Univalue Segment Assimilating Nucleus Research Articles

Addressing topology optimization with overhang constraints for structures subjected to self-weight loads

This paper investigates the topology optimization of structures subjected to self-weight loads with self-supporting constraints for additive manufacturing. The integration of topology optimization procedures and additive manufacturing techniques can make the most of their advantages, and there is significant interest today in integrating both approaches. Imposing overhang constraints in topology optimization has been addressed, but primarily for classical topology optimization problems with fixed external loads, not design-dependent loads. This work combines an effective numerical procedure for contour evaluation with a modified version of the power-law model for low densities to eliminate the problems that arise when self-weight loads are considered. The overhang edge detection is based on the Smallest Univalue Segment Assimilating Nucleus (SUSAN) method, and a variable mask size technique is used to avoid eventual dripping problems. The proposed constraint function evaluates the overhang globally and allows control of the formation of unsupported contours for maximum stiffness design problems when self-weight loads are present. Several numerical experiments demonstrate the proposed method's effectiveness and robustness.

A hybrid classical techniques and optimal decision model for iris recognition under variable image quality conditions

One of the best biometrics used for human verification and identification is iris recognition. The contrast of its unique characteristics differs from one candidate to another in which the iris pattern has numerous well-known features like uniqueness texture, stability and compactness representation for human identification. Among these facts, several approaches in these areas are localized, but there is still an abundant problems such as the low match rate of the score level and low accuracy. Therefore, a decision model should be essential for iris recognition frameworks. This paper proposes a iris recognition based on a decision model using a unified framework based on the integration of three detection schemes due to variation occurred in shading and position change using Smallest Univalue Segment Assimilating Nucleus (SUSAN), Generalized Hough Transform (GHT) and Viola–Jones (SUSANGHT-VJ) for eye detection, enhancing the dimmer and darker areas using fuzzy retinex method and normalizing the iris boundary using daugman’s Rubber Sheet Model for segmentation. Also, the iris corner points is extracted using Gabor Wavelet Transform (GWT), the vector properties of the blurred texture features are quantized using Local Phase Quantization (LPQ) and the optimal decision model based on Atom Search Optimization (ASO) and Feed Forward Counter propagation Neural Network (FFCNN) for matching score level and classification task. Furthermore, the current framework prevents false matches and inappropriate iris input, thus making the iris match score framework more reliable. The evaluation of the proposed approach is trained and tested with the employed eye template of iris and face datasets. Therefore, the results depict that the proposed technique gives a high recognition rate of 99.9% on different datasets compared to existing methods.

Analysis of controversies in the formulation and evaluation of restoration algorithms for MR Images

There is no reliable guidance available in literature so far for the selection of a suitable technique for denoising Magnetic Resonance (MR) images. The performance of edge-preserving denoising schemes like Nonlocal Means, Bilateral, Total Variation, Anisotropic Diffusion, Kuwahara, wavelet denoising, Linear Minimum Mean Square Error, Smallest Univalue Segment Assimilating Nucleus and Beltrami filters on MR images are evaluated and compared in this paper. Performance evaluation is done on real-time MR Images, Shepp–Logan Phantom images and simulated MR images. Image Quality Analysis indices used for the evaluation are Structural Similarity Index Metric, Noise Quality Measure, Peak Signal to Noise Ratio, Edge Preservation Index, MetricQ, Anisotropic Quality Index, Blind Reference Image Quality Evaluator and computational time. It has been observed that the performance of each filter is completely different on Shepp–Logan, simulated MR and real-time MR images. It is critically sensitive to the strength of noise also. No filter which can offer good performance equally on Phantom, simulated MR image and real-time MR images, is available in the literature. Values of the objective indices are not in concordance with subjective quality ratings. Filter designs optimized on Phantom or simulated MR using maximum PSNR between denoised and ground-truth images as an objective function (minimum error sense in general) do not perform well on real-time MRI.

A new overhang constraint for topology optimization of self-supporting structures in additive manufacturing

This work falls within the scope of computer-aided optimal design, and aims to integrate the topology optimization procedures and recent additive manufacturing technologies (AM). The elimination of scaffold supports at the topology optimization stage has been recognized and pursued by many authors recently. The present paper focuses on implementing a novel and specific overhang constraint that is introduced inside the topology optimization problem formulation along with the regular volume constraint. The proposed procedure joins the design and manufacturing processes into a integrated workflow where any component can directly be manufactured with no requirement of any sacrificial support material right after the topology optimization process. The overhang constraint presented in this work is defined by the maximum allowable inclination angle, where the inclination of any member is computed by the Smallest Univalue Segment Assimilating Nucleus (SUSAN), an edge detection algorithm developed in the field of image analysis and processing. Numerical results on some benchmark examples, along with the numerical performances of the proposed method, are introduced to demonstrate the capacities of the presented approach.

A FUZZY EDGE BASED MULTIFOCUS FUSION

A New method of fusion is presented to find each object present in the image. The proposed algorithm used USAN (univalue segment assimilating nucleus) technique to fuse the high and low intensity edges. The effective way of multi focus fusion is based on the fuzzy edge detection using modified fuzzy membership function. However, the proposed method computes the edge intensity region in an optimal way by using particle swarm optimization (PSO). We conduct experiments on several images and compare with different state of art methods. The effectiveness of the proposed approach outperforms than other methods in each case of performance measures.

Smallest Univalue Segment Assimilating Nucleus approach to Brain MRI Image Segmentation using Fuzzy C-Means and Fuzzy K-Means Algorithms

Image segmentation still remains an important task in image processing and analysis. Sequel to any segmentation process, preprocessing activities carried out on the images have a great effect on the accuracy of the segmentation task. This paper therefore laid emphasis on the preprocessing stage of brain Magnetic Resonance Imaging (MRI) images Smallest Univalue Segment Assimilating Nucleus (SUSAN) and bias field correction algorithms. Subsequently, brain tissue extraction tool was employed in extracting non-brain tissues from the brain image. Afterwards, Fuzzy K-Means (FKM) and Fuzzy C-Means (FCM) segmentation algorithms were employed for segmenting brain MRI images acquired from four different MRI databases into their White Matter (WM), Gray Matter (GM) and Cerebrospinal Fluid (CSF) constituents. Evaluation metrics such as cluster validity functions using partition coefficients and partition entropy; area error metrics such as false positive, true positive, true negative and false negative (FN); similarity index, sensitivity and specificity were used to evaluate the performance of both techniques. A comparative analysis of the experimental results revealed that in most instances, FKM segmentation technique is preferable to FCM segmentation technique for brain MRI segmentation task.

An Optimal Fuzzy System for Edge Detection in Color Images Using Bacterial Foraging Algorithm

This paper presents a fuzzy system for edge detection, using smallest univalue segment assimilating nucleus (USAN) principle and bacterial foraging algorithm (BFA). The proposed algorithm fuzzifies the USAN area obtained from the original image, using a USAN area histogram-based Gaussian membership function. A parametric fuzzy intensification operator (FINT) is proposed to enhance the weak edge information, which results in another fuzzy set. The fuzzy measures, i.e., fuzzy edge quality factor and sharpness factor, are defined on fuzzy sets. The BFA is used to optimize the parameters involved in the fuzzy membership function and the FINT. The fuzzy edge map is obtained using optimized parameters. The adaptive thresholding is used to defuzzify the fuzzy edge map to obtain a binary edge map. The experimental results are analyzed qualitatively and quantitatively. The quantitative measures, i.e., Pratt's figure of merit, Cohen’ Kappa, Shannon's entropy, and edge strength similarity-based edge quality metric, are used. The quantitative results are statistically analyzed using t-test. The proposed algorithm outperforms many of the traditional and state-of-the-art edge detectors.

Vehicle License Plate Detection and Tilt Correction Based on HSI Color Model and SUSAN Corner Detector

Detection and tilt correction of vehicle license plates are inevitable problems to be solved before developing Intelligent Transportation Systems. This paper describes a method of extraction of the vehicle license plate region from a vehicle image with tilt correction. The method consists of five distinct phases: segmenting color, labeling and filtering, correcting tilt, verifying candidate region, and segmenting license plate characters. Initially, an adaptive technique with labeling and filtering capability is utilized to select a statistical threshold value in the HSI color space for detecting candidate vehicle license plate regions. In this way the proposed method can overcome the limitation of the failure to detect a license plate when the vehicle body and license plate are similar colors. The slopes and tilt angles in both the horizontal and vertical directions of the skewed vehicle license plate are determined using the candidate corner points detected by the Smallest Univalue Segment Assimilating Nucleus corner detector, and the license plate region is rotated accordingly. Finally, a horizontal and vertical position histogram is employed to segment the candidate region into rows and constituent characters. The proposed tilt correction method is compared with the least square fitting with perpendicular offsets method and found to outperform results when the tilt angle gradually increases. 145 vehicle images were used for testing with different conditions and the results are presented to prove its efficiency and effectiveness.

Quantity estimation modeling of the Rice Plant-hopper infestation area on rice stems based on a 2-Dimensional Wavelet Packet Transform and corner detection algorithm

BackgroundOutbreaks of Rice Plant-hoppers (RPH) (Nilaparvata lugens, Sogatella furcifera, and Laodelphax striatellus) appear in Asia almost every year and have had significant impacts on rice yields. To implement timely, targeted pesticide applications, reduce input costs and benefit the environment, the accurate early detection and quantity estimation of RPH infestation is a critical part of integrated pest management (IPM) for rice production. To use visible images to detect and estimate RPH infestation areas on rice stems, related experiments and studies were performed to determine the feasibility of using a 2-Dimensional Wavelet Packet Transform (2DWPT) and a corner detection algorithm. Visible images of the rice stems were collected using a handheld camera. First, a series of pretreatments to these visible images were applied, including smoothing, denoising, image color space transformation and 2-Dimensional Wavelet Packet transformation. Second, the related image corner eigenvalues (i.e. the number of the corners) were extracted using a Smallest Univalue Segment Assimilating Nucleus (SUSAN) algorithm. Finally, a linear regression model was developed based on the corner eigenvalues. ResultsThe results show that the SUSAN corner detection algorithm used to extract the corner eigenvalues can also be used to distinguish the I (infestation) and N (non-infestation) areas with high accuracy. Most of the corner eigenvalues based on different image forms had a high correlation coefficient with the RPH quantity, and B-P10 (i.e., the corner eigenvalue of the RGB color space B component that was transformed via 2DWPT at node P10) had the highest correlation coefficient of 0.8277. ConclusionsIt is possible to detect and quantify the estimated RPH infestation area on rice stems by applying a 2DWPT and corner detection algorithm to visible images. Along with the micro-sensor mobile monitoring platform, the visible-image-based method is expected to be used as a redundant method in remote sensing to measure the stress induced by RPH.

Analysis and improvement of SUSAN algorithm

A modified Smallest Univalue Segment Assimilating Nucleus (SUSAN) algorithm based on the local gray value character of an image is presented here. Beginning with an explanation of the principle of edge detection and noise reduction, we find SUSAN algorithm is immune to all noise points but the isolated noise points. To improve this, an original edge response formulation is optimized by imposing constraint conditions. Then a set of anti-noise tests were run to compare our scheme with the original algorithm and other popular edge detectors. The results show that for Gaussian noise and salt-and-pepper noise, the improved SUSAN algorithm performs much better than the original one in view of sensitivity to noise and detection of edges, and especially for salt-and-pepper noise the improved SUSAN algorithm works best among the all detectors tested here.

Improved Rapid Corner Detection Algorithm in Medical Image

Improved Rapid Corner Detection Algorithm in Medical Image

A New Algorithm for Fast Corner Detection Using Line Search Mechanism

In image corner detection,classical approaches can not meet the demand of high accuracy but low compu- tational complexity.A new line search algorithm for image corner detection is proposed in this paper.The new method employs a circle mask centered at the pixel in question,called the "nucleus" of the mask,to search all the straight lines that pass through the nucleus within the mask.Then,a corner point of an image is defined as the point (nucleus) where,among those straight lines,there is at least one straight line that does not cross the remaining part of the univalue segment assimilating nucleus (USAN) except a given neighborhood of the nucleus.This paper asserts the practicability and neces- sity of using finite search lines.To reduce the computational complexity,it employs dynamic search lines,finite points on each search line,and the coarse-to-fine search strategy as well.A new non-maximum suppression for accurate localization is proposed,and some new false response suppression measures are incorporated for good detection.Experiments show good performance of the new algorithm in both accuracy and speed.

Ultrasound speckle reduction by a SUSAN-controlled anisotropic diffusion method

An ultrasound speckle reduction method is proposed in this paper. The filter, which enhances the power of anisotropic diffusion with the Smallest Univalue Segment Assimilating Nucleus (SUSAN) edge detector, is referred to as the SUSAN-controlled anisotropic diffusion (SUSAN_AD). The SUSAN edge detector finds image features by using local information from a pseudo-global perspective. Thanks to the noise insensitivity and structure preservation properties of SUSAN, a better control can be provided to the subsequent diffusion process. To enhance the adaptability of the SUSAN_AD, the parameters of the SUSAN edge detector are calculated based on the statistics of a fully formed speckle (FFS) region. Different FFS estimation schemes are proposed for envelope-detected speckle images and log-compressed ultrasonic images. Adaptive diffusion threshold estimation and automatic diffusion termination criterion are employed to enhance the robustness of the method. Both synthetic and real ultrasound images are used to evaluate the proposed method. The performance of the SUSAN_AD is compared with four other existing speckle reduction methods. It is shown that the proposed method is superior to other methods in both noise reduction and detail preservation.

Feature list object matching based on oriented SUSAN features

For the machine vision object matching,the feature list correlation algorithm can be adopted in the calculation of the matching similarity measurement.As a result,the processing time is able to be decreased considerably,and the objects can also be clearly recognized due to the high values of the coefficients and the high values of the Peak Signal-to-Noise Ratio(PSNR).A feature list object matching method based on the oriented Smallest Univalue Segment Assimilating Nucleus(SUSAN)features was proposed.The feature pixels with oriented SUSAN features were added to the feature lists and the similarity was based on the normalized absolute error of feature pixel points.The supposed algorithm is verified by the experiments.