Landmark-Based Region of Interest Dimension Determination
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Solution Overview
Problem
Conventional image processing techniques face challenges in accurately determining the dimensions of a region of interest in a target object from an image, especially when the image quality is poor, leading to errors in Optical Character Recognition (OCR) and inefficient resource usage due to incorrect identification of corners and boundaries.
Innovation Solution
The method identifies and utilizes landmarks such as headers, footers, barcodes, or QR codes within the image to determine the class and dimensions of the target object, creating restricted search areas to accurately locate and rectify the corners of the region of interest, thereby improving OCR accuracy and resource management.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If conventional image processing techniques are used to determine dimensions of region of interest, then the process is simple, but accuracy deteriorates under poor image quality
Solution Approach 1:
The patent applies preliminary action by first detecting landmarks (such as corners, edges, or specific features) in the image before performing dimension measurements. This preliminary landmark detection step prepares the image data by identifying reliable reference points that can be used subsequently for accurate dimension calculation, even when the overall image quality is poor. The system detects these landmarks and uses them to establish a reference framework before the actual measurement process begins.
Solution Approach 2:
The patent introduces landmarks as intermediary elements that mediate between the raw image data and the final dimension measurements. These landmarks serve as intermediate reference points that bridge the gap between poor quality image data and accurate measurements. By using these intermediary landmarks as known reference points, the system can calculate real-world dimensions more accurately without being directly affected by the poor overall image quality.
2Measurement precision
If landmarks are used to determine target object class and dimensions, then measurement precision improves, but processing time increases
Solution Approach 1:
The patent applies segmentation by dividing the image processing task into distinct stages: first detecting landmarks, then using those landmarks to determine target object class, and finally calculating dimensions. This segmentation allows the system to focus computational resources on specific sub-tasks rather than processing the entire image uniformly, thereby reducing overall processing time while maintaining measurement precision through the use of identified landmarks.
Solution Approach 2:
The patent implements partial action by selectively processing only the regions of the image that contain detected landmarks, rather than analyzing the entire image. Once landmarks are identified, the system focuses computational efforts only on those specific areas to determine class and dimensions, leaving the rest of the image unprocessed. This partial processing approach significantly reduces computation time while maintaining accuracy.
3Measurement precision
If restricted search areas are created based on landmarks, then corner identification accuracy improves, but algorithm complexity increases
Solution Approach 1:
The patent applies local quality by creating restricted search areas around detected landmarks rather than searching the entire image for corners. Each landmark serves as a local reference point that defines a specific search region, allowing the algorithm to focus computational resources on localized areas where corners are most likely to be found. This local approach improves corner identification accuracy while managing algorithm complexity by limiting the search scope.
Data Source
AI summary
Methods and apparatus to determine the dimensions of a region of interest of a target object and a class of the target object from an image using target object landmarks are disclosed herein. An example method includes identifying a landmark of a target object in an image based on a match between the landmark and a template landmark; classifying a target object based on the identified landmark; projecting dimensions of the template landmark based on a location of the landmark in the image; and determining a region of interest based on the projected dimensions, the region of interest corresponding to text printed on the target object.