Synthetic Aperture Radar (SAR) satellites can monitor oil spills at sea. Due to the small difference of SAR image features between oil film and oil-like film, a dense connection network model based ...on DenseNet convolution neural network is proposed in this paper. The model extracts multi-scale features of the image, and improves the ability of capturing subtle features of the image and the accuracy of classification and recognition before reusing the features of each convolution layer. After filtering and denoising, the original oil spill SAR image is input into CNN network. Then the feature of SAR image is extracted by using CNN model. Finally, the feature is classified by using Soft-max. Experiments using ERS-2 SAR image data prove the effectiveness of this method in identifying "oil slicks" and "oillike slicks " images.
Red-light running is one of the major causes for traffic crashes at signalized intersection. In addition to traffic engineering and management measures, enforcement countermeasures are employed to ...encourage drivers to comply with traffic signal through the threat of penalty points and fine. However, the fine associated with red-light running is fixed no matter how many times one vehicle violates red signal during a given period. Such issue results in little deterrence effectiveness of fine for red-light running recidivism. Therefore, the objective of this study is to explore a novel model of increasing block fine structure based on the number of one vehicle committing red-light running in 1 year so as to prevent red-light running recidivism. First, using optimal partition method, the number of one vehicle committing red-light running in 1 year is categorized into a few groups that are regarded as the blocks for an increasing block fine structure. Second, the price elasticity is introduced and discussed to determine the changed number of red-light running and corresponding fine at each block. Third, an optimization model is proposed to determine the varying fine at each block and solved via the simulated annealing algorithm. After that, a case study is conducted to verify the validity of the developed model. The results indicate that this novel fine structure can effectively deter red-light running recidivism from running red signal. In addition, the fine structure established in this research not only offsets the defect of the present fine structure but also reduces red-light running to some extent.
Rating information is usually used to calculate and predict in traditional recommendation systems. They can obtain the explicit characteristics of the users, but without implicit information and ...enough semantic interpretation, which affect recommendation results. To address the issue, this paper proposes a unified probabilistic matrix factorization recommendation algorithm fusing social tagging. The algorithm constructs user-resource rating matrix, user-tag tagging matrix, resources-tag correlation matrix and uses unified probabilistic matrix factorization to get the latent feature vectors of three matrices, to recommend for users by optimizing model parameter. The experimental results show that the proposed algorithm can effectively improve the quality of recommendation.
Under the environment of information exploding, how to find pictures from the massive information to meet their own needs, has been a major factor restricting the efficiency of UI designers. In order ...to reduce the cost of maintaining style consistence of UI for designers, this paper proposes a recommendation algorithm of pictures having the same style based on SVD collaborative filtering. This algorithm computes rating matrix by 0 or 1 according to whether the images are used or not, and it calculates the similarity of pictures which have been used and which have not during each new design process to predict whether they are in the same style, and optimize the calculation by SVD. It is shown in the experimental results that this algorithm is effective and it can recommend pictures of high-quality after being optimized by SVD.
This paper introduces a methodology for the development of robust motion trackers for video based on block motion models. According to this methodology, the motion of a site between two successive ...frames is estimated by minimizing an error function defined in terms of the intensities at these frames. The proposed methodology is used to develop robust motion trackers that rely on fractional block motion models. The motion trackers developed in this paper are utilized to extract motor activity signals from video recordings of neonatal seizures. The experimental results reveal that the proposed motion trackers are more accurate and reliable than existing motion tracking methods relying on pure translation and affine block motion models.
Purpose: The main objective of this research is the development of automated video processing and analysis procedures aimed at the recognition and characterization of the types of neonatal seizures. ...The long‐term goal of this research is the integration of these computational procedures into the development of a stand‐alone automated system that could be used as a supplement in the neonatal intensive care unit (NICU) to provide 24‐h per day noninvasive monitoring of infants at risk for seizures.
Methods: We developed and evaluated a variety of computational tools and procedures that may be used to carry out the three essential tasks involved in the development of a seizure recognition and characterization system: the extraction of quantitative motion information from video recordings of neonatal seizures in the form of motion‐strength and motor‐activity signals, the selection of quantitative features that convey some unique behavioral characteristics of neonatal seizures, and the training of artificial neural networks to distinguish neonatal seizures from random infant behaviors and to differentiate between myoclonic and focal clonic seizures.
Results: The methods were tested on a set of 240 video recordings of 43 patients exhibiting myoclonic seizures (80 cases), focal clonic seizures (80 cases), and random infant movements (80 cases). The outcome of the experiments verified that optical‐ flow methods are promising computational tools for quantifying neonatal seizures from video recordings in the form of motion‐strength signals. The experimental results also verified that the robust motion trackers developed in this study outperformed considerably the motion trackers based on predictive block matching in terms of both reliability and accuracy. The quantitative features selected from motion‐strength and motor‐activity signals constitute a satisfactory representation of neonatal seizures and random infant movements and seem to be complementary. Such features lead to trained neural networks that exhibit performance levels exceeding the initial goals of this study, the sensitivity goal being ≥80% and the specificity goal being ≥90%.
Conclusions: The outcome of this experimental study provides strong evidence that it is feasible to develop an automated system for the recognition and characterization of the types of neonatal seizures based on video recordings. This will be accomplished by enhancing the accuracy and improving the reliability of the computational tools and methods developed during the course of the study outlined here.
This paper presents an approach for improving the accuracy and reliability of motion tracking methods developed for video based on block motion models. This approach estimates the displacement of a ...block of pixels between two successive frames by minimizing an error function defined in terms of the pixel intensities at these frames. The minimization problem is made analytically tractable by approximating the error function using a second-order Taylor expansion. The improved reliability of the proposed method is illustrated by its application in the extraction of temporal motor activity signals from video recordings of neonatal seizures.
Purpose: This study aimed at the development of a seizure‐detection system by training neural networks with quantitative motion information extracted from short video segments of neonatal seizures of ...the myoclonic and focal clonic types and random infant movements.
Methods: The motion of the infants' body parts was quantified by temporal motion‐strength signals extracted from video segments by motion‐segmentation methods based on optical flow computation. The area of each frame occupied by the infants' moving body parts was segmented by clustering the motion parameters obtained by fitting an affine model to the pixel velocities. The motion of the infants' body parts also was quantified by temporal motion‐trajectory signals extracted from video recordings by robust motion trackers based on block‐motion models. These motion trackers were developed to adjust autonomously to illumination and contrast changes that may occur during the video‐frame sequence. Video segments were represented by quantitative features obtained by analyzing motion‐strength and motion‐trajectory signals in both the time and frequency domains. Seizure recognition was performed by conventional feed‐forward neural networks, quantum neural networks, and cosine radial basis function neural networks, which were trained to detect neonatal seizures of the myoclonic and focal clonic types and to distinguish them from random infant movements.
Results: The computational tools and procedures developed for automated seizure detection were evaluated on a set of 240 video segments of 54 patients exhibiting myoclonic seizures (80 segments), focal clonic seizures (80 segments), and random infant movements (80 segments). Regardless of the decision scheme used for interpreting the responses of the trained neural networks, all the neural network models exhibited sensitivity and specificity >90%. For one of the decision schemes proposed for interpreting the responses of the trained neural networks, the majority of the trained neural‐network models exhibited sensitivity >90% and specificity >95%. In particular, cosine radial basis function neural networks achieved the performance targets of this phase of the project (i.e., sensitivity >95% and specificity >95%).
Conclusions: The best among the motion segmentation and tracking methods developed in this study produced quantitative features that constitute a reliable basis for detecting neonatal seizures. The performance targets of this phase of the project were achieved by combining the quantitative features obtained by analyzing motion‐strength signals with those produced by analyzing motion‐trajectory signals. The computational procedures and tools developed in this study to perform off‐line analysis of short video segments will be used in the next phase of this project, which involves the integration of these procedures and tools into a system that can process and analyze long video recordings of infants monitored for seizures in real time.
The smart home system is residential as a platform, the use of integrated wiring technology, network communication technology, security technology, automatic control technology, audio and video ...technology, improve home safety, convenience, comfort and artistry. The living environment and realize environmental protection and energy saving. Our smart home system is composed of a lot of popular technology, for instance, speech recognition, GSM, GPRS, Sensors, camera, smart phone, etc. It is based on ARM platform and AT89s52 SCM. It is without a doubt, smart home system bring a cool experience to users.
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