Density Measuring Device Region Segmentation for Multi-Class Object Accuracy
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Existing density measuring devices face challenges in accurately calculating the density of each object class in an image when multiple types of objects are present, due to false recognition of object classes.
Innovation Solution
A density measuring device comprising a first calculator to divide an image into regions and calculate the density of each object class, a second calculator to calculate the likelihood of each object class based on density and area or similarity, and a first generator to assign the highest likelihood object class density to each region, reducing errors from false determinations.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If the number of specific objects is measured based on the area ratio of the foreground region, then the measurement process is simple, but the accuracy deteriorates when multiple types of objects are captured due to false recognition
Solution Approach 1:
The image is divided into multiple regions, and density calculations are performed separately for each region. This segmentation allows the system to handle multiple object classes more accurately by localizing measurements and reducing false recognition across the entire image.
Solution Approach 2:
The system changes from a single area ratio parameter to multiple parameters including region-based density values and likelihood probabilities for each object class. This parameter expansion enables accurate differentiation between multiple object types while maintaining measurement efficiency.
2Measurement precision
If density is calculated for each object class in multiple regions, then measurement precision improves, but device complexity increases due to multiple calculators and generators
Solution Approach 1:
The first calculator performs multiple functions by calculating both density values and serving as the basis for likelihood calculation in the second calculator. This multi-functionality reduces the need for completely separate components while maintaining the required measurement precision.
Solution Approach 2:
The first generator acts as an intermediary that assigns object class densities based on likelihood calculations, bridging the gap between raw density measurements and final accurate object class identification. This mediator component simplifies the overall system architecture while improving precision.
Data Source
AI summary
According to an embodiment, a density measuring device includes a first calculator, a second calculator, and a first generator. The first calculator calculates, from an image including objects of a plurality of classes classified according to a predetermined rule, for each of a plurality of regions formed by dividing the image, density of the objects captured in the region. The second calculator calculates, from the density of the objects captured in each of the regions, likelihood of each object class captured in each of the regions. The first generator generates density data, in which position corresponding to each of the regions in the image is assigned with the density of the object class having at least the higher likelihood than the lowest likelihood from among likelihoods calculated for object classes captured in the corresponding region.