Hierarchical Feature Transfer for Visual Data Alignment
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Solution Overview
Problem
Current methods for transferring visual features between images lack seamless alignment and blending, resulting in unnatural and imperfect outputs due to inadequate feature adaptation and alignment.
Innovation Solution
A method using a hierarchical algorithm to determine alignment between source and target data, allowing for consistent and coherent feature transfer by detecting and segmenting features, and applying linear or non-linear transformations to ensure accurate blending and integration of visual data.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If simple overlay or transplantation of features is used, then the process is simple and fast, but the alignment is incorrect and the output looks unnatural
Solution Approach 1:
The patent applies preliminary alignment determination between source and target images before performing feature transfer. This preliminary action establishes the correct spatial relationship and transformation parameters, ensuring that subsequent feature transplantation occurs with accurate alignment rather than simple overlay, thereby resolving the contradiction between speed and precision.
2Productivity
If simple overlay or transplantation of features is used, then the process is simple and fast, but the output features do not blend well with target image
Solution Approach 1:
The patent performs preliminary determination of alignment and transformation parameters before feature transfer, enabling proper blending of source features with target image. This preliminary preparation ensures that features are transferred with correct spatial relationships and can be seamlessly integrated, resolving the contradiction between fast processing and high blending quality.
3Measurement precision
If alignment determination is performed before feature detection, then the overall alignment is accurate, but feature-specific alignment may be less precise
Solution Approach 1:
The patent segments the alignment process into two stages: first determining overall alignment between source and target images, then performing feature-specific alignment on detected features. This segmentation allows the system to benefit from both approaches - the global context from overall alignment and the local precision from feature-specific alignment, resolving the contradiction between the two precision requirements.
4Manufacturing precision
If hierarchical algorithm is used for feature transfer, then the output fidelity and resolution are improved, but the computational complexity increases
Solution Approach 1:
The patent implements a hierarchical algorithm that segments the feature transfer process into multiple levels: overall image alignment, feature detection and segmentation, and then feature-specific transfer with blending. This multi-level segmentation allows the complex task to be broken down into manageable stages, improving visual fidelity while organizing computational complexity in a structured manner.
Data Source
AI summary
A method for developing visual data using source data, target data, and a hierarchical algorithm. According to a first aspect, there is provided a method for developing visual data from source data, target data and using a hierarchical algorithm, the method comprising the steps of: determining an alignment between the target data and the source data; and producing the visual data by transferring one or more features of the source data onto one or more features of the target data; wherein, the visual data is produced after the step of determining the alignment between the target data and the source data; and wherein the visual data is produced using the hierarchical algorithm.