Image Sensor Resolution Control via Variation Tendency Analysis
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Solution Overview
Problem
Conventional image processing systems face challenges in distinguishing between environment and gesture images, leading to inefficient selection of resolution and frame rate, resulting in either excessive power consumption or incorrect gesture detection.
Innovation Solution
An image processing method that captures images at a first resolution, computes image variation tendency, and adjusts to a higher resolution and frame rate when a predetermined tendency is matched, allowing for clear differentiation between environment and gesture images.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If high resolution and frame rate are used, then gesture detection accuracy is improved, but power consumption increases
Solution Approach 1:
The system dynamically adjusts the image sensor resolution and frame rate based on detected image variation tendencies. When gesture patterns are detected, the system switches to higher resolution and frame rate for accurate gesture recognition. During static periods, it operates at lower resolution to conserve power, thus resolving the contradiction between measurement precision and energy consumption.
Solution Approach 2:
The invention changes the operational parameters (resolution and frame rate) of the image sensor based on the computed image variation tendency. By adjusting these parameters dynamically rather than maintaining fixed high settings, the system achieves accurate gesture detection only when needed, thereby reducing overall power consumption while maintaining detection accuracy.
2Use of energy by moving object
If low resolution and frame rate are used, then power consumption is reduced, but gesture detection accuracy deteriorates
Solution Approach 1:
The system transitions from static low-resolution operation to dynamic resolution adjustment. By computing image variation tendencies and switching to higher resolution when gestures are detected, the system ensures accurate gesture detection is maintained when needed while consuming less power during idle periods.
Solution Approach 2:
The system performs preliminary computation of image variation tendency at lower resolution before switching to high resolution. This preliminary action allows the system to prepare for accurate gesture detection by identifying potential gesture patterns early, ensuring that high resolution is activated only when necessary for actual gesture recognition.
3Device complexity
If brightness variation threshold mechanism is used, then simple processing is achieved, but inability to distinguish gesture from environment occurs
Solution Approach 1:
The invention segments the image analysis process into two stages: first computing image variation tendency to identify potential gesture regions, then performing detailed gesture recognition only in those regions. This segmentation allows the system to maintain low overall complexity while achieving high gesture distinction accuracy by focusing computational resources where needed.
Solution Approach 2:
The image variation tendency computation acts as an intermediary step between simple brightness thresholding and complex gesture recognition. This intermediary mechanism filters and identifies regions of interest, enabling the system to distinguish gestures from environmental changes more accurately without requiring full complex processing on all images.
Data Source
AI summary
An image processing method applied to an image processing system comprising an image sensor. The image processing method comprises: (a) capturing a plurality of first images via the image sensor applying a first resolution; (b) computing an image variation tendency for the first images; and (c) controlling the image sensor to apply a second resolution higher than the first resolution if the image variation tendency matches a predetermined tendency. An image processing system applying the image processing method is also disclosed.