Image Processing Sub-Block Selection for Indoor Localization
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Solution Overview
Problem
Current image processing techniques for mobile device localization are computationally intensive, limiting their use in portable devices and achieving sub-meter accuracy, especially in indoor environments, as they require full-size image processing.
Innovation Solution
Divide image data into sub-blocks and process them in a predetermined sequence, allowing for efficient feature identification and tracking, reducing computational load and enabling accurate localization without the need for specialized equipment.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If full-size image processing is used for localization, then localization accuracy is improved, but computational complexity increases making it unsuitable for portable devices
Solution Approach 1:
The image is divided into multiple blocks, and only selected blocks are processed for feature extraction. This segmentation approach reduces the total computational load while maintaining localization accuracy by focusing processing resources on the most informative regions of the image.
Solution Approach 2:
Instead of processing the entire image, the method processes only a partial set of blocks selected according to a predetermined sequence. This partial action approach achieves sufficient localization accuracy without the excessive computational cost of full-image processing.
2Measurement precision
If full-size image processing is used for localization, then localization accuracy is improved, but processing time increases limiting real-time application
Solution Approach 1:
By segmenting the image into blocks and processing only selected blocks, the total processing time is reduced while maintaining the ability to achieve accurate localization through feature matching across multiple frames.
Solution Approach 2:
The method processes blocks in a predetermined periodic sequence across multiple video frames. This periodic processing approach ensures that localization accuracy is maintained through cumulative feature matching while reducing instantaneous processing time requirements for real-time application.
3Measurement precision
If full-size image processing is used, then feature detection accuracy is improved, but device power consumption increases
Solution Approach 1:
The image processing is segmented into block-level operations with selective processing. This reduces the total number of computational operations required for feature detection, thereby lowering power consumption on battery-powered portable devices while maintaining detection accuracy through multi-frame accumulation.
Solution Approach 2:
The method applies partial processing by selecting and processing only certain blocks rather than the entire image. This partial action reduces energy consumption significantly while the predetermined sequence and multi-frame processing ensure that feature detection accuracy is maintained.
Data Source
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AI summary
An illustrative example method of processing image data includes dividing a block of data into a plurality of sub-blocks, the block of data corresponding to at least one image; selecting at least one of the sub-blocks according to a predetermined sequence; identifying a plurality of features of interest in the selected sub-block; and processing the selected sub-block to provide information regarding at least one of the features of interest.