GPU Object Detection via Coordinate Transformations
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Solution Overview
Problem
Existing digital image processing technologies face challenges in efficiently detecting objects within images, particularly when dealing with variations in size and orientation, which requires significant data processing and can lead to computational delays.
Innovation Solution
The use of a graphics processing unit (GPU) to perform feature detection through coordinate transformations, filtering, and accumulation operations, allowing for quick and efficient object detection by iterating through various transformations and updating accumulation elements, thereby scanning multiple potential variations of an object without excessive computational resource usage.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If traditional CPU-based object detection is used to handle variations in size and orientation, then detection accuracy is improved, but computational time and processing speed deteriorate
Solution Approach 1:
The patent replaces traditional CPU-based sequential processing with GPU-based parallel processing. The GPU's architecture with thousands of cores enables simultaneous execution of multiple coordinate transformations and filtering operations, substituting the mechanical sequential approach with a parallel computational system that achieves both high accuracy and fast processing speeds
Solution Approach 2:
The patent segments the object detection process into independent parallel operations: coordinate transformation, filtering, and accumulation. Each operation can be executed independently on different GPU cores simultaneously, allowing the system to maintain high detection accuracy while reducing overall processing time through parallel execution of these segmented tasks
2Adaptability or versatility
If comprehensive coordinate transformations are applied to detect all object variations, then detection completeness is improved, but computational complexity increases
Solution Approach 1:
The patent adds the dimension of parallelism to the coordinate transformation process by utilizing the GPU's multi-core architecture. Instead of sequentially applying transformations in a single computational dimension, the system executes multiple transformations simultaneously across different processing cores, achieving comprehensive detection coverage while managing complexity through dimensional expansion
Solution Approach 2:
The patent creates a universal detection framework that handles multiple object variations (different sizes, orientations, and positions) through a single integrated GPU-based system. The same hardware and algorithmic structure can process any coordinate transformation required, making the system universally applicable to all object detection scenarios without requiring separate specialized processing paths
3Productivity
If GPU is used for parallel processing of image data, then processing speed is improved, but data transfer overhead between CPU and GPU increases
Solution Approach 1:
The patent implements self-service by allowing the GPU to independently process image data once it is transferred from the CPU. The GPU performs all subsequent operations (coordinate transformations, filtering, accumulation) autonomously without requiring continuous CPU intervention or additional data transfers, thereby minimizing communication overhead and energy loss while maintaining high processing speed
Data Source
AI summary
Methods, systems and apparatus, including computer program products, for processing electronic images. An electronic image is transformed to generate a transformed image according to a first coordinate transformation. The transformed image is filtered, and a second coordinate transformation is applied to the filtered image to align the filtered image with an accumulation array that includes multiple accumulation elements. Each accumulation element is updated based on a respective value in the aligned image. The operations of generating a transformed image according to a first transformation, filtering the transformed image, applying a second transformation and updating each accumulation element are iterated for one or more further first coordinate transformations. These techniques can be implemented to detect features in the image using a graphics processing unit.