Landmark Detection Using Variable Patch Filters and Vector Flow Maps
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Solution Overview
Problem
Constrained Local Models for landmark point detection in images face limitations in accuracy and speed due to fixed patch sizes and independent confidence map generation, which restricts the precision and efficiency of filter generation and landmark point adaptation.
Innovation Solution
The method involves generating filters using linear support vector machines with patches of varying sizes and shapes, determining optimal confidence map region sizes based on training data variance, and normalizing the entire image instead of individual confidence map regions, along with pre-computing vector flow maps for efficient landmark point adjustment.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Device complexity
If fixed patch sizes are used for filter generation, then the device complexity is reduced, but the measurement precision of landmark points deteriorates
Solution Approach 1:
The patent applies local quality by using variable patch sizes and shapes for different landmark points based on their local image characteristics. Each landmark point is processed with a patch size and shape optimized for its specific region, improving measurement precision while maintaining manageable device complexity through localized adaptation rather than global uniformity.
2Device complexity
If confidence maps are generated independently for each landmark point, then the computational complexity is reduced, but the measurement precision of landmark points deteriorates
Solution Approach 1:
The patent merges the generation of confidence maps by computing them jointly across all landmark points rather than independently. This combines computational resources to improve the precision of each landmark point's confidence map while distributing the computational load, thereby improving measurement precision without proportionally increasing device complexity.
3Productivity
If individual confidence map regions are normalized separately, then the processing speed is reduced, but the measurement precision of confidence maps is improved
Solution Approach 1:
The patent merges the normalization process by applying it to the entire image or large regions rather than individual confidence map regions. This reduces the total number of normalization operations, improving processing speed while maintaining sufficient precision through the broader context provided by larger normalization regions.
4Measurement precision
If multiple iterations are performed for landmark point adaptation, then the measurement precision is improved, but the productivity of the detection process deteriorates
Solution Approach 1:
The patent applies preliminary action by pre-computing confidence maps and normalization factors before the actual landmark point adaptation process. This preliminary computation provides more accurate starting conditions that reduce the number of iterations needed for convergence, thereby improving measurement precision while maintaining high productivity through fewer adaptation cycles.
Data Source
AI summary
Techniques are provided to improve the performance and accuracy of landmark point detection using a Constrained Local Model. The accuracy of feature filters used by the model may be improved by supplying positive and negative sets of image data from training image regions of varying shapes and sizes to a linear support vector machine training algorithm. The size and shape of regions within which a feature filter is to be applied may be determined based on a variance in training image data for a landmark point with which the feature filter is associated. A sample image may be normalized and a confidence map generated for each landmark point by applying the feature filters as a convolution on the normalized image. A vector flow map may be pre-computed to improve the efficiency with which a mean landmark point is adjusted toward a corresponding landmark point in a sample image.