Image Sensor Impact Time Estimation via Local Extreme Points
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Solution Overview
Problem
Existing methods for estimating Time-to-Impact (TTI) in image processing are noise-sensitive and require a longer sequence of image frames, making them resource-intensive and complex.
Innovation Solution
A method that uses local extreme points (LEPs) identified in two consecutive image frames to compute a measure indicative of impact time, providing improved noise resistance and faster computation.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If prior art methods using LEP runs are used for TTI estimation, then TTI measurement can be obtained, but the method is sensitive to noise and requires longer sequences of image frames
Solution Approach 1:
The patent extracts only the essential feature of LEP presence/absence between two frames, discarding the complex temporal analysis of LEP runs. By focusing solely on whether LEPs are present in one frame but absent in the next (or vice versa), the method eliminates noise sensitivity while requiring minimal frame sequences, directly resolving the contradiction between reliability and time loss.
2Measurement precision
If conventional spatial motion estimation methods are used, then motion information can be obtained, but fast computing hardware and data storage are required
Solution Approach 1:
The patent replaces complex mechanical/computational image processing systems with a simple LEP detection and comparison mechanism. Instead of using intensive optical flow algorithms requiring fast computing hardware, the method uses basic LEP identification and presence/absence comparison, achieving motion estimation with minimal hardware resources while maintaining measurement precision.
3Measurement precision
If real-time TTI estimation is performed using conventional methods, then accurate TTI can be obtained, but a fair amount of hardware resources are required
Solution Approach 1:
The patent uses simple, computationally inexpensive LEP detection operations that can be performed rapidly with minimal hardware resources. By replacing expensive, resource-intensive conventional methods with this simpler approach, accurate TTI estimation is achieved while dramatically reducing hardware resource consumption and energy usage.
Data Source
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AI summary
Provision of measure indicative of impact time between an image sensor (1201) and an object (1210) relatively moving towards the image sensor (1201). Image data comprising a first and second set of pixel data are obtained (1401) for pixel positions (1..N). Pixel data in the first set relate to sensed pixel data by the image sensor (1201) when imaging said object (1210) a first time (t1) and the second set relate to pixel data sensed by the image sensor (1201) when subsequently imaging said object a later second time (t2). Local extreme Points, LEPs, are identified (1402) in the first set. A first value (Σi f(i)) proportional to the total number of identified LEPs in the first set of pixel data may be computed. New LEPs in the second set of pixel data are identified (1404). A second value (Σi f'(i)) proportional to the total number of said identified new LEPs is computed (1405). Said measure indicative of impact time is provided (1407), based on at least the first value.