Cascade contour-enhanced panoptic segmentation for robotic vision perception.

Panoptic segmentation plays a crucial role in enabling robots to comprehend their surroundings, providing fine-grained scene understanding information for robots' intelligent tasks. Although existing methods have made some progress, they are prone to fail in areas with weak textures, small objects, etc. Inspired by biological vision research, we propose a cascaded contour-enhanced panoptic segmentation network called CCPSNet, attempting to enhance the discriminability of instances through structural knowledge.

From picture to 3D hologram: end-to-end learning of real-time 3D photorealistic hologram generation from 2D image input.

In this Letter, we demonstrate a deep-learning-based method capable of synthesizing a photorealistic 3D hologram in real-time directly from the input of a single 2D image. We design a fully automatic pipeline to create large-scale datasets by converting any collection of real-life images into pairs of 2D images and corresponding 3D holograms and train our convolutional neural network (CNN) end-to-end in a supervised way. Our method is extremely computation-efficient and memory-efficient for 3D hologram generation merely from the knowledge of on-hand 2D image content.

Three-dimensional computer holography enabled from a single 2D image.

To compute a high-quality computer-generated hologram (CGH) for true 3D real scenes, a huge amount of 3D data must be physically acquired and provided depending on specific devices or 3D rendering techniques. Here, we propose a computational framework for generating a CGH from a single image based on the idea of 2D-to-3D wavefront conversion. We devise a deep view synthesis neural network to synthesize light-field contents from a single image and convert the light-field data to the diffractive wavefront of the hologram using a ray-wave algorithm.

Deep-learning-based computer-generated hologram from a stereo image pair.

We propose a deep-learning-based approach to producing computer-generated holograms (CGHs) of real-world scenes. We design an end-to-end convolutional neural network (the Stereo-to-Hologram Network, SHNet) framework that takes a stereo image pair as input and efficiently synthesizes a monochromatic 3D complex hologram as output. The network is able to rapidly and straightforwardly calculate CGHs from the directly recorded images of real-world scenes, eliminating the need for time-consuming intermediate depth recovery and diffraction-based computations.

In vitro enzymatic electrochemical monitoring of glucose metabolism and production in rat primary hepatocytes on highly O permeable plates.

In situ continuous glucose monitoring under physiological culture conditions is imperative in understanding the dynamics of cell and tissue behaviors and their physiological responses since glucose plays an important role in principal source of biological energy. We therefore examined physiologically relevant dynamic changes in glucose levels based on glucose metabolism and production during aerobic culture (10% O) of rat primary hepatocytes stimulated with insulin or glucagon on a highly O permeable plate, which can maintain the oxygen concentration close to the periportal zone of the liver. As glucose monitoring devices, we used oxygen-independent glucose dehydrogenase-modified single-walled carbon nanotube electrodes placed close to the surface of the hepatocytes.

Semantic Edge Based Disparity Estimation Using Adaptive Dynamic Programming for Binocular Sensors.

Disparity calculation is crucial for binocular sensor ranging. The disparity estimation based on edges is an important branch in the research of sparse stereo matching and plays an important role in visual navigation. In this paper, we propose a robust sparse stereo matching method based on the semantic edges.

A mitochondrial revelation of early human migrations to the Tibetan Plateau before and after the last glacial maximum.

As the highest plateau surrounded by towering mountain ranges, the Tibetan Plateau was once considered to be one of the last populated areas of modern humans. However, this view has been tremendously changed by archeological, linguistic, and genetic findings in the past 60 years. Nevertheless, the timing and routes of entry of modern humans into the Tibetan Plateau is still unclear.