Vision System Image Multiprocessing via Adaptive Region Splitting
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Solution Overview
Problem
Existing image processing systems in vision systems do not optimally allocate processing resources, leading to inefficient use of processing units due to varying data content across image portions, which results in unequal processing times.
Innovation Solution
A method for image multiprocessing that involves splitting images into portions based on the execution time of a predefined image processing algorithm, identifying regions requiring more time for denser splitting, and allocating these regions to multiple processing units for optimized resource allocation.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If images are divided into equal slices for multiprocessing, then parallel processing capability is utilized, but processing resource allocation becomes inefficient due to varying data content characteristics
Solution Approach 1:
The patent applies local quality by dynamically adjusting the number of slices based on the specific characteristics of each image region. Instead of using a uniform slicing approach, the system analyzes image features (such as texture complexity, edge density, or color variation) and assigns different numbers of slices to different regions. This ensures that regions with more complex data content receive more processing resources (more slices) while simpler regions receive fewer slices, thereby optimizing overall processing efficiency and resource utilization.
2Ease of manufacture
If simple rectangular slicing is used, then implementation is straightforward, but processing time varies significantly across different image portions
Solution Approach 1:
The patent implements dynamics by making the slicing configuration adaptive rather than static. The system dynamically determines the optimal number of slices for each image region based on real-time analysis of image characteristics. This dynamic adjustment allows the slicing strategy to respond to varying data content, ensuring more balanced processing times across different regions while maintaining the simplicity of the overall slicing framework.
3Adaptability or versatility
If more slices are created to balance processing times, then resource allocation improves, but system complexity increases
Solution Approach 1:
The patent applies self-service by implementing an automated slicing configuration system that independently analyzes image characteristics and determines optimal slice distributions without requiring manual intervention. The system self-adjusts the number of slices based on detected image features, automatically balancing processing loads and optimizing resource allocation. This self-service approach reduces the complexity burden on users while maintaining sophisticated adaptive slicing capabilities.
Data Source
AI summary
The present disclosure relates to a method for image classes definition and to a method for image multiprocessing and related vision system, which implement said method for image classes definition. The latter comprising an image splitting operation for each image of M input images, the image splitting operation comprising the steps of: a) splitting the image into image portions; b) executing the algorithm onto each image portion with at least one processing unit; c) identifying the image portion associated with a maximum execution time of said algorithm; d) splitting said identified image portion into further image portions; e) checking if a stop criterion is met: e1) if the stop criterion is met, iterating steps a) to e) onto another one of the M input images; e2) if the stop criterion is not met, executing a predefined image processing algorithm onto each of the further image portions; identifying the image portion or further image portion associated with a maximum execution time of said algorithm; and iterating steps d) to e) on the so identified image portions portion or further image portion; wherein after executing steps a) to e) on all of the M input images, the method for image classes definition further comprises the steps of: f) identifying in an image space, for all the M input images, the positions of each split image portion/further image portion and defining clusters (A, B, C) based thereon; g) defining a set of Q image classes (A′, B′, C, AC) based on said clusters (A, B, C), each class (A′, B′, C, AC) being univocally associated with a splitting pattern representing in the image space a plurality of regions to be allocated to a corresponding plurality of processing units of the vision system for image multiprocessing.