Mutation of DEFECTIVE EMBRYO SAC1 results in a low seed-setting rate in rice by regulating embryo sac development.

The seed-setting rate has a significant effect on grain yield in rice (Oryza sativa L.). Embryo sac development is essential for seed setting; however, the molecular mechanism underlying this process remains unclear. Here, we isolated defective embryo sac1 (des1), a rice mutant with a low seed-setting rate. Cytological examination showed degenerated embryo sacs and reduced fertilization capacity in des1. Map-based cloning revealed a nonsense mutation in OsDES1, a gene that encodes a putative nuclear envelope membrane protein (NEMP)-domain-containing protein that is preferentially expressed in pistils. The OsDES1 mutation disrupts the normal formation of functional megaspores, which ultimately results in a degenerated embryo sac in des1. Reciprocal crosses showed that fertilization is abnormal and that the female reproductive organ is defective in des1. OsDES1 interacts with LONELY GUY (LOG), a cytokinin-activating enzyme that acts in the final step of cytokinin synthesis; mutation of LOG led to defective female reproductive organ development. These results demonstrate that OsDES1 functions in determining the rice seed-setting rate by regulating embryo sac development and fertilization. Our study sheds light on the function of NEMP-type proteins in rice reproductive development.

Large-aperture, widely and linearly tunable, electromagnetically actuated MEMS Fabry-Perot filtering chips for longwave infrared spectral imaging.

Longwave infrared spectral imaging (LWIR-SI) has potential in many important civilian and military fields. However, conventional LWIR-SI systems based on traditional dispersion elements always suffer the problems of high cost, large volume and complicated system structure. Micro-electro-mechanical systems Fabry-Perot filtering chips (MEMS-FPFC) give a feasible way for realizing miniaturized, low cost and customizable LWIR-SI systems. The LWIR MEMS-FPFC ever reported can't meet the demands of the next-generation LWIR-SI systems, due to the limitation of small aperture size and nonlinear actuation. In this work, we propose a large-aperture, widely and linearly tunable electromagnetically actuated MEMS-FPFC for LWIR-SI. A multi-field coupling simulation model is built and the wafer-scale bulk-micromachining process is applied to realize the design and fabrication of the proposed MEMS-FPFC. Finally, with the rational structural design and fabrication process, the filtering chip after packaging has an aperture size of 10 mm, which is the largest aperture size of LWIR MEMS-FPFC ever reported. The fabricated electromagnetically actuated MEMS-FPFC can be tuned continuously across the entire LWIR range of 8.39-12.95 µm under ±100 mA driving current with a pretty good linear response of better than 98%. The developed electromagnetically actuated MEMS-FPFC can be directly used for constructing miniaturized LWIR-SI systems, aiming for such applications as military surveillance, gas sensing, and industry monitoring.

OsCUL3a-Associated Molecular Switches Have Functions in Cell Metabolism, Cell Death, and Disease Resistance.

This study applies parallel reaction monitoring (PRM) proteomics and CRISPR-Cas9 mutagenesis to identify relationships between cell metabolism, cell death, and disease resistance. In oscul3a (oscullin3a) mutants, OsCUL3a-associated molecular switches are responsible for disrupted cell metabolism that leads to increased total lipid content in rice grain, a late accumulation of H2O2 in leaves, enhanced Xanthomonas oryzae pv. oryzae disease resistance, and suppressed panicle and first internode growth. In oscul3a mutants, PRM-confirmed upregulated molecular switch proteins include lipoxygenases (CM-LOX1 and CM-LOX2), suggesting a novel connection between ferroptosis and rice lesion mimic formation. Rice immunity-associated proteins OsNPR1 and OsNPR3 were shown to interact with each other and have opposing regulatory effects based on the cell death phenotype of osnpr1/oscul3a and osnpr3/oscul3a double mutants. Together, these results describe a network that regulates plant growth, disease resistance, and grain quality that includes the E3 ligase OsCUL3a, cell metabolism-associated molecular switches, and immunity switches OsNPR1 and OsNPR3.

High-Performance Visual Tracking With Extreme Learning Machine Framework.

In real-time applications, a fast and robust visual tracker should generally have the following important properties: 1) feature representation of an object that is not only efficient but also has a good discriminative capability and 2) appearance modeling which can quickly adapt to the variations of foreground and backgrounds. However, most of the existing tracking algorithms cannot achieve satisfactory performance in both of the two aspects. To address this issue, in this paper, we advocate a novel and efficient visual tracker by exploiting the excellent feature learning and classification capabilities of an emerging learning technique, that is, extreme learning machine (ELM). The contributions of the proposed work are as follows: 1) motivated by the simplicity and learning ability of the ELM autoencoder (ELM-AE), an ELM-AE-based feature extraction model is presented, and this model can provide a compact and discriminative representation of the inputs efficiently and 2) due to the fast learning speed of an ELM classifier, an ELM-based appearance model is developed for feature classification, and is able to rapidly distinguish the object of interest from its surroundings. In addition, in order to cope with the visual changes of the target and its backgrounds, the online sequential ELM is used to incrementally update the appearance model. Plenty of experiments on challenging image sequences demonstrate the effectiveness and robustness of the proposed tracker.

Blind Noisy Image Quality Assessment Using Sub-Band Kurtosis.

Noise that afflicts natural images, regardless of the source, generally disturbs the perception of image quality by introducing a high-frequency random element that, when severe, can mask image content. Except at very low levels, where it may play a purpose, it is annoying. There exist significant statistical differences between distortion-free natural images and noisy images that become evident upon comparing the empirical probability distribution histograms of their discrete wavelet transform (DWT) coefficients. The DWT coefficients of low- or no-noise natural images have leptokurtic, peaky distributions with heavy tails; while noisy images tend to be platykurtic with less peaky distributions and shallower tails. The sample kurtosis is a natural measure of the peakedness and tail weight of the distributions of random variables. Here, we study the efficacy of the sample kurtosis of image wavelet coefficients as a feature driving, an extreme learning machine which learns to map kurtosis values into perceptual quality scores. The model is trained and tested on five types of noisy images, including additive white Gaussian noise, additive Gaussian color noise, impulse noise, masked noise, and high-frequency noise from the LIVE, CSIQ, TID2008, and TID2013 image quality databases. The experimental results show that the trained model has better quality evaluation performance on noisy images than existing blind noise assessment models, while also outperforming general-purpose blind and full-reference image quality assessment methods.

State-aware Anti-drift Object Tracking.

Correlation filter (CF) based trackers have aroused increasing attentions in visual tracking field due to the superior performance on several datasets while maintaining high running speed. For each frame, an ideal filter is trained in order to discriminate the target from its surrounding background. Considering that the target always undergoes external and internal interference during tracking procedure, the trained tracker should not only have the ability to judge the current state when failure occurs, but also to resist the model drift caused by challenging distractions. To this end, we present a State-aware Anti-drift Tracker (SAT) in this paper, which jointly model the discrimination and reliability information in filter learning. Specifically, global context patches are incorporated into filter training stage to better distinguish the target from backgrounds. Meanwhile, a color-based reliable mask is learned to encourage the filter to focus on more reliable regions suitable for tracking. We show that the proposed optimization problem could be efficiently solved using Alternative Direction Method of Multipliers and fully carried out in Fourier domain. Furthermore, a Kurtosis-based updating scheme is advocated to reveal the tracking condition as well as guarantee a high-confidence template updating. Extensive experiments are conducted on OTB-100 and UAV-20L datasets to compare the SAT tracker with other relevant state-of-the-art methods. Both quantitative and qualitative evaluations further demonstrate the effectiveness and robustness of the proposed work.

Edge-Aware Unidirectional Total Variation Model for Stripe Non-Uniformity Correction.

The problem of stripe non-uniformity in array-based infrared imaging systems has been the focus of many research studies. Among the proposed correction techniques, total variation models have been proven to significantly reduce the effect of this type of noise on the captured image. However, they also cause the loss of some image details and textures due to over-smoothing effect. In this paper, a correction scheme is proposed based on unidirectional variation model to exploit the direction characteristic of the stripe noise, in which an edge-aware weighting is incorporated to convey image structure retaining ability to the overall algorithm. Moreover, a statistical-based regularization is also introduced to further enhance correction performance around strong edges. The proposed approach is thoroughly scrutinized and compared to the state-of-the-art de-striping techniques using real stripe non-uniform images. Results demonstrate a significant improvement in edge preservation with better correction performance.

Robust Object Tracking With Discrete Graph-Based Multiple Experts.

Variations of target appearances due to illumination changes, heavy occlusions, and target deformations are the major factors for tracking drift. In this paper, we show that the tracking drift can be effectively corrected by exploiting the relationship between the current tracker and its historical tracker snapshots. Here, a multi-expert framework is established by the current tracker and its historical trained tracker snapshots. The proposed scheme is formulated into a unified discrete graph optimization framework, whose nodes are modeled by the hypotheses of the multiple experts. Furthermore, an exact solution of the discrete graph exists giving the object state estimation at each time step. With the unary and binary compatibility graph scores defined properly, the proposed framework corrects the tracker drift via selecting the best expert hypothesis, which implicitly analyzes the recent performance of the multi-expert by only evaluating graph scores at the current frame. Three base trackers are integrated into the proposed framework to validate its effectiveness. We first integrate the online SVM on a budget algorithm into the framework with significant improvement. Then, the regression correlation filters with hand-crafted features and deep convolutional neural network features are introduced, respectively, to further boost the tracking performance. The proposed three trackers are extensively evaluated on three data sets: TB-50, TB-100, and VOT2015. The experimental results demonstrate the excellent performance of the proposed approaches against the state-of-the-art methods.

NMF-Based Image Quality Assessment Using Extreme Learning Machine.

Numerous state-of-the-art perceptual image quality assessment (IQA) algorithms share a common two-stage process: distortion description followed by distortion effects pooling. As for the first stage, the distortion descriptors or measurements are expected to be effective representatives of human visual variations, while the second stage should well express the relationship among quality descriptors and the perceptual visual quality. However, most of the existing quality descriptors (e.g., luminance, contrast, and gradient) do not seem to be consistent with human perception, and the effects pooling is often done in ad-hoc ways. In this paper, we propose a novel full-reference IQA metric. It applies non-negative matrix factorization (NMF) to measure image degradations by making use of the parts-based representation of NMF. On the other hand, a new machine learning technique [extreme learning machine (ELM)] is employed to address the limitations of the existing pooling techniques. Compared with neural networks and support vector regression, ELM can achieve higher learning accuracy with faster learning speed. Extensive experimental results demonstrate that the proposed metric has better performance and lower computational complexity in comparison with the relevant state-of-the-art approaches.

Robust Approach for Nonuniformity Correction in Infrared Focal Plane Array.

In this paper, we propose a new scene-based nonuniformity correction technique for infrared focal plane arrays. Our work is based on the use of two well-known scene-based methods, namely, adaptive and interframe registration-based exploiting pure translation motion model between frames. The two approaches have their benefits and drawbacks, which make them extremely effective in certain conditions and not adapted for others. Following on that, we developed a method robust to various conditions, which may slow or affect the correction process by elaborating a decision criterion that adapts the process to the most effective technique to ensure fast and reliable correction. In addition to that, problems such as bad pixels and ghosting artifacts are also dealt with to enhance the overall quality of the correction. The performance of the proposed technique is investigated and compared to the two state-of-the-art techniques cited above.

Extreme Learning Machine for Multilayer Perceptron.

Extreme learning machine (ELM) is an emerging learning algorithm for the generalized single hidden layer feedforward neural networks, of which the hidden node parameters are randomly generated and the output weights are analytically computed. However, due to its shallow architecture, feature learning using ELM may not be effective for natural signals (e.g., images/videos), even with a large number of hidden nodes. To address this issue, in this paper, a new ELM-based hierarchical learning framework is proposed for multilayer perceptron. The proposed architecture is divided into two main components: 1) self-taught feature extraction followed by supervised feature classification and 2) they are bridged by random initialized hidden weights. The novelties of this paper are as follows: 1) unsupervised multilayer encoding is conducted for feature extraction, and an ELM-based sparse autoencoder is developed via l1 constraint. By doing so, it achieves more compact and meaningful feature representations than the original ELM; 2) by exploiting the advantages of ELM random feature mapping, the hierarchically encoded outputs are randomly projected before final decision making, which leads to a better generalization with faster learning speed; and 3) unlike the greedy layerwise training of deep learning (DL), the hidden layers of the proposed framework are trained in a forward manner. Once the previous layer is established, the weights of the current layer are fixed without fine-tuning. Therefore, it has much better learning efficiency than the DL. Extensive experiments on various widely used classification data sets show that the proposed algorithm achieves better and faster convergence than the existing state-of-the-art hierarchical learning methods. Furthermore, multiple applications in computer vision further confirm the generality and capability of the proposed learning scheme.

Scale and orientation invariant text segmentation for born-digital compound images.

Many recent applications require text segmentation for born-digital compound images. To this end, we propose a coarse-to-fine framework for segmenting texts of arbitrary scales and orientations in born-digital compound images. In the coarse stage, the local image activity measure is designed based upon the variation distribution of characters, to highlight the difference between textual and pictorial regions. This stage outputs a coarse textual layer including textual regions as well as a few pictorial regions with high activity. In the fine stage, a textual connected component (TCC) based refinement is proposed to eliminate the survived pictorial regions. In particular, a scale and orientation invariant grouping algorithm is proposed to adaptively generate TCCs with uniform statistical features. The minimum average distance and morphological operations are employed to assist the formation of candidate TCCs. Then, three string-level features (i.e., shapeness, color similarity, and mean activity level) are designed to distinguish the true TCCs from the false positive ones that are formed by connecting the high activity pictorial components. Extensive experiments show that the proposed framework can segment textual regions precisely from born-digital compound images, while preserving the integrity of texts with varied scales and orientations, and avoiding over-connection of textual regions.

Optimal compression plane for efficient video coding.

All existing video coding standards developed so far deem video as a sequence of natural frames (formed in the XY plane), and treat spatial redundancy (redundancy along X and Y directions) and temporal redundancy (redundancy along T direction) differently and separately. In this paper, we investigate into a new compression (redundancy reduction) method for video in which the frames are allowed to be formed in a non-XY plane. We are to exploit fuller extent of video redundancy, and propose an adaptive optimal compression plane determination process to be used as a preprocessing step prior to any standard video coding scheme. The essence of the scheme is to form the frames in the plane formed by two axes (among X, Y, and T) corresponding to signal correlation evaluation, which enables better prediction (therefore better compression). In spite of the simplicity of the proposed method, it can be used for both lossless and lossy compression, and with and without interframe prediction. Extensive experimental results show that the new coding method improves the performance of the video coding for a number of coding methods (inclusive of lossless and near-lossless Motion JPEG-LS, Motion JPEG, Motion JPG2K, H.264 intraonly profile, and H.264) and videos with different visual content.