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Brand logo detection is a special aspect of machine vision. However, Video logo detection benchmarks are scarce in the public domain. We exploit the power of a deep convolutional neural network (DCNN) and leverage established datasets related to existing applications to develop a deep-transfer active-learning (DTAL) algorithm to select the most valuable samples so that the smallest number possible needs to be labeled to achieve maximum performance improvements for video object detection model training. By exploiting the possible shared deep feature space between static and video datasets through transfer learning based on highly adaptable DCNN features, DTAL implements diversity-based active learning to select the most informative samples from a sequence of similar image frames for video object detection. We successfully apply the new DTAL algorithm to implement active learning for logo detection from live streaming sports videos as well as pedestrian and face detection from video data. We show that DTAL is a better active-learning method than state-of-the-art deep-learning-based active-learning object detection techniques. We also contribute one of the largest video-based logo resources, the Sports Match Video Logo (SMVL) dataset, to facilitate general logo detection research using transfer- and active-learning applications for video object detection. [ABSTRACT FROM AUTHOR]

Brain oedema is a common pathological phenomenon following many diseases and may lead to severe secondary damage. Astrocytes are the most numerous cells in the brain. Five aquaporins (AQPs) have been found in mature astrocytes, which play crucial roles in water transportation. However, most studies have focused on AQP4 or AQP9 and whether another aquaporin such as AQP5 involved in brain oedema is unclear. Here, we addressed the issue that the expression pattern of AQP5 in rat astrocytes in vitro was altered in the hypotonic condition through some mitogen‐activated protein kinases (MAPK) pathways. Primary astrocytes were randomly divided into the control group and the hypotonic group. Cell viability was evaluated by MTT test. Immunofluorescence, Western blotting and real‐time PCR were used to detect the expression of AQP5. Western blotting was used to detect the variation of MAPK pathway. The present study demonstrated that incubation of astrocytes in the hypotonic medium produced an increase inAQP5 expression, and AQP5 peaked at 6–12 h after hypotension solution exposure. In addition, MAPK pathways were set in motion under hypotension, but not all branches. Only the p38 inhibitor can inhibit AQP5 expression in cultured astrocytes. AQP5 is directly related to the extracellular hypotonic stimuli in astrocytes, which could be regulated through the p38 MAPK pathway. [ABSTRACT FROM AUTHOR]

Several compressor inlet guide vanes (IGV) with cracks of 9F gas turbine generator unit have been found fractured during the inspection process. The purpose of this paper is to find out the failure cause of those fractured IGV in terms of visual inspection, metallographic examination, chemical component analysis, microscopic fracture analysis, hardness test and energy spectrum analysis and so on. Macro-observations showed that all the breaking points were located at the chamfering position of IGV root, where exits the stress concentration confirmed by the stress analysis. According to the results of energy spectrum analysis, the corrosive element such as chlorine and sulfur were enriched on the IGV surface, producing local pitting corrosion and formation of corrosion fatigue source. In addition, the coarse martensite structure and organizational segregation of individual IGV by means of metallographic examination resulted in the performance degradation including strength and corrosion resistance, which accelerated the formation and propagation of the stress corrosion fatigue crack. [ABSTRACT FROM AUTHOR]

In supervised deep learning, learning good representations for remote-sensing images (RSI) relies on manual annotations. However, in the area of remote sensing, it is hard to obtain huge amounts of labeled data. Recently, self-supervised learning shows its outstanding capability to learn representations of images, especially the methods of instance discrimination. Comparing methods of instance discrimination, clustering-based methods not only view the transformations of the same image as "positive" samples but also similar images. In this paper, we propose a new clustering-based method for representation learning. We first introduce a quantity to measure representations' discriminativeness and from which we show that even distribution requires the most discriminative representations. This provides a theoretical insight into why evenly distributing the images works well. We notice that only the even distributions that preserve representations' neighborhood relations are desirable. Therefore, we develop an algorithm that translates the outputs of a neural network to achieve the goal of evenly distributing the samples while preserving outputs' neighborhood relations. Extensive experiments have demonstrated that our method can learn representations that are as good as or better than the state of the art approaches, and that our method performs computationally efficiently and robustly on various RSI datasets. [ABSTRACT FROM AUTHOR]

Image clustering is a very useful technique that is widely applied to various areas, including remote sensing. Recently, visual representations by self-supervised learning have greatly improved the performance of image clustering. To further improve the well-trained clustering models, this paper proposes a novel method by first ranking samples within each cluster based on the confidence in their belonging to the current cluster and then using the ranking to formulate a weighted cross-entropy loss to train the model. For ranking the samples, we developed a method for computing the likelihood of samples belonging to the current clusters based on whether they are situated in densely populated neighborhoods, while for training the model, we give a strategy for weighting the ranked samples. We present extensive experimental results that demonstrate that the new technique can be used to improve the state-of-the-art image clustering models, achieving accuracy performance gains ranging from 2.1 % to 15.9 % . Performing our method on a variety of datasets from remote sensing, we show that our method can be effectively applied to remote sensing images. [ABSTRACT FROM AUTHOR]

Facial attributes in StyleGAN generated images are entangled in the latent space which makes it very difficult to independently control a specific attribute without affecting the others. Supervised attribute editing requires annotated training data which is difficult to obtain and limits the editable attributes to those with labels. Therefore, unsupervised attribute editing in an disentangled latent space is key to performing neat and versatile semantic face editing. In this paper, we present a new technique termed Structure-Texture Independent Architecture with Weight Decomposition and Orthogonal Regularization (STIA-WO) to disentangle the latent space for unsupervised semantic face editing. By applying STIA-WO to GAN, we have developed a StyleGAN termed STGAN-WO which performs weight decomposition through utilizing the style vector to construct a fully controllable weight matrix to regulate image synthesis, and employs orthogonal regularization to ensure each entry of the style vector only controls one independent feature matrix. To further disentangle the facial attributes, STGAN-WO introduces a structure-texture independent architecture which utilizes two independently and identically distributed (i.i.d.) latent vectors to control the synthesis of the texture and structure components in a disentangled way. Unsupervised semantic editing is achieved by moving the latent code in the coarse layers along its orthogonal directions to change texture related attributes or changing the latent code in the fine layers to manipulate structure related ones. We present experimental results which show that our new STGAN-WO can achieve better attribute editing than state of the art methods. [ABSTRACT FROM AUTHOR]

In order to compare the effects of iopromide and isoxazole on postoperative contrast-induced nephropathy in patients with renal insufficiency, the paper searches for randomized controlled trials and retrospective cohort studies comparing the effects of iopromide and iodixanol on renal function in patients with renal insufficiency after surgery. The data are extracted from eligible studies. We tried to assess the incidence of contrast-agent nephropathy, preoperative and postoperative serum creatinine indicators, and mortality. This paper includes 8 studies with a total of 1243 patients. The incidence of contrast-induced nephropathy in the iopromide group is higher than that in the iodixanol group, and there is no significant difference between the two groups in postoperative mortality and preoperative serum creatinine expression. Sensitivity analysis and funnel chart show that our research is robust and has low publication bias. Our research shows that in patients with renal insufficiency, the incidence of contrast-medium nephropathy in the iopromide group is higher than that in the iodixanol group. Iodixanol is safer and has less effect on patients' serum creatinine levels. [ABSTRACT FROM AUTHOR]

The Huff model is designed to estimate the probability of shopping centre patronage based on a shopping centre's attractiveness and the cost of a customer's travel. In this paper, we attempt to discover some general shopping trends by calibrating the Huff model in Shenzhen, China, and New York, USA, using taxi trajectory GPS data and sharing bikes GPS data. Geographical and Temporal Weighted Regression (GTWR) is used to fit the model, and calibration results are compared with Ordinary Least Squares (OLS) regression, Geographical Weighted Regression (GWR), and Temporal Weighted Regression (TWR). Results show that GTWR gives the highest performance due to significant geographical and temporal variation in the Huff model parameters of attractiveness and travel cost. To explain the geographical variation, we use residential sales' and rental prices in Shenzhen and New York as a proxy for customers' wealth in each region. Pearson product-moment correlation results show a medium relationship between localised sales' and rental prices and the Huff model parameter of attractiveness: that is, customer wealth explains geographic sensitivity to shopping area attractiveness. To explain temporal variation, we use census data in both Shenzhen and New York to provide job profile distributions for each region as a proxy to estimate customers' spare leisure time. Regression results demonstrate that there is a significant linear relationship between the length of spare time and the parameter of shopping area attractiveness. In particular, we demonstrate that wealthy customers with less spare time are more sensitive to a shopping centre's attractiveness. We also discover customers' sensitivities to travel distance are related to their travel mode. In particular, people riding bikes to shopping areas care much more about trip distance compared with people who take taxi. Finally, results show a divergence in behaviours between customers in New York and Shenzhen at weekends. While customers in New York prefer to shop more locally at weekends, customers in Shenzhen care less about trip distance. We provide the GTWR calibration of the Huff model as our theoretical contribution. GTWR extends the Huff model to two dimensions (time and space), so as to analyse the differences of residents' travel behaviours in different time and locations. We also provide the discoveries of factors affecting urban travel behaviours (wealth and employment) as practical contributions that may help optimise urban transportation design. In particular, the sensitivity of residents to the attraction of shopping areas has a significant positive linear relationship with the housing price and a significant negative linear relationship with the residents' length of spare time. [ABSTRACT FROM AUTHOR]

Accurately locating the fovea is a prerequisite for developing computer aided diagnosis (CAD) of retinal diseases. In colour fundus images of the retina, the fovea is a fuzzy region lacking prominent visual features and this makes it difficult to directly locate the fovea. While traditional methods rely on explicitly extracting image features from the surrounding structures such as the optic disc and various vessels to infer the position of the fovea, deep learning based regression technique can implicitly model the relation between the fovea and other nearby anatomical structures to determine the location of the fovea in an end-to-end fashion. Although promising, using deep learning for fovea localisation also has many unsolved challenges. In this paper, we present a new end-to-end fovea localisation method based on a hierarchical coarse-to-fine deep regression neural network. The innovative features of the new method include a multi-scale feature fusion technique and a self-attention technique to exploit location, semantic, and contextual information in an integrated framework, a multi-field-of-view (multi-FOV) feature fusion technique for context-aware feature learning and a Gaussian-shift-cropping method for augmenting effective training data. We present extensive experimental results on two public databases and show that our new method achieved state-of-the-art performances. We also present a comprehensive ablation study and analysis to demonstrate the technical soundness and effectiveness of the overall framework and its various constituent components. [ABSTRACT FROM AUTHOR]

Fog removal from an image is an active research topic in computer vision. However, current literature is weak in the following two areas which in many ways are hindering progress for developing defogging algorithms. First, there is no true real-world and naturally occurring foggy image datasets suitable for developing defogging models. Second, there is no suitable mathematically simple and easy to use image quality assessment (IQA) methods for evaluating the visual quality of defogged images. We address these two aspects in this paper. We first introduce a new foggy image dataset called multiple real-world foggy image dataset (MRFID). MRFID contains foggy and clear images of 200 outdoor scenes. For each scene, one clear image and 4 foggy images of different densities defined as slightly foggy, moderately foggy, highly foggy, and extremely foggy, are manually selected from images taken from these scenes over the course of one calendar year. We then process the foggy images of MRFID using 16 defogging methods to obtain 12,800 defogged images (DFIs) and perform a comprehensive subjective evaluation of the visual quality of the DFIs. Through collecting the mean opinion score (MOS) of 120 subjects and evaluating a variety of fog-relevant image features, we have developed a new Fog-relevant Feature based SIMilarity index (FRFSIM) for assessing the visual quality of DFIs. We present extensive experimental results to show that our new visual quality assessment measure, the FRFSIM, is more consistent with the MOS than other IQA methods and is therefore more suitable for evaluating defogged images than other state-of-the-art IQA methods. Our dataset and relevant code are available at http://www.vistalab.ac.cn/MRFID-for-defogging/. [ABSTRACT FROM AUTHOR]

We present a new local window based image processing framework, which is particularly effective on edge-preserving and texture-removing. This seemingly contradictive effect is achieved by combining the traditional full window filtering strategy (FWF), which is good at removing noise, and the recently proposed side window filtering (SWF) strategy, which is good at preserving edges, so the new framework is called combined window filtering (CWF). By using window inherent variation method, we can easily distinguish the edges of structures from the texture. For the pixels on edges, SWF is used to preserve them and for the pixels on texture, FWF with multiple scales is used to remove them. This technique is surprisingly simple yet very effective in practice. We show that many traditional linear and nonlinear filters can be easily implemented under CWF framework. Extensive analysis and experiments show that implementing the CWF principle can significantly improve their edge-preserving and texture-removing capabilities and achieve state of the art performances in applications. [ABSTRACT FROM AUTHOR]

One of the challenges in the study of generative adversarial networks (GANs) is the difficulty of its performance control. Lipschitz constraint is essential in guaranteeing training stability for GANs. Although heuristic methods such as weight clipping, gradient penalty and spectral normalization have been proposed to enforce Lipschitz constraint, it is still difficult to achieve a solution that is both practically effective and theoretically provably satisfying a Lipschitz constraint. In this paper, we introduce the boundedness and continuity (BC) conditions to enforce the Lipschitz constraint on the discriminator functions of GANs. We prove theoretically that GANs with discriminators meeting the BC conditions satisfy the Lipschitz constraint. We present a practically very effective implementation of a GAN based on a convolutional neural network (CNN) by forcing the CNN to satisfy the BC conditions (BC–GAN). We show that as compared to recent techniques including gradient penalty and spectral normalization, BC–GANs have not only better performances but also lower computational complexity. [ABSTRACT FROM AUTHOR]

Background: Specific highly polarized aquaporin‐4 (AQP4) expression is reported to play a crucial role in blood‐brain barrier (BBB) integrity and brain water transport balance. The upregulation of polymerase δ‐interacting protein 2 (Poldip2) was involved in aggravating BBB disruption following ischemic stroke. This study aimed to investigate whether Poldip2‐mediated BBB disruption and cerebral edema formation in mouse bacterial meningitis (BM) model occur via induction of AQP4 polarity loss. Methods and Results: Mouse BM model was induced by injecting mice with group B hemolytic streptococci via posterior cistern. Recombinant human Poldip2 (rh‐Poldip2) was administered intranasally at 1 hour after BM induction. Small interfering ribonucleic acid (siRNA) targeting Poldip2 was administered by intracerebroventricular (i.c.v) injection at 48 hours before BM induction. A specific inhibitor of matrix metalloproteinases (MMPs), UK383367, was administered intravenously at 0.5 hour before BM induction. Western blotting, immunofluorescence staining, quantitative real‐time PCR, neurobehavioral test, brain water content test, Evans blue (EB) permeability assay, transmission electron microscopy (TEM), and gelatin zymography were carried out. The results showed that Poldip2 was upregulated and AQP4 polarity was lost in mouse BM model. Both Poldip2 siRNA and UK383367 improved neurobehavioral outcomes, alleviated brain edema, preserved the integrity of BBB, and relieved the loss of AQP4 polarity in BM model. Rh‐Poldip2 upregulated the expression of MMPs and glial fibrillary acidic protein (GFAP) and downregulated the expression of β‐dystroglycan (β‐DG), zonula occludens‐1 (ZO‐1), occludin, and claudin‐5; whereas Poldip2 siRNA downregulated the expression of MMPs and GFAP, and upregulated β‐DG, ZO‐1, occludin, and claudin‐5. Similarly, UK383367 downregulated the expression of GFAP and upregulated the expression of β‐DG, ZO‐1, occludin, and claudin‐5. Conclusion: Poldip2 inhibition alleviated brain edema and preserved the integrity of BBB partially by relieving the loss of AQP4 polarity via MMPs/β‐DG pathway. [ABSTRACT FROM AUTHOR]

Deep neural networks (DNNs) have been extensively applied in image processing, including visual saliency map prediction of images. A major difficulty in using a DNN for visual saliency prediction is the lack of labeled ground truth of visual saliency. A powerful DNN usually contains a large number of trainable parameters. This condition can easily lead to model over-fitting. In this study, we develop a novel method that overcomes such difficulty by embedding hierarchical knowledge of existing visual saliency models in a DNN. We achieve the objective of exploiting the knowledge contained in the existing visual saliency models by using saliency maps generated by local, global, and semantic models to tune and fix about 92.5% of the parameters in our network in a hierarchical manner. As a result, the number of trainable parameters that need to be tuned by the ground truth is considerably reduced. This reduction enables us to fully utilize the power of a large DNN and overcome the issue of over-fitting at the same time. Furthermore, we introduce a simple but very effective center prior in designing the learning cost function of the DNN by attaching high importance to the errors around the image center. We also present extensive experimental results on four commonly used public databases to demonstrate the superiority of the proposed method over classical and state-of-the-art methods on various evaluation metrics. [ABSTRACT FROM AUTHOR]

Single image defogging is a classical and challenging problem in computer vision. Existing methods towards this problem mainly include handcrafted priors based methods that rely on the use of the atmospheric degradation model and learning-based approaches that require paired fog-fogfree training example images. In practice, however, prior-based methods are prone to failure due to their own limitations and paired training data are extremely difficult to acquire. Moreover, there are few studies on the unpaired trainable defogging network in this field. Thus, inspired by the principle of CycleGAN network, we have developed an end-to-end learning system that uses unpaired fog and fogfree training images, adversarial discriminators and cycle consistency losses to automatically construct a fog removal system. Similar to CycleGAN, our system has two transformation paths; one maps fog images to a fogfree image domain and the other maps fogfree images to a fog image domain. Instead of one stage mapping, our system uses a two stage mapping strategy in each transformation path to enhance the effectiveness of fog removal. Furthermore, we make explicit use of prior knowledge in the networks by embedding the atmospheric degradation principle and a sky prior for mapping fogfree images to the fog images domain. In addition, we also contribute the first real world nature fog-fogfree image dataset for defogging research. Our multiple real fog images dataset (MRFID) contains images of 200 natural outdoor scenes. For each scene, there is one clear image and corresponding four foggy images of different fog densities manually selected from a sequence of images taken by a fixed camera over the course of one year. Qualitative and quantitative comparison against several state-of-the-art methods on both synthetic and real world images demonstrate that our approach is effective and performs favorably for recovering a clear image from a foggy image. [ABSTRACT FROM AUTHOR]

Structure-texture image decomposition is a fundamental but challenging topic in computational graphics and image processing. In this paper, we introduce a structure-aware and a texture-aware measures to facilitate the structure-texture decomposition (STD) of images. Edge strengths and spatial scales that have been widely-used in previous STD researches cannot describe the structures and textures of images well. The proposed two measures differentiate image textures from image structures based on their distinctive characteristics. Specifically, the first one aims to measure the anisotropy of local gradients, and the second one is designed to measure the repeatability degree of signal patterns in a neighboring region. Since these two measures describe different properties of image structures and textures, they are complementary to each other. The STD is achieved by optimizing an objective function based on the two new measures. As using traditional optimization methods to solve the optimization problem will require designing different optimizers for different functional spaces, we employ an architecture of deep neural network to optimize the STD cost function in a unified manner. The experimental results demonstrate that, as compared with some state-of-the-art methods, our method can better separate image structure and texture and result in shaper edges in the structural component. Furthermore, to demonstrate the usefulness of the proposed STD method, we have successfully applied it to several applications including detail enhancement, edge detection, and visual quality assessment of super-resolved images. [ABSTRACT FROM AUTHOR]

Deep learning approaches are widely applied to histopathological image analysis due to the impressive levels of performance achieved. However, when dealing with high-resolution histopathological images, utilizing the original image as input to the deep learning model is computationally expensive, while resizing the original image to achieve low resolution incurs information loss. Some hard-attention based approaches have emerged to select possible lesion regions from images to avoid processing the original image. However, these hard-attention based approaches usually take a long time to converge with weak guidance, and valueless patches may be trained by the classifier. To overcome this problem, we propose a deep selective attention approach that aims to select valuable regions in the original images for classification. In our approach, a decision network is developed to decide where to crop and whether the cropped patch is necessary for classification. These selected patches are then trained by the classification network, which then provides feedback to the decision network to update its selection policy. With such a co-evolution training strategy, we show that our approach can achieve a fast convergence rate and high classification accuracy. Our approach is evaluated on a public breast cancer histopathological image database, where it demonstrates superior performance compared to state-of-the-art deep learning approaches, achieving approximately 98% classification accuracy while only taking 50% of the training time of the previous hard-attention approach. [ABSTRACT FROM AUTHOR]

Global positioning system (GPS) data generated from taxi trips is a valuable source of information that offers an insight into travel behaviours of urban populations with high spatio-temporal resolution. However, in its raw form, GPS taxi data does not offer information on the purpose (or intended activity) of travel. In this context, to enhance the utility of taxi GPS data sets, we propose a two-layer framework to identify the related activities of each taxi trip automatically and estimate the return trips and successive activities after the trip, by using geographic point-of-interest (POI) data and a combination of spatio-temporal clustering, Bayesian inference and Monte Carlo simulation. Two million taxi trips in New York, the United States of America, and ten million taxi trips in Shenzhen, China, are used as inputs for the two-layer framework. To validate each layer of the framework, we collect 6,003 trip diaries in New York and 712 questionnaire surveys in Shenzhen. The results show that the first layer of the framework performs better than comparable methods published in the literature, while the second layer has high accuracy when inferring return trips. [ABSTRACT FROM AUTHOR]

Motivation For the diagnosis of cancer, manually counting nuclei on massive histopathological images is tedious and the counting results might vary due to the subjective nature of the operation. Results This paper presents a new segmentation and counting method for nuclei, which can automatically provide nucleus counting results. This method segments nuclei with detected nuclei seed markers through a modified simple one-pass superpixel segmentation method. Rather than using a single pixel as a seed, we created a superseed for each nucleus to involve more information for improved segmentation results. Nucleus pixels are extracted by a newly proposed fusing method to reduce stain variations and preserve nucleus contour information. By evaluating segmentation results, the proposed method was compared to five existing methods on a dataset with 52 immunohistochemically (IHC) stained images. Our proposed method produced the highest mean F1-score of 0.668. By evaluating the counting results, another dataset with more than 30 000 IHC stained nuclei in 88 images were prepared. The correlation between automatically generated nucleus counting results and manual nucleus counting results was up to R 2 = 0.901 (P  < 0.001). By evaluating segmentation results of proposed method-based tool, we tested on a 2018 Data Science Bowl (DSB) competition dataset, three users obtained DSB score of 0.331 ± 0.006. Availability and implementation The proposed method has been implemented as a plugin tool in ImageJ and the source code can be freely downloaded. Supplementary information Supplementary data are available at Bioinformatics online. [ABSTRACT FROM AUTHOR]

With the rapid growing of remotely sensed imagery data, there is a high demand for effective and efficient image retrieval tools to manage and exploit such data. In this letter, we present a novel content-based remote sensing image retrieval (RSIR) method based on Triplet deep metric learning convolutional neural network (CNN). By constructing a Triplet network with metric learning objective function, we extract the representative features of the images in a semantic space in which images from the same class are close to each other while those from different classes are far apart. In such a semantic space, simple metric measures such as Euclidean distance can be used directly to compare the similarity of images and effectively retrieve images of the same class. We also investigate a supervised and an unsupervised learning methods for reducing the dimensionality of the learned semantic features. We present comprehensive experimental results on two public RSIR datasets and show that our method significantly outperforms state-of-the-art. [ABSTRACT FROM AUTHOR]

One of the methods for stratifying different molecular classes of breast cancer is the Nottingham prognostic index plus, which uses breast cancer relevant biomarkers to stain tumor tissues prepared on tissue microarray (TMA). To determine the molecular class of the tumor, pathologists will have to manually mark the nuclei activity biomarkers through a microscope and use a semi-quantitative assessment method to assign a histochemical score (H-Score) to each TMA core. Manually marking positively stained nuclei is a time-consuming, imprecise, and subjective process, which will lead to inter-observer and intra-observer discrepancies. In this paper, we present an end-to-end deep learning system, which directly predicts the H-Score automatically. Our system imitates the pathologists’ decision process and uses one fully convolutional network (FCN) to extract all nuclei region (tumor and non-tumor), a second FCN to extract tumor nuclei region, and a multi-column convolutional neural network, which takes the outputs of the first two FCNs and the stain intensity description image as an input and acts as the high-level decision making mechanism to directly output the H-Score of the input TMA image. To the best of our knowledge, this is the first end-to-end system that takes a TMA image as the input and directly outputs a clinical score. We will present experimental results, which demonstrate that the H-Scores predicted by our model have very high and statistically significant correlation with experienced pathologists’ scores and that the H-Score discrepancy between our algorithm and the pathologists is on par with the inter-subject discrepancy between the pathologists. [ABSTRACT FROM AUTHOR]

This paper presents an advanced urban traffic density estimation solution using the latest deep learning techniques to intelligently process ultrahigh-resolution traffic videos taken from an unmanned aerial vehicle (UAV). We first capture nearly an hour-long ultrahigh-resolution traffic video at five busy road intersections of a modern megacity by flying a UAV during the rush hours. We then randomly sampled over 17 K 512×512 pixel image patches from the video frames and manually annotated over 64 K vehicles to form a dataset for this paper, which will also be made available to the research community for research purposes. Our innovative urban traffics analysis solution consists of an advanced deep neural network (DNN) based vehicle detection and localization, type (car, bus, and truck) recognition, tracking, and vehicle counting over time. We will present extensive experimental results to demonstrate the effectiveness of our solution. We will show that our enhanced single shot multibox detector (Enhanced-SSD) outperforms other DNN-based techniques and that deep learning techniques are more effective than traditional computer vision techniques in traffic video analysis. We will also show that ultrahigh-resolution video provides more information that enables more accurate vehicle detection and recognition than lower resolution contents. This paper not only demonstrates the advantages of using the latest technological advancements (ultrahigh-resolution video and UAV), but also provides an advanced DNN-based solution for exploiting these technological advancements for urban traffic density estimation. [ABSTRACT FROM AUTHOR]

The classification of habitats is crucial for structuring knowledge and developing our understanding of the natural world. Currently, most successful methods employ human surveyors -a laborious, expensive and subjective process. In this paper, we formulate habitat classification as a fine-grained visual categorization problem. We build on previous work and propose an image annotation framework that uses a novel automatic random forest-based method and that takes into consideration visual and geographical closeness in the classification process. During training, low-level visual features and medium-level contextual information are extracted. For the latter, we use a human-in-the-loop methodology by asking humans a set of 17 questions about the appearances of the image that can be easily answered by non-ecologists to extract medium-level knowledge about the images. During testing, and considering that close areas have similar ecological properties, we weigh the influence of the prediction of each tree of the forest according to their distance to the unseen test photography. Additionally, we present an updated version of a geo-referenced habitat image database containing over 1,000 high-resolution ground photographs that have been manually annotated by habitat classification experts. This has been made publicly available image database specifically designed for the development of multimedia analysis techniques for ecological applications. We show experimental recall and precision results which illustrate that our image annotation framework is able to annotate with a reasonable degree of confidence four of the main habitat classes: woodland and scrub, grassland and marsh, heathland and miscellaneous. [ABSTRACT FROM AUTHOR]

Background: Colour is the most important feature used in quantitative immunohistochemistry (IHC) image analysis; IHC is used to provide information relating to aetiology and to confirm malignancy.Methods: Statistical modelling is a technique widely used for colour detection in computer vision. We have developed a statistical model of colour detection applicable to detection of stain colour in digital IHC images. Model was first trained by massive colour pixels collected semi-automatically. To speed up the training and detection processes, we removed luminance channel, Y channel of YCbCr colour space and chose 128 histogram bins which is the optimal number. A maximum likelihood classifier is used to classify pixels in digital slides into positively or negatively stained pixels automatically. The model-based tool was developed within ImageJ to quantify targets identified using IHC and histochemistry.Results: The purpose of evaluation was to compare the computer model with human evaluation. Several large datasets were prepared and obtained from human oesophageal cancer, colon cancer and liver cirrhosis with different colour stains. Experimental results have demonstrated the model-based tool achieves more accurate results than colour deconvolution and CMYK model in the detection of brown colour, and is comparable to colour deconvolution in the detection of pink colour. We have also demostrated the proposed model has little inter-dataset variations.Conclusions: A robust and effective statistical model is introduced in this paper. The model-based interactive tool in ImageJ, which can create a visual representation of the statistical model and detect a specified colour automatically, is easy to use and available freely at http://rsb.info.nih.gov/ij/plugins/ihc-toolbox/index.html . Testing to the tool by different users showed only minor inter-observer variations in results. [ABSTRACT FROM AUTHOR]

Long non-coding RNAs (lncRNAs), a recently discovered class of non-coding genes, are transcribed throughout the genome. Emerging evidence suggests that lncRNAs may be involved in modulating various aspects of tumor biology, including regulating gene activity in response to external stimuli or DNA damage. No data are available regarding the expression of lncRNAs during genotoxic stress-induced apoptosis and/or necrosis in human glioma cells. In this study, we detected a change in the expression of specific candidate lncRNAs (neat1, GAS5, TUG1, BC200, Malat1, MEG3, MIR155HG, PAR5, and ST7OT1) during DNA damage-induced apoptosis in human glioma cell lines (U251 and U87) using doxorubicin (DOX) and resveratrol (RES). We also detected the expression pattern of these lncRNAs in human glioma cell lines under necrosis induced using an increased dose of DOX. Our results reveal that the lncRNA expression patterns are distinct between genotoxic stress-induced apoptosis and necrosis in human glioma cells. The sets of lncRNA expressed during genotoxic stress-induced apoptosis were DNA-damaging agent-specific. Generally, MEG3 and ST7OT1 are up-regulated in both cell lines under apoptosis induced using both agents. The induction of GAS5 is only clearly detected during DOX-induced apoptosis, whereas the up-regulation of neat1 and MIR155HG is only found during RES-induced apoptosis in both cell lines. However, TUG1, BC200 and MIR155HG are down regulated when necrosis is induced using a high dose of DOX in both cell lines. In conclusion, our findings suggest that the distinct regulation of lncRNAs may possibly involve in the process of cellular defense against genotoxic agents. [ABSTRACT FROM AUTHOR]