Gradient Direction Transform for Curved Item Detection
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Solution Overview
Problem
Conventional methods for detecting naturally curved items in digital images, such as the Hough Transform, are computationally expensive and inefficient for real-time applications with limited memory and processing resources, particularly in mobile devices.
Innovation Solution
A Gradient Direction Transform (GDT) is introduced, which uses simple integer operations and emphasizes gradient normal vector directions to efficiently detect curved items by incrementing or decrementing values in a transform space, reducing computational complexity and resource requirements.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If conventional methods such as Hough Transform are used to detect curved items in digital images, then detection accuracy is maintained, but computational complexity and processing time increase significantly
Solution Approach 1:
The patent extracts and emphasizes only the gradient normal vector directions from the complete gradient information, discarding magnitude and other redundant components. This selective extraction simplifies the input data for curved item detection while preserving the essential directional information needed for accurate detection, thereby reducing computational complexity without sacrificing detection accuracy.
Solution Approach 2:
The patent transforms the image processing parameters by converting pixel intensity and gradient magnitude information into a simplified transform space that only retains gradient normal vector directions. This parameter transformation reduces the dimensional complexity of the data structure from multi-dimensional gradient information to a simplified directional representation, enabling faster processing while maintaining detection precision.
2Measurement precision
If conventional image processing methods are used, then detection accuracy is achieved, but energy consumption increases due to complex operations
Solution Approach 1:
The patent extracts only the essential gradient normal vector direction information from complete gradient calculations, eliminating redundant computations of gradient magnitudes and other non-essential parameters. This extraction approach reduces the number of arithmetic operations required per pixel, directly lowering energy consumption while preserving the structural information needed for accurate curved item detection.
Solution Approach 2:
The patent employs simplified integer operations and approximate transformations that require less computational power and energy compared to conventional floating-point operations. By using computationally cheaper operations that still achieve the necessary detection accuracy, the system reduces energy consumption suitable for mobile devices with limited power resources.
3Loss of information
If complex transform operations are performed on each pixel, then detailed structural information is obtained, but processing speed decreases
Solution Approach 1:
The patent extracts and processes only the gradient normal vector directions for each pixel, omitting redundant structural information that would require more complex transformations. This selective extraction maintains the essential structural information needed for curved item detection while reducing the computational workload per pixel, thereby improving processing speed without significant loss of detection-relevant information.
Solution Approach 2:
The patent changes the transformation parameters from complex multi-step operations to simplified integer-based transformations in a reduced transform space. By parameterizing the transformation to use only gradient normal directions and simple increment/decrement operations, the system achieves faster processing speeds while retaining sufficient structural information for accurate curved item detection.
4Reliability
If conventional Hough Transform is used for curved item detection, then detection capability is maintained, but memory requirements increase
Solution Approach 1:
The patent extracts and processes only the gradient normal vector directions, creating a simplified transform space that requires less memory storage compared to the complete Hough Transform parameter space. By eliminating redundant parameter storage and processing only essential directional information, the system reduces memory requirements while maintaining the core detection capability for curved items.
Solution Approach 2:
The patent transforms the parameter representation from the traditional Hough space (requiring storage of multiple parameters per detected feature) to a simplified gradient direction space that uses fewer parameters. This parameter change reduces the memory footprint of the transform space and the data structures needed for detection, making the system more suitable for mobile devices with limited memory resources.
Data Source
AI summary
A method for obtaining structural information from a digital image by an electronic device is described. The method includes obtaining a digital image. The method also includes determining a gradient vector for each pixel in a region of interest of the digital image. The method further includes transforming each pixel in the region of interest in accordance with a transform. Transforming each pixel includes determining, for each pixel, a first set of pixels. The first set of pixels includes any pixel along a line that is collinear with or perpendicular to the gradient vector and passes through a pixel location. Transforming each pixel includes incrementing with signed integer values, for each pixel, a first set of values in a transform space corresponding to any of the first set of pixels that are in a first direction of the line.