Search

In the era of deep learning, it's important to obtain enough qualified data for model training. For the labor-intensive and time-consuming characteristics of data collection and annotation, date augmentation has been widely employed in deep learning methods to mitigate over-fitting. It has been proven to be one of the most effective ways to ensure the performance of deep models. In the field of computer vision, the principal goal of data augmentation usually is enlarging both the scale and diversity of the dataset, and maintaining the quality of both the image and the annotation data simultaneously. Data augmentation methods at the raw input data level are intuitive to obtain semantic meaningful images. In this paper, we provide a brief survey on some data augmentation studies conducted at image level and briefly categorize the studies into two main classes: those based on image editing and those based on image generation. We also make an analysis of the semantic-preserving considerations within data augmentation techniques. [ABSTRACT FROM AUTHOR]

Abstract Locating human action in spatio-temporal domain among untrimmed videos is an important but challenging task. Recent works have shown that incorporating contextual information leads to a significant improvement in action recognition, but there is still no existing work taking full advantage of context for action localization. While the popular target-centered methods have achieved promising results, they fail to exploit contexts and capture temporal dynamics in actions. In this paper, we propose a principled dynamic model, called spatio-temporal context model (STCM), to simultaneously locate and recognize actions. The STCM integrates various kinds of contexts, including the temporal context that consists of the sequences before and after action as well as the spatial context in the surrounding of target. Meanwhile, a novel dynamic programming approach is introduced to accumulate evidences collected at a small set of candidates in order to detect the spatio-temporal location of action effectively and efficiently. We report encouraging results on the UCF-Sports and UCF-101. It demonstrates that the contextual information is not only helpful for action recognition, but also contributes to action localization. [ABSTRACT FROM AUTHOR]

Recent works have shown that mid-level feature is superior to low-level feature, which can not only improve discriminative power, but also enhance descriptive capability. In this paper, the classical STIP, spatial star-graph and temporal star-graph are first extracted to represent human action from multi-perspectives. Then a principled feature learning algorithm is proposed to embed these multi-cues into a unified space and enhance all low-level features using diffusion map. Unlike treating spatio-temporal patch as mid-level primitive, we use a graph to model different types of primitives, then apply graph partitioning to co-cluster them into visual-word clusters called mid-level distinctive feature, which can bridge the semantic gap across low-level features. Experimental results show that our approach can successfully classify human activities with higher accuracies both on single-person actions (KTH and UCF) and complex interactional activities (UT-Interaction and HMDB51). [ABSTRACT FROM AUTHOR]

In this study, we present a new image completion method based on image entropy reduction. We complete the missing region with semantically matching images, which maximizes the reduction in the combined entropy of all regions in the image. We use labeled regions (high confidence regions) to complete the uncertain regions. By contrast, existing image completion methods focus on simple filling and ignore creative and semantic matching completion. Entropy reduction can yield higher accuracy of semantic image matching than existing image completion methods. We use Poisson blending and blending optimization (color handling) to complete the missing region with higher-quality results. The superiority of our method to existing image completion algorithms is validated. Experiments using an image database show that our method significantly improves the completion results. [ABSTRACT FROM AUTHOR]

We study the task of object recognition with no or few instances in its category. There are thousands of categories and only a few attributes. In other words, some attributes can be shared with different categories. In some case, it is difficult for some special categories to search enough positive instances. Even if it is easy for some category to search enough positive instances, it still waste time and resources. In this paper, we present an innovative image recognition method with no or few learning instances. We use attributes shared by other categories to learn the new category. It can make full use of the attributes and solve the problem that some categories do not have enough positive learning instances. Other objects or scenes in image also make contributions to improve recognition. At last, we set up a new database to do some experiments. The experimental results show our method's superiority. Although our method learns new category with no or few positive instances, it still keeps the high accuracy compared to traditional methods. [ABSTRACT FROM AUTHOR]

In this paper, we present a new model applied to watershed segmentation optimization. Watershed segmentation is widely used in the domain of computer vision. However, in some cases, there are some unobvious gradient changing between different regions or obvious gradient changing in the same region. So the images are divided into a number of regions either smaller (under-segmentation) or larger (over-segmentation) than the expected numbers of regions. We use a new filter (OLS operator) to pre-process the image. The new filter can smooth the image but keep the edge between different regions sharper based on image decompositions. Compared to traditional watershed segmentation, our method can segment images more accurately. Experiments using an image database show the superiority of our method. Even if we apply our method to complex images with more regions, it is still effective. Furthermore, our model can be applied to other domains of computer vision (e.g., image classification, image recognition, image matting). [ABSTRACT FROM AUTHOR]

In this paper, we propose a new image completion method based on high-accuracy semantic matching. Existing image completion approaches mainly focus on simple filling, ignoring creativity and accuracy in semantic matching. Our method can complete missing regions with more creative and semantically matching images as well as create a more seamless and consistent completed image. We use global and local features to search matching images for the target image. We then complete the missing region using Poisson blending and blending optimization. Results of experiments on challenging image databases show that the images are greatly improved. Thus, these results demonstrate the superiority of the proposed algorithm over existing image completion approaches. [ABSTRACT FROM AUTHOR]

Abstract: Herein an effective method for fabrication of three dimensional networks of starch is presented. In this method, the native starch was dissolved in hot water to form solution, resulting in the formation of starch gel after cooled to room temperature. Then the water in the starch gel was exchanged by ethanol, followed by drying with supercritical (SC) CO2 extraction, the nano-networks of starch were prepared. The obtained starch aerogel was composed of starch fibers and presented porous structure. Changing the initial concentration of native starch and controlling the drying conditions could control the microstructure of the starch networks. The resulting starch networks were utilized as a directing template to prepare the hierarchically ordered TiO2 networks. In this process, TiO2 precursor dissolved in SC CO2 diffused onto the surface of starch gel, and reacted with the hydroxyl groups and adsorbed water molecules on the starch surface, to form an organic/inorganic hybrid material. After calcining the composites at 500°C, the networks composed of anatase TiO2 nanocrystals were produced, which accurately replicated the fine structures of the starch gel and possessed higher BET surface area and total pore volume compared with those of other TiO2 materials templated by some biomaterials or polymer gels. [Copyright &y& Elsevier]

Abstract: In this work, a green route to prepare TiO2 nanotube networks using swim bladder (SB) membrane as a bio-template was reported. In this method, the SB membrane was first dried with CO2 extraction, which resulted in the fibrous structures of SB membrane. With the CO2-dried SB membrane as bio-template, TiO2 were obtained via the surface sol–gel reaction of titanium isopropoxide on the fibers of SB membrane in supercritical CO2, followed by the removal of SB membrane calcined at 600°C. The resulting TiO2 was characterized with scanning electron microscopy, transmission electron microscopy and X-ray diffraction. It was indicated that ordered anatase nanotube networks were produced. Compared to the conventional sol–gel method, the protocol adopted in this work could produce TiO2 nanotubes with regular and ordered structures. [Copyright &y& Elsevier]

Abstract: Mesoporous TiO2 films were successfully prepared via calcining ionic liquid solution containing titanium tetrabutyloxide and cellulose. The as-prepared TiO2 films were characterized by means of different techniques including X-ray diffraction, transmission electron microscopy, scanning electron microscopy and nitrogen sorption analysis. It was indicated that the resulting TiO2 films were composed of anatase nanocrystals with size of about 14nm, and the pore size of the films could be adjusted by the concentration of cellulose in IL. The possible formation mechanism of TiO2 films was also discussed. The prepared anatase films showed a high catalytic activity in photocatalytic reduction of Ag(I) to Ag and Au(III) to Au(0) under UV irradiation. [Copyright &y& Elsevier]

Abstract: This paper describes a rose variety recognition program and proposes Modified Fourier Descriptor. Based on the modified descriptor, a new measurement, Angle Measurement, is proposed for image shape analysis. The rationality of this measurement is analyzed and its mathematical definition is given. We use it for rose flower shape analysis and the experiment results show that this measurement is quite efficient for image shape analysis. [Copyright &y& Elsevier]

• To remove the low variations and noise of objects in the background, we extract the motion descriptor of the foreground by integrating background subtraction with binarization of surveillance videos. • In the training stage, to obtain a low-rank dictionary based on the similarity of normal training samples and a compact cluster of reconstruction coefficient vectors surrounding a center in the meantime, we propose a new joint optimization of the nuclear-norm and l 2, 1 -norm. • In the detection stage, to obtain a large gap between the reconstruction errors of abnormal testing samples and those of normal testing samples, we force the reconstruction coefficient vectors of abnormal frames to distribute so that they resemble those of normal ones by solving an l 2, 1 -norm optimization problem. In this paper, a novel approach to abnormal event detection in crowded scenes is presented based on a new low-rank and compact coefficient dictionary learning (LRCCDL) algorithm. First, based on the background subtraction and binarization of surveillance videos, we construct a feature space by extracting the histogram of maximal optical flow projection (HMOFP) feature of the foreground from a normal training frame set. Second, in the training stage, a new joint optimization of the nuclear-norm and l 2, 1 -norm is applied to obtain a compact coefficient low-rank dictionary. Third, in the detection stage, l 2, 1 -norm optimization is utilized to obtain the reconstruction coefficient vectors of the testing samples. Note that the l 2, 1 -norm forces the reconstruction vectors of all the testing samples to compactly surround the same center in the training stage, such that the reconstruction errors of abnormal testing samples are different from those of normal ones. Finally, a reconstruction cost (RC) is introduced to detect abnormal frames. Experimental results on both global and local abnormal event detection show the effectiveness of our algorithm. Based on comparisons with state-of-the-art methods employing various criteria, the proposed algorithm achieves comparable detection results. [ABSTRACT FROM AUTHOR]

Abstract: In this work, mesoporous CeO2 crystalline film was prepared via calcining the solution of Ce(NO3)3 in ionic liquid (1-allyl-3-methylimidazolium chloride) containing cellulose. The as-prepared CeO2 film was characterized by means of different techniques including X-ray diffraction, transmission electron microscopy equipped with energy dispersive X-ray spectroscopy, scanning electron microscopy and nitrogen sorption analysis. It was indicated that the as-prepared CeO2 film was composed of numerous CeO2 crystals with size of ∼9nm, which assembled with each other to form interconnected channels within the film. The BET specific surface area and the pore volume of the CeO2 film were estimated to be 62m2/g and 0.15cm3/g, respectively, based on the N2 sorption isotherms. Being used as a support, the CeO2 film was loaded with Au nanoparticles of ∼5nm in size via the reduction of HAuCl4 with NaBH4 in aqueous solution. The as-synthesized Au/CeO2 nanocomposite demonstrated a good catalytic activity for aerobic oxidation of benzyl alcohol. [Copyright &y& Elsevier]

Abstract: Many real-world classification tasks involve discriminations between two unbalanced classes in imprecise environments, in which either the training data do not represent a random sample of the target population or the class distribution may shift over time in the target population. In such situations, in order to minimize the misclassification costs, the class distribution in target population must be known for selecting the optimal threshold. Forman has presented a method, based on the distribution generated on training data and the distribution on unlabeled test data, for estimating the number of positives in target population. However, when the data size is small, it is difficult to reliably generate these distributions for estimating the number of positives. This paper presents a novel algorithm to generate these distributions based on the bootstrap and Fisher discriminant analysis. Experiment results on five UCI data sets demonstrate its effectiveness. [Copyright &y& Elsevier]

Abstract: SnO2 nanofibers were obtained by electrospinning of a poly(vinyl alcohol) (PVA)/SnCl4·5H2O solution. A novel micro gas sensor based on the SnO2 nanofibers was successfully fabricated. The morphology and crystal structure of the fibers were analyzed by scanning electron microscope (SEM), transmission electron microscope (TEM) and X-ray diffraction (XRD), and the ethanol-sensing properties of the sensor were tested. The results showed that the SnO2 nanofibers with an average diameter of ∼100nm could be directly deposited on a microhotplate by “near-field” electrospinning. The micro gas sensor exhibited large response, low detection limit, fast response/recovery and good reproducibility. The detection limit was <10ppb and the response/recovery time towards 10ppm ethanol was <14s. [Copyright &y& Elsevier]

Abstract: Metal oxide films, including Ce2O3 and/or CeO2, Al2O3, La2O3, were deposited on the outer surfaces of carbon nanotubes (CNTs) through the decomposition of metal nitrate precursors in supercritical CO2 modified with ethanol. Transmission electron microscopy showed that CNTs could be coated with metal oxide layers that were nominally complete and uniform. The thickness of the coating could be readily tailored by tuning the ratio of the initial mass of precursors to CNTs. The as-prepared CeO2–CNT composites showed high sensitivity and selectivity to acetone on the basis of chemiluminescence detection. [Copyright &y& Elsevier]

Abstract: Titanium dioxide (anatase, TiO2) nanoparticles have been successfully deposited onto multi-walled carbon nanotubes (MWCNTs) via hydrolysis of titanium isopropoxide in supercritical ethanol. The as-prepared composites were characterized by X-ray diffraction, transmission electron microscopy and X-ray photoelectron spectroscopy. It was demonstrated that the MWCNTs were decorated with well-dispersed anatase nanoparticles less than 7nm in diameter. The size and loading content of the nanoparticles on MWCNTs could be tuned by manipulating the ratio of precursor to MWCNTs, and the formation mechanism of the composites was also discussed. The absorbance spectrum of the resultant TiO2/MWCNT composites extended to the whole UV–visible region due to the decoration of TiO2 on MWCNTs. The TiO2/MWCNT composites were used as photocatalyst for phenol degradation under irradiation of visible light, which showed higher efficiency compared to a mixture of TiO2 and MWCNTs. [Copyright &y& Elsevier]

Abstract: PtRu nanoparticles were decorated on multi-walled carbon nanotubes (MWCNTs) using H2PtCl6 and RuCl3 as precursors with the aid of supercritical CO2, and the resulting composites were characterized by transmission electron microscopy (TEM), X-ray diffraction (XRD) and X-ray photoelectron spectroscopy. TEM observation showed that nanoparticles of size about 5nm were distributed evenly on the MWCNTs, and XRD analysis showed that the particles had a face-centered cubic crystal structure. The loading content of the nanoparticles on the MWCNTs could be adjusted by manipulating the relative ratio of the precursor to MWCNTs. The as-prepared PtRu/MWCNT composites exhibited high activity for methanol electro-oxidation. [Copyright &y& Elsevier]

Abstract: A simple and efficient route to decorate carbon nanotubes (CNTs) with nanocrystalline RuO2 has been developed. In this method, RuCl3 ·3H2O was oxidized into RuO2 by oxygen in supercritical diethyl amine, and the produced RuO2 deposited on CNTs, resulting in RuO2/CNT nanocomposites. The as-prepared composites were structurally and morphologically characterized by X-ray diffraction, X-ray photoelectron spectroscopy, and transmission electron microscopy (TEM). TEM images showed that RuO2 nanoparticles attached on CNTs had uniform shape and a narrow particle size distribution. The loading content and the size of RuO2 particles on CNTs could be tuned by changing the mass ratio of RuCl3 ·3H2O/CNT. Electrochemical measurements by cyclic voltammetry demonstrated a substantial increment of the specific capacitance of CNTs due to a pseudocapacitance originated from the deposited RuO2 nanoparticles. [Copyright &y& Elsevier]

Abstract: Mesoporous aluminosilicate molecular sieves have been synthesized using K-feldspar, a natural ore rich in silicon and aluminum sources, as starting material. In this method, the mixture of K-feldspar and K2CO3 with mass ratio of 2:3 was first calcined at 880°C for 1.2h. The calcined materials were then dissolved in water together with cetyltrimethylammonium bromide as structure-directing agent, resulting in mesoporous molecular sieves after heating the solution at 130°C for 60h. Scanning electron microscopy and transmission electron microscopy observations indicated that the resulting materials were spherical particles with size of about 100nm. The mesoporous structure of the as-synthesized materials was confirmed by low angle X-ray diffraction and nitrogen sorption analysis. The BET surface area of the as-prepared material after calcined at 550°C was 507m2 g−1 and the pore volume was 0.854cm3 g−1. 27Al MAS NMR analysis showed that nearly all of aluminium is incorporated into the framework of the mesoporous material with 4-coordinated state. The acidity of the product was analyzed by FTIR spectroscopy of pyridine-adsorbed product. The product was treated in water at 100°C for 360h, and its mesoporous structure was still intact, suggesting its high hydrothermal stability. [Copyright &y& Elsevier]

Homogeneity fiber/matrix composites of isotactic polypropylene (iPP) were prepared with both partially carbon-coated and non-carbon-coated iPP fibers. Their morphologies produced by melt recrystallization were studied by means of polarized optical microscopy. The results show that through vacuum evaporating a thin carbon film partially on the surface of iPP fiber, the nucleation ability of the molten iPP matrix during the course of recooling has been enhanced tremendously. The early formation and high density of the iPP row nuclei formed along the partially carbon-coated iPP fibers lead to the formation of an apparent iPP transcrystalline zone in the vicinity of its precoated fiber. The high nucleation ability of the carbon-coated iPP fiber towards its homogeneity matrix may originate from the surface fixing effect of the vacuum evaporated carbon layer on the polymer samples. [Copyright &y& Elsevier]