Robust Method of Vote Aggregation and Proposition Verification for Invariant Local Features
This addresses the need for more robust and controllable object detection in computer vision, though it appears incremental as it builds on existing local features and multi-detection frameworks.
The paper tackles the problem of analyzing vote spaces from local features in multi-detection systems by proposing a method that replaces classic clustering, offering high control over cluster composition and verification steps. It achieves an exceptionally high detection rate with a low false detection chance compared to alternatives.
This paper presents a method for analysis of the vote space created from the local features extraction process in a multi-detection system. The method is opposed to the classic clustering approach and gives a high level of control over the clusters composition for further verification steps. Proposed method comprises of the graphical vote space presentation, the proposition generation, the two-pass iterative vote aggregation and the cascade filters for verification of the propositions. Cascade filters contain all of the minor algorithms needed for effective object detection verification. The new approach does not have the drawbacks of the classic clustering approaches and gives a substantial control over process of detection. Method exhibits an exceptionally high detection rate in conjunction with a low false detection chance in comparison to alternative methods.