An improved morphological edge detection algorithm of medical image

Medical images usually contain much noise which affects the edge detection accuracy. Focusing on this problem, based on the edge detection operator in mathematical morphology, an improved edge detection algorithm is presented by combining the features of the multi structure elements and the multi scale edge detection algorithm. The algorithm performs opening and closing operations on the data with the alternative sequence filters and the structure elements. The weighting operation is applied with different weight coefficients for horizontal, vertical and diagonal directions, while the edge detection operator with dilation type is calculated to obtain the improved edge detection algorithm. The steps of the algorithm are described. The algorithm is used to extract the edge of MRI image as well as the image of Lena. The experimental results indicate the algorithm can considerably improve the edge resolution of the traditional morphological edge detection methods and is practical.     

An edge detection method based on a good point set genetic algorithm

In order to improve the convergence rate of genetic algorithms based on edge detection, a novel edge detection method based on a good point set genetic algorithm (GGA) was proposed. The proposed method designed the crossover operation with the theory of good point set in which the progeny inherits the common genes of the parents which represent its family so as to improve the convergence rate of the genetic algorithm. Furthermore, before the algorithm was used for edge detection, the feature space of the image grey level was transformed into the feature space of the fuzzy entropy. Dissimilarity enhancement processing next was applied to the image by using a fuzzy entropy theory to filter the non edge pixels so as to reduce the scale of the solution domain. This approach offered another efficient way to improve the convergence rate. Experimental results show the proposed algorithm performs very well in terms of convergence rate. The detected edge image is well localized, thin, and robustly resistant to noise.     

Precise vessel centerline extraction of retinal images using ridge detection

An efficient and precise retinal vessel centerline extraction approach based on ridge detection is proposed. Firstly, the candidate retinal vessel centerline is obtained by using ridge detection. Then, illumination equalization and the multi-scale morphological enhancement are applied. Finally, the Otsu dual threshold segmentation method and Otsu single threshold segmentation method are respectively applied to the enhanced image for each scale in the retina ROI. The final vessel centerline is obtained with logic AND operation between the candidate centerline and the summation of segmentation results of each scale. The proposed method is evaluated by using database of 25 fluorescein retinal images and some color retinal images. The experiments demonstrate the method can extract the slim vessels and low contrast vessels well, while persevering vessels connectivity well. The extracted vessels are compared with the skeleton of the manual vessel image, and the average overlap rate is 83.5%. The proposed method outperforms the Hoover algorithm and conservatively hand-labeled method for the small vessels extraction and connectivity.     

Multi scale and multi orientation features for image de noising

An adaptive algorithm for image de noising is proposed based on the multi scale and multi orientation features. The coefficients in different scales and different directions are obtained by image decomposition using the nonsubsampled contourlet transform. Then thresholds functions are adaptively set with these coefficients. The texture of the image information is introduced by using the mean of decomposition scale and the energy of regional. The greater the energy, the more information of the texture while the same decomposition scales, the smaller the threshold is set. On the contrary, the greater the threshold is set. After the de noising and then reconstruction of these coefficients, image de noising is implemented. Compare to the wavelet transform threshold and contourlet transform threshold, the nonsubsampled contourlet transform pick up the image detail better and improve the quality of the image.     

A cooperative spectrum sensing algorithm with double-threshold relative distance weighting

Abstract：The single-node energy detection has the drawbacks of low accuracy and “Hidden terminal”, while the cooperative spectrum sensing algorithms usually use equivalent weights for data fusion instead of considering the influences to the detection performance resulting from the communication environment of different nodes. In order to solve these problems, a novel weighted cooperative spectrum sensing algorithm is proposed. The proposed algorithm is based on the double-threshold energy detection. According to the relation between the single-node dual-threshold setting and the node perceiving performance, the single-node dual-threshold relative distance is used as a credit for weighted cooperative data fusion. The simulation result proves that, compared with the single node double-threshold energy detection and the cooperative spectrum sensing algorithm based on OR-rule, the proposed algorithm can achieve reliable sensing performance with low SNR.     

A novel shuffled frog leaping algorithm for ICPT power programming

A new mode of inductively coupled power transfer (ICPT) is presented to city electrization traffic vehicle power supply optimization. The power supply distribution plan and ICPT technology are investigated. To avoid the local optimal of shuffled frog leaping algorithm (SFLA),a novel algorithm based on mutative scale chaos search and SFLA is presented. It is applied to inductively coupled power transfer substation optimal planning. The advantages of global and local search strategies for SFLA are combined with the proposed algorithm. In order to implement local refined search to improve local chaotic search ability and to enhance the solution accuracy, mutative scale chaos search is introduced to the proposed algorithm. The minimum annual expense of the proposed algorithm is 2.39% less than that of SFLA, which shows its advantage.     

Watermarking algorithm based on statistical property

A novel robust blind watermarking algorithm in DCT domain was proposed. The DCT coefficients are arranged in a specific way, and then the watermark is embedded based on odd even difference for optimal embedding. This algorithm works in a new pattern other than block division and search of the DCT coefficients in the middle frequencies in the traditional pattern, which can overcome the problem in searching suitable coefficients in the middle frequencies for watermark embedding and the small embedding capacity. Experimental results show that the algorithm is simple and good at perceptual transparency as well as robustness against noise and JPEG compression.     

Automatic head recognition by integrating mean shift with multiple features

To improve the head detection accuracy in video sequences captured with fixed vertical monocular camera, a novel method of head recognition based on mean shift and multiple features is proposed. Firstly, mean shift based image segmentation algorithm with color information and spatial information is suggested to derive the candidate head components in images. Furthermore, by referring to two features that the contour of human head regions are approximate round and the hair color distribution is clustered, the evaluation models based on the contour information and inside color information of candidate head components are presented for head recognition. The experimental results show that the proposed algorithm can effectively reduce the light interfere and eliminate fake target whose color information is similar to hair color distribution. The detection rate for static images can reach about 89.4%.     

Adaptive window smoothing method for 4f system imagesin wavelet domain

Due to the features of limited frequency band and existing noise for the output images of the conventional 4f information optics system, it is required to protect the image detail regions much better when restoring the images. Based on the classic AWM method, an adaptive window smoothing method which can adaptively adjust the shape of the smoothing window in wavelet domain is proposed to remove the random noise in the system output images. The method utilizes the direction of the high pass sub images decomposed by wavelets to determine the direction of the smoothing window, and adjusts the size of the smoothing window based on the advantage of energy concentration characteristics of sub images in wavelet domain to adjust the shape of the window adaptively. The output image of the 4f system is smoothed with the adjusted windows. The theoretical analysis and experiments show that the method can de noise the image in visual sensation and protect the image detail well, while PSNR is improved about 2 dB. It is suitable for the cases which require a certain image precision and has low pass characters, such as the 4f optic system.     

Image edge detection algorithm in fuzzy domain

An image edge detection algorithm in fuzzy domain is proposed, which combines adaptive fuzzy enhancement and multi direction fuzzy morphology to detect the edges of fuzzy image. The adaptive fuzzy enhancement method enhances the fuzzy image within blocks with sliding windows to avoid losses of the real edges resulting from enhancing with single threshold for the whole image and lead strong adaptive ability to image region variance. The multi direction fuzzy mathematical morphology operates on the enhanced fuzzy image with structure elements of multiple different directions to extract the real edges with directionality and restrain non directional noise. Experiments show the algorithm can detect fuzzy image edge effectively with strong antinoise ability.     

Optimal sensor deployment scheme for heterogeneous wireless sensor networks

For the high density of distributed heterogeneity nodes in wireless sensor networks (WSNs), the optimal sensor deployment is studied. A cost optimal heterogeneous sensor deployment scheme based on genetic algorithm is proposed. The cost of sensor node deployment is used as objective function for optimization computation subjected to network coverage and fault tolerance to obtain the suitable types and positions of the sensors. The algorithm can be used for boolean sensing model as well as probabilistic sensing model. The simulation results demonstrate that the proposed algorithm converges fast toward the optimal solutions and reduces the cost of sensor deployment, which is feasible for heterogenous nodes deployment in WSNs.     

Measurement of interharmonics in power network based on all phase FFT time shifting and phase difference

The harms brought by interharmonics have attracted people’s attention more and more. The research of interharmonics is based on the actual measurement of the interharmonics in power network. An interharmonics detection method of all phase fast Fourier transform (apFFT) time shifting and phase difference is proposed in order to overcome the defects of the bigger measurement error brought by traditional FFT algorithm. By adopting apFFT algorithm and adjusting sample rate, we suppress spectrum leakage of all 0.1 times integer time harmonics including fundamental. Frequency and amplitude corrections are obtained through main spectrum line phase difference. The apFFT main spectrum lines phase values without correction are directly taken as phase values. The concept is clear and the results are direct. The algorithm has the advantage of strong anti-interference between harmonics. The simulation results show that the proposed method can detect amplitude, phase and frequency of interharmonics with high precision. The algorithm provides a new effective way for power network interharmonic detection and analysis.     

Image denoising based on the complex direction of filter banks and blockshrink

A new image denoising algorithm based on the complex direction of filter bands and BlockShrink is proposed. The image is decomposed based on the complex direction of filter bands to obtain the coefficients. Then, according to the coefficient correlation, the block threshold method is used to select the best block size and the threshold. In this way, the unbiased risk estimation of each sub-band is minimized. The algorithm is entirely data-driven easy to implement and with good visual effects. Experiments show that the proposed algorithm increase the PSNR by 0.6 percent compared with the algorithm based on DWT-BlockShrink transform, and the edge and countour information are more clearly.     

Novel Image-adaptive Watermarking Algorithm

Digital watermarking is an effective method to provide copyright protection for digital media. A blind image- adaptive watermarking algorithm based on DCT domain is presented. The algorithm selects adaptively the step size of quantization to embed the watermark bits by modifying DC components via the human visual system model and local characteristics of image; at the same time some AC components of the blocks with less perceptual capacity are also modified to embed the same watermark bits by using quantization. Thus the relation between robustness and imperceptibility can be well balanced. The algorithm recovers the watermark without any reference to the original image. Compared with the similar algorithm reported in the literature, this method can give better image quality and is better robust against noise and commonly used image processing techniques.     

Recognition Algorithm of Motorway Curl Road

Road detection is a sub-system of vehicle intelligent auxiliary drive system, which includes such sub-projects as road segmentation and road recognition. The image from CCD camera is processed, and road line is abstracted from it through image process. Then the direction of the road, such as left or right, can be identified according to the road line. Because of the special requirement, i.e. the rigid real-time requirement, the algorithm adopted must have the very low time cost. Through many experiments, a kind of recognition algorithm is proposed based on region-growing process and curve fitting, which was verified to have good effect.     

Fusion method for infrared and visible light images based on image segmentation and stationary wavelet transform

A novel image fusion method based on image segmentation and stationary wavelet transform (SWT) is proposed to improve the visual effect of fused infrared and visible light images. Infrared image is firstly separated into object and background region utilizing Otsu combined with edge detection. Then a multiresolution decomposition using SWT is made to the background region of the infrared image and the visible light image. Neighborhood spatial frequency and absolute value are adopted as fusion rules in low-frequency and high-frequency coefficients. The background fused image is reconstructed by inverse SWT. The final infrared and visible light fused image is obtained by fusing the background fused image and the object region of infrared image base on weighted fusion rule. The experimental results show that the object information of the infrared image is obviously highlighted and the scene information of the visible light image is well represented. The visual effect of fused image is improved efficiently by utilizing the proposed method. The proposed method works better than the traditional Laplacian Pyramid and wavelet transform fusion algorithms in terms of standard deviation, comentropy and mutual information. Experimental results verify its effectiveness.     

Image denoising based on adaptive graph regularization

Adaptive regularization can select different parameters based on the features of local areas in an image, which can differentiate the edges and noise in an image flexibly. An adaptive graph regularization is proposed based on graph spectral theory and adaptive regularization, which uses the Non local means to generate the weighting function of graph. The adaptive graph regularization equation is used to filter the noisy image. Simulation results show that the proposed method can effectively remove the noise and is superior to other graph theory based partial differential equation methods.     

Design of PID control algorithm on large diameter tiled-grating

To realize the long-time dynamic stability for the tiled-grating control system,a PID algorithm based on the analysis of the four-dimensional degree of freedom mathematical model for the tiled-grating is developed. Two improvements are made in incremental PID algorithm. The latches compensation method is used to avoid saturated loss,and the four-point center difference method is applied to avoid interference. The splicing spot photos of the tiled-grating’s closed-loop control experiments demonstrate that the tiled-grating which employs the improved PID algorithm can acquire a long-time stability as well as the stability can keep one hour.     

Preprocess Method of PCB Line Detection

In the visual inspection systems of PCB line detection, a good preprocess effect of test image is the precondition of the system with fine performance, while the its speed is the determinant of the real-time inspection performance. This paper proposed a preprocess algorithms of PCB test image, which includes two parts-binarization and denoising. The experiment shows that the preprocess algorithm is able to obtain an effective threshold for the binarization of the defect image, eliminating the noise and enhancing the speed highly.     

Single sample face recognition based on B-spline and image gradient

A single sample face recognition algorithm based on B-spline and image gradient is proposed. Image gradient method for face recognition has advantage of illumination invariant. But the recognition rate will be greatly decreased when the image contains noise which will seriously influence gradient information. Traditional methods to reduce noise smooth image at the same time and image gradient reorganization rate will be reduced. As the B-spline filter has the feature which can adjust the order, B-spline filter with different orders can be selected according to the image noise value to minimize noise while preserve image gradient information. Experiments prove that using B-spline and image gradient algorithm can achieve a better recognition rate than traditional filtering method on single sample face recognition problem.