Image segmentation using superpixel ensembles

Recently there has been an increasing interest in image segmentation due to the needs of locating objects with high segmentation accuracy as required by many computer vision and image processing tasks. While image segmentation remains a research challenge, 'superpixel' as the perceptual meaningful grouping of pixels has become a popular concept and a number of superpixel-based image segmentation algorithms have been proposed. The goal of this thesis is to examine the state-of-the-art superpixel algorithms and introduce new methods for achieving better image segmentation outcome. To improve the accuracy of superpixel-based segmentation, we propose a colour covariance matrix-based segmentation algorithm (CCM). This algorithm employs a novel colour covariance descriptor and a corresponding similarity measure method. Moreover, based on the CCM algorithm, we propose a multi-layer bipartite graph model (MBG-CCM) and a low-rank representation technique based algorithm (LRR-CCM). In MBG-CCM, different superpixel descriptors are fused by a multi-layer bipartite graph, and in LRR-CCM, the similarities of the covariance descriptors of the superpixel are measured by the subspace structure. Besides, we develop a new over-segmentation, called superpixel association, and propose a novel segmentation algorithm (SHST) which is able to generate hierarchical segmentation from superpixel associations. In addition to those unsupervised segmentation algorithms, we also explore the algorithms for supervised segmentation. We propose a model for semantic segmentation, named 'generalized puzzle game', by which the segmentation information contained in the superpixels can be integrated into the supervised segmentation

Projection Convolutional Neural Networks for 1-bit CNNs via Discrete Back Propagation

The advancement of deep convolutional neural networks (DCNNs) has driven significant improvement in the accuracy of recognition systems for many computer vision tasks. However, their practical applications are often restricted in resource-constrained environments. In this paper, we introduce projection convolutional neural networks (PCNNs) with a discrete back propagation via projection (DBPP) to improve the performance of binarized neural networks (BNNs). The contributions of our paper include: 1) for the first time, the projection function is exploited to efficiently solve the discrete back propagation problem, which leads to a new highly compressed CNNs (termed PCNNs); 2) by exploiting multiple projections, we learn a set of diverse quantized kernels that compress the full-precision kernels in a more efficient way than those proposed previously; 3) PCNNs achieve the best classification performance compared to other state-of-the-art BNNs on the ImageNet and CIFAR datasets

One-Two-One Network for Compression Artifacts Reduction in Remote Sensing

Compression artifacts reduction (CAR) is a challenging problem in the field of remote sensing. Most recent deep learning based methods have demonstrated superior performance over the previous hand-crafted methods. In this paper, we propose an end-to-end one-two-one (OTO) network, to combine different deep models, i.e., summation and difference models, to solve the CAR problem. Particularly, the difference model motivated by the Laplacian pyramid is designed to obtain the high frequency information, while the summation model aggregates the low frequency information. We provide an in-depth investigation into our OTO architecture based on the Taylor expansion, which shows that these two kinds of information can be fused in a nonlinear scheme to gain more capacity of handling complicated image compression artifacts, especially the blocking effect in compression. Extensive experiments are conducted to demonstrate the superior performance of the OTO networks, as compared to the state-of-the-arts on remote sensing datasets and other benchmark datasets. The source code will be available here: https://github.com/bczhangbczhang/

Exogenous Melatonin Improves Cold Tolerance of Strawberry (Fragaria × ananassa Duch.) through Modulation of DREB/CBF-COR Pathway and Antioxidant Defense System

The strawberry (Fragaria × ananassa Duch.) is an important fruit crop cultivated worldwide for its unique taste and nutritional properties. One of the major risks associated with strawberry production is cold damage. Recently, melatonin has emerged as a multifunctional signaling molecule that influences plant growth and development and reduces adverse consequences of cold stress. The present study was conducted to investigate the defensive role of melatonin and its potential interrelation with abscisic acid (ABA) in strawberry plants under cold stress. The results demonstrate that melatonin application conferred improved cold tolerance on strawberry seedlings by reducing malondialdehyde and hydrogen peroxide contents under cold stress. Conversely, pretreatment of strawberry plants with 100 μM melatonin increased soluble sugar contents and different antioxidant enzyme activities (ascorbate peroxidase, catalase, and peroxidase) and non-enzymatic antioxidant (ascorbate and glutathione) activities under cold stress. Furthermore, exogenous melatonin treatment stimulated the expression of the DREB/CBF—COR pathways’ downstream genes. Interestingly, ABA treatment did not change the expression of the DREB/CBF—COR pathway. These findings imply that the DREB/CBF-COR pathway confers cold tolerance on strawberry seedlings through exogenous melatonin application. Taken together, our results reveal that melatonin (100 μM) pretreatment protects strawberry plants from the damages induced by cold stress through enhanced antioxidant defense potential and modulating the DREB/CBF—COR pathway. View Full-Tex

Molecular Packing Control Enables Excellent Performance and Mechanical Property of Blade-Cast All-Polymer Solar Cells

All-polymer solar cells (all-PSCs) are the most promising power generators for flexible and portable devices due to excellent morphology stability and outstanding mechanical property. Previous work indicates high crystallinity is beneficial to device performance but detrimental to mechanical property, therefore identifying the optimized ratio between crystalline and amorphous domains becomes important. In this work, we demonstrated highly efficient and mechanically robust all-PSCs by blade-coating technology in ambient environment based on PTzBI:N2200 system. By controlling the aggregation in solution state and ultrafast film formation process, a weakly ordered molecular packing morphology as well as small phase separation is obtained, which leads to not only the good photovoltaic performance (8.36%-one of the best blade-cast device in air) but also prominent mechanical characteristic. The controlled film shows a remarkable elongation with the crack onset strain of 15.6%, which is the highest result in organic solar cells without adding elastomers. These observations indicate the great promise of the developed all-PSCs for practical applications toward large-area processing technology

Image segmentation using superpixel ensembles

Recently there has been an increasing interest in image segmentation due to the needs of locating objects with high segmentation accuracy as required by many computer vision and image processing tasks. While image segmentation remains a research challenge, 'superpixel' as the perceptual meaningful grouping of pixels has become a popular concept and a number of superpixel-based image segmentation algorithms have been proposed. The goal of this thesis is to examine the state-of-the-art superpixel algorithms and introduce new methods for achieving better image segmentation outcome. To improve the accuracy of superpixel-based segmentation, we propose a colour covariance matrix-based segmentation algorithm (CCM). This algorithm employs a novel colour covariance descriptor and a corresponding similarity measure method. Moreover, based on the CCM algorithm, we propose a multi-layer bipartite graph model (MBG-CCM) and a low-rank representation technique based algorithm (LRR-CCM). In MBG-CCM, different superpixel descriptors are fused by a multi-layer bipartite graph, and in LRR-CCM, the similarities of the covariance descriptors of the superpixel are measured by the subspace structure. Besides, we develop a new over-segmentation, called superpixel association, and propose a novel segmentation algorithm (SHST) which is able to generate hierarchical segmentation from superpixel associations. In addition to those unsupervised segmentation algorithms, we also explore the algorithms for supervised segmentation. We propose a model for semantic segmentation, named 'generalized puzzle game', by which the segmentation information contained in the superpixels can be integrated into the supervised segmentation

Transcriptome Analysis Reveals Differential Gene Expression and a Possible Role of Gibberellins in a Shade-Tolerant Mutant of Perennial Ryegrass

The molecular basis behind shade tolerance in plants is not fully understood. Previously, we have shown that a connection may exist between shade tolerance and dwarfism, however, the mechanism connecting these phenotypes is not well understood. In order to clarify this connection, we analyzed the transcriptome of a previously identified shade-tolerant mutant of perennial ryegrass (Lolium perenne L.) called shadow-1. shadow-1 mutant plants are dwarf, and are significantly tolerant to shade in a number of environments compared to wild-type controls. In this study, we treated shadow-1 and wild-type plants with 95% shade for 2 weeks and compared the transcriptomes of these shade-treated individuals with both genotypes exposed to full light. We identified 2,200 differentially expressed genes (DEGs) (1,096 up-regulated and 1,104 down-regulated) in shadow-1 mutants, compared to wild type, following exposure to shade stress. Of these DEGs, 329 were unique to shadow-1 plants kept under shade and were not found in any other comparisons that we made. We found 2,245 DEGs (1,153 up-regulated and 1,092 down-regulated) in shadow-1 plants, compared to wild-type, under light, with 485 DEGs unique to shadow-1 plants under light. We examined the expression of gibberellin (GA) biosynthesis genes and found that they were down-regulated in shadow-1 plants compared to wild type, notably gibberellin 20 oxidase (GA20ox), which was down-regulated to 3.3% (96.7% reduction) of the wild-type expression level under shade conditions. One GA response gene, lipid transfer protein 3 (LTP3), was also down-regulated to 41.5% in shadow-1 plants under shade conditions when compared to the expression level in the wild type. These data provide valuable insight into a role that GA plays in dwarfism and shade tolerance, as exemplified by shadow-1 plants, and could serve as a guide for plant breeders interested in developing new cultivars with either of these traits

Borate-Based Artificial Solid-Electrolyte Interphase Enabling Stable Lithium Metal Anodes

Lithium (Li) metal is considered as the “holy grail” of anode materials for next-generation high energy batteries. However, notorious dendrite growth and interfacial instability could induce irreversible capacity loss and safety issues, limiting the practical application of Li metal anodes. Herein, we develop a novel approach to construct a borate-based artificial solid-electrolyte interphase (designated as B-SEI) through the reaction of metallic Li with triethylamine borane (TEAB). According to our cryogenic electron microscopy (Cryo-EM) characterization results, the artificial SEI adopts a glass-crystal bilayer structure, which facilitates uniform Li-ion transport and inhibits dendrite growth during Li plating. Benefiting from such an artificial SEI, the Li anode delivers an improved rate performance and prolonged cycle life. The symmetric Li/B-SEI||Li/B-SEI cell can maintain stable cycling for 700 h at a high current density of 3 mA cm–2. The full-cell pairing Li/B-SEI with LiFePO4 only exhibits minimal capacity decay after 500 cycles in a conventional carbonate-based electrolyte. This work demonstrates the feasibility of building a boride-based artificial SEI to stabilize the Li metal anode based on microscopic characterization results and comprehensive electrochemical data, which represents a promising avenue to develop practical Li metal batteries