BMAN: Bidirectional Multi-scale Aggregation Networks for Abnormal Event Detection.

Abnormal event detection is an important task in video surveillance systems. In this paper, we propose a novel bidirectional multi-scale aggregation networks (BMAN) for abnormal event detection. The proposed BMAN learns spatiotemporal patterns of normal events to detect deviations from the learned normal patterns as abnormalities.

Endometrium segmentation on transvaginal ultrasound image using key-point discriminator.

Purpose: Transvaginal ultrasound imaging provides useful information for diagnosing endometrial pathologies and reproductive health. Endometrium segmentation in transvaginal ultrasound (TVUS) images is very challenging due to ambiguous boundaries and heterogeneous textures. In this study, we developed a new segmentation framework which provides robust segmentation against ambiguous boundaries and heterogeneous textures of TVUS images.

VRSA Net: VR Sickness Assessment Considering Exceptional Motion for 360° VR Video.

The viewing safety is one of the main issues in viewing virtual reality (VR) content. In particular, VR sickness could occur when watching immersive VR content. To deal with the viewing safety for VR content, objective assessment of VR sickness is of great importance.

Ultrafast layer based computer-generated hologram calculation with sparse template holographic fringe pattern for 3-D object.

In this paper, we propose a new ultrafast layer based CGH calculation that exploits the sparsity of hologram fringe pattern in 3-D object layer. Specifically, we devise a sparse template holographic fringe pattern. The holographic fringe pattern on a depth layer can be rapidly calculated by adding the sparse template holographic fringe patterns at each object point position.

Iterative deep convolutional encoder-decoder network for medical image segmentation.

In this paper, we propose a novel medical image segmentation using iterative deep learning framework. We have combined an iterative learning approach and an encoder-decoder network to improve segmentation results, which enables to precisely localize the regions of interest (ROIs) including complex shapes or detailed textures of medical images in an iterative manner. The proposed iterative deep convolutional encoder-decoder network consists of two main paths: convolutional encoder path and convolutional decoder path with iterative learning.

Acceleration of the calculation speed of computer-generated holograms using the sparsity of the holographic fringe pattern for a 3D object.

In computer-generated hologram (CGH) calculations, a diffraction pattern needs to be calculated from all points of a 3-D object, which requires a heavy computational cost. In this paper, we propose a novel fast computer-generated hologram calculation method using sparse fast Fourier transform. The proposed method consists of two steps.

Experimental investigation of the effect of binocular disparity on the visibility threshold of asymmetric noise in stereoscopic viewing.

Stereoscopic images could have asymmetric distortions caused by image processing in capture, synthesis, and compression of them. In 3D perception in stereoscopic display, the visibility threshold of the asymmetric distortions in the left and right images is important, which is tolerable to the human visual system. In this paper, we investigate the effect of the binocular disparity on the visibility threshold of asymmetric noises in stereoscopic images via subjective assessments.