Image Processing Apparatus Region Segmentation Histogram Calculation
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Solution Overview
Problem
Conventional image processing techniques are complex and inefficient, particularly when dealing with rapidly moving living tissues, as they require step-by-step searches that complicate the image reading process and hinder real-time processing.
Innovation Solution
An image processing apparatus and method that divides the detection region into multiple areas, performs decimation processing on pixel values, and interpolates these values to calculate histograms, reducing processing time and complexity.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Loss of time
If step-by-step search is performed to reduce search range, then processing time is shortened, but device complexity increases due to complicated processing steps
Solution Approach 1:
The detection region is divided into multiple sub-regions, and histogram calculation is performed independently for each sub-region. This segmentation allows parallel processing of different regions, reducing overall processing time while maintaining a unified and simple processing framework without complex step-by-step searches.
Solution Approach 2:
Instead of performing full-resolution histogram calculation on the entire image, the method calculates histograms on down-sampled images and selectively processes only necessary regions. This partial action approach reduces computational load and processing time while avoiding the complexity of multi-step search algorithms.
2Loss of time
If step-by-step search is performed to reduce search range, then processing time is shortened, but ease of operation deteriorates due to complicated processing
Solution Approach 1:
The image processing is segmented into independent regional histogram calculations that can be executed in parallel. This simplifies the operational workflow compared to sequential step-by-step searches, making the system easier to operate while maintaining fast processing speed.
Solution Approach 2:
The method performs histogram calculation on down-sampled images rather than full-resolution images, significantly reducing computational complexity and making the system easier to operate. Only essential processing steps are performed, avoiding complicated multi-step procedures.
3Measurement precision
If full image reading is performed to maintain detection accuracy, then measurement precision is improved, but productivity decreases due to long processing time
Solution Approach 1:
The detection region is divided into multiple sub-regions, and histogram calculations are performed independently for each sub-region on down-sampled images. This segmentation enables parallel processing that maintains detection accuracy through comprehensive regional analysis while significantly improving processing speed through efficient resource utilization.
Solution Approach 2:
The method changes the resolution parameter by performing histogram calculation on down-sampled images rather than full-resolution images. This parameter change reduces computational load and improves processing speed while the regional division strategy maintains detection accuracy by ensuring thorough analysis of all detection regions.
4Productivity
If down-sampling is performed to reduce calculation load, then productivity is improved, but measurement precision deteriorates due to loss of image detail
Solution Approach 1:
The detection region is divided into multiple sub-regions, and histogram calculation is performed for each sub-region on down-sampled images. This segmentation compensates for the information loss from down-sampling by ensuring that regional characteristics are captured independently, maintaining detection accuracy while achieving high processing speed through reduced computational load.
Solution Approach 2:
The method changes the resolution parameter by performing calculations on down-sampled images to improve processing speed. The regional division strategy compensates for the loss of detail by ensuring that each region's characteristics are adequately captured, thereby maintaining detection accuracy despite the reduced resolution.
Data Source
AI summary
[Problem] To shorten the processing time without performing complicated processing during image reading.[Solution] The present disclosure provides an image processing apparatus that includes: a dividing unit that divides a detection region for detecting a feature value of an image into a plurality of regions; a decimation processing unit that performs a decimation process on a pixel value for each of the regions; and a histogram calculating unit that interpolates a pixel value having undergone the decimation process to calculate a histogram of pixel values of the regions. With this configuration, it is possible to shorten the processing time without performing complicated processing during image reading.