Local Remote Object Classifier Segmentation for Image Processing
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Solution Overview
Problem
Current image processing methods for detecting characteristics of images are computationally intensive and require more efficient approaches to handle the processing of image data effectively.
Innovation Solution
An image processing system comprising a local object classifier, a remote object classifier, and storage for object definitions, where the local classifier detects objects, determines if to transmit data to the remote classifier, and receives classification results, optimizing processing by leveraging both local and remote resources.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If image processing is performed using a single local classifier, then processing speed is maintained, but object detection accuracy deteriorates
Solution Approach 1:
The system segments the classification task by dividing objects into two categories: common objects classified locally by the first object classifier, and rare objects transmitted to the second object classifier for specialized classification. This segmentation allows the system to maintain high processing speed for common objects while achieving high accuracy for rare objects through specialized remote classification.
Solution Approach 2:
The system introduces a communication interface as an intermediary between the first local object classifier and the second remote object classifier. This intermediary manages the transmission of image data and classification results, enabling coordinated operation between local and remote classifiers to achieve both speed and accuracy.
2Measurement precision
If all image processing is performed remotely, then object detection accuracy is improved, but computational efficiency deteriorates
Solution Approach 1:
The classification workload is segmented between local and remote classifiers based on object type. The first object classifier handles common objects locally, while the second object classifier specializes in rare objects. This segmentation prevents unnecessary remote transmission for common objects, maintaining processing efficiency while ensuring accurate classification for rare objects.
Solution Approach 2:
The first local object classifier performs self-service by independently classifying common objects without requiring remote assistance. This self-service capability maintains processing efficiency for the majority of common objects, while the system automatically engages the second remote classifier only when needed for rare objects.
3Measurement precision
If a hierarchical classification system is implemented, then object detection accuracy is improved, but device complexity increases
Solution Approach 1:
The hierarchical system is segmented into two distinct classification levels: a first object classifier for common objects and a second object classifier for rare objects. Each classifier is optimized for its specific domain, reducing the complexity individual classifiers would face if they attempted to handle all object types alone, while still achieving high overall accuracy.
Solution Approach 2:
A communication interface acts as an intermediary that manages the complexity of coordinating between two classifiers. This intermediary handles the logic of determining when to transmit images remotely and when to classify locally, simplifying the overall system architecture while enabling hierarchical classification benefits.
4Measurement precision
If image data is transmitted to remote classifier, then classification accuracy for rare objects is improved, but data transmission overhead increases
Solution Approach 1:
The system extracts and transmits only the specific image data required for rare object classification to the second object classifier, rather than transmitting all image data. This selective extraction minimizes data transmission overhead and energy consumption while ensuring accurate classification for rare objects that require remote analysis.
Solution Approach 2:
The first local object classifier performs self-service by handling common objects locally without any data transmission. This eliminates unnecessary data transmission energy consumption for the majority of common objects, while the system efficiently uses transmission resources only when rare objects require remote classification assistance.
Data Source
AI summary
A local object classifier using a set of object definitions to perform object classification in image frames. The local object classifier is arranged to detect an object in an image frame and determine whether to transmit image data for the detected object to a remote object classifier. In response to said determining, the local object classifier is arranged to transmit image data, derived from the image data representative of the image frame, to the remote object classifier. The local object classifier is also arranged to receive object data, representative of the detected object, from the remote object classifier.