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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.

Stereo high dynamic range (HDR) image/video can be generated by using a pair of stereo cameras with different exposure parameters. This paper proposes a new stereo HDR imaging method using generative adversarial networks (GAN) with a low dynamic range (LDR) stereo imaging system. It is assumed here that the left-view (LV) image is under-exposed and the right-view (RV) image is over-exposed.

We detect and classify Table Tennis strokes in videos recorded in natural condition. The goal is to develop an intelligent computer environment where teachers and students can analyse their games for improving players performance.

The study of vascular morphometry requires segmenting vessels with high precision. Of particular clinical interest is the morphometric analysis of arterial bifurcations in Adaptive Optics Ophthalmoscopy (AOO) images of eye fundus. In this paper, we extend our previous approach for segmenting retinal vessel branches to the segmentation of bifurcations. This enables us to recover the microvascular tree and extract biomarkers that charactarize the blood flow.

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.

The region proposal task is generating a set of candidate regions that contain an object. In this task, it is most important to propose as many candidates of ground-truth in a fixed number of proposals. However, in an image, there are too small number of hard negative examples compared to the vast number of easy negatives, so the region proposal networks struggle to train hard negatives. Because of these problem, network tends to propose hard negatives as the candidates and fails to propose the ground-truth, which leads poor performance.