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The traditional reservoir water level monitoring system utilizes the sensors placed in the water has drawbacks of: 1) high possibility of water seepage; 2) short life cycle; 3) difficulty to achieve real-time monitoring; and 4) not easy to maintain. This paper presents a design of water level measurement sensor which relies on mechanical transmission tracking method. Since there is no need to place the sensor into the water, the life cycle is improved. Furthermore, by remote data transmission over the internet network, A real-time monitoring of the water level can be achieved, and thus significantly improve the overall management efficiency of reservoir resource utilization.

According to the current status for measuring water-level of the river, a river water-level remote monitoring system has been designed, which consists of a water-level sensor, a single chip microcomputer, a wireless module and user terminals. The designed contact type water-level sensor based on a simple principle with a high performance-cost ratio, has eliminated the shortcomings of the current sensors, which must be placed under the water with difficulty of water proof. The water level information collected can be real-time transmitted with signal of GPRS to the server terminal, users can easily transfer real-time data and history of data record from the server terminal. The experimental results show that the system can work stably, with a high accuracy, low cost, and easy to market.

Virtual instrument is a result of the computer combined with some instrument hardware, working for the instrumentation applications, it is one trend of the instrumentation development .This paper presents a design of a high-power and high-precision constant current source, studied three different electrical testing technologies based on constant current source and propose the principle of integrated the three electrical test technologies on a virtual instrument platform. A multi-function virtual instrument system based on LabView language, USB data acquisition card and a constant current source has been developed. This system has a high accuracy, high integration and a thecapability of further development etc..

Due to the issues of recent traffic situation and the problems of current vehicle recognition systems, a high resolution video image based real-time monitoring system for multi-lane violation trucks is presented and implemented in this paper. A soft-trigger technique based background-blocks is utilized to detect the big vehicles in real-time, then a step-by-step feature extraction and recognition approach effects from the facial window to entire vehicle body is proposed based on Sobel operator and Hough transform. Experimental results indicate the accuracy and efficient of proposed system, which has high practical value for several applications.

For intelligent transportation surveillance, a novel background model based on Marr wavelet kernel and a background subtraction technique based on binary discrete wavelet transforms were introduced. The background model kept a sample of intensity values for each pixel in the image and used this sample to estimate the probability density function of the pixel intensity. The density function was estimated using a new Marr wavelet kernel density estimation technique. Since this approach was quite general, the model could approximate any distribution for the pixel intensity without any assumptions about the underlying distribution shape. The background and current frame were transformed in the binary discrete wavelet domain, and background subtraction was performed in each sub-band. After obtaining the foreground, shadow was eliminated by an edge detection method. Experimental results show that the proposed method produces good results with much lower computational complexity and effectively extracts the moving objects with accuracy ratio higher than 90%, indicating that the proposed method is an effective algorithm for intelligent transportation system.

In order to solve problems such as traffic jams and poor traffic management, a kind of illegal lane changing monitoring system based on traffic video is presented. The hardware system is composed of industrial computers and cameras fixed above the road. The software part consists of video processing, background extraction and updating, lane line extraction, vehicle recognition, moving shadow analysis, illegal lane change detection, vehicle tracking and prediction. First, the video collected is transformed into a series of images. Then a background is obtained by using averaging, and the lane lines are extracted by an improved Hough transform. In order to obtain complete and accurate outlines of the moving vehicles, a method of obtaining a vehicle profile combined with background subtraction and edge detection is proposed. A simple method for illegal lane change discrimination is also put forward. A mean-shift algorithm combined with a Kalman filter is used to track and predict the locations of ve...

This paper presents a moving object detection method with cast shadow suppression in the RGB color space. Firstly, the background model is constructed by the metrically trimmed mean and updated adaptively. Then, the information of brightness, texture, and chromaticity is fused to discriminate the shadow and the moving object. Our experimental results demonstrate the good performance of the proposed method.

A face tracking algorithm based on LBP (Local Binary Pattern) histograms and particle filtering is presented in this paper. The mean shift algorithm is adopted to locate the face region coarsely. Then the particles can be propagated efficiently with small random noise added. Since color histograms as face representation can be disturbed easily by the background with skin color. LBP histograms are used as the representation of faces. In order to alleviate the influence of light variance, the face template is updated under certain condition. The experimental results show that the proposed algorithm is superior to the CAMSHIFT algorithm and the particle filter based on color histograms.

The texture characteristic is one of the important factors of infrared image. For detecting the region of interest, a method of target extraction from the infrared image with complex texture background was presented. First, Mean-shift smoothing algorithm was used to smooth the image pixels, and then an eight directions difference clustering process combined with Mean Shift segmentation was used to extract the region of target. The method is relatively simple making it easy for practical applications. Experimental results show that the method can extract the information of target from infrared images without surveillance and has better adaptability, indicating that it is an effective algorithm for military field with a certain practical value.

On account of restriction of man-made and collection environment, the fingerprint image generally has low quality, especially a contaminated background. In this paper, an enhancement algorithm based on edge filter and Gabor filter is proposed to solve this kind of fingerprint image. Firstly, a gray-based algorithm is used to enhance the edge and segment the image. Then, a multilevel block size method is used to extract the orientation field from segmented fingerprint image. Finally, Gabor filter is used to fulfill the enhancement of the fingerprint image. The experiment results show that the proposed enhancement algorithm is effective than the normal Gabor filter algorithm. The fingerprint image enhance by our algorithm has better enhancement effect, so it is helpful for the subsequent research, such as classification, feature exaction and identification.

With the extensive application of digital television, video compression technology has become one of the topics which the telecommunication industry concerns most. A video compression method based on redundant discrete wavelet transforms, Delaunay triangle and triangular affine transformation is presented. The feature points and potential motion areas (PMA) are extracted in the wavelet transform domain, and motion estimation is done in the time domain. Experiment results show that the algorithm can increase the PSNR value and reduce the video coding complexity. It has a certain practical value.

In this paper, a region of interest (ROI) MRI image coding scheme is proposed. This scheme use the Maxshift ROI image coding method supported in JPEG 2000 image coding standard. An automatic ROI detection method is also introduced. Experiments results demonstrate that the proposed method has good performance on MRI image compression.

For particle filtering tracking method, particle choosing is random to some degree according to the dynamics equation, which may cause inaccurate tracking results. To compensate, an improved particle filtering tracking method is presented. A moving vehicle is detected by redundant discrete wavelet transforms method (RDWT), and then the key points are obtained by scale invariant feature transform. The matching key points in the follow-up frames obtained by SIFT method are used as the initial particles to improve the tracking performance. Experimental results show that more particles centralize in the region of motion area by the presented method than traditional particle filtering, and tracking results of moving vehicles are more accurate. The method has been adopted by Tianjin traffic bureau of China, and has a certain actual application prospect.

A novel background model based on Marr wavelet kernel and a background subtraction technique based on binary discrete wavelet transforms are introduced. The background model keeps a sample of intensity values for each pixel in the image and uses this sample to estimate the probability density function of the pixel intensity. The density function is estimated using a new Marr wavelet kernel density estimation technique. Since this approach is quite general, the model can approximate any distribution for the pixel intensity without any assumptions about the underlying distribution shape. The background and current frame are transformed in the binary discrete wavelet domain, and background subtraction is performed in each sub-band. After obtaining the foreground, shadow is eliminated by an edge detection method. Experiments show that the simple method produces good results with much lower computational complexity and can effectively extract the moving objects, even though the objects are similar to the background, and shadows can be successfully eliminated, thus good moving objects segmentation can be obtained.

A novel background model based on Marr wavelet kernel and a background subtraction technique based on binary discrete wavelet transforms are introduced. The background model keeps a sample of intensity values for each pixel in the image and uses this sample to estimate the probability density function of the pixel intensity. The density function is estimated using a new Marr wavelet kernel density estimation technique. Since this approach is quite general, the model can approximate any distribution for the pixel intensity without any assumptions about the underlying distribution shape. The background and current frame are transformed in the binary discrete wavelet domain, and background subtraction is performed in each sub-band. Experiments show that the simple method produces good results with much lower computational complexity and can effectively extract the moving objects, even though the objects are similar to the background, thus good moving objects segmentation can be obtained.

For the requirement of monitoring cross road violation in intelligent traffic system, a method to recognize and track the peccant vehicle is presented. The static background is modeled by mixture Gaussian model, and the location of lane line is detected by Hough transformation, thus, coordinated series can be obtained from the monitor image. Information of vehicles can be obtained by background-frame binary discrete wavelet transforms (BDWT) method, and according to the distance between the vehicle and line, the peccant vehicle can be detected. An improved mean-shift method is used to track the peccant vehicle, and a close range camera is used to snapshoot the license plate according to the center of tracking window. Actual road tests show that the work efficiency of this method is high, and the accuracy is up to 80%; run-time of mean-shift tracking system is about 0.085s for each frame. So it has a certain practical value in the field of intelligent traffic.

For the requirement of monitoring cross road violation in intelligent traffic system, a method to recognize and track the peccant vehicle is presented. The static background is modeled by mixture Gaussian model, and the location of lane line is detected by Hough transformation, thus, the coordinated series can be obtained from the monitor image. The information of vehicles can be obtained by background-frame difference method, and according to the distance between the vehicle and line, the peccant vehicle can be detected. A mean-shift method is used to track the peccant vehicle, and the close range camera is used to snapshoot the license plate according to the center of tracking window. Actual road tests show that the work efficiency of the method is high, and the accuracy is up to 80%; so it has a certain practical value.

For particle filtering tracking method, particle choosing was random to some degree according to the dynamics equation, which may cause inaccurate tracking results. To compensate, an improved particle filtering tracking method was presented. The motion region was detected by redundant discrete wavelet transforms method (RDWT), and then the key points were obtained by scale invariant feature transform. The matching key points in the follow-up frames obtained by SIFT method were used as the initial particles to improve the tracking performance. Experimental results show that more particles centralize in the region of motion area by the presented method than traditional particle filtering, and tracking results are more accurate and robust of occlusion.

A method for moving multi-target recognition and tracking in stable scene is presented. Optical flow is used to extract the velocity of pixels, and targets are recognized by combining motion character points obtained by binary discrete wavelet transforms (BDWT). A discrete kalman filter is used to track targets in the follow-up frames; the center and scale of tracking window are updated by a Mexico wavelet kernel function mean shift method which is embedded into the discrete kalman filter framework to stabilize the trajectories of the targets for robust tracking during mutual occlusion. The method is tested on several frame sequences and shown to achieve robust and reliable frame-rate recognition and tracking.

Moving vehicle recognition and tracking is the key technology in the intelligent traffic monitoring system. For the shortcomings and deficiencies of the frame-subtraction method, a binary discrete wavelet transforms based moving object recognition algorithm is put forward, which directly detects moving vehicles in the binary discrete wavelet transforms domain. For the shortages of RGB or HSV color space based vehicle shadow segmentation algorithms, shadow segmentation algorithm based on YCbCr color space is proposed. First, the motion area which includes the vehicle and the shadow is selected by binary discrete wavelet transforms, and then the original data of the shadow according to the characteristics of the occurrence of shadow is chose, finally, the shape and location of the vehicle region is determined. An automatic particle filtering algorithm is used to track the vehicle after recognition and obtaining the center of the object. The actual road test shows that the algorithm can effectively remove the influence of pedestrians, cyclists in the complex environment, and can track the moving vehicle exactly. The algorithm with better robustness has a practical value in the field of intelligent traffic monitoring, and it is adopted by Tianjin Traffic Bureau.

A redundant wavelet transforms based stereo video objects segmentation algorithm is presented. First, the redundant wavelet transforms is used to extract the feature points of stereo video images, and then according to the feature points, the disparity estimation is done to form a disparity map. The stationary video objects are segmented from the stereo images by disparity map. For the moving video objects,a redundant wavelet transforms based moving object extraction algorithm is used to segment the moving objects from the redundant wavelet domain.Experimental results show that the algorithm can segment video objects from stereo video images,including stationary objects and moving objects with good accuracy and robustness.

Vehicle license plate is a special region of vehicle image, which has many features and can not be located by only one of these characteristics. Based on multi- characteristics a novel real-time license plate detection method is presented, which consists of two main steps: license plate area's rough detection and license plate segmentation & verification. The former is stimulated by the fact that the front characters' color and the background's color are didymous and their run- lengths are alternant and restricted, while the latter is based on edge and size of license plate region. Experimental results show that this approach not only has a high locating rate, but also can achieve the realtime processing since it costs 20 ms only.

The segmentation of video object is an important technique of multimedia processing. The biggest characteristic of MPEG-4 which is the multimedia compressing standard is that it puts forward the video object based compressing. Currently, there is no method which can segment the moving object effectively. This paper puts forward a new method to obtain the motion area based on redundant wavelet transforms and then to segment the moving video object. Experimental results show that the method can obtain the moving object effectively and with strong robustness.

Locality preserving projections (LPP) is a linear method that optimally preserves the local structure of the data set. However, because of the continuing existence of transformation difference, LPP subspace is failed to detect the important nonlinear variations of the face manifold. In order to improve the recognition performance, we propose to use radial basis function network (RBFN) to classify the features in the LPP subspace. The multi-quadrics function is taken as the activation function of the RBFN and the hidden layer of RBFN is trained via the gradient descent algorithm in our approach. The experimental results show that the LPP+RBFN method has achieved a better rate than Laplacianfaces, Fisherfaces and Eigenfaces.

Level set methods provide powerful numerical techniques for analyzing and solving interface evolution problems based on partial differential equations. Level sets display interesting elastic behaviors and can handle topological changes. Although level set methods have many advantages, they still often face difficult challenges such as poor image contrast, noise, and missing or diffuse boundaries. The robust level set method of this paper is based on the anisotropic diffusion method. The fast marching method provides a fast implementation for level set methods, the anisotropic diffusion is allowed to better control the amount of smoothing effect and this process can get both noise smoothing and edge enhancement at the same time. Experimental results indicate that the method can greatly reduce the noise without distorting the image and made the level set methods more robust and accurate.

Although the snake model has been widely used nowadays and obtained quite good results, there are still some key difficulties with it: the narrow capture range and the disability to move into boundary concavities. A new snake model, Gradient Vector Flow snake, can overcome this difficulty. GVF snake model creates its own external force field called GVF force field, this make it insensitive to the initialization and able to move into concave boundary regions. However, GVF snake need large amount of computation and is easily interfered by noise. Accordingly, the wavelet-based GVF snake model can lessen the amount of computation because the multi-scale character of wavelet transform. Due to the different singularities of signal and noise, the module local maxima of their wavelet coefficients vary in different way in multi resolution, so noise can also be distinguished from signal with wavelet-based GVF snake model. The wavelet-based GVF snake model is more quickly and robust contrast to traditional snake model.

Due to the large requirement for memory and the high complexity of computation, JPEG2000 cannot be used in many conditions especially in the memory constraint equipment. The line-based wavelet transform was proposed and accepted because lower memory is required without affecting the result of wavelet transform. In this paper, the improved lifting scheme is introduced to perform wavelet transform to replace Mallat method that is used in the original line-based wavelet transform. The three-adder unit is adopted to realize lifting scheme. The corresponding context-based arithmetic coding is designed here so that the proposed algorithm is more suitable for equipment.