accuracy.
2Ease of operation
If pixel rearrangement procedures are used for direction detection, then directional analysis is simplified, but favored directions appear causing measurement errors
Solution Approach 1:
The patent applies segmentation by dividing the image into multiple blocks at different resolution levels through non-decimated multi-resolution transformation. Each block is independently analyzed for direction detection, and the results are combined to determine the predominant direction. This segmentation approach avoids the need for pixel rearrangement while maintaining computational simplicity and eliminating the favored direction problem.
Solution Approach 2:
The patent applies local quality by analyzing direction characteristics locally within each image block rather than globally across the entire image. Each block's direction properties are determined independently based on its local wavelet coefficients, allowing the method to adapt to local variations in contour orientation without imposing global constraints that cause favored directions.
3Device complexity
If fixed resolution approaches are used for contour analysis, then processing simplicity is maintained, but optimal representation of contours at varying scales is lost
Solution Approach 1:
The patent applies dynamics by using a non-decimated multi-resolution transformation that dynamically adapts to the scale and orientation of contours at different locations in the image. Unlike fixed-resolution approaches, this method provides multiple resolution levels that can be selectively applied to different image regions based on their specific characteristics, allowing optimal contour representation while maintaining manageable processing complexity through systematic decomposition.
Data Source
AI summary
A method for detecting orientation of the contours in an image, performs an initial transformation of the image using a non-decimated multi-resolution transform, segments the image into a plurality of blocks, determines the optimal resolution for each block, and detects the predominant direction of contour for each of the blocks.