DRL-GAN: dual-stream representation learning GAN for low-resolution image classification in UAV applications.

Identifying tiny objects from extremely low resolution (LR) UAV-based remote sensing images is generally considered as a very challenging task, because of very limited information in the object areas. In recent years, there have been very limited attempts to approach this problem. These attempts intend to deal with LR image classification by enhancing either the poor image quality or image representations. In this paper, we argue that the performance improvement in LR image classification is affected by the inconsistency of the information loss and learning priority on Low-Frequency (LF) components and High-Frequency (HF) components. To address this LF-HF inconsistency problem, we propose a Dual-Stream Representation Learning Generative Adversarial Network (DRL-GAN).The core idea is to produce super image representations optimal for LR recognition by simultaneously recovering the missing information in LF and HF components, respectively, under the guidance of high-resolution (HR) images. We evaluate the performance of DRL-GAN on the challenging task of LR image classification. A comparison of the experimental results on the LR benchmark, namely HRSC and CIFAR-10, and our newly collected “WIDER-SHIP” dataset demonstrates the effectiveness of our DRL-GAN, which significantly improves the classification performance, with up to 10% gain on average

MoWLD: A Robust Motion Image Descriptor for Violence Detection

Abstract Automatic violence detection from video is a hot topic for many video surveillance applications. However, there has been little success in designing an algorithm that can detect violence in surveillance videos with high performance. Existing methods typically apply the Bagof-Words (BoW) model on local spatiotemporal descriptors. However, traditional spatiotemporal features are not discriminative enough, and also the BoW model roughly assigns each feature vector to only one visual word and therefore ignores the spatial relationships among the features. To tackle these problems, in this paper we propose a novel Motion Weber Local Descriptor (MoWLD) in the spirit of the well-known WLD and make it a powerful and robust descriptor for motion images. We extend the WLD spatial descriptions by adding a temporal component to the appearance descriptor, which implicitly captures local motion information as well as low-level image appear information. To eliminate redundant and irrelevant features, the nonparametric Kernel Density Estimation (KDE) is employed on the MoWLD descriptor. In order to obtain more discriminative features, we adopt the sparse coding and max pooling scheme to further process the selected MoWLDs. Experimental results on three benchmark datasets have demonstrated the superiority of the proposed approach over the state-of-the-arts

Micro-CT imaging reveals<i> Mekk3 </i>heterozygosity prevents cerebral cavernous malformations in <i>Ccm2</i>-deficient mice

Mutations in CCM1 (aka KRIT1), CCM2, or CCM3 (aka PDCD10) gene cause cerebral cavernous malformation in humans. Mouse models of CCM disease have been established by deleting Ccm genes in postnatal animals. These mouse models provide invaluable tools to investigate molecular mechanism and therapeutic approaches for CCM disease. However, the full value of these animal models is limited by the lack of an accurate and quantitative method to assess lesion burden and progression. In the present study we have established a refined and detailed contrast enhanced X-ray micro-CT method to measure CCM lesion burden in mouse brains. As this study utilized a voxel dimension of 9.5μm (leading to a minimum feature size of approximately 25μm), it is therefore sufficient to measure CCM lesion volume and number globally and accurately, and provide high-resolution 3-D mapping of CCM lesions in mouse brains. Using this method, we found loss of Ccm1 or Ccm2 in neonatal endothelium confers CCM lesions in the mouse hindbrain with similar total volume and number. This quantitative approach also demonstrated a rescue of CCM lesions with simultaneous deletion of one allele of Mekk3. This method would enhance the value of the established mouse models to study the molecular basis and potential therapies for CCM and other cerebrovascular diseases

Hydrolytic synthesis and structural characterization of lanthanide-acetylacetonato/hydroxo cluster complexes - A systematic study

Lanthanide hydroxide cluster complexes with acetylacetonate were synthesized by the hydrolysis of the corresponding hydrated lanthanide acetylacetonates in methanol in the presence of triethylamine. Polymeric lanthanide hydroxide complexes based on diamond-shaped dinuclear repeating units of [Ln(2)(CH(3)CO(3))(2)](4+) (Ln = La, Pr) and discrete complexes featuring a tetranuclear distorted cubane core of [Ln(4)(mu(3)-OH)(2)(mu(3)-OCH(3))(2)](8+) (Ln = Nd, Sm) and a nonanuclear core of [Ln(9)(mu(4)-O)(mu(4)-OH)(mu(3)-OH)(8)](16+) (Ln = Eu-Dy, Er, Yb) were obtained. The dependence of the cluster nuclearity on the identity of the lanthanide ion is rationalized in terms of the influences of a metal ion's Lewis acidity and the sterics about the Ln-OH unit on the kinetics of the assembly process that leads to a particular cluster.U.S. NSF[CHE-0238790

Classification techniques in pattern recognition

In this paper, we review some pattern recognition schemes published in recent years. After giving the general processing steps of pattern recognition, we discuss several methods used for steps of pattern recognition such as Principal Component Analysis (PCA) in feature extraction, Support Vector Machines (SVM) in classification, and so forth. Different kinds of merits are presented and their applications on pattern precognition are given. The objective of this paper is to summarize and compare some of the methods for pattern recognition, and future research issues which need to be resolved and investigated further are given along with the new trends and ideas

An algorithm for accuracy enhancement of license plate recognition

This paper presents an algorithm for extraction (detection) and recognition of license plates in traffic video datasets. For license plate detection, we introduce a method that applies both global edge features and local Haar-like features to construct a cascaded classifier consisting of 6 layers with 160 features. The characters on a license plate image are extracted by a method based on an improved blob detection algorithm for removal of unwanted areas. For license plate recognition (i.e., character recognition), an open source OCR is modified and used. Our proposed system is robust under poor illumination conditions and for moving vehicles. Our overall system is efficient and can be applied in real-time applications. Experimental results are demonstrated using a traffic video

Visualization and Comparison of the Level of Apurinic/Apyrimidinic Endonuclease 1 in Live Normal/Cancerous and Neuron Cells with a Fluorescent Nanoprobe

As a major apurinic/apyrimidinic endonuclease and a redox signaling protein in human cells, APE1 plays a crucial role in cellular function and survival. The relationship between alterations of APE1 expression and subcellular localization and the initiation, development and treatment of various cancers has received extensive attention. However, comparing the in-vivo activity of APE1 in normal and cancerous breast live cells remains challenging due to the low efficiency of commonly used liposome transfection methods in delivering DNA substrate probes into human normal breast epithelial cells (MCF-10A). In this work, we develop a DNA/RNA hybrid-based small magnetic fluorescent nanoprobe (25 ± 3 nm) that can be taken up by various live cells under magnetic transfection. The D0/R-nanoprobe demonstrates an outstanding specificity toward APE1 and strong resistance to the cellular background interference. Using this nanoprobe, we are not only able to visualize the intracellular activity of APE1 in breast ductal carcinoma (MCF-7) live cells, but also demonstrate the APE1 activity in MCF-10A live cells for the first time. The method is then extended to observe the changes in APE1 levels in highly metabolically active neuroendocrine cells under normal conditions and severe attacks by reactive oxygen species in real-time. The fluorescent nanoprobe provides a useful tool for studying the dynamic changes of intracellular APE1 in normal or cancerous live cells. It also displays the potential for visible and controllable release of miRNA drugs within live cells for therapeutic purposes