Application Of Image Processing Techniques Research Articles

Investigation of the Quality of Microholes Machined by µEDM Using Image Processing

Micromachining processes have become more imperative in this modern era of miniaturization. Micro-EDM (µEDM) is an excellent micromachining process capable of producing microholes and microfeatures in all electrically conductive materials, irrespective of their mechanical properties. µEDM is a proven process for producing microholes. The quality of microholes is imperative in many applications. The aim of the present study is to investigate the role of capacitance and voltage in determining the perfection of microholes edges that machined using die sinking µEDM by applying image processing techniques. The experimental results reveal that the capacitance significantly influences circularity more than open circuit voltage. The quality of the microholes is also affected by flushing force of dielectric fluid and tool adhesion. Best circularity was achieved with capacitance values between 0.001–0.00001 µF and voltage between 80–100. Discharge energies greater than 50 µJ gradually reduce the quality of microhole edges.

Bearing diagnostics using image processing methods

In complex machines, the failure signs of an early bearing damage are weak compared to other sources of excitations (e.g. gears, shafts, rotors, etc.). The task of emphasizing the failure signs is complicated by the fact that changes in operating conditions influence vibrations sources and change the frequency and amplitude characteristics of the signal, making it non-stationary. As a result, a joint time-frequency representation is required. Previous vibration based diagnostic techniques focused on either the time domain or the frequency domain.The proposed method suggests a different solution that applies image processing techniques to time-frequency or RPM-order representations (TFR) of the vibration signals in the orders-RPM domain.In the first stage, TFRs of healthy machines are used to create a baseline. The TFRs can be obtained using various methods (Wigner-Ville, wavelets, STFT, etc).In the next stage, the distance TFR between the inspected recording and the baseline is computed. In the third stage, the distance TFR is analyzed using ridge tracking and other image processing algorithms. In the fourth stage, the relations between the detected ridges are compared to the characteristic patterns of the bearing failure modes and the matching ridges are selected.The different stages of analysis: baselines, distance TFR, ridges detection and selection, are illustrated with actual data of damaged bearings.

Identification of Composite Delamination Using the Krawtchouk Moment Descriptor

Damage assessment of composite materials is crucial for health monitoring of engineering structures. It is particularly important to detect damage invisible to the human eye caused by low speed impact. Optical non-contact sensing techniques enable full-field measurements from structural responses. However, damage is generally associated with its local effect on deformation and strain patterns while full-field measured data is highly information redundant. It is possible to apply image processing techniques [1, 2] to extract succinct and efficient features, or attributes, from full-field data. Iterative reanalysis of large and detailed numerical models is generally very expensive and may not be feasible. Meta modeling is one of the practical ways to overcome the problem of high computational cost. In this paper, a case study of composite delamination assessment based on simulation is discussed. The damage mode in the composite plate is assumed to be a delamination of elliptical shape. Surface strain of a specimen under tensile loading is considered to be the measured structural output. The Krawtchouk moment descriptor is applied to extract a small number of features from the strain map. A meta model in the form of a Kriging predictor [3] is constructed to map the damage parameters to Krawtchouk shape features of the strain distribution. The delamination region is quantified using an inverse procedure based on the trained Meta model. Furthermore, a biomimicry algorithm, particle swarm optimisation, is applied to detect the location of the delamination.

Image processing technique to estimate geometric parameters and volume of selected dry beans

The geometric parameters along with related physical properties of agricultural material are vital to characterize and describe its quality. The application of image processing technique for this purpose can certainly reduce the human drudgery while ensuring quality of produce. The experiments were conducted to classify the shape and then workout the volume of the selected beans using the image processing technique. Green pea, gar- banzo, kidney bean, navy bean and pinto bean were pro- cured from the local grocery store for this study. A digital camera was used to capture the images of the ten different beans of each type. The beans were placed in two different orientations (longitudinal and lateral) and image processing technique was used to quantify and process the digital images. The results of image analysis were compared with the data obtained with actual measurements using digital vernier caliper. The related observations like thousand grain weight, bulk density, true density, porosity and weight of selected single grain were also recorded. A linear relationship was seen between the axial dimensions of beans and the pixel value with R 2 in the range of 0.64-0.96. The bulk density and true density of the beans were observed to be in the range of 0.71-0.80 and 1.21-1.29 g/cc respectively. The sphericity of the beans varied in the range of 0.55-0.89. Analyses of the acquired images indicate convex geometry for the beans with ellipsoid shape while the same observation was recorded by physical measurements also. A linear relationship was observed between the volumes estimated by image analysis and true volumes of the beans with R 2 in the range of 0.80-0.96. The circularity and compactness of the beans lied in the range of 0.69-0.90 and 0.56-0.71.

Particle flow modeling of dry induced roll magnetic separator

Abstract Magnetic separators have unrestricted industrial application and are widely used in mineral beneficiation, food, textiles, plastic and ceramic processing industry. Separation efficiency of magnetic separator depends on material characteristics and the design features of equipment along with the optimization of process variables. This study is based on a novel application of image processing technique to capture the particle trajectory on induced roll in an electromagnetic separator while treating magnetic particles. Three different minerals of different magnetic susceptibility were selected to investigate this approach. Trajectories of 3 mm diameter balls of mineral fines were captured using an image analyzer at different operating conditions. The particle trajectory captured was compared with the trajectory predicted by Cakir mathematical model. The separation angle between roll and particles was considered as a separation performance measure. It was found that Cakir mathematical model produce reliable result and a new model was proposed to increase the reliability of separation angle prediction by including the particle shape factor. The proposed model can be utilized to optimize the magnetic separation process while treating paramagnetic minerals.

An improved simple morphological filter for the terrain classification of airborne LIDAR data

Terrain classification of LIDAR point clouds is a fundamental problem in the production of Digital Elevation Models (DEMs). The Simple Morphological Filter (SMRF) addresses this problem by applying image processing techniques to the data. This implementation uses a linearly increasing window and simple slope thresholding, along with a novel application of image inpainting techniques. When tested against the ISPRS LIDAR reference dataset, SMRF achieved a mean 85.4% Kappa score when using a single parameter set and 90.02% when optimized. SMRF is intended to serve as a stable base from which more advanced progressive filters can be designed. This approach is particularly effective at minimizing Type I error rates, while maintaining acceptable Type II error rates. As a result, the final surface preserves subtle surface variation in the form of tracks and trails that make this approach ideally suited for the production of DEMs used as ground surfaces in immersive virtual environments.

Facies and Fracture Network Modeling by a Novel Image Processing Based Method

A wide range of methods for geological reservoir modeling has been offered from which a few can reproduce complex geological settings, especially different facies and fracture networks. Multi Point Statistic (MPS) algorithms by applying image processing techniques and Artificial Intelligence (AI) concepts proved successful to model high-order relations from a visually and statistically explicit model, a training image. In this approach, the patterns of the final image (geological model) are obtained from a training image that defines a conceptual geological scenario for the reservoir by depicting relevant geological patterns expected to be found in the subsurface. The aim is then to reproduce these training patterns within the final image. This work presents a multiple grid filter based MPS algorithm to facies and fracture network images reconstruction. Processor is trained by training images (TIs) which are representative of a spatial phenomenon (fracture network, facies...). Results shown in this paper give visual appealing results for the reconstruction of complex structures. Computationally, it is fast and parsimonious in memory needs.

Quantification of corrugation in simulated graphene by electron tomography techniques

Electron tomography (ET) can be used to determine the shape, and therefore the properties, of an object. It deals with retrieving tridimensional (3D) information from bidimensional (2D) projections of the object under study. While ET has been used widely in biology, its application to the nanoscale world is relatively recent because of the limitations in resolution and aberration of electron microscopes. At atomic scale, classical tomography reconstruction can be replaced by tomography through points reconstruction, that is, the 3D information can be obtained by applying image processing techniques to the projections and by using geometrical models instead of the classical integral approach. In this paper, a methodology for the study of corrugated graphene will be presented. It will be shown how to apply tomographic techniques to the determination of the corrugations in graphene layers with error in 3D location in the range of pm.

영상처리에 의한 위상잠금 열화상기법의 최적화 연구

This study examined the use of LIT (lock-in infrared thermography) to detect defects in the welded parts of ships and offshore structures. A quantitative analysis was used with the filtering and texture measurement of image processing techniques to find the optimized experimental condition. We verified the reliability of our methods by applying image processing techniques in order to normalize the evaluations of comparative images that showed a phase difference. In addition, it was found that a low to mid-range intensity of light exposure on the surface showed good results, whereas high exposure did not provide significant results. A lock-in frequency of around 0.1 Hz was satisfactory regardless of the intensity of the light source. In addition, making the integration time of the thermography camera inversely proportional to the intensity of the exposed light source during the experiment provided good results.

Image based in situ electron-beam drift detection by silicon photodiodes in scanning-electron microscopy and an electron-beam lithography system

► Silicon-photodiode detector for in-situ beam position sensing in an scanning electron microscope (SEM). ► Applying image processing technique to the backscattered-electron image to reach nanometer resolution. ► The performance in detecting the drift of the electron beam over time was analyzed. ► This detection mechanism is useful for beam position feedback in the electron-beam direct-write lithography system. A silicon-photodiode detector can be used to sense the position of the electron beam in a scanning-electron microscope. In order to validate the implementation of a electron beam drift detector, a silicon photodiode was constructed with a low profile and small working distance. The performance in detecting the drift of the electron beam over time was analyzed. It was also shown that a back scattered-electron image can be created with electron scanning, which allows the development of highly sensitive in situ beam position feedback in the electron-beam direct-write lithography system.

A Study on the Optimized Test Condition of Lock-in IR Thermography by Image Processing

In this study, it was studies the utilization of LIT(lock-in infrared thermography) which can detect defects in welded parts of ship and offshore structures. Quantitative analysis was used through methods of filtering and texture measurement of image processing techniques to find the optimized experimental condition. We verified reliability in our methods by applying image processing techniques in order to normalize evaluations of comparative images that show phase difference. In addition, low to mid exposure showed good results whereas high exposure did not provide significant results in regards to intensity of light exposure on surface. Lock-in frequency was satisfactory around 0.1 Hz regardless of intensity of light source we had. In addition, having the integration time of thermography camera inversely proportional to intensity of exposed light source during the experiment allowed good outcome of results.

Numerical Simulation of Fracture Propagation of Heterogeneous Material

For the Three Gorges granite, a finite difference software package FLAC was used to study failure process of heterogeneous rock material. Based on mineral components identification results of the granite and fuzzy clustering method, the actual image date was transformed into the finite difference grid by applying image processing techniques. A convenient and efficient two-dimensional numerical modeling method for heterogeneous geomaterials was presented. Then, failure process of the granite were simulated in uniaxial compression test based on experimental strain soft model, and the stress concentration phenomenon was analyzed. The results show that the numerical modeling method based on digital image processing can be used to calculate the mechanical responses of geomaterials by taking their heterogeneities into considerations.

Frequency pattern of turbulent flow and sediment entrainment over ripples using image processing

Abstract. River channel change and bed scourings are source of major environmental problem for fish and aquatic habitat. The bed form such as ripples and dunes is the result of an interaction between turbulent flow structure and sediment particles at the bed. The structure of turbulent flow over ripples is important to understand initiation of sediment entrainment and its transport. The focus of this study is the measurement and analysis of the dominant bursting events and the flow structure over ripples in the bed of a channel. Two types of ripples with sinusoidal and triangular forms were tested in this study. The velocities of flow over the ripples were measured in three dimensions using an Acoustic Doppler Velocimeter with a sampling rate of 50 Hz. These velocities were measured at different points within the flow depth from the bed and at different longitudinal positions along the flume. A CCD camera was used to capture 1500 sequential images from the bed and to monitor sediment movement at different positions along the bed. Application of image processing technique enabled us to compute the number of entrained and deposited particles over the ripples. From a quadrant decomposition of instantaneous velocity fluctuations close to the bed, it was found that bursting events downstream of the second ripple, in Quadrants 1 and 3, were dominant whereas upstream of the ripple, Quadrants 2 and 4 were dominant. More importantly consideration of these results indicates that the normalized occurrence probabilities of sweep events along the channel are in phase with the bed forms whereas those of ejection events are out of phase with the bed form. Therefore entrainment would be expected to occur upstream and deposition occurs downstream of the ripple. These expectations were confirmed by measurement of entrained and deposited sediment particles from the bed. These above information can be used in practical application for rivers where restoration is required.

Performance Analysis of Text-Independent Speaker Identification System

In this paper, the text independent speaker identification system is implemented and analyzed using the speaker specific features extracted by applying image processing techniques. The speech is converted into spectrogram, an efficient representation of the speech signal in the form of pattern. Radon Transform (RT) and Discrete Cosine Transform (DCT) are applied on the spectrogram to extract the features. Radon Transform is applied for eight orientations on the acoustic characteristics of the spectrogram. After applying RT, the two dimensional DCT has been applied on Radon projections yields low dimensional feature vectors. The performance of the algorithm has been evaluated with our own created BCS database using sound forge version 5.0. The effect of number of Radon projections and DCT coefficients are analyzed. The demonstration program is coded by MATLAB V 7.0.4. The recognition rate of this algorithm is 96% on BCS database

Performance Analysis of Text-Independent Speaker Identification System

In this paper, the text independent speaker identification system is implemented and analyzed using the speaker specific features extracted by applying image processing techniques. The speech is converted into spectrogram, an efficient representation of the speech signal in the form of pattern. Radon Transform (RT) and Discrete Cosine Transform (DCT) are applied on the spectrogram to extract the features. Radon Transform is applied for eight orientations on the acoustic characteristics of the spectrogram. After applying RT, the two dimensional DCT has been applied on Radon projections yields low dimensional feature vectors. The performance of the algorithm has been evaluated with our own created BCS database using sound forge version 5.0. The effect of number of Radon projections and DCT coefficients are analyzed. The demonstration program is coded by MATLAB V 7.0.4. The recognition rate of this algorithm is 96% on BCS database.

Computer-Aided Diagnosis of Schistosomiasis

Schistosomiasis is a parasitic infection affecting more than 200 million persons in developing countries. The parasite multiplication and transmission are very fast and constitute a heavy burden to the public health and medical services. The parasites are characterized by different shaped eggs. Conventional detection techniques consume a considerable amount of time and are costly. Human factors affect the accuracy of the results, particularly in mass screening campaigns. This article shows how to detect the isolated schistosoma egg by developing an algorithm applying image processing techniques. The designed algorithm is based on calculation of cross-correlation coefficient of the 2 sets of invariant moments for both the reference and the sample images. Using 0.99 as a threshold value of cross-correlation coefficient, the algorithm decides whether an egg is detected or not. This algorithm is implemented using MATLAB software. A graphical user interface was designed for the implemented algorithm. Ultimately, the designed software is capable of automatic detection of schistosoma eggs. The detection accuracy of our algorithm is 100% as compared with conventional techniques.

An Interactive System for Sensory and Gustatory Impaired People Based On Hand Gesture Recognition

Sign Language is an interactive system for sensory and gustatory impaired people for communicating information among them. Hand Gesture Recognition (HGR) is a system for sign language detection which uses computers for enabling communication with sensory impaired people. In this research work, an efficient and effective technique is introduced for the identification of the number of fingers opened or closed in a gesture representing an alphabet of the American Sign Language (ASL). Finger Detection is accomplished with the use of Contour Hull and Convexity Defects. Contour Hull helps to form the boundary of the hand gestures whereas the Convexity Defects helps to find tip of the fingers captured by the web camera. This proposed system applies image processing techniques to store the pre-processed data, Machine learning technique is used in supervised clustering and computer vision techniques is used to recognize the hand gestures. Hence this intelligent system does not require the hand to be perfectly aligned to the camera for detecting the signs. In addition to this, to detect the signs, this system does not need any colored markers or gloves to be wearied on the hands.

Microscopic Image Recognition-based Fish Disease Diagnosis System

This paper presents a microscopic image recognition-based fish disease diagnosis system capable of faster treatment of diseased fish in order to prevent the spread of disease. The system is composed of three steps: 1) extracting the pathogen area from the microscopic images of diseased fish tissue by applying image processing techniques such as noise reduction, edge detection, and object detection, 2) confirming the pathogen and making the final diagnosis by feature extraction, and correlation matching compared to the registered pathogen database, and 3) proving the information of diagnosed disease, treatment and prevention methods by a connected web server through the Internet and SMS message by mobile communication. The designed fish disease diagnosis system was implemented to diagnose 3 parasitic diseases of olive flounder. The system can support fish farmers and veterinarians by providing easy and rapid diagnosis of diseased olive flounder, guidance in the use of appropriate drugs and a suitable treatment method for the diagnosed disease.

Application of Image Processing Technique for Evaluation of Garment Pleats

The trends and results achieved on application of image processing technique in the textile and garment were reviewed. The basis and principle of application of image technique to the garment pleat evaluation were studied. A blouse wearer trial is conducted so that the images of wrinkles developed on the surface of the blouse with different postures of the wearer were collected. The visual effects of the garment pleats created by various techniques and wearing were analyzed, and methods for extracting pleat images form the surface of the fabric were also introduced. After image processing of the extracted pleats, an evaluation system of garment pleats based on the grey scale was recommended, which included such indexes as the number, depth and width of pleat, and unevenness of pleats. The study took an example of the wrinkles changing in a blouse sleeve when arm lifting, which proved the feasibility of digital expression of wrinkle characteristics by mean s of image technique theoretically and practically. Research results indicated that this evaluation system can truly reflect the form and characteristics of garment pleats．

Text-independent speaker identification using Radon and discrete cosine transforms based features from speech spectrogram

This paper presents a new feature extraction technique for speaker recognition using Radon transform (RT) and discrete cosine transform (DCT). The spectrogram is compact, efficient in representation and carries information about acoustic features in the form of pattern. In the proposed method, speaker specific features have been extracted by applying image processing techniques to the pattern available in the spectrogram. Radon transform has been used to derive the effective acoustic features from the speech spectrogram. Radon transform adds up the pixel values in the given image along a straight line in a particular direction and at a specific displacement. The proposed technique computes Radon projections for seven orientations and captures the acoustic characteristics of the spectrogram. DCT applied on Radon projections yields low dimensional feature vector. The technique is computationally efficient, text-independent, robust to session variations and insensitive to additive noise. The performance of the proposed algorithm has been evaluated using the Texas Instruments and Massachusetts Institute of Technology (TIMIT) and our own created Shri Guru Gobind Singhji (SGGS) databases. The recognition rate of the proposed algorithm on TIMIT database (consisting of 630 speakers) is 96.69% and for SGGS database (consisting of 151 speakers) is 98.41%. These results highlight the superiority of the proposed method over some of the existing algorithms.