Top PDF Detection of foliar diseases using image processing techniques

Detection of foliar diseases using image processing techniques

Detection of foliar diseases using image processing techniques

Other approaches implement machine learning algorithms to detect diseases in plants, where different procedures are carried out for the extraction of characteristics, among which are the Color Co-occurrence method for an ANNs and a multiclass SVM, the SIFT transformed, the normalization of the RGB color space, the color difference in the LAB for a multiclass SVM, with K-means it has been used between 3 and 4 clusters corresponding to the leaf, the disease and the background in the RGB color space (Chaudhary et al., 2012; Arakeri et al., 2015; Sharma et al., 2017); obtaining efficiencies of up to 99.35% with a high computational cost and low statistical relevance due to the quantity of images evaluated. However, in Mohanty et al. (2016) they worked with 54306 images where a CNN was trained to identify 4 crop species and 26 diseases, however there are limitations due to the variation of the conditions of the images during the training phase, substantially reducing the accuracy of this model. Although it is important to identify the type of disease that plants have, for epidemiological studies it is necessary to study the progress of diseases over time in populations of plants by means of mathematical models that allow the farmer to take the most appropriate control measures according to the state of the leaf, among these models are the estimation of the affected area in the foliage that is given in percentage to determine the rate of development of the disease (Escalante & Farrera, 2004; Patil & Bodhe, 2011).
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Detection and alignment of 3D domain swapping proteins using angle-distance image-based secondary structural matching techniques.

Detection and alignment of 3D domain swapping proteins using angle-distance image-based secondary structural matching techniques.

This work presents a novel detection method for three-dimensional domain swapping (DS), a mechanism for forming protein quaternary structures that can be visualized as if monomers had ‘‘opened’’ their ‘‘closed’’ structures and exchanged the opened portion to form intertwined oligomers. Since the first report of DS in the mid 1990s, an increasing number of identified cases has led to the postulation that DS might occur in a protein with an unconstrained terminus under appropriate conditions. DS may play important roles in the molecular evolution and functional regulation of proteins and the formation of depositions in Alzheimer’s and prion diseases. Moreover, it is promising for designing auto-assembling biomaterials. Despite the increasing interest in DS, related bioinformatics methods are rarely available. Owing to a dramatic conformational difference between the monomeric/closed and oligomeric/open forms, conventional structural comparison methods are inadequate for detecting DS. Hence, there is also a lack of comprehensive datasets for studying DS. Based on angle-distance (A-D) image transformations of secondary structural elements (SSEs), specific patterns within A-D images can be recognized and classified for structural similarities. In this work, a matching algorithm to extract corresponding SSE pairs from A-D images and a novel DS score have been designed and demonstrated to be applicable to the detection of DS relationships. The Matthews correlation coefficient (MCC) and sensitivity of the proposed DS-detecting method were higher than 0.81 even when the sequence identities of the proteins examined were lower than 10%. On average, the alignment percentage and root-mean-square distance (RMSD) computed by the proposed method were 90% and 1.8A˚ for a set of 1,211 DS-related pairs of proteins. The performances of structural alignments remain high and stable for DS-related homologs with less than 10% sequence identities. In addition, the quality of its hinge loop determination is comparable to that of manual inspection. This method has been implemented as a web-based tool, which requires two protein structures as the input and then the type and/or existence of DS relationships between the input structures are determined according to the A-D image-based structural alignments and the DS score. The proposed method is expected to trigger large-scale studies of this interesting structural phenomenon and facilitate related applications.
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Recognition of leishmania parasite and macrophage infection rate using image processing techniques

Recognition of leishmania parasite and macrophage infection rate using image processing techniques

Cell Segmentation is the process in which Cell portions are extracted from input DAPI images. This process has received a lot of attention between researchers in last decades; however it seemed to be a non trivial task for automated image segmentation packages. Illumination inhomogeneity across the cells’ contours, the existence of intensity saturated and overlapped cells and variety of shape and orientation of cell portions are between the most challenging issues which make cell segmentation a complex task. Furthermore, dif- ferent image acquisition techniques and tools(stains, microscopes, imaging wavelengths, etc) in laboratories often lead researchers to use a variety of approaches towards solving the problem. Intensity Thresholding methods try to segment cells assuming their intensi- ties are significantly different from the background, locally or globally. Feature Detection methods, using linear image filters, derives cell portions intensity based features and seg- ment cells based on them. Morphological Based methods acquire topological properties of cells in the image by using a combination of its non linear operators such as erosion, dilation, opening and closing. Region Accumulation methods identify cell regions start- ing from initial seed points and iteratively add connected points to previously labeled regions. Finally, Deformable Model Fitting methods try to fit some deformable model to the image data and extract cells areas with assigned set of information.
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Early Detection of Lung Cancer Using Neural Network  Techniques

Early Detection of Lung Cancer Using Neural Network Techniques

Cancer is the most disastrous and life threatening disease to human beings globally among various diseases. Cancer is the second largest disease in India which is responsible for maximum mortality about 0.3 million deaths per year [1]. According to GLOBOCAN 2012 statistics, it was found that around 14.1 million new cases were diagnosed and around 8.2 million deaths occurred in 2012, due to cancer which is quiet high when compared to statistics of 2008 which was 12.7 million new cases and 7.6 million deaths due to cancer. According to study [2] it has been found that out of all the cancers, Lung Cancer is the main cause of mortality worldwide amongst all types of cancers. Main reason behind high rate of mortality due to lung cancer is that it is not easily detected in the initial stage and it is very difficult to overcome this disease at later stages of cancer [3]. If lung nodules can be identified accurately at an early stage, the patient’s survival rate can be increased by a significant percentage. In today’s era, the field of automated diagnostic systems plays crucial role in the diagnosis of any disease. Image Processing is one such field where automated diagnostic system designed especially for medical diagnosis leads to solution which will help in decreasing the mortality rate and these medical diagnostics systems helps in detecting the disease at initial filed which is very remarkable in the field of bioinformatics [5].
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Red Palm Weevil (<i>Rynchophorus Ferrugineous, Olivier</i>) Recognition by Image Processing Techniques

Red Palm Weevil (<i>Rynchophorus Ferrugineous, Olivier</i>) Recognition by Image Processing Techniques

Abstract: Problem statement: Red palm weevil is the most destructive insect for palm trees all over the world. This research is part of developing an automated wireless red palm weevil detection and control system. The focus for this study was to develop red palm Weevil recognition system which can detect RPW in an image and can be used in wireless image sensor network which will be part of entire proposed system. Approach: Template based recognition techniques were used. Two general recognition methods i.e., Zernike and Regional Properties and an algorithm combining them were used. Besides that, a novel technique for detecting Rostrum of RPW named as ‘Rostrum Analysis’ was proposed and used for recognition, a conclusive algorithm based on all three techniques was also proposed, 319 test images of RPW and 93 images of other insects which found in RPW habitat were used. Results: It was found that both general techniques i.e., Regional Properties and Zernike Moments methods perform reasonably in recognizing RPW. The algorithm based on both these methods performs better than individual methods. The Rostrum Analysis outperforms better than both the earlier methods and proposed algorithm using all three analytical techniques gives best results among all discussed techniques in recognizing RPW as well as other insects. Conclusion: The most balanced and efficient recognition technique is to use the proposed conclusive algorithm which is combination of Regional Properties, Zernike Moments and Rostrum Analysis techniques. The maximum time for processing an image is 0.47 sec and the results obtained in recognizing the RPW and other insects are 97 and 88% respectively.
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SAR Image Processing

SAR Image Processing

Abstract - Several techniques have been studied, invented and reformulated to enhance automatic ships detection using SAR images. Depending on how the information is gathered and processed each technique presents different performances and results. Nowadays there are several missions on going, and the need to get better results in ships detection, oil-spills or any kind of sea activity is fundamental to preserve and promote navigation safety as well as a constant and accurate monitoring of the surroundings for, for example, illegal fishing activities, pollution or drug trafficking. This paper is written in the context of a MSc dissertation which the purpose of looking on a full hand of recent and most used techniques for ships detection and summarizing them in terms of basic principles, advantages and disadvantages and performance in order to provide a basis guideline to choose which technique shall provide the best and more promising results for a task at hands and also give a practical example on Sentinel data set material.
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Image processing techniques to evaluate mammography screening quality

Image processing techniques to evaluate mammography screening quality

However, the ranking obtained using SNR values was very similar to those obtained from DR, CNR, H, and Q presenting only some minor differences. This parameter weights the average value of intensity, resulting in a non- suitable ranking, which does not refl ect the ability to µCa detection. As example, it can be seen from Figure 4 that central axis profi les for a 24 kV incident spectrum and 30 mm thickness of glandular breast tissue (green line) should not be better ranked than the image corresponding to red line. However, the obtained ranking predicts the opposite. The ranking using SNR values for these three examples was fi fth, sixth and eight for the red, black and green lines, respectively.
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Research on Defects Detection by Image Processing of Thermographic Images

Research on Defects Detection by Image Processing of Thermographic Images

Currently, the use of STS materials has been gradually extended to various application fields such as nuclear pipes, automobile, railroad and building structure. Increased usage of STS materials has led to an increased interest to the production processes [1,2]. High quality of materials and structures is an important factor in many areas of human activities [3]. A major effort to reach the high level of quality is to implement various inspection tasks. Non-destructive testing (NDT) is one of the most important means to detect and verify the quality of items [4]. In this context, Infrared thermographic (IRT) technologies are used nowadays as a very fast NDT tool for examination of a wide range of materials. NDT using active IRT provides information on material, structure, physical & mechanical properties and discontinuities & defects present on the analyzed specimen [5]. The inspection of a material or component by means of thermographic techniques consists of the measurement and interpretation of the temperature field over the component. The detecting device (Infrared Camera) receives different levels of infrared radiation from the surface of the sample generating a map of its distribution, thus creating an image called thermogram [6-8].
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ADVANCED IMAGE PROCESSING TECHNIQUES FOR DETECTION AND QUANTIFICATION OF DRUSEN

ADVANCED IMAGE PROCESSING TECHNIQUES FOR DETECTION AND QUANTIFICATION OF DRUSEN

In figure 3.12 are presented two examples of illumination compensation using the Gaussian Blurring algorithm. As it can be observed, the technique is well suited for images containing medium sized drusen. In the retinas with large sized drusen (b) the centre of the confluent drusen had a slight reduction of contrast, but the overall result is a normalized illumination and contrast. The filter mask can be adjusted to have a lower cut-off frequency and improve the behaviour in these cases; however this is a compromise in any situation. One disadvantage is the computation required for the filter convolution. For example, an image identical to the one shown has a filter size of 150x150 pixels wide. Therefore, for every pixel in the image (typically 450x450) it is needed to compute the convolution, which is time consuming. Some strategies as the image down-sampling or the use of one-dimensional kernel that calculates the horizontal, vertical and other two or more directions independently, reduce computation time.
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Leaf Disease Detection Using Arm7 and Image Processing

Leaf Disease Detection Using Arm7 and Image Processing

Generally there are many causes of leaf diseases and perennials landscape plant problems and no. of these problems can appear to have the same symptoms on the plant hence the naked eyes fails to detect the diseases. In the proposed system we use the image processing technique. Any image can be described by its Red,Green,&Blue co-ordinates. By using Ycbcrthe RGB image is converted into grayscale. The Otsu threshold method used for image segmentation [7]. In which the diseased area is a foreground &undiseased area is background. The Gabor filter is applied on this image for texture feature extraction [4]. SVM(support vector machine) is used for classification [3].
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Embedded real-time speed limit sign recognition using image processing and machine learning techniques

Embedded real-time speed limit sign recognition using image processing and machine learning techniques

Abstract The number of traffic accidents in Brazil has reached alarming lev- els, and is currently one of the leading causes of death in the country. With the number of vehicles on the roads increasing rapidly, these problems will tend to worsen. Consequently, huge investments in resources to increase road safety will be required. The vertical R-19 system for optical character recogni- tion of regulatory traffic signs (maximum speed limits) according to Brazilian standards developed in this work uses a camera positioned at the front of the vehicle, facing forward. This is so that images of traffic signs can be cap- tured, enabling the use of image processing and analysis techniques for sign detection. This paper proposes the detection and recognition of speed limit signs based on a cascade of boosted classifiers working with haar-like features. The recognition of the sign detected is achieved based on the Optimum-path Forest classifier (OPF), Support Vector Machines (SVM), Multi-layer Percep- tron (MLP), k-Nearest Neighbor (kNN), Extreme Learning Machine (ELM), Least Mean Squares (LMS), and Least Squares (LS) machine learning tech-
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Disease Detection of Cotton Leaves Using Advanced Image Processing

Disease Detection of Cotton Leaves Using Advanced Image Processing

In this research, identification and classification of cotton diseases is done. The pattern of disease is important part where some features like the colour of actual infected image are extracted from image. There are so many diseases occurred on cotton leaf so the leaf color is different for different diseases. This paper uses k-mean clustering with Discrete Wavelet Transform for efficient plant leaf image segmentation and classification between normal & diseased images using neural network technique. Segmentation is basic pre-processing task in image processing applications and it is required to extract diseased plant leaf from normal plant leaf image and image background. Image segmentation is necessary to detect objects and borderlines in images. Even though different methods are already proposed, it is still hard to accurately segment a random image by one specific method. In last years, additional attention has been given to merge segmentation algorithm and feature extraction algorithm to enhance segmentation results.
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Improvement of Modal Matching Image Objects in Dynamic Pedobarography using Optimization Techniques

Improvement of Modal Matching Image Objects in Dynamic Pedobarography using Optimization Techniques

Figure 15 displays a diagram of the adopted physical matching methodology. The locations of the objects data points in each image, X = [ X 1 … X m ] , are used as the nodes of a finite elements model made of an elastic material. Next, the eigenmodes { } φ i of the model are computed, providing an orthogonal description of the object and its natural deformations, ordered by frequency. Using a matrix based notation, the eigenvectors matrix [ ] Φ and the eigenvalues diagonal matrix [ ] Ω can be written as in equation (1) for 2D objects and as in equation (2) for 3D objects. The eigenvectors, also called shape vectors [1], [2], [16], [17], describe how each vibration mode deforms the object by changing the original data point locations: X deformed = X a + { } φ i .
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Analysis of gated myocardial perfusion SPECT images using computational image registration techniques

Analysis of gated myocardial perfusion SPECT images using computational image registration techniques

The spatial resolution of gamma cameras can achieve a sub-millimeter range, being even possible sub-half millimeters when it is used a specialized dedicated multi-pinhole geometry. Furthermore, it is improved with the application of a distance function of the object from the aperture and its distance from the detector surface, by minimizing the aperture size and specialized collimator geometry [6]. However, this causes the reduction of both detection efficiency, partially tackled by increasing the number of holes, and image field of view, such as insufficient data acquisition. Besides, given SPECT collimation is geometrical, spatial resolution depends on the distance between the source and the detector head, leading to distortions in the image if the process of reconstruction is not taken into account. This can be minimized with the use of reconstruction algorithms as descriptions of the resolution degradation based on measurements [11].
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Surveillance of arthropod vector-borne infectious diseases using remote sensing techniques: a review.

Surveillance of arthropod vector-borne infectious diseases using remote sensing techniques: a review.

The use of remote sensing techniques to map vector species distribution and disease risk has evolved considerably during the past two decades. The complexity of techniques range from using simple correlations between spectral signatures from different land use–land cover types and species abundance (e.g., [8,9]) to complex techniques that link satellite-derived seasonal environmental variables to vector biology [10]. While a variety of numerical techniques to create maps of vector distribution in time and space from satellite data are available, only those techniques that aid our understanding of biological processes provide meaningful information in epidemiology and vector control. A review of different modeling approaches for mapping vector and vector-borne diseases is discussed by Rogers [11]. The typical modeling approach is to use either logistic regression or discriminant analysis techniques that investigate associations between multivariate environmental data and patterns of vector presence or absence for mapping vectors and vector- borne diseases. Both of these methods are capable of predicting the a posteriori probability of the presence of the dependent variable (e.g., either the vector or the disease) from a set of independent variables (e.g., climate and land cover data) and can be used to make risk maps from sample data sets (i.e., training datasets) based on the observed similarity of environmental conditions to sites. The choice of techniques should be able to accommodate both categorical (e.g., disease presence or absence) as well as continuous data (e.g., surface temperature data) spatial data.
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Applying Gabor Wavelet in Image Processing for Defect Detection on Steel plates

Applying Gabor Wavelet in Image Processing for Defect Detection on Steel plates

Quality control is one of the most important issues in the industry of steed sheet production. Detection of surface defects allocates a high percent of quality control process to itself. Nowadays in most production lines of steel sheets, quality control is executed manually by expert personnel. The lack of an automated system for quality control causes a decrease in efficiency, increases costs and makes inaccuracy. Image Processing is dominant technology today for inspection of different tissues and recognition of available diversity. The power of this technology, especially in two fields of detection and classification of the template, makes it possible to utilize in quality control of such industries as textile, paper and ceramic. With regard to such practical background of techniques for image processing and the sort of surface
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Improvement of modal matching image objects in dynamic pedobarography using optimization techniques

Improvement of modal matching image objects in dynamic pedobarography using optimization techniques

There is an eigen methods class 2 that derives its parameterization directly from objects data shape. Some of these techniques also try to determine, explicitly and automatically, the correspondences between characteristic points sets, while others try to match images using more global approaches instead of local ones. Each eigen method decomposes the object deformation in an orthogonal and ordered base.

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Detection of production defects using Machine Learning based Image Classification Algorithms

Detection of production defects using Machine Learning based Image Classification Algorithms

Since the conditions of the assembly line mentioned before are present, this implies that, at the moment of image acquisition, the area of interest, in this case the area of the contacts, will be positioned, considering some geometric tolerances, in a certain position. Considering the characteristics of an object detection model, being composed of two portions, the first extracts the features that it has learned, related to the region of interest, and predicts these positional values. The following portion works on the classification of this area. Even considering the higher adaptability of these models, when there is a great variation of background or the position of the object, in this particular case it is redundant to find this position, given that this is defined by the position of the handle in the workpiece carrier. Accordingly, utilizing a classification model will permit the application of a much simpler labelling process, where text labels instead of bounding boxes coupled with a much lower computational requirement and a greater, even if less flexible, capacity for classification will be implemented.
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ROBOT NAVIGATION USING IMAGE PROCESSING AND ISOLATED WORD RECOGNITION

ROBOT NAVIGATION USING IMAGE PROCESSING AND ISOLATED WORD RECOGNITION

In very rough terrain a robot can be controlled using speech commands. this method can be especially useful in the places where the terrain is uneven or the traffic signs and lanes are not present. Much work is done in isolated word recognition in English and many other languages. Kuldeep Kumar and R. K. Aggarwal of Nation Institute of Technology Kurukshetra developed a speech recognition system for Hindi language using HTK toolkit [11]. Sukhminder Singh Grewal and Dinesh Kumar worked on speaker independent isolated word recognition system for English language with 81.23 % success rate [12].
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MODELING AND SIMULATION OF PARALLEL PROCESSING ARCHITECTURE FOR IMAGE PROCESSING

MODELING AND SIMULATION OF PARALLEL PROCESSING ARCHITECTURE FOR IMAGE PROCESSING

that the parallel routines developed can be used in other tasks which involve remote distribution, remote execution and remote collection of data. Because of time constraint, complex MATLAB data types such as struct matrix, cell matrix, sparse matrix, objects etc are not handled. The software has been tested to work with double matrices. Thus these are also the areas where functionality can be built to extend the software. There are some future improvements to be made to our work. One of the issues would be to improve the data redistribution scheme. Data redistribution is critical for implementing a task and data parallel execution scheme. Our implementation was very simple; the master processor gathers the data processed by the allocated processors in a task and sends it to the master processor of the successor task in the graph. It would be more efficient if the processors allocated to a task can send the computed data directly to the processors allocated to the successor task in the graph, as it was proposed in [84]. Another extension would be performing the data dependency analysis of a given image processing application. In our research, we started from the assumption that we already have the information related to data dependencies in the form of the Image Application Task Graph. It only inserts Inter- processor communication when data is missing or outdated on a certain processor. This method would be an excellent tool to replace our simple data redistribution scheme, yielding a system that has to be best of both worlds.
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