Sparse Zone Frequency Domain Image Processing for Mobile Object Detection
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Solution Overview
Problem
Current object detection technologies in image processing face challenges with high processing power requirements, battery drain, and inability to handle real-time frame rates on mobile devices, especially when dealing with high-definition video streams and temporal data, leading to the need for faster and more efficient methods.
Innovation Solution
The method involves transforming image data into the frequency domain using sparse zones and optimized 2D L-Transformations to create normalized complex vectors, allowing for efficient processing and classification of content in video streams without the need for extensive spatial domain calculations.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Speed
If traditional spatial domain calculations are used for content detection, then detection accuracy can be maintained, but processing speed decreases and power consumption increases
Solution Approach 1:
The patent transforms image data from the spatial domain to the frequency domain using 2D L-Transformation. This dimensional change allows processing to occur in a different mathematical space where certain operations become more efficient. The frequency domain representation enables faster computation of detection features while reducing the computational load compared to traditional spatial domain methods, thereby improving processing speed and reducing power consumption.
Solution Approach 2:
The patent extracts only the necessary frequency components from the transformed image data using sparse zones. Instead of processing the entire frequency spectrum, the method selectively extracts relevant features from specific zones in the frequency domain. This extraction approach reduces the amount of data that needs to be processed, leading to faster computation and lower power consumption while maintaining detection accuracy.
2Measurement precision
If high-definition video streams are processed in real-time, then detection accuracy improves, but processing speed decreases
Solution Approach 1:
The patent divides the frequency domain into multiple sparse zones and processes each zone independently. This segmentation allows parallel processing of different regions, enabling the system to handle high-definition video streams more efficiently. By segmenting the processing task and working on multiple zones simultaneously, the method maintains high detection accuracy while improving frame processing rate to meet real-time requirements.
Solution Approach 2:
The patent applies partial action by processing only the most informative frequency zones rather than the entire frequency spectrum. The sparse zone approach selectively focuses computational resources on regions of the frequency domain that contain the most relevant information for detection. This partial processing strategy maintains detection accuracy while reducing the overall computational burden, enabling real-time processing of high-definition video.
3Measurement precision
If comprehensive feature extraction is performed, then detection accuracy improves, but computational load increases
Solution Approach 1:
The patent extracts only the essential features from the frequency domain data using sparse zones. Instead of performing comprehensive feature extraction across the entire image, the method identifies and extracts only the most discriminative features from specific frequency regions. This selective extraction reduces computational load and device complexity while preserving the detection accuracy needed for effective content detection.
Solution Approach 2:
The patent changes the parameter representation from spatial coordinates to frequency domain parameters through L-Transformation. This parameter transformation allows the system to work with a different set of features that are more compact and computationally efficient. The frequency domain parameters provide the necessary information for accurate detection while requiring less computational resources to process compared to traditional spatial domain parameters.
Data Source
AI summary
A method for content detection based on images or a digital video stream of images, to enhance and isolate frequency domain signals representing content to be identified, and decrease or ignore frequency domain noise with respect to the content. A digital image or sequence of digital images defined in a spatial domain are obtained. One or more pairs of sparse zones are selected, each pair generating a feature, each zone defined by two sequences of spatial data. The selected features are transformed into frequency domain data. The transfer function, shape and direction of the frequency domain data are varied for each zone, thus generating a normalized complex vector for each feature. The normalized complex vectors are then combined to define a model of the content to be identified.