Mobile Terminal Automatic Foreground Blur Adjustment
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Solution Overview
Problem
Current mobile terminal image capturing technologies require users to manually select and set the blur for foreground objects, making the process laborious and inefficient, as it cannot perform automatic blurring of images.
Innovation Solution
A method in a mobile terminal that receives a virtual adjustment instruction to determine if the blur adjustment signal is a dynamic short or long change, acquiring corresponding blur mapping signals to adjust the target object's image-capturing state, allowing for automatic blurring by dynamically highlighting the object based on its movement.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Extent of automation
If manual selection and setting of blur for foreground objects is implemented, then the blurring function can be achieved, but the image capturing process becomes laborious and inefficient
Solution Approach 1:
The system automatically detects the foreground object and applies blurring without requiring user intervention. The processor identifies the subject in the captured image and autonomously determines the blur parameters, allowing the system to serve itself rather than requiring manual operation from the user.
Solution Approach 2:
The system performs preliminary detection and analysis of the foreground object before the actual image capture is completed. By pre-identifying the subject and pre-calculating the blur parameters based on the detected object's characteristics, the system prepares the blurring effect in advance, making the final image processing automatic and efficient.
2Productivity
If automatic blurring is implemented without manual selection, then the image capturing efficiency is improved, but the precision of blur application may be compromised
Solution Approach 1:
The system uses feedback from the image detection process to continuously adjust and refine the blur application. The processor analyzes the detected foreground object's characteristics and uses this feedback information to optimize the blur parameters, ensuring precise application while maintaining high processing efficiency through automated iterative refinement.
Solution Approach 2:
The system dynamically changes the blur parameters based on the detected foreground object's properties. By adjusting parameters such as blur radius, intensity, and distribution according to the object's size, shape, and position, the system achieves precise blur application automatically, matching the blur effect to the specific characteristics of each detected subject.
Data Source
AI summary
Provided is a method for generating a blurred photo graph, comprising: receiving a blurring adjustment instruction and determining a blurring adjustment signal trajectory of the blurring adjustment instruction; if the trajectory is a dynamic short change, according to a first blurring mapping signal corresponding to the dynamic forward movement of a target object, obtaining a virtual photographing state and adjusting the target object to be in the virtual photographing state; and if the trajectory is a dynamic long change, according to a second blurring mapping signal corresponding to the dynamic back ward movement of the target object, obtaining a virtual photographing state and adjusting the target object to be in the virtual photographing state.