Development Of Image Processing Algorithms Research Articles

COMPUTER VISION SYSTEM FOR AUTOMATIC CERAMIC TILES INSPECTION

Ponte Academic JournalSep 2016, Volume 72, Issue 9 COMPUTER VISION SYSTEM FOR AUTOMATIC CERAMIC TILES INSPECTIONAuthor(s): POLEPOGU RAJESH ,KUNDURU UMAMAHESWARIJ. Ponte - Sep 2016 - Volume 72 - Issue 9 doi: 10.21506/j.ponte.2016.9.16 Abstract:Quality control is an important issue in the ceramic tile industry. On the other hand maintaining the rate of production with respect to time is also a major issue in ceramic tile manufacturing. Due to massive production of ceramic tiles, using human resources for defect detection is quite tedious and time consuming process. So we need to automate the process. Various optical technology based approaches came into play such as Automatic Optical Imaging. It is effective but very expensive technique for small-scale manufacturers. Therefore, many researchers started developing image processing algorithms for defect detection in ceramic tiles using less expensive cameras. In this paper we proposed an automated computer vision system with efficient defect detection technique, which can detect surface defects like blobs and cracks on tiles with high accuracy and fast response. This automated method helps us to acquire knowledge about the pattern of defect within a very short span of time. To automate the whole process, we have developed a ‘Graphical User Interface’ (GUI) in MATLAB using GUIDE (Graphical user interface design environment). We have used Sobel operator for edge detection and image subtraction techniques for fault detection in ceramic tiles. Index Terms— CV (Computer Vision), GUI (Graphical User Interface), GUIDE (Graphical user interface design environment), Threshold segmentation. Download full text:Check if you have access through your login credentials or your institution Username Password

Computer vision for automatic quality inspection of dried figs (Ficus carica L.) in real-time

This work reports the development of automated systems based on computer vision to improve the quality control and sorting of dried figs of Cosenza (protected denomination of origin) focusing on two research issues. The first was based on qualitative discrimination of figs through colour assessment comparing the analysis of colour images obtained using a digital camera with those obtained according to conventional instrumental methods, i.e. colourimetry currently done in laboratories. Data were expressed in terms of CIE XYZ, CIELAB and HunterLab colour spaces, as well as the browning index measurement of each fruit, and then, analysed using PCA and PLS-DA based methods. The results showed that both chroma meter and image analysis allowed a complete distinction between high quality and deteriorated figs, according to colour attributes. The second research issue had the purpose of developing image processing algorithms to achieve real-time sorting of figs using an experimental prototype based on machine vision, simulating an industrial application. An extremely high 99.5% of deteriorated figs were classified correctly as well as 89.0% of light coloured good quality figs A lower percentage was obtained for dark good quality figs but results were acceptable since the most of the confusion was among the two classes of good product.

Quantitative analyses of Ag3Sn intermetallic compound formation in SnAgCu solder alloys

In this paper, a new explicit state variable was introduced which describes the Ag3Sn intermetallic morphology inside of lead-free solder joints at different cooling rates. In the experiment, samples were prepared by melting standard Ag-content (4 % in wt%) and low Ag-content (0.3 % in wt%) solder alloys, and solidifying them at five different cooling rates between 0.8 and 3 K/s. Images were taken about the cross-section of solder joints by brightfield light-microscope to calculate the explicit state variables, which describe the morphology of precipitating Ag3Sn intermetallic compounds. The captured images were processed then with an in-house developed automatic image processing algorithm. It was found that the area of Ag3Sn intermetallic compounds followed exponential distribution, so a new state variable could be introduced, such as the amplitude of a certain exponential curve fitted to the area histogram of the Ag3Sn IMCs. It was proven that our new variable described the different Ag3Sn IMC morphologies obtained at different cooling rates better; it was more sensitive at both aforementioned solder alloys (4 and 0.3 % Ag content) than the widely applied parameters, like the mean-intercept length or the average area of particles.

Vineyard detection from unmanned aerial systems images

In viticulture, the adoption of precision agriculture techniques is nowadays increasingly essential to reach required high product quality standards. New reliable tools for mapping crop variability indexes in a vineyard or a single parcel are necessary to deploy site-specific management practices. In this paper, a new method to automatically detect vine rows in gray-scale aerial images is presented. The developed image processing algorithm is constituted by three main steps based on dynamic segmentation, Hough Space Clustering and Total Least Squares techniques. The procedure’s reliability has also been proven in the presence of disturbance elements, like dense inter-row grassing, bushes and trees shadows, by properly detecting vine rows in the vineyard images. Moreover, its adaptive features allow it to obtain optimal results in the presence of uneven image illumination due, for example, to the presence of clouds or steep terrain slopes. The extracted row and inter-row information, besides being the basis for vineyard characterization maps computation, like vine plants vigor maps, could also be used as a reference for other precision viticulture tasks such as, for example, path planning of unmanned ground vehicles.

Target Recognition based on Machine Vision for Industrial Sorting Robot

In order to improve the applications for an industrial sorting robot, it is necessary to increase its flexibility and control accuracy. Here an industrial robot arm is designed and set up for experiment simulation with machine vision. Some target recognition experiments are designed for vision recognition. The flying badminton tracking experiment is given to verify the efficiency of the developed algorithm. Moreover, the multiple target recognition are also tested with our developed algorithms. The machine vision technology is an effective solution. In order to reduce the influence of the size, deformation, and lighting etc., the target recognition and location method with fusion of scale invariant feature transform (SIFT) and moment invariants is developed. The experiments results showed that the developed image processing algorithms are robust, and the flexibility of the industrial robot can be improved by machine vision.

The Use of Regularized Least Squares Minimization for the Deconvolution of SEM Images

Major advances are currently taking place in image processing algorithm development. When combined with today’s high speed computers utilizing multicore processing and very large memories the door is opened to the practical application of regularization techniques to the restoration of SEM images with improved spatial resolution. It is well recognized that an observed SEM image can be thought of as the convolution of a point spread function (psf) arising from measurement broadening and the true structure imaged. However, even with the abovementioned advances, a variety of additional limitations arise that are preventing the practical implementation of deconvolution to SEM image restoration. These factors include noise as well as a lack of detailed knowledge of the psf since no experimental technique is currently available to directly measure it with level of spatial resolution required. Furthermore, the problem is complicated by other factors such as specimen drift, contamination, the three dimensionality of specimen, signal excitation volume considerations, non-linearity in the scanning system and also nonlinear behavior in the detection chain [1].

Visual Servoing of Automatic Alignment System Using Model Predictive Control

Over the past few decades, vision based alignment has been accepted as an important technique to achieve higher economic benefits for precision manufacturing and measurement applications. Also referred to as visual servoing, this technique basically applies the vision feedback information and drives the moving parts to the desired target location using some appropriate control laws. Although recently rapid development of advanced image processing algorithms and hardware have made this alignment process an easier task, some fundamental issues including inevitable system constraints and singularities, still remain as a challenging research topic for further investigation. This paper aims to develop a visual servoing method for automatic alignment system using model predictive control (MPC). The reason for using this optimal control for visual servoing design is because of its capability of handling constraints such as motor and image constraints in precision alignment systems. In particular, a microassembly system for peg and hole alignment application is adopted to illustrate the design process. The goal is to perform visual tracking of two image feature points based on a XYθ motor-stage system. From the viewpoint of MPC, this is an optimization problem that minimizes feature errors under given constraints. Therefore, a dynamic model consisting of camera parameters and motion stage dynamics is first derived to build the prediction model and set up the cost function. At each sample step the control command is obtained by solving a quadratic programming optimization problem. Finally, simulation results with comparison to a conventional image based visual servoing method demonstrate the effectiveness and potential use of this method.

Computer simulation of magnetic resonance angiography imaging: model description and validation.

With the development of medical imaging modalities and image processing algorithms, there arises a need for methods of their comprehensive quantitative evaluation. In particular, this concerns the algorithms for vessel tracking and segmentation in magnetic resonance angiography images. The problem can be approached by using synthetic images, where true geometry of vessels is known. This paper presents a framework for computer modeling of MRA imaging and the results of its validation. A new model incorporates blood flow simulation within MR signal computation kernel. The proposed solution is unique, especially with respect to the interface between flow and image formation processes. Furthermore it utilizes the concept of particle tracing. The particles reflect the flow of fluid they are immersed in and they are assigned magnetization vectors with temporal evolution controlled by MR physics. Such an approach ensures flexibility as the designed simulator is able to reconstruct flow profiles of any type. The proposed model is validated in a series of experiments with physical and digital flow phantoms. The synthesized 3D images contain various features (including artifacts) characteristic for the time-of-flight protocol and exhibit remarkable correlation with the data acquired in a real MR scanner. The obtained results support the primary goal of the conducted research, i.e. establishing a reference technique for a quantified validation of MR angiography image processing algorithms.

Observations on the non-mixed length and unsteady shock motion in a two dimensional supersonic ejector

Key features that drive the operation of a supersonic ejector are the complex gasdynamic interactions of the primary and secondary flows within a variable area duct and the phenomenon of compressible turbulent mixing between them, which have to be understood at a fundamental level. An experimental study has been carried out on the mixing characteristics of a two dimensional supersonic ejector with a supersonic primary flow (air) of Mach number 2.48 and the secondary flow (subsonic) which is induced from the ambient. The non-mixed length, which is the length within the ejector for which the primary and secondary flow remain visually distinct is used to characterize the mixing in the ejector. The operating pressures, flow rates and wall static pressures along the ejector have been measured. Two flow visualization tools have been implemented—time resolved schlieren and laser scattering flow visualization. An important contribution has been the development of in-house image processing algorithms on the MATLAB platform to detect the non-mixed length from the schlieren and laser scattering images. The ratio of mass flow rates of the secondary flow to primary flow (entrainment ratio) has been varied in a range of 0.15–0.69 for two locations of the primary nozzle in the ejector duct. Representative cases have been computed using commercial CFD tool (Fluent) to supplement the experiments. Significant outcomes of the study are—the non-mixed length quantified from the flow visualization images is observed to lie within 4.5 to 5.2 times the height of the mixing duct which is confirmed by the wall static pressure profiles. The flow through the supersonic ejector in the mixed regime is explained using corroborative evidences from different diagnostic tools. A reduction of the non-mixed length by 46.7% is observed at operating conditions when the nozzle is sufficiently overexpanded. The disturbance caused to the mixing layer due to unsteady shock-boundary layer interactions within the nozzle at such conditions enhances mixing. The analysis of time resolved schlieren images have provided interesting observations on repetitive back and forth motion of the shock cells in the primary flow with a co-flowing secondary flow in the confines of the supersonic ejector. The oscillations have significant amplitudes (order of the nozzle height) at the centerline. The details of these experiments followed by the analysis of data and the inferences drawn from the results are discussed in this article.

Benchmarking Framework for SAR Despeckling

Objective performance assessment is a key enabling factor for the development of better and better image processing algorithms. In synthetic aperture radar (SAR) despeckling, however, the lack of speckle-free images precludes the use of reliable full-reference measures, leaving the comparison among competing techniques on shaky bases. In this paper, we propose a new framework for the objective (quantitative) assessment of SAR despeckling techniques, based on simulation of SAR images relevant to canonical scenes. Each image is generated using a complete SAR simulator that includes proper physical models for the sensed surface, the scattering, and the radar operational mode. Therefore, in the limits of the simulation models, the employed simulation procedure generates reliable and meaningful SAR images with controllable parameters. Through simulating multiple SAR images as different instances relevant to the same scene we can therefore obtain, a true multilook full-resolution SAR image, with an arbitrary number of looks, thus generating (by definition) the closest object to a clean reference image. Based on this concept, we build a full performance assessment framework by choosing a suitable set of canonical scenes and corresponding objective measures on the SAR images that consider speckle suppression and feature preservation. We test our framework by studying the performance of a representative set of actual despeckling algorithms; we verify that the quantitative indications given by numerical measures are always fully consistent with the rationale specific of each despeckling technique, strongly agrees with qualitative (expert) visual inspections, and provide insight into SAR despeckling approaches.

Identification of crack features in fresh jujube using Vis/NIR hyperspectral imaging combined with image processing

Detection of crack defect in fresh jujube is a critical process to guarantee jujube quality and meet processing demands of fresh jujube fruit. This study presented a novel method for identification of fresh jujube crack feature using hyperspectral imaging in visible and near infrared (Vis/NIR) region (380–1030nm) combined with image processing. Hyperspectral image data of samples were used to extract the characteristic wavebands by chemometrics, which integrated the method of partial least squares regression (PLSR), principal component analysis (PCA) of spatial hyperspectral image (SPCA) and independent component analysis (ICA) of spatial hyperspectral image (SICA). On the basis of the selected wavebands, least-squares support vector machine (LS-SVM) discrimination models were established to correctly distinguish between cracked and sound fresh jujube. The performance of discriminating model was evaluated using receiver operating characteristics (ROC) curve analysis. The results demonstrated that PLSR–LS-SVM discrimination model with a high accuracy of 100% had the optimal performance of “area”=1 and “std”=0. For acquiring rich crack feature information, SPCA was also carried on images at the five characteristic wavebands (467, 544, 639, 673 and 682nm) selected by PLSR. Finally, the SPC-4 image was explored to identify the location and area of crack feature through a developed image processing algorithm. The results revealed that hyperspectral imaging combined with image processing technique could achieve the rapid identification of crack features in fresh jujube.

Study on the Target Recognition and Location Technology of industrial Sorting Robot based on Machine Vision

In order to improve the applications for an industrial sorting robot, it is necessary to increase its flexibility and control accuracy. The prerequisite is to automatically extract the multiple target positions accurately and robustly. The machine vision technology is an effective solution. Here an industrial robot arm is designed and set up for experiment simulation with machine vision. In order to reduce the influence of the size, deformation, and lighting etc., the target recognition and location method with fusion of scale invariant feature transform (SIFT) and moment invariants is developed. The experiments results showed that the developed image processing algorithms are robust, and the flexibility of the industrial robot can be improved by machine vision.

Automated long-term monitoring of parallel microfluidic operations applying a machine vision-assisted positioning method.

As microfluidics has been applied extensively in many cell and biochemical applications, monitoring the related processes is an important requirement. In this work, we design and fabricate a high-throughput microfluidic device which contains 32 microchambers to perform automated parallel microfluidic operations and monitoring on an automated stage of a microscope. Images are captured at multiple spots on the device during the operations for monitoring samples in microchambers in parallel; yet the device positions may vary at different time points throughout operations as the device moves back and forth on a motorized microscopic stage. Here, we report an image-based positioning strategy to realign the chamber position before every recording of microscopic image. We fabricate alignment marks at defined locations next to the chambers in the microfluidic device as reference positions. We also develop image processing algorithms to recognize the chamber positions in real-time, followed by realigning the chambers to their preset positions in the captured images. We perform experiments to validate and characterize the device functionality and the automated realignment operation. Together, this microfluidic realignment strategy can be a platform technology to achieve precise positioning of multiple chambers for general microfluidic applications requiring long-term parallel monitoring of cell and biochemical activities.

Application of Neutron Tomography in Culture Heritage Research

Neutron Tomography (NT) investigation of Culture Heritages (CH) is an efficient tool for understanding the culture of ancient civilizations. Neutron imaging (NI) is a-state-of-the-art non-destructive tool in the area of CH and plays an important role in the modern archeology. The NI technology can be widely utilized in the field of elemental analysis.At Egypt Second Research Reactor (ETRR-2), a collimated Neutron Radiography (NR) beam is employed for neutron imaging purposes. A digital CCD camera is utilized for recording the beam attenuation in the sample. This helps for the detection of hidden objects and characterization of material properties. Research activity can be extended to use computer software for quantitative neutron measurement. Development of image processing algorithms can be used to obtain high quality images.In this work, full description of ETRR-2 was introduced with up to date neutron imaging system as well. Tomographic investigation of a clay forged artifact represents CH object was studied by neutron imaging methods in order to obtain some hidden information and highlight some attractive quantitative measurements. Computer software was used for imaging processing and enhancement. Also the Astra Image 3.0 Pro software was employed for high precise measurements and imaging enhancement using advanced algorithms.This work increased the effective utilization of the ETRR-2 Neutron Radiography/Tomography (NR/T) technique in Culture Heritages activities.

Surface Casting Defects Inspection Using Vision System and Neural Network Techniques

Abstract The paper presents a vision based approach and neural network techniques in surface defects inspection and categorization. Depending on part design and processing techniques, castings may develop surface discontinuities such as cracks and pores that greatly influence the material’s properties Since the human visual inspection for the surface is slow and expensive, a computer vision system is an alternative solution for the online inspection. The authors present the developed vision system uses an advanced image processing algorithm based on modified Laplacian of Gaussian edge detection method and advanced lighting system. The defect inspection algorithm consists of several parameters that allow the user to specify the sensitivity level at which he can accept the defects in the casting. In addition to the developed image processing algorithm and vision system apparatus, an advanced learning process has been developed, based on neural network techniques. Finally, as an example three groups of defects were investigated demonstrates automatic selection and categorization of the measured defects, such as blowholes, shrinkage porosity and shrinkage cavity.

Development of Image Processing Algorithm for Cytological Diagnosis of Uterine Cervical Cancer Tissue Examination

In this study is relevant to cell area extraction for cytological diagnosis of liquid-based cell examination being used for early diagnosis of uterine cervical cancer by utilizing image processing. As existing cytological diagnosis process has been performed manually by cytotechnologist, the amount of cell image that could be processed was limited. Therefore, in this study, cellular domain extraction through which automatic processing of cytological diagnosis process is enabled was performed and its condition was established depending on the size of cell nucleus based on cytological diagnosis standard of uterine cervical cancer cell. Obtained cell image was processed by hough transform matching with cell image by using matlab through preprocessing phase. As a result, 28 sample images among total 30 sample images were succeeded in finding out target cell and two sample images failed to find it. In the future, modification and verification for such failure case may be required to be performed. It is expected that the result of this study could be utilized for diagnosis process automation of liquid-based cell examination.

ImageNets — Framework for Fast Development of Robust and High Performance Image Processing Algorithms

Zusammenfassung In this publication, we describe the novel ImageNets framework which enables developers with low programming knowledge to rapidly create efficient high performance image processing algorithms and which is available under an open source license. The performance of this novel tool has been compared with the simple to use Image Processing Toolbox of MATLAB/Simulink. A group of 49 students, attendees of a robot vision lecture, had to solve an image processing task using both tools. Different evaluation criteria have been measured or determined by interrogation of the students. The advantage of the new tool ImageNets has become clearly visible.

Non-destructive and rapid analysis of moisture distribution in farmed Atlantic salmon (Salmo salar) fillets using visible and near-infrared hyperspectral imaging

This study was conducted to explore the potential of hyperspectral imaging (HSI) technique in visible and near-infrared (VIS–NIR) region (400–1700 nm) for determining the spatial distribution of moisture content (MC) in farmed Atlantic salmon fillets rapidly and non-destructively. The quantitative relationship between spectral data and the reference MC values was successfully established by partial least squares regression (PLSR). Three spectral ranges of 400–1000 nm (Spectral Range I), 900–1700 nm (Spectral Range II) and 400–1700 nm (Spectral Range III) were considered, and their results were compared to choose the best spectral range. The established PLSR models had coefficients of determination (RP2) of 0.893, 0.902 and 0.849, and root-mean-square errors of prediction (RMSEP) of 1.513%, 1.450% and 1.800% for three spectral ranges, respectively. Important wavelengths were then selected by using regression coefficients of PLSR models for three spectral ranges, and optimised PLSR models were built using only the important wavelengths, resulting in RP2 of 0.893, 0.888 and 0.884 with RMSEP of 1.517%, 1.553% and 1.578% for three spectral ranges, respectively. PLSR model with eight important wavelengths (420, 445, 545, 585, 635, 870, 925 and 955 nm) selected from Spectral Range I was considered as the best model for MC determination and was transferred to each pixel within the image for visualising MC in all locations of salmon fillets with an aid of a developed image processing algorithm. The results revealed that hyperspectral imaging technique has a great potential to predict the MC distribution of salmon fillets non-destructively and accurately. In addition to realising the MC difference within salmon fillets, it could be possible for hyperspectral imaging to classify and grade salmon fillets based on different MC levels. The results revealed that hyperspectral imaging technique has a great potential to predict the MC distribution of salmon fillets non-destructively and accurately for the food industry.

Challenges of Video Monitoring for Phenomenological Diagnostics in Present and Future Tokamaks

With the development of heterogeneous camera networks working at different wavelengths and frame rates and covering a large surface of a vacuum vessel, the visual observation of a large variety of plasma and thermal phenomena (e.g., hot spots, ELMs, MARFE, arcs, dusts, etc.) becomes possible. In the domain of machine protection, a phenomenological diagnostic is a key element toward plasma/thermal event dangerousness assessment during real-time operation. It is also of primary importance to automate the extraction and the storage of phenomena information for further offline event retrieval and analysis, thus leading to a better use of massive image databases for plasma physics studies. To this end, efforts have been devoted to the development of image processing algorithms dedicated to the recognition of specific events. However, a need arises now for the integration of techniques developed so far in both hardware and software directions. We present in this paper our latest results in the field of real-time phenomenon recognition and management through our image understanding software platform. This platform has been validated on Tore Supra during operation and is under evaluation for other present Tokamaks and for the foreseen imaging diagnostic of ITER.

Integration of the OpenIGTLink Network Protocol for image‐guided therapy with the medical platform MeVisLab

AbstractBackgroundOpenIGTLink is a new, open, simple and extensible network communication protocol for image‐guided therapy (IGT). The protocol provides a standardized mechanism to connect hardware and software by the transfer of coordinate transforms, images, and status messages. MeVisLab is a framework for the development of image processing algorithms and visualization and interaction methods, with a focus on medical imaging.MethodsThe paper describes the integration of the OpenIGTLink network protocol for IGT with the medical prototyping platform MeVisLab. The integration of OpenIGTLink into MeVisLab has been realized by developing a software module using the C++ programming language.ResultsThe integration was evaluated with tracker clients that are available online. Furthermore, the integration was used to connect MeVisLab to Slicer and a NDI tracking system over the network. The latency time during navigation with a real instrument was measured to show that the integration can be used clinically.ConclusionsResearchers using MeVisLab can interface their software to hardware devices that already support the OpenIGTLink protocol, such as the NDI Aurora magnetic tracking system. In addition, the OpenIGTLink module can also be used to communicate directly with Slicer, a free, open source software package for visualization and image analysis. Copyright © 2012 John Wiley & Sons, Ltd.