Image Object Resolution via Local Max Pixel Partitioning
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Current image processing methods struggle to accurately and efficiently distinguish and track closely spaced objects in images, often requiring excessive time, effort, and processing resources, and are unable to resolve clusters of more than two objects effectively.
Innovation Solution
A method and apparatus that generate partitions of contiguous object pixels in images, identify local max pixels, and perform quantitative resolution to determine object centroids and amplitudes, enabling faster and more accurate identification and tracking of objects, even in clusters of multiple objects.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If current image processing methods are used to distinguish closely spaced objects, then object detection can be performed, but the processing time and computational resources required are excessive
Solution Approach 1:
The patent applies segmentation by dividing the image into multiple partitions or regions of interest, then processing each partition separately to identify local maxima. This breaks down the complex task of resolving all closely spaced objects in the entire image into smaller, more manageable sub-tasks, reducing overall processing time while maintaining detection accuracy for each object cluster.
Solution Approach 2:
The patent performs preliminary actions by first identifying local maxima pixels and generating initial object hypotheses before conducting full quantitative resolution. This preliminary identification of potential object locations allows the system to focus computational resources only on regions containing objects, rather than processing the entire image uniformly, thus reducing processing time while preserving measurement precision.
2Measurement precision
If current image processing methods are used to track closely spaced objects, then object tracking can be performed, but the processing resources required are excessive
Solution Approach 1:
The patent segments the image processing task into distinct phases: local maxima identification, partition generation, and quantitative resolution. By processing only relevant regions containing local maxima rather than the entire image, the system reduces computational resource consumption while maintaining the ability to accurately track objects across frames through consistent local maxima detection.
Solution Approach 2:
The patent applies partial action by performing quantitative resolution only on partitions containing local maxima pixels, rather than processing the entire image. This selective processing approach reduces computational resource usage by focusing efforts only on regions where objects are likely present, while still achieving accurate object tracking through consistent identification of local maxima across image sequences.
3Measurement precision
If current image processing methods are used to resolve object clusters, then two objects can be distinguished, but clusters of more than two objects cannot be resolved
Solution Approach 1:
The patent segments complex multi-object clusters into smaller sub-partitions based on local maxima identification. Each partition containing a local maximum is processed independently through quantitative resolution, allowing the system to handle arbitrary numbers of objects within each partition. This segmentation approach enables the resolution of clusters with any number of objects, not limited to pairs, by breaking down large clusters into manageable subsets.
Solution Approach 2:
The patent introduces an additional dimension of processing by organizing objects into hierarchical partitions based on local maxima. Instead of attempting to resolve all objects in a cluster simultaneously in a single step, the system creates a multi-level partitioning structure where each level handles a subset of objects, enabling scalable resolution of clusters regardless of their size through this dimensional organization approach.
Data Source
AI summary
A method and apparatus for resolving a set of objects in an image of an area. A partition that captures a set of objects is identified using the image. The partition is comprised of a group of contiguous object pixels. A number of local max pixels are identified from the group of contiguous object pixels in the partition. A quantitative resolution of the set of objects captured in the partition is performed based on the number of local max pixels identified.