Image Object Location via Pixel Run Voting
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Solution Overview
Problem
Current image processing methods are inadequate for locating objects with repetitive patterns, such as barcodes and character strings, especially when they are touching other objects, as they fail to accurately distinguish separate elements within repetitive patterns.
Innovation Solution
The method involves binarizing the image to identify pixel runs, defining regions, and using a vote accumulator to locate objects by adjusting metrics based on characteristics such as run lengths and intensities, allowing for the identification of objects regardless of their orientation or exact size.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If current blob-based approaches are used to locate objects with repetitive patterns, then the method is simple to implement, but it fails to locate separate elements when they touch another object because the blob includes the separate elements within it
Solution Approach 1:
The patent segments the image processing task into multiple stages: defining candidate regions, extracting pixel runs, and applying voting metrics. This segmentation allows separate elements within touching objects to be identified individually through pixel run analysis rather than treating them as a single blob, thereby improving location accuracy while maintaining manageable complexity
Solution Approach 2:
The patent applies local quality by analyzing pixel run characteristics (length, intensity, orientation) at specific locations within candidate regions. By evaluating local pixel properties rather than global blob characteristics, the method can distinguish separate elements even when they touch, improving measurement precision without requiring overly complex global analysis
2Adaptability or versatility
If current approaches are used to locate objects, then they work for simple objects, but they are not robust enough to locate objects with repetitive patterns without being provided with an exact description of the object and knowledge of the object's size and orientation
Solution Approach 1:
The patent changes parameters by evaluating multiple pixel run characteristics (length, intensity, orientation) and using a voting metric that accumulates evidence across different regions. This parameter-based approach allows the system to adapt to different object types with repetitive patterns without requiring prior knowledge of exact object descriptions, sizes, or orientations, thereby improving versatility while keeping detection complexity manageable through systematic parameter evaluation
3Reliability
If blob-based methods are used to find aligned regions, then the process is fast and simple, but separate elements within touching objects cannot be located because the blob includes them within it
Solution Approach 1:
The patent applies preliminary action by first defining candidate regions and extracting pixel runs before performing the actual object location determination. This preliminary segmentation of the image into analyzable pixel runs enables reliable separation of touching elements while maintaining processing efficiency through systematic pre-processing that simplifies subsequent analysis
Data Source
AI summary
A method for locating an object in an image is provided for an object that exhibits, in at least one direction, foreground pixel runs and background pixel runs consistent with at least characteristic of the object. In one embodiment, the method includes the acts of defining a plurality of regions in the image, associating a metric with each of the regions, finding pixel runs in the image, and identifying at least one pixel run in one of the plurality of regions where the at least one pixel run is consistent with the at least one characteristic of the object. According to one embodiment, the method includes the acts of adjusting the metric associated with the region where the at least one pixel run is identified and identifying, from among the plurality of regions, a region where the object is located based on a metric determined for a plurality of regions. In various embodiments, the object includes a repetitive pattern such as those found in barcodes and character strings.