Edge-Pixel-Feature Descriptors for Texture-Less Object Detection
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Solution Overview
Problem
Existing object-detection techniques based on descriptors struggle with texture-less objects due to poor performance in scenarios with significant occlusion and clutter, and face challenges in scaling with large model libraries, leading to inaccurate matching and increased false detections.
Innovation Solution
A computer system generates edge-based descriptors by extracting line segments from images, determining their orientations, and calculating pairs of angles representing relative displacement and orientation, which are then used to create 2D histograms for object description, allowing for robust detection and identification of texture-less objects without relying on texture information.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If edge-based template matching is used to detect texture-less objects, then detection capability for texture-less objects is improved, but false detections increase significantly in cluttered environments
Solution Approach 1:
The patent segments the edge-based detection process into multiple hierarchical levels: local descriptors are computed for individual edge pixels, then aggregated into superpixel-level descriptors, and finally combined into global object descriptors. This segmentation allows the system to capture both fine-grained local geometry and coarse-grained global structure, enabling discrimination between true object edges and clutter in a multi-scale manner that reduces false detections while maintaining sensitivity to texture-less objects
Solution Approach 2:
The patent applies local quality by computing descriptors with different granularities at different spatial scales. Local descriptors capture fine geometric details at the edge pixel level, while superpixel and global descriptors capture broader structural patterns. This multi-granularity approach allows the system to adaptively weigh local versus global evidence, improving reliability for texture-less objects while using global context to suppress false detections from clutter
2Adaptability or versatility
If a large pose space is explored for pose-invariant detection, then detection robustness to transformations is improved, but search time grows linearly with model library size
Solution Approach 1:
The patent transforms the pose search problem from an exhaustive spatial-temporal search into a geometric constraint satisfaction problem by working in angle-space. Instead of searching through all possible poses, the system formulates detection as finding consistent angular relationships between edge pixels that satisfy geometric constraints. This dimensional transformation from pose-space to angle-space enables polynomial-time complexity solutions while maintaining pose invariance, as the angular relationships are inherently invariant to rigid transformations
3Reliability
If tolerance for high occlusion is increased, then detection robustness to occlusion is improved, but uniqueness of detection cue decreases leading to more false detections
Solution Approach 1:
The patent changes the parameter space from raw pixel intensity or simple edge presence to sophisticated angular relationship descriptors that capture the geometric configuration of multiple edges. By transforming the detection cues into angular relationship space and computing histograms of these relationships at multiple scales, the system maintains cue uniqueness even when individual edges are occluded. The multi-parameter angular descriptors provide sufficient discriminative power to tolerate high occlusion while avoiding false detections from clutter
Data Source
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AI summary
During a description technique (100), a local descriptor for an object (300) is generated (122) by computing a 2-dimensional histogram (600) of pairs of angles (514, 516) between pairs of line segments (510, 512) that are aligned with edge pixels associated with the object (300). The pairs of line segments (510, 512) belong to a subset of k neighboring or proximate line segments (310). Moreover, this 2D histogram (600) may represent the relative displacement and the relative orientations of the pairs of line segments (510, 512) in the subset as weights in bins or cells defined by angular quantization values, and the 2D histogram (600 may exclude lengths of the line segments. Subsequently, the generated 2D histogram (600) may be compared (210) to predefined sets of descriptors in a model library that are associated with a set of objects, and the object may be identified (212) as one of the set of objects based on a group of match scores determined in the comparisons.