Pixel Summation via Integral Column and Row Sums
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Solution Overview
Problem
Existing methods for determining pixel summation in images, especially for non-rectangular areas, require extensive computational capacity and large memory, making them inefficient for object detection in computer vision.
Innovation Solution
A method involving hierarchical decomposition to calculate integral column and row sums, allowing for efficient determination of column-wise and row-wise sums of pixel values within specific areas of an image, using a processor to identify and sum pixel values across portions of columns and rows, thereby reducing computational complexity and memory requirements.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If existing methods for determining pixel summation are used, then object detection can be performed, but computational capacity and memory requirements become excessively large
Solution Approach 1:
The image is divided into multiple blocks, and pixel summation is performed independently for each block. This segmentation allows the computational task to be distributed and managed in smaller units, reducing the memory burden and computational complexity while maintaining accurate object detection capability across the entire image.
2Adaptability or versatility
If non-rectangular areas are used for pixel summation, then detection of various object shapes is enabled, but computational complexity increases
Solution Approach 1:
Non-rectangular areas are segmented into multiple rectangular sub-regions, allowing the use of efficient rectangular summation techniques for each sub-region. The results are then combined to obtain the total summation for the complex shape, maintaining versatility in detecting various object shapes while avoiding the computational complexity of direct non-rectangular summation.
Solution Approach 2:
Multiple rectangular sub-regions that form a non-rectangular area are processed individually and then merged by summing their results. This combining approach enables detection of complex shapes by leveraging the simplicity of rectangular region processing, thus reducing overall computational complexity.
3Productivity
If rectangular areas are used for pixel summation, then computational efficiency is improved, but detection of non-rectangular objects is limited
Solution Approach 1:
Non-rectangular objects are segmented into multiple rectangular regions, allowing efficient rectangular summation to be applied to each region. The individual rectangular sums are then combined to represent the entire non-rectangular object, thus maintaining computational efficiency while enabling detection of diverse object shapes.
Data Source
AI summary
In one example embodiment, a method includes determining at least one of integral column sums and integral row sums for pixels of an image and determining at least one of a column-wise sum of pixel values and a row-wise sum of pixel values associated with an area within the image based on at least one of the determined integral column sums and the determined integral row sums corresponding to a plurality of the pixels forming the area.