Image Frame Spectral Selection for Object Detection Noise Reduction
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Solution Overview
Problem
Existing object detection algorithms face inefficiencies in processing video frames due to the presence of pixel noise, background surfaces, and textures, leading to prolonged processing times and resource utilization issues.
Innovation Solution
The implementation of spectral and spatial transformations to enhance the visibility of objects of interest by identifying dominant hues, modifying color characteristics, and applying blurring processes to isolate high-frequency images, thereby removing extraneous data and improving processing efficiency.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If object detection algorithms process all pixels in video frames, then object detection completeness is improved, but processing time increases
Solution Approach 1:
The patent segments the image processing task by dividing pixels into two categories: high-frequency pixels (potential objects) and low-frequency pixels (background). This segmentation allows the algorithm to focus computational resources only on relevant regions, improving detection speed while maintaining completeness through iterative refinement of the frequency threshold.
Solution Approach 2:
The patent performs preliminary spectral transformation and frequency analysis on the entire image frame before object detection. By pre-identifying and masking low-frequency background regions, the algorithm reduces the search space for object detection, thereby decreasing processing time without compromising detection accuracy.
2Measurement precision
If object detection algorithms analyze all image data, then detection accuracy is improved, but resource utilization deteriorates
Solution Approach 1:
The patent extracts and removes low-frequency background components from the image data before object detection. By separating the background (low-frequency) from potential objects (high-frequency), the algorithm reduces the amount of data that needs to be analyzed, improving resource utilization while maintaining detection accuracy through targeted analysis of high-frequency regions.
Solution Approach 2:
The patent applies different processing quality levels to different regions of the image. High-frequency regions (potential objects) receive full analytical processing, while low-frequency regions (background) are simplified or masked. This local quality differentiation optimizes resource allocation, directing computational resources only where they are most needed for accurate detection.
3Measurement precision
If spectral transformations are applied to modify color characteristics, then object visibility is improved, but processing complexity increases
Solution Approach 1:
The patent transforms the image from the spatial domain to the frequency domain using spectral transformation (e.g., Fourier transform). This parameter change allows the algorithm to manipulate image characteristics in the frequency domain, where background and object components are separated by frequency content, improving object visibility while enabling efficient processing through frequency-domain operations.
Data Source
AI summary
An image comprising a set of pixels is obtained and a dominant hue is determined. A color value of individual pixels in a subset of the set of pixels that fall within the hue range is modified to produce a first transformed image. A filter is applied to the first transformed image or an image derived from the first transformed image to reduce spatial high frequency noise and derive a low frequency image. A high frequency image is generated based on the first transformed image and the low frequency image. Whether the high frequency image depicts the object at a target image frequency is determined based on specified criteria for an object, and if the high frequency image corresponds to the target image frequency, the high frequency image is stored.