Single Decision Tree for Image Landmark Localization
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Solution Overview
Problem
Existing image processing methods for target localization in images, such as Hough Forests, face challenges in achieving high accuracy and reducing the size of the prediction model when implemented on computers.
Innovation Solution
A computer-implemented image processing method using a single decision tree with optimized decision rules based on all available image features, rather than random subsets, to improve detection performance and reduce memory usage.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If a random forest with multiple decision trees is used for target localization, then the detection accuracy is improved, but the memory usage increases significantly
Solution Approach 1:
The patent extracts and removes the unnecessary randomness from the random forest algorithm, keeping only the essential decision tree structure. By eliminating the random sampling of features and bags of patches, the invention reduces the model from multiple trees to a single optimized tree, thereby reducing memory usage while preserving the core localization functionality.
Solution Approach 2:
The patent changes the parameter of the number of trees in the forest from multiple (random forest) to one (single decision tree). This parameter change is compensated by optimizing the single tree to use all available image features and all patches, which maintains or improves accuracy while reducing the memory footprint by a factor of ten or more.
2Device complexity
If random subsets of image features are used in each decision node, then the model size is reduced, but the detection accuracy decreases
Solution Approach 1:
The patent makes the single decision tree universal by enabling it to process all image features and all patches, rather than using random subsets. This universal approach allows the tree to leverage the complete information available in the image, improving detection accuracy while maintaining a compact model structure through the single-tree architecture.
3Reliability
If multiple decision trees are used in the random forest, then the robustness of the prediction is improved, but the processing time increases
Solution Approach 1:
The patent extracts the essential robustness mechanism from the random forest while removing the time-consuming multiple-tree structure. By using a single decision tree that processes all features and patches systematically, the invention achieves reliable predictions without the iterative random sampling overhead of multiple trees, thereby reducing processing time.
Data Source
AI summary
One embodiment of this invention provides an image processing method for use in locating a landmark in an acquired image. The method comprises a method to sample several features from an image patch, and a decision tree, which performs a regression to the location of the landmark relative to the image patch. The image is scanned by extracting an image patch in many translated locations and for each patch applies the regression decision tree to produce one or more votes for the location of the given target point within the acquired image. The method further accumulates the regression votes for all of the patches in the scan to generate a response image corresponding to the given target point. The method finally performs an estimate of the local maxima of the voting map as the likely locations of the landmark.