A new approach of hybrid decision tree based floor state recognition

International audienceBlind people need some aid to interact with their environment with more security, especially, to avoid collision and falling. The process of staircase negotiation is complex for visually impaired people. Therefore, an intelligent system is worth helping them. In this paper, we investigate incorporating only one ultrasonic sensor within an electronic white cane to recognize floor state and classify the environment in three classes, even surface, ascending stair case and descending stair case. In our knowledge, no previous work was concerned with such context. The performance of floor state recognition system depends, firstly, on signal processing strategy, then, on objects representation and, finally, on classification approach, making the decision appropriate. In this paper, the proposed strategy consists in merging more than one transformation of ultrasonic signals, mostly Fourier transform and wavelet transform, used as features in a hybrid decision tree

Feature selection based on discriminative power under uncertainty for computer vision applications

Feature selection is a prolific research field, which has been widely studied in the last decades and has been successfully applied to numerous computer vision systems. It mainly aims to reduce the dimensionality and thus the system complexity. Features have not the same importance within the different classes. Some of them perform for class representation while others perform for class separation. In this paper, a new feature selection method based on discriminative power is proposed to select the relevant features under an uncertain framework, where the uncertainty is expressed through a possibility distribution. In an uncertain context, our method shows its ability to select features that can represent and discriminate between classes

An Evidence Theory-Based Approach to Handling Conflicting Temporal Data in OWL 2

International audienc