Abstract In the classical contamination models, such as gross-error (Huber and Tukey model or case-wise contamination), observations are considered units to be identified outliers not. This is very useful when number of variables moderately small. Alqallaf et al. (Ann Stat 37(1):311–331, 2009) show limits this approach for a larger introduced independent (cell-wise contamination) where now cell...

The recent pandemic emergency raised many challenges regarding the countermeasures aimed at containing virus spread, and constraining minimum distance between people resulted in one of most effective strategies. Thus, implementation autonomous systems capable monitoring so-called social distance</i...

A main challenge for tasks on panorama lies in the distortion of objects among images. In this work, we propose a Distortion-Aware Monocular Omnidirectional (DAMO) dense depth estimation network to address indoor panoramas with two steps. First, introduce distortion-aware module extract calibrated semantic features from omnidirectional Specifically, exploit deformable convolution adjust its sam...

Depth estimation is a classic task in computer vision, which of great significance for many applications such as augmented reality, target tracking and autonomous driving. Traditional monocular depth methods are based on cues prediction with strict requirements, e.g. shape-from-focus/ defocus require low field the scenes images. Recently, large body deep learning have been proposed has shown pr...

Monocular depth estimation is a hot research topic in autonomous car driving. Deep convolution neural networks (DCNN) comprising encoder and decoder with transfer learning are exploited the proposed work for monocular map of two-dimensional images. Extracted CNN features from initial stages later upsampled using sequence Bilinear UpSampling layers to reconstruct map. The forms feature extractio...