Contour Detection Using Differential Kernels for Object Recognition
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Solution Overview
Problem
Current edge detection technologies rely heavily on color-texture analysis, which is not effective for distinguishing between similar objects, such as different mammalian species or man-made objects, and lack efficient methods for characterizing objects based on shape and spatial relationships in 2D image space, limiting their accuracy in object recognition.
Innovation Solution
A contour/shape detection model using differential kernels to select edge segments based on probabilistic relationships between edge features, promoting target edges while suppressing background edges, by calculating co-occurrence probabilities and extracting prominent edge features for accurate object identification.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If color-texture analysis is used for object detection, then local features can be identified, but accuracy in distinguishing between similar objects is limited
Solution Approach 1:
The patent segments the object detection process into multiple stages: initial edge detection using color-texture, followed by shape-based refinement using contour analysis and spatial relationship modeling. This segmentation allows the system to first identify potential objects using simple features, then progressively refine detection accuracy using more complex shape characteristics, thereby resolving the contradiction between initial detection capability and final discrimination accuracy
Solution Approach 2:
The patent transitions from 2D color-texture analysis to 3D shape analysis by incorporating depth information and spatial relationships. By adding the dimension of shape characterization through contour analysis and geometric modeling, the system can distinguish between objects that appear similar in 2D but have different three-dimensional structures, thus improving the ability to differentiate similar objects
2Measurement precision
If computing power is increased to match human visual system, then object recognition accuracy improves, but system complexity and resource requirements increase
Solution Approach 1:
The patent performs preliminary edge detection and feature extraction using efficient algorithms before applying more computationally intensive shape analysis. By pre-processing the image data to identify and filter potential objects of interest, the system reduces the amount of data that requires complex processing, thereby achieving high accuracy without requiring excessive computing power for the entire image
Solution Approach 2:
The patent applies different levels of processing complexity to different regions of the image based on their relevance. Areas containing potential objects of interest receive more sophisticated shape-based analysis, while background regions use simpler processing. This localized approach to quality processing maintains high accuracy for critical regions while reducing overall system complexity and computational burden
Data Source
AI summary
A contour/shape detection model may use relatively simple and efficient kernels to detect target edges in an object within an image or video. A co-occurrence probability may be calculated for two or more edge features in an image or video using an object definition. Edge features may be differentiated between in response to measured contextual support, and prominent edge features may be extracted based on the measured contextual support. The object may then be identified based on the extracted prominent edge features.