Local Motion Detection Using Pixel Delta Functions
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Solution Overview
Problem
Existing image processing technologies face challenges in detecting local motion between image frames, particularly in non-static video, as simple temporal filters can cause undesirable artifacts like blurring or ghosting of moving objects.
Innovation Solution
A method for detecting local motion between image frames involves calculating delta functions based on pixel value differences between corresponding locations in two frames, with thresholds determined by intensity and system components, allowing for precise identification of motion without applying unnecessary temporal filters.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If a simple temporal filter is applied to remove temporal noise, then noise removal is improved, but moving objects may appear to blur or ghost
Solution Approach 1:
The patent applies different filtering operations to different spatial locations based on local motion detection. Specifically, it performs motion detection for each pixel location and conditionally applies temporal filtering only to locations where no motion is detected, while preserving moving objects without filtering. This local differentiation resolves the contradiction by maintaining object clarity in moving regions while still removing noise in static regions.
Solution Approach 2:
The patent dynamically adjusts the filtering operation based on detected motion characteristics. The system transitions from a static filtering approach to a dynamic one where the filtering behavior changes according to the presence or absence of motion at each pixel location. This dynamic adaptation allows the system to maintain reliability for noise removal while preserving manufacturing precision for moving objects.
2Reliability
If a low pass filter combines objects that move over an area with objects that are behind the moving objects, then temporal noise is reduced, but moving objects appear to ghost
Solution Approach 1:
The patent performs motion detection at each pixel location and uses this local motion information to determine whether to apply temporal filtering. By making the filtering decision local rather than global, the system preserves motion information in regions where motion is detected while still achieving temporal noise reduction in regions where no motion is present. This prevents the ghosting effect that occurs when moving objects are incorrectly combined with background objects.
Solution Approach 2:
The patent segments the image into motion and non-motion regions based on pixel-level motion detection. This segmentation allows independent processing of different regions: temporal filtering is applied to non-motion regions for noise reduction, while motion regions are preserved without filtering to maintain motion information integrity. This segmentation resolves the contradiction by preventing the mixing of moving and stationary objects in the filtering process.
Data Source
AI summary
A system and method for detecting local motion between image frames are presented. Each image frame may include a plurality of pixels. A first image frame and a second image frame may be received. Values of a first plurality of pixels at first pixel locations of the first image frame may be read. Values of a second plurality of pixels at second pixel locations of the second image frame may be read. The first pixel locations of the first image frame may correspond to the second pixel locations of the second image frame. A first delta function, based on differences between pixel values between the first and second image frames, may be calculated to produce a first output. The first output may be compared to a first one or more thresholds. It may be determined whether local motion has occurred in the second image frame based on said comparing.