Image Processing Apparatus Local Feature Integration
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Solution Overview
Problem
Current image processing technologies face challenges in accurately classifying objects within images by relying solely on global features, which can lead to misclassification due to variations in object orientation, scale, and background noise.
Innovation Solution
An image processing apparatus that divides images into multiple partial areas, learns features using a machine learning model, and integrates classification results from these areas to determine the presence of specific objects, employing local feature extraction and classification models like SIFT and HOG to enhance accuracy.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If global features are used for object classification, then the classification process is simple, but classification accuracy deteriorates due to variations in object orientation, scale, and background noise
Solution Approach 1:
The image is divided into multiple partial image areas, and each area is processed independently to extract local features. This segmentation approach allows the system to capture detailed local characteristics while maintaining manageable processing complexity through modular operations.
Solution Approach 2:
Different processing approaches are applied to different regions of the image. Local feature extraction is performed on partial areas that may contain objects, while global features are processed separately. This allows the system to adapt processing methods to local image characteristics, improving accuracy without uniformly increasing complexity across the entire image.
2Measurement precision
If multiple partial image areas are processed independently, then classification accuracy improves through local feature extraction, but processing time increases
Solution Approach 1:
Classification results from multiple partial image areas are integrated through a result integration unit that combines local classification outcomes with global image features. This merging process achieves comprehensive classification accuracy by synthesizing information from different regions while avoiding redundant processing of identical features across overlapping areas.
Solution Approach 2:
The system processes only certain partial image areas that are likely to contain objects of interest, rather than uniformly processing the entire image. By identifying and focusing on relevant regions, the system achieves high classification accuracy for target objects while reducing unnecessary processing time on background or empty areas.
3Reliability
If local feature extraction is performed on overlapping partial areas, then robustness to object orientation and scale variations improves, but computational load increases
Solution Approach 1:
The image is divided into multiple partial image areas with overlaps to ensure that objects appearing at different orientations or scales are captured in at least one region. This segmentation strategy provides robustness by ensuring multiple views of the same object feature, while the modular nature of processing each segment keeps computational energy requirements manageable through parallel processing capabilities.
Data Source
AI summary
An image processing apparatus includes a first acquiring unit that acquires an image to be processed; a setting unit that sets multiple partial image areas in the image to be processed; a second acquiring unit that acquires a first classification result indicating a possibility that an object of a specific kind is included in each of the multiple partial image areas; and a generating unit that generates a second classification result indicating a possibility that the object of the specific kind is included in the image to be processed on the basis of the first classification result of each of the multiple partial image areas.