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Gait recognition is a leading remote-based identification method, suitable for applications in forensic cases, surveillance, and medical studies. We present Glidar3DJ, a model-based gait recognition methodology, using a skeleton model extracted from sequences generated by a single flash lidar camera. Compared with Kinect, a flash lidar camera has a drastically extended range (> 1000 meters) and its performance is not affected in outdoor.

Bacterial segmentation poses significant challenges due to
lack of structure, poor imaging resolution, limited contrast
between touching cells and high density of cells that overlap.
Although there exist bacterial segmentation algorithms in the

existing art, they fail to delineate cells in dense biofilms,
especially in 3D imaging scenarios in which the cells are growing

and subdividing in an unstructured manner. A graph-based

The underwater moving object segmentation is a challenging task. The problems like absorbing, scattering and attenuation of light rays between the scene and the imaging platform degrades the visibility of image or video frames. Also, the back-scattering of light rays further increases the problem of underwater video analysis, because the light rays interact with underwater particles and scattered back to the sensor. In this paper, a novel Motion Saliency Based Generative Adversarial Network (GAN) for Underwater Moving Object Segmentation (MOS) is proposed.

The underwater moving object segmentation is a challenging task. The problems like absorbing, scattering and attenuation of light rays between the scene and the imaging platform degrades the visibility of image or video frames. Also, the back-scattering of light rays further increases the problem of underwater video analysis, because the light rays interact with underwater particles and scattered back to the sensor. In this paper, a novel Motion Saliency Based Generative Adversarial Network (GAN) for Underwater Moving Object Segmentation (MOS) is proposed.

The underwater moving object segmentation is a challenging task. The problems like absorbing, scattering and attenuation of light rays between the scene and the imaging platform degrades the visibility of image or video frames. Also, the back-scattering of light rays further increases the problem of underwater video analysis, because the light rays interact with underwater particles and scattered back to the sensor. In this paper, a novel Motion Saliency Based Generative Adversarial Network (GAN) for Underwater Moving Object Segmentation (MOS) is proposed.

The underwater moving object segmentation is a challenging task. The problems like absorbing, scattering and attenuation of light rays between the scene and the imaging platform degrades the visibility of image or video frames. Also, the back-scattering of light rays further increases the problem of underwater video analysis, because the light rays interact with underwater particles and scattered back to the sensor. In this paper, a novel Motion Saliency Based Generative Adversarial Network (GAN) for Underwater Moving Object Segmentation (MOS) is proposed.

Scene understanding is an active area of research in computer vision that encompasses several different problems. The LiDARs and stereo depth sensor have their own restrictions such as light sensitiveness, power consumption and short-range. In this paper, we propose a two-stream deep adversarial network for single image depth estimation in RGB images. For stream I network, we propose a novel encoder-decoder architecture using residual concepts to extract course-level depth features.

Learning the compatibility relationship is of vital importance to a fashion recommendation system, while existing works achieve this merely on product images but not on street images in the complex daily life scenario. In this paper, we propose a novel fashion recommendation system: Given a query item of interest in the street scenario, the system can return the compatible items. More specifically, a two-stage curriculum learning scheme is developed to transfer the semantics from the product to street outfit images.

The recently-proposed reinforcement learning for mapless visual navigation can generate an optimal policy for searching different targets. However, most state-of-the-art deep reinforcement learning (DRL) models depend on hard rewards to learn the optimal policy, which can lead to the lack of previous diverse experiences. Moreover, these pre-trained DRL models cannot generalize well to un-trained tasks. To overcome these problems above, in this paper, we propose a Memorybased Parameterized Skills Learning (MPSL) model for mapless visual navigation.

Non-local sparsity has been widely concerned in image compressive
sensing. Considering the difference of distribution
characteristic of among group-based sparse coefficients of
image, a new method for image compressive sensing reconstruction
(ICSR) is proposed based on the z-scores standardized
group sparse representation (ZSGSR). Here, the
similar patch groups of the image are firstly extracted and
decomposed by adaptive PCA dictionary, then the resulting
coefficients are normalized using z-score standardization in