This paper presents a generic video vehicle detection approach through multiple background-based features and statistical learning. The main idea is to configure several virtual loops (as detection zones) on the image...
详细信息
This paper presents a generic video vehicle detection approach through multiple background-based features and statistical learning. The main idea is to configure several virtual loops (as detection zones) on the image, assuming moving vehicles may cause pixel intensities and local texture to change, and then by identifying such pixel changes to detect vehicles. In this research, multiple pattern classifiers including LDA + Adaboost, SVM, and Random Forests are used to detect vehicles that are passing through virtual loops. We extract fourteen pattern features (related to foreground area, texture change, and luminance and contrast in the local virtual loop zone and the global image) to train pattern classifiers and then detect vehicles. As experimental results illustrate, the proposed approach is quite robust to detect vehicles under complex dynamic environments, and thus is able to improve the accuracy of traffic data collection in all weather for long term.
The Non-Local Means (NLM) filter uses the redundancy of information in the image to remove noise, this scheme gives some of the best results among other powerful methods such as wavelet based approaches or diffusion t...
详细信息
The Non-Local Means (NLM) filter uses the redundancy of information in the image to remove noise, this scheme gives some of the best results among other powerful methods such as wavelet based approaches or diffusion techniques. Though simple to implement and efficient in practice, the classical NLM suffers from ringing artifacts around edges when using square patches, due to an abrupt lack of redundancy of the image. This paper presents an extended NLM based on Multi-Shape Patches Aggregation (NLM-MSPA) to overcome this problem, and uses it to remove medical ultrasound images corrupted by multiplicative speckle noise. We have incorporated a preprocessing step to make the speckle noise much closer to the real additive white Gaussian noise, hence more amenable to a denoising algorithm such as NLM-MSPA. Results on real images and artificially speckled images show that the proposed scheme outperforms several classical methods chosen for comparison in its ability to reduce speckle and preserve edge details.
An automatic recognition method for steel billet images with different orientations is proposed in this paper. A crucial part of this method is to segment the image firstly, and then use the projection features of the...
详细信息
In this paper, we propose a method to predict the outcome of Bevacizumab therapy on Glioblastoma Multiform (GBM) tumors. The method uses diffusion anisotropy indices (DAI) and spatial information to predict the treatm...
详细信息
In this paper, we propose a method to predict the outcome of Bevacizumab therapy on Glioblastoma Multiform (GBM) tumors. The method uses diffusion anisotropy indices (DAI) and spatial information to predict the treatment response of each tumor voxel. These DAIs are Fractional Anisotropy, Mean Diffusivity, Relative Anisotropy, and Volume Ratio, extracted from Diffusion Tensor Imaging (DTI) data before treatment. The spatial information is considered as the distance of each tumor voxel from the tumor center, extracted from pre-treatment post-contrast T1-weighted Magnetic Resonance images (pc-T1-MRI). DAIs and spatial information of each tumor voxel are considered as feature vector. DTI and pc-T1-MRI are gathered before and after the treatment of seven GBM patients. First, DAIs of all brain voxels and the distance of each tumor voxel from the tumor center are calculated. Second, the method registers pre-treatment DAI maps and post-treatment pc-T1-MRI to pre-treatment pc-T1-MRI. Next, the tumor is segmented using thresholding technique from pc-T1-MRI. Then, Gd-enhanced voxels of the pre- and post-treatment pc-T1-MRI are compared to label the feature vectors. Three classifiers were evaluated, including Support Vector Machine, K-Nearest Neighbor, and Artificial Neural Network. Classification results show a preference for K-Nearest Neighbor based on well-established performance measures.
Recent research has demonstrated that the ultra-scale computation by self-assembly DNA tiles can be implemented in the laboratory. One of the significant applications is the DNA-based cryptography systems. In this pap...
详细信息
Cognitive radio is used for enhancement of spectrum *** many works have been accomplished on the power allocation of cognitive radio,limited efforts have considered evolutionary *** this paper,we study this problem in...
详细信息
Cognitive radio is used for enhancement of spectrum *** many works have been accomplished on the power allocation of cognitive radio,limited efforts have considered evolutionary *** this paper,we study this problem in the cognitive radio networks where interference constraints are defined for protection of quality of service(QoS)for both primary and secondary *** defined as functions of the signal-to-interference-plus-noise ratio(SINR) are matched for each secondary user which meets Nash’s *** general,the region of utilities that meets the constraints is *** is possible to make simplifications,generate a convex region,and then use common convex optimization approaches to obtain a ***,Particle Swarm Optimization(PSO)does not need such simplifications and thus its results are superior to those of the convex optimization *** is an evolutionary algorithm based on social intelligence,utilized in many optimization *** is a global optimizations algorithm that does not require the objective function be differentiable as required in classic optimization methods.
We consider the problem of looking for small universal spiking neural P systems with exhaustive use of rules, which was formulated as an open problem by Andrei PǍun and Gheorghe PǍun in a survey paper. Here, spiking...
详细信息
In the "standard" way of simulating register machines by spiking neural P systems (in short, SN P systems), one neuron is associated with each instruction of the register machine that we want to simulate. In...
详细信息
Looking for small universal computing devices is a natural and well investigated topic in computer science. Recently, this topic started to be considered also in the framework of (synchronized) spiking neural P system...
详细信息
The degradation of AlGaN/GaN high electron mobility transistors (HEMTs) has a close relationship with a model of traps in AlGaN barriers as a result of high electric field. We mainly discuss the impacts of strong el...
详细信息
The degradation of AlGaN/GaN high electron mobility transistors (HEMTs) has a close relationship with a model of traps in AlGaN barriers as a result of high electric field. We mainly discuss the impacts of strong electrical field on the AlGaN barrier thickness of AlGaN/GaN HEMTs. It is found that the device with a thin AlGaN barrier layer is more easily degraded. We study the degradation of four parameters, i.e. the gate series resistance RGate, channel resistance R channel, gate current IG,off at VGS=-5 and VDS=0.1 V, and drain current ID,max at VGS=2 and VDS=5 V. In addition, the degradation mechanisms of the device electrical parameters are also investigated in detail.
暂无评论