Top PDF A NEW FRAMEWORK FOR OBJECT-BASED IMAGE ANALYSIS BASED ON SEGMENTATION SCALE SPACE AND RANDOM FOREST CLASSIFIER

A NEW FRAMEWORK FOR OBJECT-BASED IMAGE ANALYSIS BASED ON SEGMENTATION SCALE SPACE AND RANDOM FOREST CLASSIFIER

A NEW FRAMEWORK FOR OBJECT-BASED IMAGE ANALYSIS BASED ON SEGMENTATION SCALE SPACE AND RANDOM FOREST CLASSIFIER

Image patches also could be defined using image segmentation algorithms. This solution opens a new area in the classification of high resolution imagery using per-field category which are also known as object-based or object-oriented image analysis methods (Benz et al., 2004). In object-based classification methods an extra pre-processing step is employed to produce the image objects. Image segmentation algorithms are the most widely used methods for this goal. Segmentation is defined as the process of dividing an image scene into homogenous parts which inherently contains similar pixels and completely different from neighbouring parts (Pal and Pal, 1993). Homogenous image patches as the output of segmentation step are known as image objects and considered as the processing units in the object-based classification. The quality of image objects affects directly the final results of image classification. Ideally, the image object ’s boarders should be coincide to the real objects in the image scene. Shape and size of the image segments are important parameters here. In the most of segmentation algorithms, the size and the shape of image segments controlled by some input parameters. In (Wu and Li, 2009) geographical variance, wavelet transform, local variance, semi-variogram and fractals methods are introduced as quantitative methods to deal with the scale issue in the remote sensing imageries. Local variance method has been more frequently considered in the estimation of segmentation scale parameter (Drăguţ et al., 2014; Drǎguţ et al., 2010).
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Information Extraction of High Resolution Remote Sensing Images Based on the Calculation of Optimal Segmentation Parameters.

Information Extraction of High Resolution Remote Sensing Images Based on the Calculation of Optimal Segmentation Parameters.

Many scholars have applied the object-oriented method to extract information from high- resolution remote sensing images because of its rich geometry and the texture characteristics. A series of experimental studies has shown that the object-oriented method can add value to information extraction conducted on the same data using different methods[4–9]. In the object-oriented information extraction from high-resolution images, the segmentation is one of the most important steps. The appropriate segmentation parameters, such as the optimal segmentation scale and the shape and structure factors, are the key factors in image segmenta- tion. At present, the calculation method of the optimal scale mainly uses expert experience, cal- culation models, objective functions, and so on. For example, Yan[10] presented an object- oriented typical ground object extraction on the basis of multi-level rules and improved the image segmentation method based on region growing. Hu[11]proposed an optimal segmenta- tion-scale calculation model to improve the accuracy of object-oriented image interpretation. Huang[12] developed mean variance and object max-area method to calculate the scale factor. Tian[13] proposed a framework to identify optimal segmentation scale for a given feature type. Yu[14] proposed a new method of optimal segmentation scale selection for the object-oriented remote sensing image classification-vector distance index method. According to the analysis of the above studies, the current calculation methods of segmentation parameter, such as expert experience and object function method[12, 15], focus on the scale factor, depend on expert experience, and is restricted by the lack of mathematical law. Furthermore, the calculation method of the shape and firmness factor is lacking and mainly relies on subjective judgments. Therefore, examining object-oriented high-resolution remote sensing image segmentation, as well as the calculation method of the optimal segmentation parameters and thematic infor- mation extraction according to these segmentation parameters, has important the oretical research significance and practical application value. So, the purpose of this study is to present a calculation method of the optimal segmentation parameters and extract classification infor- mation from high-resolution remote sensing imagebased on the calculation of optimal segmen- tation parameters.
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MULTISCALE SEGMENTATION OF POLARIMETRIC SAR IMAGE BASED ON SRM  SUPERPIXELS

MULTISCALE SEGMENTATION OF POLARIMETRIC SAR IMAGE BASED ON SRM SUPERPIXELS

Image segmentation is an important pre-processing step in the object-based image analysis (OBIA) technology. In the use of single-scale segmentation algorithm for image segmentation, it is often required to segment a few times, and then choose a best segmentation result. On the one hand, the efficiency of this approach is low due to multiple segmentations. On the other hand, the final segmentation result is usually not optimal since it is chosen from limited number of results. In order to overcome the defects of single-scale segmentation, a number of multi-scale segmentation algorithms have been proposed, such as quadtrees (Samet, 1985), pyramid (Jolion and Montanvert, 1992; Koepfler et al., 1994), and binary partition trees (BPT) (Salembier, 2000). Inspired by the scale space theory (Witkin, 1983; Koenderink, 1984), Guigues et al. (2003) proposed a scale-sets image analysis method by putting these multi-scale image representation algorithms into the framework of scale- sets theory. Compared with the single-scale segmentation, the multi-scale segmentation can obtain multi-scale segmentation results which constitute a complete hierarchical structure on different scales by only once segmentation. Therefore, multi- scale segmentation is more systematic and more convenient for OBIA.
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New Approach for Image Fusion Based on Curvelet Approach

New Approach for Image Fusion Based on Curvelet Approach

The focus of this paper is developing a new approach to fuse a color image (visual image) and a corresponding grayscale one (Infrared image – IR) using the curvelet approach using different fusion rules in new fields. The fused image obtained by the proposed approach maintain the high resolution of the colored image (visual image), incorporate any hidden object given by the IR sensor as an example, or complements the two input images and keep the natural color of the visual image.
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Gray Scale Image Compression Based on Wavelet Transform and Linear Prediction

Gray Scale Image Compression Based on Wavelet Transform and Linear Prediction

Every year, several terabytes of image data- both medical and non medical- are engendered so that the requisition for image compression is substantiated. In this paper, the correlation properties of wavelets are harnessed in linear predictive coding to compress images. The image is decomposed using a one dimensional wavelet transform. The highest level wavelet transform coefficients and few detail coefficients in every level are retained. Using linear prediction on these coefficients the image is reconstructed. The prediction is done in both the dimensions, so the numbers of coefficients retained in detail subbands are less. With less predictors and samples from the original wavelet coefficients compression can be achieved. The results are appraised in objective and subjective manner with real world and medical images. The results are also verified on ModelFest database.
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NEGOSEIO: Framework for the Sustainability of Model-oriented Enterprise Interoperability

NEGOSEIO: Framework for the Sustainability of Model-oriented Enterprise Interoperability

TC#2 – After NEGOSEIO: The negotiation mechanism has the formalisation of the changes that need to be performed and has a parametric analysis of the impact of each factor change, hence it can determine various scenarios from past experience or simulation to analyse the scenario with least effort/time/impact. For each of these actions, the previous experience determines the set of services which need to be enabled and the best solutions for the required needs. Despite being complex, the negotiations have a proper development environment to maximise performance and minimise the impacts. It can make use of the intelligent agents which don’t have human limitations and may continue processing new possibilities and scenarios and assess its feasibility. Nevertheless the measured average negotiation time was in average of about 3-4 days to reach a solution which was validated by the various stakeholders. The changes to be performed by reusing services and strategies took an average length of 2 days.
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A New Approach in Design and Operating Principle of Silicone Tactile Sensor

A New Approach in Design and Operating Principle of Silicone Tactile Sensor

Abstract: Problem statement: Research and development in tactile sensor are escalating due to the fact that advanced robot needs to interact with surrounding environments which is very complex, dynamic, uncontrolled and difficult to perceive reliably. Recent research has been focusing in development of new tactile sensor that takes advantage of advances in materials, Micro- Electromechanical Systems (MEMS) and semiconductor technology. To date, several basic sensing principles are commonly used in tactile sensor such as capacitive sensor, piezoelectric sensor, inductive sensor, opto-electrical and piezo-resistive sensor. However they are still lack of sensitivity and low dynamic range in sensing the changes of forces in 3 axes and not durable enough to perform in various working environments. Approach: Three different designs of optical tactile sensor was proposed and analyzed. The overall design of the test-rig of the system was presented. The working principle was based on the deformation of the silicone tactile sensor. The deformation image will be transferred through high quality medical fiberscope and will be recorded using a CCD camera. The image will be stored in a computer for further analysis to relate the image with the given forces. These data can be used to control a robotic gripper so that it can perform gently and precisely like human tactile sensing capability but with greater strength and durability in various working environments. Results: The sensor had been designed and an experimental test rig was developed. Initial experiment was carried out to check the potential of this technique. Based on results, there is almost a linear relationship between the forces and the deformation of the tactile sensor. The amount of deformation is calculated based on the analyzed image data. Conclusion: The results of the experiment gave a convincing idea and provide a ground for further research to enhance this system to be an alternative tactile sensor in future.
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Real-Time Object Recognition Based on Cortical Multi-scale Keypoints

Real-Time Object Recognition Based on Cortical Multi-scale Keypoints

Object recognition and categorisation has seen tremendous progress in recent years. As noted by Boiman et al. [1], in a few years the best-performing methods went from scoring around 20% on Caltech 101 to almost 90%. This has led to newer, more challenging datasets like Caltech 256 [2] and PASCAL VOC [3], with similar progress. These improvements went hand-in-hand with increased computational power and improvements in machine learning methods, allowing for learning very complex relationships from training images. But while this re- search has pushed the boundaries of what is possible, most of the best-performing methods are very slow. While directly comparing reported runtimes is difficult due to differences in implementation details and different hardware used, most authors who report how long it takes to finish processing a benchmark like Cal- tech 101 mention hours and days. Even with more powerful computers, most of these methods will not be usable on mobile robots for years.
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USING MORPHLET-BASED IMAGE REPRESENTATION FOR OBJECT DETECTION

USING MORPHLET-BASED IMAGE REPRESENTATION FOR OBJECT DETECTION

To sum up, the proposed method for selective objects detection based on trees of morphlets provides a robust search of objects even in case of minimal prior knowledge about their type and shape. The method is convenient to use for the search of large objects of complex shape in various scales. It is necessary to mention that the method does not employ a running window and image or feature pyramids. This property provides the robust search of objects of complex shape as well as possibility of testing hypotheses about their size and shape. Additionally, the method does not require the analysis of color information, which can also be useful in some tasks.
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A Piecewise Constant Region Based Simultaneous Image Segmentation and Registration

A Piecewise Constant Region Based Simultaneous Image Segmentation and Registration

Image registration is the process of overlaying two or more images of the same scene taken at different times, and/or by different sensors. Area based methods or feature based methods have been developed to match given images. For the details of the image registration techniques, please refer to [27].

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Fish Classification Based on Robust Features Extraction From Color Signature Using Back-Propagation Classifier

Fish Classification Based on Robust Features Extraction From Color Signature Using Back-Propagation Classifier

(CV) map and then find its representative vector based on the concept of force equilibrium. After rotating the representative vector into the canonical orientation, every unknown object can be compared with the model objects efficiently. An image-processing algorithm developed by Zion et al. (1999) and Shutler and Nixon (2001), has been used for discrimination between images of three fish species for use on freshwater fish farms. Zernike velocity moments were developed by (Dudani et al., 2000), to describe an object using not only its shape, but also its motion throughout an image as claimed by (Mercimekm et al., 2005).Classification is the final stage of any image-processing system where each unknown pattern is assigned to a category. The degree of difficulty of the classification problem depends on the variability in feature values for objects in the same category, relative to the difference between feature values for objects in different categories. Mercimekm et al. (2005) and Gupta et al. (2007) and Lee et al. have proposed shape analysis of images of fish to deal with the fish classification problem. A new shape analysis algorithm was developed for removing edge noise and redundant data point such as short straight line. A curvature function analysis was used to locate critical landmark points. The fish contour segments of interest patterns were then extracted based on landmark points for species classification, which were done by comparing individual contour segments to the curves in the database. Regarding the feature extraction process, the authors tackled in their research the following features: fish contour extraction; fish detection and tracking; shape measurement and descriptions (i.e., shape characters (features), anal and caudal fin and size); data reduction; landmark points; landmark points statistics (i.e., curve segment of interest). In their study, they have chosen nine species of fishes that have similar shape characters and the total of features was nine features. Also, they recommended that the decision tree is considered as a suitable method to obtain high accurate results of fish images based on the common characters used, such as: caudal, anal and adipose fin. Furthermore, the authors claimed that the number of shape characters needed to be used and how to use them depending on the number of species and what kind of species are required by the system to be classified. Their experiments conducted 22 fish images that belong to 9 species, where the detection percentage of the classification process was 90%.
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A Comparative Study on CT Image Segmentation Using FCM-based Clustering Methods

A Comparative Study on CT Image Segmentation Using FCM-based Clustering Methods

CT scan is an imaging modality which uses X-rays to obtain structural and functional information about the human body [1]. Because animal tissues have various degrees of X-ray absorption, they can be imaged in a CT scan as pixels with different intensity. For example, dense tissues such as bones are white in a CT image, soft tissues such as brain or liver are gray, tissues filled of air or cavity may be black, etc. With the help of the CT scan technology, medical diagnosis advances effectively and more accurately. The investigation of CT images usually relies on human medical doctors or experts, which is time-consuming and error-prone. Automated analysis of CT images can reduce human’s efforts and provide summarized information for fast diagnosis and has received increasing attention[2].
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An Automated System To Classify Alloy Steel Surface Using  Contourlet Transform

An Automated System To Classify Alloy Steel Surface Using Contourlet Transform

A novel technique for detecting defects in fabric image based on the features extracted using a new multi resolution analysis tool called digital curvelet transform is proposed in [8]. The extracted features are direction features of curvelet coefficients and texture features based on GLCM of curvelet coefficients. K-nearest neighbor is used as a classifier for detecting the surface. A new method to detect the defect of texture images by using curvelet transform is presented in [9]. The curvelet transform can easily detect defects in texture, like one-dimensional discontinuities or in two dimensional signal or function of image. The extracted features are energy and standard deviation of division sub-bands.
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A Framework for Enterprise Context Analysis Based on Semantic Principles

A Framework for Enterprise Context Analysis Based on Semantic Principles

In addition to the above, there are additional sources of data and information that are just recently being explored. An example is the usage of Google Analytics data for economic indicator prediction. In [3], it details how the Bank of England has already been using Google Trends as a tool to obtain more current information than the official statistics. The results so far have been very interesting, as “the Bank believes that trends in searches for estate agents have been a better predictor of future house prices than even established surveys by property bodies such as the Royal Institute for Chartered Surveyors or the Home Builders Federation” [3]. Additionally, “Searches for Jobseeker’s Allowance, meanwhile, are at least as reliable as a way of gauging current unemployment rates as the actual number of claimants for the main jobless benefit” [3]. Such economic indicators could be of use to many enterprises in order to determine economic cycles or business trends in their specific sectors.
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Applications of Image Filtration Based on Principal Component Analysis and Nonlocal Image Processing

Applications of Image Filtration Based on Principal Component Analysis and Nonlocal Image Processing

As we stated before computation costs of the sequential and parallel scheme algorithms are relatively high in comparison with APCA+Wiener and other existed denoising algorithms. There are several possible approaches which can be used to decrease the cost: (1) calculate only first largest eigenvalues and correspondent eigenvectors for creation of principa l components’ basis [20]; (2) during the processing of a noised image change a procedure of searching a local principal component basis with a creation of global hierarchical principal component basis [21]; (3) while using a non-local processing algorithm [6, 9-11] implement it in a vector form [9,10], or, alternatively, use a global principal components’ basis separately calculated for a processed image – this will reduce size of compared similarity areas of pixels being processed and analyzed, and speed up calculation of weight coefficients used to form a final evaluation of an unnoised pixel [22].
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Barriers And Profits Of Distan ce Education In Operations Research Based Decision Analysis

Barriers And Profits Of Distan ce Education In Operations Research Based Decision Analysis

Combine! framework will take care displaying the matrix and storing the data after it has been changed by the user). The application logic can be programmed with Java or Octave numerical computation language. The Octave code can be placed either within an HTML file or in an external file – all variables defined in the standard form can be accessed and modified within the Octave code. After a DSS is implemented it needs to be tested and debugged. Combine! supports reporting errors in Octave code, what makes them easy to find. Deployment requires copying the DSS files into an appropriate folder in the production server – plug-in architecture will take care of displaying new DSS in the list of available DSSs.
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Analysis of Sequence Based Classifier Prediction for HIV Subtypes

Analysis of Sequence Based Classifier Prediction for HIV Subtypes

A HIV is human immunodeficiency virus causes AIDS (Acquired Immunodeficiency Virus) [1] which leads to life threatening opportunistic infections. It is one of the most serious, deadly diseases in human history. In the last two decades, over more than 60 million people have been infected with HIV. After getting into the body, the virus kills or damages cells of the body's immune system. The body tries to keep up by making new cells or trying to contain the virus, but eventually the HIV wins out and progressively destroys the body's ability to fight infections and certain cancers.HIV is of two types HIV-1 and HIV-2[1,2]. HIV is different in structure from other retroviruses. It is roughly spherical with a diameter of about 120nm, around 60 times smaller than a red blood cell, yet large for a virus. It is composed of two copies of positive single-stranded RNA enclosed by a conical capsid comprising the viral protein p24, typical of lentiviruses. HIV contains nine gene made of 9749 base pairs.
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Image-based theatre: a new pedagogy for a “new” theatre

Image-based theatre: a new pedagogy for a “new” theatre

I would like to emphasise that it is not about bringing new technologies into theatre. Over time, performing arts have always embraced what was new in terms of architecture, stage technique, lighting and so on. This has been done. Is about re-organising the whole structure of theatre performances to harmonise with our capacity to perceive time and space simultaneously, from different perspectives. This is what technologies have brought into our every- day life: we are surrounded by tons of information – most of them visual – which have no immediate connection with one another, but nevertheless they are being delivered to us simultaneously. And this fact has dramatic consequences – literally speaking. That’s why we have to reconsider the main idea of “image” in order to understand how it is, or how it can be used, in today’s theatre. The image has a paradoxically status: it is far more imprecise than the written language but it is infinitely more complex than words. It is, at the same time, empty of meanings/full of meanings. So, how does an image become meaningful, or what makes it expressive?
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SATELLITE STEREO BASED DIGITAL SURFACE MODEL GENERATION USING SEMI  GLOBAL MATCHING IN OBJECT AND IMAGE SPACE

SATELLITE STEREO BASED DIGITAL SURFACE MODEL GENERATION USING SEMI GLOBAL MATCHING IN OBJECT AND IMAGE SPACE

This paper presents methodology and evaluation of Digital Surface Models (DSM) generated from satellite stereo imagery using Semi Global Matching (SGM) applied in image space and georeferenced voxel space. SGM is a well known algorithm, used widely for DSM generation from airborne and satellite imagery. SGM is typically applied in the image space to compute disparity map corresponding to a stereo image pair. As a different approach, SGM can be applied directly to the georeferenced voxel space similar to the approach of volumetric multi-view reconstruction techniques. The matching in voxel space simplifies the DSM generation pipeline because the stereo rectification and triangulation steps are not required. For a comparison, the complete pipeline for generation of DSM from satellite pushbroom sensors is also presented. The results on the ISPRS satellite stereo benchmark using Worldview stereo imagery of 0.5m resolution shows that the SGM applied in image space produce slightly better results than its object space counterpart. Furthermore, a qualitative analysis of the results on Worldview-3 stereo and Pleiades tri-stereo images are presented.
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Carbonated soft drink classification based on image analysis and PCA.

Carbonated soft drink classification based on image analysis and PCA.

CARBONATED SOFT DRINK CLASSIFICATION BASED ON IMAGE ANALYSIS AND PCA. This paper describes an approach for the colour-based classification of RGB (red-green-blue) images, acquired using a common scanner, of commercial carbonated soft drinks. Mean histograms of image colour channels were evaluated for the PCA classification of 29 brands of Guaraná, Cola, and orange flavors. Loadings for principal component axes resulted in different patterns for sample grouping on score plots according to RGB histograms. pH, sorbic acid and sucrose measurements were also correlated to the analyzed brands through PCA score plots of the digitalized images.
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