Area-of-Interest DCT Classification for Low-Quality Image Objects
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Solution Overview
Problem
Low-quality images, particularly those captured by amateur users with limited camera capabilities, often suffer from poor resolution and distortion, making it difficult to accurately identify and classify objects of interest, such as birds, due to user error and hardware limitations.
Innovation Solution
Applying a two-dimensional discrete cosine transform on an area of interest within a spatial image to convert it into a frequency domain image, followed by threshold-based identification and classification of sub-areas using intensity analysis, allowing for the isolation and classification of objects of interest.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If a two-dimensional discrete cosine transform is applied to an area of interest in a spatial image, then the ability to identify and classify objects of interest is improved, but the computational complexity and processing time increase
Solution Approach 1:
The patent applies DCT transform specifically to identified areas of interest rather than processing the entire image, segmenting the processing task to focus computational resources only on regions containing potential objects of interest, thereby improving identification accuracy while limiting overall computational complexity
Solution Approach 2:
The patent transforms spatial image data into frequency domain representation using DCT, changing the dimension of analysis from spatial domain to frequency domain, which enables better object identification and classification by revealing patterns not visible in the original spatial representation
2Measurement precision
If threshold-based identification is used to classify sub-areas in frequency domain images, then classification accuracy is improved, but the processing time and computational resources increase
Solution Approach 1:
The patent applies threshold-based analysis only to sub-areas within the frequency domain representation that contain relevant information, rather than analyzing the entire transformed image, achieving accurate classification while reducing overall processing time
Solution Approach 2:
The system performs preliminary threshold-based identification on the frequency domain image before final classification, pre-sorting and identifying candidate regions to reduce the computational burden of subsequent detailed analysis
Data Source
AI summary
Various embodiments relate to application of a two-dimensional discrete cosine transform upon an area of interest of a spatial image to create a frequency domain image. A sub-area of the frequency domain image can be identified and its inverse can be taken. The intensity of the inverse can be used to classify the sub-area. In one example, how the intensity changes over time can be used to classify the sub-area.