Bitmap-Based Texture Classification for Robot Object Recognition
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Solution Overview
Problem
Current systems face challenges in accurately classifying images as textured or textureless, which affects object recognition and robot interaction, particularly in environments with varying lighting conditions and visual features.
Innovation Solution
A computing system generates bitmaps from image portions to detect visual features and intensity variations, using a fused bitmap to classify images as textured or textureless, and adjusts motion planning for robot interaction based on these classifications.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If image classification is performed using traditional methods, then the system can process images, but the classification accuracy between textured and textureless images deteriorates in varying lighting conditions
Solution Approach 1:
The patent transforms the image classification problem from analyzing raw pixel values to analyzing frequency domain characteristics through bitmaps. By converting spatial domain image data into frequency domain bitmaps that represent visual features and intensity variations, the system achieves dimensionality change that makes classification robust to lighting variations.
Solution Approach 2:
The system changes the parameters used for classification from direct pixel intensity values to derived bitmap characteristics. By generating bitmaps that encode visual features and intensity variations, and classifying based on bitmap properties rather than raw pixel values, the system achieves parameter transformation that improves accuracy under varying lighting conditions.
2Measurement precision
If the system generates multiple bitmaps for feature detection, then the classification accuracy improves, but the computational complexity increases
Solution Approach 1:
The patent segments the image analysis process into distinct bitmap generation stages, where different bitmaps capture different visual features. By dividing the complex classification task into multiple specialized bitmap analyses (edge detection, intensity variation, etc.), the system achieves accurate classification while organizing computational complexity into manageable segments.
Solution Approach 2:
The system performs preliminary actions by pre-generating multiple bitmaps from the image before classification. These bitmaps pre-process and encode various visual features in advance, so that the actual classification step can efficiently combine these pre-computed features without redundant calculations, balancing accuracy with computational efficiency.
Data Source
AI summary
Systems and methods for classifying at least a portion of an image as being textured or textureless are presented. The system receives an image generated by an image capture device, wherein the image represents one or more objects in a field of view of the image capture device. The system generates one or more bitmaps based on at least one image portion of the image. The one or more bitmaps describe whether one or more features for feature detection are present in the at least one image portion, or describe whether one or more visual features for feature detection are present in the at least one image portion, or describe whether there is variation in intensity across the at least one image portion. The system determines whether to classify the at least one image portion as textured or textureless based on the one or more bitmaps.