An Iterative Co-Saliency Framework for RGBD Images

As a newly emerging and significant topic in computer vision community, co-saliency detection aims at discovering the common salient objects in multiple related images. The existing methods often generate the co-saliency map through a direct forward pipeline which is based on the designed cues or initialization, but lack the refinement-cycle scheme. Moreover, they mainly focus on RGB image and ignore the depth information for RGBD images. In this paper, we propose an iterative RGBD co-saliency framework, which utilizes the existing single saliency maps as the initialization, and generates the final RGBD cosaliency map by using a refinement-cycle model. Three schemes are employed in the proposed RGBD co-saliency framework, which include the addition scheme, deletion scheme, and iteration scheme. The addition scheme is used to highlight the salient regions based on intra-image depth propagation and saliency propagation, while the deletion scheme filters the saliency regions and removes the non-common salient regions based on interimage constraint. The iteration scheme is proposed to obtain more homogeneous and consistent co-saliency map. Furthermore, a novel descriptor, named depth shape prior, is proposed in the addition scheme to introduce the depth information to enhance identification of co-salient objects. The proposed method can effectively exploit any existing 2D saliency model to work well in RGBD co-saliency scenarios. The experiments on two RGBD cosaliency datasets demonstrate the effectiveness of our proposed framework.Comment: 13 pages, 13 figures, Accepted by IEEE Transactions on Cybernetics 2017. Project URL: https://rmcong.github.io/proj_RGBD_cosal_tcyb.htm

Deep edge map guided depth super resolution

Accurate edge reconstruction is critical for depth map super resolution (SR). Therefore, many traditional SR methods utilize edge maps to guide depth SR. However, it is difficult to predict accurate edge maps from low resolution (LR) depth maps. In this paper, we propose a deep edge map guided depth SR method, which includes an edge prediction subnetwork and an SR subnetwork. The edge prediction subnetwork takes advantage of the hierarchical representation of color and depth images to produce accurate edge maps, which promote the performance of SR subnetwork. The SR subnetwork is a disentangling cascaded network to progressively upsample SR result, where every level is made up of a weight sharing module and an adaptive module. The weight sharing module extracts the general features in different levels, while the adaptive module transfers the general features to the specific features to adapt to different degraded inputs. Quantitative and qualitative evaluations on various datasets with different magnification factors demonstrate the effectiveness and promising performance of the proposed method. In addition, we construct a benchmark dataset captured by Kinect-v2 to facilitate research on real-world depth map SR

Functional Genomic Analysis of Variation on Beef Tenderness Induced by Acute Stress in Angus Cattle

Beef is one of the leading sources of protein, B vitamins, iron, and zinc in human food. Beef palatability is based on three general criteria: tenderness, juiciness, and flavor, of which tenderness is thought to be the most important factor. In this study, we found that beef tenderness, measured by the Warner-Bratzler shear force (WBSF), was dramatically increased by acute stress. Microarray analysis and qPCR identified a variety of genes that were differentially expressed. Pathway analysis showed that these genes were involved in immune response and regulation of metabolism process as activators or repressors. Further analysis identified that these changes may be related with CpG methylation of several genes. Therefore, the results from this study provide an enhanced understanding of the mechanisms that genetic and epigenetic regulations control meat quality and beef tenderness

Young plasma reverses anesthesia and surgery-induced cognitive impairment in aged rats by modulating hippocampal synaptic plasticity

We investigated the protective effect of young plasma on anesthesia- and surgery-induced cognitive impairment and the potential underlying mechanism using bioinformatics, functional enrichment analysis, gene set enrichment analysis, Golgi-Cox staining, dendritic spine analysis, immunofluorescence assay, western blot analysis, and transmission electron microscopy. Furthermore, we performed behavioral assessments using the open field test, the novel object recognition test, and the Morris water maze test. We identified 1969 differentially expressed genes induced by young plasma treatment, including 800 upregulated genes and 1169 downregulated genes, highlighting several enriched biological processes (signal release from synapse, postsynaptic density and neuron to neuron synapse). Anesthesia- and surgery-induced cognitive impairment in aged rats was comparatively less severe following young plasma preinfusion. In addition, the decreased levels of synapse-related and tyrosine kinase B/extracellular signal-regulated protein kinase/cyclic adenosine monophosphate response element-binding protein (TrkB/ERK/CREB) signaling pathway-related proteins, dendritic and spine deficits, and ultrastructural changes were ameliorated in aged mice following young plasma preinfusion. Together, these findings suggest that young plasma reverses anesthesia- and surgery-induced cognitive impairment in aged rats and that the mechanism is associated with the activation of the TrkB/ERK/CREB signaling pathway and improvement in hippocampal synaptic plasticity

Direction finding and mutual coupling estimation for uniform rectangular arrays

A novel two-dimensional (2-D) direct-of-arrival (DOA) and mutual coupling coefficients estimation algorithm for uniform rectangular arrays (URAs) is proposed. A general mutual coupling model is first built based on banded symmetric Toeplitz matrices, and then it is proved that the steering vector of a URA in the presence of mutual coupling has a similar form to that of a uniform linear array (ULA). The 2-D DOA estimation problem can be solved using the rank-reduction method. With the obtained DOA information, we can further estimate the mutual coupling coefficients. A better performance is achieved by our proposed algorithm than those auxiliary sensor-based ones, as verified by simulation results

ESPRIT-like two-dimensional direction finding for mixed circular and strictly noncircular sources based on joint diagonalization

In this paper, a two-dimensional (2-D) direction-of-arrival (DOA) estimation method for a mixture of circular and strictly noncircular signals is presented based on a uniform rectangular array (URA). We first formulate a new 2-D array model for such a mixture of signals, and then utilize the observed data coupled with its conjugate counterparts to construct a new data vector and its associated covariance matrix for DOA estimation. By exploiting the second-order non-circularity of incoming signals, a computationally effective ESPRIT-like method is adopted to estimate the 2-D DOAs of mixed sources which are automatically paired by joint diagonalization of two direction matrices. One particular advantage of the proposed method is that it can solve the angle ambiguity problem when multiple incoming signals have the same angle θ or β. Furthermore, the theoretical error performance of the proposed method is analyzed and a closed-form expression for the deterministic Cramer-Rao bound (CRB) for the considered signal scenario is derived. Simulation results are provided to verify the effectiveness of the proposed method

Modeling of transmission distortion for multi-view video in packet lossy networks

In this paper, a mathematical model is proposed to estimate the distortion caused by random packet losses for multi-view video transmission. Based on the study of multiview video coding, the proposed model takes into account the disparity/motion compensation which relates the channel-induced distortion in the current frame with that in the previous frame or the adjacent view, and allows for any motion-compensated and disparity-compensated concealment method at the decoder. Comparative studies between the modeled and simulated distortion results demonstrates that the proposed model is able to estimate the transmission distortion of multi-view video with high accuracy