scholarly journals OPTIMIZATION OF SEGMENTATION QUALITY OF INTEGRATED CIRCUIT IMAGES / INTEGRINIŲ GRANDYNŲ VAIZDŲ SEGMENTAVIMO KOKYBĖS OPTIMIZAVIMAS

2012 ◽  
Vol 4 (1) ◽  
pp. 5-8
Author(s):  
Gintautas Mušketas
Keyword(s):  
Genetic Algorithms ◽  
Image Segmentation ◽  
Integrated Circuit ◽  
Optimization Problem ◽  
Active Regions ◽  
Genetic Optimization ◽  
Segmentation Accuracy ◽  
Segmentation Quality ◽  
Sequence Parameters

The paper presents investigation into the application of genetic algorithms for the segmentation of the active regions of integrated circuit images. This article is dedicated to a theoretical examination of the applied methods (morphological dilation, erosion, hit-and-miss, threshold) and describes genetic algorithms, image segmentation as optimization problem. The genetic optimization of the predefined filter sequence parameters is carried out. Improvement to segmentation accuracy using a non optimized filter sequence makes 6%. Santrauka Nagrinėjamas genetinių algoritmų taikymas integrinių grandynų aktyviųjų sričių vaizdams segmentuoti. Teoriškai aprašomi taikomi segmentavimo metodai (morfologinis plėtimas, erozija, pataikymo-nepataikymo transformacija, slenksčio funkcija), genetiniai algoritmai. Vaizdo segmentavimas apibrėžiamas kaip optimizavimo problema. Atliekamas eksperimentiškai parinktos filtrų sekos parametrų optimizavimas ir parodoma, kad optimizuota filtrų seka gaunami 6 % geresni rezultatai.

A Novel Image Segmentation Method for Cardiac MRI Using Support Vector Machine Algorithm Based on Particle Swarm Optimization

2021 ◽  
Vol 11 (12) ◽  
pp. 3174-3180
Author(s):  
Guanghui Wang ◽  
Lihong Ma
Keyword(s):  
Support Vector Machine ◽  
Particle Swarm Optimization ◽  
Image Segmentation ◽  
Heart Disease ◽  
Cardiac Mri ◽  
Support Vector ◽  
Swarm Optimization ◽  
Running Time ◽  
Segmentation Accuracy

At present, heart disease not only has a significant impact on the quality of human life but also poses a greater impact on people’s health. Therefore, it is very important to be able to diagnose heart disease as early as possible and give corresponding treatment. Heart image segmentation is the primary operation of intelligent heart disease diagnosis. The quality of segmentation directly determines the effect of intelligent diagnosis. Because the running time of image segmentation is often longer, coupled with the characteristics of cardiac MR imaging technology and the structural characteristics of the cardiac target itself, the rapid segmentation of cardiac MRI images still has challenges. Aiming at the long running time of traditional methods and low segmentation accuracy, a medical image segmentation (MIS) method based on particle swarm optimization (PSO) optimized support vector machine (SVM) is proposed, referred to as PSO-SVM. First, the current iteration number and population number in PSO are added to the control strategy of inertial weight λ to improve the performance of PSO inertial weight λ. Find the optimal penalty coefficient C and γ in the gaussian kernel function by PSO. Then use the SVM method to establish the best classification model and test the data. Compared with traditional methods, this method not only shortens the running time, but also improves the segmentation accuracy. At the same time, comparing the influence of traditional inertial weights on segmentation results, the improved method reduces the average convergence algebra and shortens the optimization time.

scholarly journals A Multiscale CNN-CRF Framework for Environmental Microorganism Image Segmentation

2020 ◽  
Vol 2020 ◽  
pp. 1-27
Author(s):  
Jinghua Zhang ◽  
Chen Li ◽  
Frank Kulwa ◽  
Xin Zhao ◽  
Changhao Sun ◽  
...  
Keyword(s):  
Image Segmentation ◽  
State Of The Art ◽  
Conditional Random Field ◽  
Segmentation Method ◽  
Recall Accuracy ◽  
Evaluation Indexes ◽  
Segmentation Quality ◽  
Segmentation Approach ◽  
Overall Evaluation

To assist researchers to identify Environmental Microorganisms (EMs) effectively, a Multiscale CNN-CRF (MSCC) framework for the EM image segmentation is proposed in this paper. There are two parts in this framework: The first is a novel pixel-level segmentation approach, using a newly introduced Convolutional Neural Network (CNN), namely, “mU-Net-B3”, with a dense Conditional Random Field (CRF) postprocessing. The second is a VGG-16 based patch-level segmentation method with a novel “buffer” strategy, which further improves the segmentation quality of the details of the EMs. In the experiment, compared with the state-of-the-art methods on 420 EM images, the proposed MSCC method reduces the memory requirement from 355 MB to 103 MB, improves the overall evaluation indexes (Dice, Jaccard, Recall, Accuracy) from 85.24%, 77.42%, 82.27%, and 96.76% to 87.13%, 79.74%, 87.12%, and 96.91%, respectively, and reduces the volume overlap error from 22.58% to 20.26%. Therefore, the MSCC method shows great potential in the EM segmentation field.

Research on the Application of College Automated Course Scheduling System Based on Genetic Algorithm

2013 ◽  
Vol 760-762 ◽  
pp. 1782-1785
Author(s):  
Xiu Ying Li ◽  
Dong Ju Du
Keyword(s):  
Genetic Algorithm ◽  
Genetic Algorithms ◽  
Optimization Problem ◽  
Optimization Problems ◽  
Teaching Quality ◽  
Combinatorial Optimization Problem ◽  
Scheduling System ◽  
Course Scheduling ◽  
Strong Ability

A reasonable curriculum contributes to the improvement of the training and teaching quality of college students. Using computer which is speed and strong ability to arrange curriculum automatically is imperative. Automatically curriculum arrangement is a constrained, multi-objective and intricate combinatorial optimization problem. Based on genetic algorithm of population search, it is suitable to process complex and nonlinear optimization problems which it difficult to solve for traditional search methods. In this paper solves complex automated course scheduling using genetic algorithms.

scholarly journals A Wavelet Relational FuzzyC-Means Algorithm for 2D Gel Image Segmentation

2013 ◽  
Vol 2013 ◽  
pp. 1-13 ◽  
Author(s):  
Shaheera Rashwan ◽  
Mohamed Talaat Faheem ◽  
Amany Sarhan ◽  
Bayumy A. B. Youssef
Keyword(s):  
Pattern Recognition ◽  
Image Segmentation ◽  
Data Analysis ◽  
The Other ◽  
Fetal Alcohol ◽  
High Quality ◽  
Analysis Pattern ◽  
Segmentation Quality ◽  
2D Gel

One of the most famous algorithms that appeared in the area of image segmentation is the FuzzyC-Means (FCM) algorithm. This algorithm has been used in many applications such as data analysis, pattern recognition, and image segmentation. It has the advantages of producing high quality segmentation compared to the other available algorithms. Many modifications have been made to the algorithm to improve its segmentation quality. The proposed segmentation algorithm in this paper is based on the FuzzyC-Means algorithm adding the relational fuzzy notion and the wavelet transform to it so as to enhance its performance especially in the area of 2D gel images. Both proposed modifications aim to minimize the oversegmentation error incurred by previous algorithms. The experimental results of comparing both the FuzzyC-Means (FCM) and the Wavelet FuzzyC-Means (WFCM) to the proposed algorithm on real 2D gel images acquired from human leukemias, HL-60 cell lines, and fetal alcohol syndrome (FAS) demonstrate the improvement achieved by the proposed algorithm in overcoming the segmentation error. In addition, we investigate the effect of denoising on the three algorithms. This investigation proves that denoising the 2D gel image before segmentation can improve (in most of the cases) the quality of the segmentation.

A Genetic Algorithm Based Optimization for Laminated Dies Manufacturing

2010 ◽  
Author(s):  
Hossein Ahari ◽  
Amir Khajepour ◽  
Sanjeev Bedi
Keyword(s):  
Genetic Algorithms ◽  
Processing Time ◽  
Optimization Problem ◽  
New Technologies ◽  
Cnc Machining ◽  
Manufacturing Cost ◽  
Local Optimum ◽  
Reliable Solution ◽  
Niching Method

Laminated tooling is one of the new technologies which helps companies to manufacture parts with lower costs and higher accuracy. It is base on dividing entire CAD model of the part to slices and then cutting each layer profile utilizing laser cut or other techniques. Finally the layers are stacked together to make the final product. CNC machining removes the extra material and brings the part to the specific tolerances. In order to minimize the manufacturing cost, one option is reduction in the amount of the extra material and the number of slices likewise. This is considered as an optimization problem in this research. Then a genetic algorithms (G.A.) based method is offered to solve this optimization problem. However, as a common problem in most instances of genetic algorithms, premature convergence prevents system to continue searching for a more reliable solution after finding a local optimum. To address this problem, a novel niching method is presented in this paper. Results show a significant improvement in the quality of the solution as well as a considerable reduction in processing time.

scholarly journals Assessment of the Segmentation of RGB Remote Sensing Images: A Subjective Approach

2020 ◽  
Vol 12 (24) ◽  
pp. 4152
Author(s):  
Giruta Kazakeviciute-Januskeviciene ◽  
Edgaras Janusonis ◽  
Romualdas Bausys ◽  
Tadas Limba ◽  
Mindaugas Kiskis
Keyword(s):  
Remote Sensing ◽  
Image Segmentation ◽  
Quality Assessment ◽  
Satellite Images ◽  
Clustering Algorithm ◽  
Satellite Image ◽  
Quality Metrics ◽  
Data Set ◽  
Segmentation Quality

The evaluation of remote sensing imagery segmentation results plays an important role in the further image analysis and decision-making. The search for the optimal segmentation method for a particular data set and the suitability of segmentation results for the use in satellite image classification are examples where the proper image segmentation quality assessment can affect the quality of the final result. There is no extensive research related to the assessment of the segmentation effectiveness of the images. The designed objective quality assessment metrics that can be used to assess the quality of the obtained segmentation results usually take into account the subjective features of the human visual system (HVS). A novel approach is used in the article to estimate the effectiveness of satellite image segmentation by relating and determining the correlation between subjective and objective segmentation quality metrics. Pearson’s and Spearman’s correlation was used for satellite images after applying a k-means++ clustering algorithm based on colour information. Simultaneously, the dataset of the satellite images with ground truth (GT) based on the “DeepGlobe Land Cover Classification Challenge” dataset was constructed for testing three classes of quality metrics for satellite image segmentation.

Scanning Electron Microscopy in Hybrid Microelectronics

1976 ◽  
Vol 34 ◽  
pp. 456-457
Author(s):  
S. Khadpe ◽  
R. Faryniak
Keyword(s):  
Integrated Circuits ◽  
Thick Film ◽  
Integrated Circuit ◽  
Failure Modes ◽  
Three Dimensional ◽  
Circuit Components ◽  
Hybrid Microcircuit ◽  
Electrical Connections ◽  
Scanning Electron

The Scanning Electron Microscope (SEM) is an important tool in Thick Film Hybrid Microcircuits Manufacturing because of its large depth of focus and three dimensional capability. This paper discusses some of the important areas in which the SEM is used to monitor process control and component failure modes during the various stages of manufacture of a typical hybrid microcircuit.Figure 1 shows a thick film hybrid microcircuit used in a Motorola Paging Receiver. The circuit consists of thick film resistors and conductors screened and fired on a ceramic (aluminum oxide) substrate. Two integrated circuit dice are bonded to the conductors by means of conductive epoxy and electrical connections from each integrated circuit to the substrate are made by ultrasonically bonding 1 mil aluminum wires from the die pads to appropriate conductor pads on the substrate. In addition to the integrated circuits and the resistors, the circuit includes seven chip capacitors soldered onto the substrate. Some of the important considerations involved in the selection and reliability aspects of the hybrid circuit components are: (a) the quality of the substrate; (b) the surface structure of the thick film conductors; (c) the metallization characteristics of the integrated circuit; and (d) the quality of the wire bond interconnections.

Imaging through Scattering Media with a Learning Based Prior

2020 ◽  
Vol 2020 (14) ◽  
pp. 306-1-306-6
Author(s):  
Florian Schiffers ◽  
Lionel Fiske ◽  
Pablo Ruiz ◽  
Aggelos K. Katsaggelos ◽  
Oliver Cossairt
Keyword(s):  
Optimization Problem ◽  
Autonomous Driving ◽  
Scattering Media ◽  
Photon Scattering ◽  
Point Spread ◽  
Spread Function ◽  
Radiative Transport Equation ◽  
Spatio Temporal ◽  
Transient Imaging

Imaging through scattering media finds applications in diverse fields from biomedicine to autonomous driving. However, interpreting the resulting images is difficult due to blur caused by the scattering of photons within the medium. Transient information, captured with fast temporal sensors, can be used to significantly improve the quality of images acquired in scattering conditions. Photon scattering, within a highly scattering media, is well modeled by the diffusion approximation of the Radiative Transport Equation (RTE). Its solution is easily derived which can be interpreted as a Spatio-Temporal Point Spread Function (STPSF). In this paper, we first discuss the properties of the ST-PSF and subsequently use this knowledge to simulate transient imaging through highly scattering media. We then propose a framework to invert the forward model, which assumes Poisson noise, to recover a noise-free, unblurred image by solving an optimization problem.

Comparative analysis of intelligent planning methods

2012 ◽  
Vol 9 (2) ◽  
pp. 53-57 ◽  
Author(s):  
O.V. Darintsev ◽  
A.B. Migranov
Keyword(s):  
Neural Networks ◽  
Genetic Algorithms ◽  
Fuzzy Logic ◽  
Comparative Analysis ◽  
Mobile Robots ◽  
Sensory Information ◽  
Working Environment ◽  
Planning Methods ◽  
Intelligent Planning

The main stages of solving the problem of planning movements by mobile robots in a non-stationary working environment based on neural networks, genetic algorithms and fuzzy logic are considered. The features common to the considered intellectual algorithms are singled out and their comparative analysis is carried out. Recommendations are given on the use of this or that method depending on the type of problem being solved and the requirements for the speed of the algorithm, the quality of the trajectory, the availability (volume) of sensory information, etc.

State-of-the-Art: A Systematic Literature Review on Image Segmentation in Latent Fingerprint Forensics

2019 ◽  
Vol 12 ◽  
Author(s):  
Megha Chhabra ◽  
Manoj Kumar Shukla ◽  
Kiran Kumar Ravulakollu
Keyword(s):  
Image Segmentation ◽  
Background Noise ◽  
State Of The Art ◽  
Recognition Rate ◽  
Poor Quality ◽  
Latent Fingerprint ◽  
In The Beginning ◽  
The Comparative Study ◽  
Finger Skin

: Latent fingerprints are unintentional finger skin impressions left as ridge patterns at crime scenes. A major challenge in latent fingerprint forensics is the poor quality of the lifted image from the crime scene. Forensics investigators are in permanent search of novel outbreaks of the effective technologies to capture and process low quality image. The accuracy of the results depends upon the quality of the image captured in the beginning, metrics used to assess the quality and thereafter level of enhancement required. The low quality of the image collected by low quality scanners, unstructured background noise, poor ridge quality, overlapping structured noise result in detection of false minutiae and hence reduce the recognition rate. Traditionally, Image segmentation and enhancement is partially done manually using help of highly skilled experts. Using automated systems for this work, differently challenging quality of images can be investigated faster. This survey amplifies the comparative study of various segmentation techniques available for latent fingerprint forensics.

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