Machine learning models are increasingly used in time series prediction with promising results. The model explanation of falls behind the development and makes less sense to users understanding decisions. This paper proposes ES-Mask, a post-hoc model-agnostic evolutionary strip mask-based saliency approach for applications. ES-Mask designs mask consisting strips same salient value consecutive s...

We present in this paper a study of concavity detection using binary edge mask information, and consistency links between them. A model for a corner is proposed as a particular arrangement of edge masks. In a second step, we merge these corner in order to build concavities. Enclosures are obtained as a particular case of concavities. Examples are shown in the context of suburban area and more p...

In recent years, speech enhancement by analysis-resynthesis has emerged as an alternative to conventional noise filtering approaches. Analysis-resynthesis replaces noisy speech with a signal that has been reconstructed from a clean speech model. It can deliver high-quality signals with no residual noise, but at the expense of losing information from the original signal that is not well-represen...

Inspired by recent evidence that a binary pattern may provide sufficient information for human speech recognition, this letter proposes a fundamentally different approach to robust automatic speech recognition. Specifically, recognition is performed by classifying binary masks corresponding to a word utterance. The proposed method is evaluated using a subset of the TIDigits corpus to perform is...

Rigid registration of bone structures in stacks of CT images can be improved by using a binary segmentation mask for the registration compared to using the segmented grey values directly. Three criteria are applied to the different reference and template datasets in order to quantify registration results. For all three criteria a statistically significant superiority of the technique using the ...