MATLAB Image Processing Toolbox Research Articles

Fuzzy Logic Image Processing

With facial recognition software becoming more widely used, especially in mobile apps such as Snapchat, boundary detection will continue to be one of the primary areas of interest in enhancing software performance. Edge detection is paramount in discriminating objects so they can be used and processed. This paper will present a fuzzy system for boundary detection. The proposed system will then compare it to traditional methods of edge detection using MATLAB's image processing toolbox. The proposed fuzzy system allows for more effective tool since the membership functions can be more defined and robust to accommodate different images, as well as tailored to result in a more effective processed image due to ambiguity. The results of the simulation of the fuzzy system shows that it was more capable of dealing with ambiguities in the input images, so in pictures where the edges were not so clear or required more detail, the fuzzy system was more capable since it can be better tailored for use.

Fuzzy logic image processing

With facial recognition software becoming more widely used, especially in mobile apps such as Snapchat, boundary detection will continue to be one of the primary areas of interest in enhancing software performance. Edge detection is paramount in discriminating objects so they can be used and processed. This paper will present a fuzzy system for boundary detection. The proposed system will then compare it to traditional methods of edge detection using MATLAB's image processing toolbox. The proposed fuzzy system allows for more effective tool since the membership functions can be more defined and robust to accommodate different images, as well as tailored to result in a more effective processed image due to ambiguity. The results of the simulation of the fuzzy system shows that it was more capable of dealing with ambiguities in the input images, so in pictures where the edges were not so clear or required more detail, the fuzzy system was more capable since it can be better tailored for use.

Reading and Interpreting Machine Printed Text in Camera-Captured Document Images

Aims: To introduce a cost-effective tool for reading and interpreting machine printed text in document images and save as computer-processable codes.
 Study Design: In this work, emphasize is given on extracting uppercase & lowercase letters and numerals from document images by the technique of segmentation and feature extraction using MATLAB Image Processing toolbox.
 Place and Duration of Study: Department of Engineering, Ibri College of Technology, between September 2017 and May 2018.
 Methodology: Necessary information about existing algorithms on character recognition is collected by review of relevant literature available in journals, books, manuals and related documents. Suitable architecture and novel algorithm for a simple, low cost, low complexity, highly accurate system is developed as per the specifications and reviewed literature. Functionality of the design is verified using simulation software MATLAB.
 Results: The proposed method can extract characters from document image (which may be scanned or camera captured) of any font size, colour, space and can be rewritten in an editable window like Notepad, WordPad where the characters can even be edited; thus, improving accuracy and hence, saves time.
 Conclusion: This algorithm gives promising results that have been obtained on a number of images in which almost all characters are retrieved. It also gives 90 percent accuracy for all printed characters.

Designing an Object Tracker Self-Balancing Robot

Real-time robots are quite common in our daily life. These robots are working as a part of the process in industry or a medical assistance in hospitals to serve humanity. Designing the robots according to the desired referent and making the given tasks with high accuracy makes them more and more popular in these days. In this work, the designed two-wheeled balancing robots with integrated camera track object autonomously. This work has two important stages. The first stage is about balancing the robot with the angle information taken from IMU sensor and implementation of PID control. IMU sensors create lots of noisy signals because of its natural structures. Kalman filter was used to denoise these noisy signals to have a smooth signal for a better balance control. The second stage is about image processing and objects recognition. This section was completed by using Matlab Image Processing Toolbox which can be used Arduino microcontroller board synchronously. In this section, algorithm infers motion information of objects. Motors were controlled according to motion information of moving objects. In the end, an object tracker self-balance robot was constructed. Balance control of the robot was managed by PID controller and accelerometer signals were denoised by a Kalman Filter. It was clarified that using PID controller and Kalman Filter together have a positive effect to balance the robot on the desired angle.

Computer-aided design-based guided endodontic: A novel approach for root canal access cavity preparation.

In cases of teeth with unusual morphology like calcified pulp canal, guided endodontic treatment is suggested. An endodontic guide which navigates burs according to a preplanned path is used. Existing approaches of endodontic guide design are based on visual observation and analysis of tomographic scan of teeth. Hence, they are time-consuming and expert-dependent. Computer-aided design-based methodology was employed to design and fabricate a customized endodontic guide. A cone beam computed tomographic scan with MIMICS software was used to create a solid model of the teeth. The solid model generated was sliced through the developed program in MATLAB. The geometric centers of consecutive slices were joined to plot the root canals central axis. To gauge the optimum bur angulation for guide design, a straight line fitted in the data set of the geometric center helped create minimally invasive access. Methodology involved simulated verification of the drill path to judge the accuracy and feasibility of root canal access cavity preparation. Next, endodontic guides for extracted teeth were designed and fabricated using a three-dimensional printer, followed by guided root canal access cavity preparation for extracted teeth. To validate the proposed methodology, using a MATLAB image processing tool box, the deviation between the prepared root canal access cavity axis and root canal axis was analyzed in radiographs of post-treated teeth. The deviation between the tool path axis and root canal axis in simulated root canals was found to be not more than 0.210 ± 0.04 mm. The deviation between the axis of the planned root canal access cavity and the prepared root canal access cavity was 0.07 ± 0.02 mm. The proposed method reveals encouraging results for endodontic guide design.

Use of GreenZyme® for remediation of porous media polluted with jet fuel JP-5

ABSTRACTJet fuel may be released in the environment either by in-flight fuel jettisoning (fuel dumping) or accidentally from spills and leaks, and eventually can reach subsurface formations where it can remain as long-term source of pollution. Remediation of aquifers contaminated by jet fuels is not a trivial task. This experimental study examined the effectiveness of a water-soluble, DNA–protein-based biodegradable non-living catalyst, with commercial name GreenZyme® for the remediation of water saturated porous media polluted with jet fuel (JP-5). Also for comparison purposes, the commercial surfactant sodium dodecyl sulfate (SDS) was used. Bench scale experiments were conducted in a glass column packed with glass beads. The migration of JP-5 in the glass column under various conditions, with and without the presence of GreenZyme® was monitored by a well-established photographic method. Digital photographs of the packed column were captured under fluorescent lighting. The fluorescent intensity of JP-5 dyed with Red Oil O within the column was analyzed using the Matlab Image Processing Toolbox. The colour intensities were converted to concentrations via appropriate calibration curves. The experimental results suggested that GreenZyme® was an efficient biosurfactant capable of enhancing significantly the migration of JP-5 in the glass column, which performed considerably better that SDS under the experimental conditions of this study.

Development of optokinetic tracking software for objective evaluation of visual function in rodents

The aim of this study was to develop software that performs the optokinetic tracking assessment without the involvement of experimenters to increase the objectivity of the test. To check the effectiveness of the software, several videos were analyzed and the results were compared to those produced by two experimenters. Videos consisted of visual acuity and contrast sensitivity tests on normal animals and pigmented P23H rats (animal model of retinitis pigmentosa). Our software showed a reasonably high success rate: in approximately 78% of the cases, both the software program and the experimenters were in agreement, including the direction of rotation. The software detected 7% false positive cases, 10% false negative cases, and it was wrong in 5% of the cases. Decrease in visual function with age in pigmented P23H rats was observed from the first time interval, although minimum thresholds were found in visual parameters at advanced ages. We developed simple software based on current functions included in the Matlab image processing toolbox that was able to recognize, with a reasonably high percentage of success, the subtle head movements of the rodent produced when visual perception of the optokinetic optotype occurs.

Curvelet transforms and flower pollination algorithm based machine vision system for roughness estimation

Machine vision based systems aided in approximation of the surface roughness in a nondestructive means and relieved the process of automation. The advent of computers and computational tools in manufacturing, assisted in reduction of ideal time by their computational knack to estimate the micron level and uncertain in line values of surface roughness. In the present work, to develop a machine vision based intelligent roughness estimation system; high-resolution surface images of the machine surface are captured. The images captured are processed in MATLAB image processing toolbox for Curvelet Transform texture features. The measured surface roughness values and the texture features are mapped using the advanced computational tool Flower Pollination Algorithm (FPA). The FPA closely estimated the surface roughness with precise percentile of error when compared with another support vector machine model.

Assessment of the feasibility of detecting concrete cracks in images acquired by unmanned aerial vehicles

An 8-rotor unmanned aerial vehicle is used as a working platform. Its motion characteristics in a hovering state are obtained using a non-contact measurement instrument, which, along with the modulation transfer function of its airborne images, indicates the reliability of the airborne images of unmanned aerial vehicles in a hovering state. By installing a laser range finder on the cradle synchronized with the camera shutter to measure the object distance, the pixel resolution of the object distance is obtained. The airborne images are then processed using the MATLAB image processing toolbox, from which the pixels of concrete cracks are extracted. Compared to a static image and direct manual measurements, the airborne image of the unmanned aerial vehicle has higher precision, indicating its wide potential applications as an alternative of the conventional inspection methods of bridge-inspection vehicle and working platforms.

Indian Fake Currency Detection using Image Processing: A Review

Paper currency identification is one of the image processing techniques i.e. clothed to recognize currency of different countries. The paper currencies of different countries are collectively rises ever more. However, the main intention of most of the standard currency recognition systems and machines is on recognizing fake currencies. The features are extracted by using image processing toolbox in MATLAB and preprocessed by reducing the data size in captured image. The expose pluck out is discharged by considering HSV (Hue Saturation Value). The chief is neural network classifier and the next step is recognition. MATLAB is used to evolve this program. The new source of paper currency recognition is pattern recognition. But for currency recognition, converter system is an image processing method which is used to identify currency and transfer it into the other currencies as the users need. The need of currency recognition and converters is accurately to recognize the currencies and transfer the currency immediately into the other currency. This application uses the computing energy in differentiation among different kinds of currencies are differentiated with their suitable class using power computing. Fake note at present plays a key topic for the researchers. The recognition system is composed of two parts. First is the captured image and the second is recognition. Forged currencies recognition is the main aim of the standard paper currency identification system. The most mandatory system is currency identification system and it should be very accurate. The performance of different methods are surveyed to refine the exactness of currency recognition system.

Braille2Alpha: Braille Dots Recognition with Alphanumeric Conversion

The Braille system is a tactile language where each element is represented by a cell with six dot positions, arranged in three rows and two columns. Each dot position can be raised or otherwise allowing different configurations which can be felt by trained fingers. For caregivers with normal sights, training their fingers is not naturally easy. This paper presents an image processing approach to Braille dots recognition with alphanumeric conversion, whereby a person with normal eyesight is able to translate a raw Braille image captured by a mobile camera into the corresponding alphanumeric texts. Image pre-processing relies heavily on various segmentation algorithms in the MATLAB image processing Toolbox. This application is hoped to help the blind and the visually impaired community as well as their caregivers to translate Braille texts digitally, hence enhancing the communication and collaboration possibilities.

A multi-objective simulation-optimization model for in situ bioremediation of groundwater contamination: Application of bargaining theory

In the present study, a BIOPLUME III simulation model is coupled with a non-dominating sorting genetic algorithm (NSGA-II)-based model for optimal design of in situ groundwater bioremediation system, considering preferences of stakeholders. Ministry of Energy (MOE), Department of Environment (DOE), and National Disaster Management Organization (NDMO) are three stakeholders in the groundwater bioremediation problem in Iran. Based on the preferences of these stakeholders, the multi-objective optimization model tries to minimize: (1) cost; (2) sum of contaminant concentrations that violate standard; (3) contaminant plume fragmentation. The NSGA-II multi-objective optimization method gives Pareto-optimal solutions. A compromised solution is determined using fallback bargaining with impasse to achieve a consensus among the stakeholders. In this study, two different approaches are investigated and compared based on two different domains for locations of injection and extraction wells. At the first approach, a limited number of predefined locations is considered according to previous similar studies. At the second approach, all possible points in study area are investigated to find optimal locations, arrangement, and flow rate of injection and extraction wells. Involvement of the stakeholders, investigating all possible points instead of a limited number of locations for wells, and minimizing the contaminant plume fragmentation during bioremediation are new innovations in this research. Besides, the simulation period is divided into smaller time intervals for more efficient optimization. Image processing toolbox in MATLAB® software is utilized for calculation of the third objective function. In comparison with previous studies, cost is reduced using the proposed methodology. Dispersion of the contaminant plume is reduced in both presented approaches using the third objective function. Considering all possible points in the study area for determining the optimal locations of the wells in the second approach leads to more desirable results, i.e. decreasing the contaminant concentrations to a standard level and 20% to 40% cost reduction.

Image analysis for detection of bugholes on concrete surface

Surface bughole of concrete is one of surface imperfections of concrete appearance, and the detection and evaluation of surface bugholes have always been a difficult problem. This paper established a method to detect the surface bugholes of concrete by the image analysis, and put forward the evaluation parameters of the surface bugholes. The shooting distance can affect the shooting area and the detection accuracy of the image, the method of partition shooting must be used to ensure the detection accuracy according to the size of the concrete member. Based on the image processing toolbox of MATLAB, image gray, contrast enhancement and OTSU image threshold segmentation technology are used to extract the characteristics of bugholes on the concrete surface, and the threshold value of shape characteristic coefficient to distinguish the cracks and the surface bugholes is chosen as 45. The relationship between CIB scale and the area ratio of bughole is established. The parameters of area ratio and maximum diameter of bugholes can comprehensively evaluate the surface quality of concrete.

Detection of Lung Cancer Using Digital Image Processing Techniques: A Comparative Study

This paper focuses on early stage lung cancer detection. Lung cancer is prominent cancer as it states large number of deaths of more than a million every year. It creates need of detecting the lung nodule at early stage in Computer Tomography medical images. So to detect the occurrence of cancer nodule at early stage, the requirement of methods and techniques is increasing. There are different methods and techniques existing but none of them provide a better accuracy of detection. One of the techniques is content based image retrieval Computer Aided Diagnosis System (CAD) for early detection of lung nodules from the Chest Computer Tomography (CT) images. This optimization algorithm allows physicians to identify the nodules present in the CT lung images in the early stage hence the lung cancer. The MATLAB image processing toolbox based implementation is done on the CT lung images and the classifications of these images are carried out. The performance measures like the classification rate and the false positive rates are analyzed.

Parts Surface Structure Image Classification Detection System Design

In order to accomplish the automatic nondestructive testing, a parts surface structure image classification detection system is designed. A series of parts surface texture images have been obtained from different processing methods for feature analysis and the combination of pre-processing method by MATLAB image processing toolbox has been put forward, using statistical analysis method for feature extraction. Based on the established BP neural network training optimization identification system, this paper realized the recognition of parts surface resulted from four kinds of processing methods: turning, milling, planing and grinding. The research results show that the deficit value of gray level co-occurrence matrix and the histogram matrix variance value can be regarded as characteristic parts of the surface texture structure value, providing foundations for further development of parts surface structure detection.

Development of Computed Tomography Scanner Application: Prototype Model Approach

The development of software application for image reconstruction system is essential in the research study of computed tomography (CT). An experimental prototype was the research tool used in developing a series of useful prototype to evaluate the reconstruction methods. A CT scanner software prototype was created using the MATLAB image processing toolbox together with the Graphical User Interface (GUI) and Graphical User Interface Development Environment (GUIDE). The prototype model was selected to guide the development of the prototype application and mainly to provide a platform to update a system from the available knowledge. The prototype model was capable in creating a CT scanner synthetic datasets, display a sinogram image from the synthetic datasets, construct images using back projection technique and displaying cross-section images. Two types of images were acquired from this research, the first type was the unfiltered and blurred image and the second type was the filtered and clear image. This research also focused on two-dimensional parallel-beam and fan-beam geometry configuration; the principle of transforming fan-beam datasets into parallel-beam datasets was carried out using the classical rebinning concept.

As-Applied Estimation of Volumetric Flow Rate from a Single Sprayer Nozzle Series Using Water-Sensitive Spray Cards

<abstract> The objective of this study was to test the feasibility of using coverage measurements from water-sensitive spray cards to estimate the volumetric flow rate at an individual sprayer nozzle. TeeJet VisiFlow Even Flat Spray Tips were selected due to their uniform distribution of coverage. Spray distribution for each nozzle was validated using a spray patternator table with 2.5 cm sampling widths. A rotary test fixture translated water-sensitive spray cards through the spray dispersion (water at ambient conditions) at a constant angular velocity and a radius of 1.2 m. The test fixture measured volumetric flow and pressure at the nozzle and recorded data at a rate of 10 Hz. A helical gear pump and a piston-type pressure regulating valve were used to provide constant pressure. The first experiment fixed the test fixture speed at 3.14 rad s-1 and used varying pressures from 70 to 552 kPa (10 to 80 psi) in 70 kPa (10 psi) increments. First-order and second-order regression models were developed for the nozzle series, and validation data were collected at intermediate pressures to test the ability of the model to predict volumetric flow rates. The second experiment fixed the system pressure at 310 kPa (45 psi) and varied the speed of the test fixture at seven increments between 2.0 and 3.8 rad s-1. Spray cards were digitized using a scanner and processed for coverage using the MATLAB image processing toolbox. Results showed that the accuracy of the spray card method was within 1% full-scale of a commercial impeller flowmeter for a single series of nozzles moving at constant speed. Varying speed could be accounted for but required knowledge of the individual nozzle model.

Designing and Performance Evaluation of an Advanced Method for Corner Detection using Harris Technique

Corner detection is a significant task in different machine vision and image processing systems because corners play an important role in describing object unique features for recognition and identification. Corner detection comes within reach of computer visualization systems to extract certain kinds of characteristics and understand the contents of an image. In this work modify the Harris technique in order to enhance the performance of corner detection. To include the effect of Morphology technique on the modified Harris corner technique. Observe the simulation results of proposed detection technique & compare results with traditional method. MATLAB R2013a has been used as an implementation platform using generalized MATLAB toolbox and image processing tool box.

Quadrotor with Image Processing Capabilities

Quadrotor helicopters or simply called as Quadrotors represent a useful class of flying robots and are an emerging popular platform for unmanned aerial vehicle (UAV) research and have applications of their own. Due to their simplicity of construction, easy maintenance, their ability to hover effortlessly, strong abilities of VTOL, vertical take off and landing (VTOL) capabilities, high manoeuvrability and controllability (especially in indoor areas). In this paper, we present the development of an unmanned UAV, called Quadrotor, which has the abilities to perform acrobatic movements like flips, rolls, barrels etc. It is also able to capture real time videos using a RF wireless camera. These videos are converted to snapshots which are then processed using MATLAB-Image Processing Toolbox to count the number of persons in that specific captured image.

A camera and image processing system for floc size distributions of suspended particles

The observation of floc size distribution (FSD) for quantitative description of its properties over a sufficient size range, from a few microns to a few millimeters, is still a challenge in most marine environments. In this study, an inexpensive image acquisition and processing system is developed for achieving this objective. Images are acquired by using a Sony Alpha NEX-5R camera body with a CMOS sensor, a Sony E-mount to Nikon F-mount adaptor, extension tubes, a Nikon macro lens, and a close-up lens that can identify flocs with a minimum size of 5μm. The light source and camera trigger are controlled by a micro-controller that was assembled for this project. These images are analyzed by using MATLAB Image Processing Toolbox. The processed floc size and their statistical distributions are confirmed with two available sample sets. The validated process is then applied to measure the steady state FSDs of kaolinite suspensions for different shear rates, suspended sediment concentrations, salinities, and a selected organic matter, guar gum. Although the presence of salt promotes flocculation of suspended kaolinite, only a small amount of salt, around 0.5ppt, is needed to reach the saturation status. Thus for the test salinities (0.5, 1, 2, 3, 5, and 9ppt), the measured FSDs show little difference among each other. On the other hand, guar gum affects kaolinite flocculation significantly and the maximum effect happens at a guar gum concentration around 15mg/L when the kaolinite concentration is around 0.52g/L. At this dosage the characteristic floc size is the largest.