Search results for: Sorted Histogram

Authors: Hanan Ahmed-Hosni Mahmoud, Nadia Al-Ghreimil

Abstract:

In-place sorting algorithms play an important role in many fields such as very large database systems, data warehouses, data mining, etc. Such algorithms maximize the size of data that can be processed in main memory without input/output operations. In this paper, a novel in-place sorting algorithm is presented. The algorithm comprises two phases; rearranging the input unsorted array in place, resulting segments that are ordered relative to each other but whose elements are yet to be sorted. The first phase requires linear time, while, in the second phase, elements of each segment are sorted inplace in the order of z log (z), where z is the size of the segment, and O(1) auxiliary storage. The algorithm performs, in the worst case, for an array of size n, an O(n log z) element comparisons and O(n log z) element moves. Further, no auxiliary arithmetic operations with indices are required. Besides these theoretical achievements of this algorithm, it is of practical interest, because of its simplicity. Experimental results also show that it outperforms other in-place sorting algorithms. Finally, the analysis of time and space complexity, and required number of moves are presented, along with the auxiliary storage requirements of the proposed algorithm.

Keywords: Auxiliary storage sorting, in-place sorting, sorting.

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Authors: B. Al-Naami, N. Gharaibeh, A. AlRazzaq Kheshman

Abstract:

Alzheimer is known as the loss of mental functions such as thinking, memory, and reasoning that is severe enough to interfere with a person's daily functioning. The appearance of Alzheimer Disease symptoms (AD) are resulted based on which part of the brain has a variety of infection or damage. In this case, the MRI is the best biomedical instrumentation can be ever used to discover the AD existence. Therefore, this paper proposed a fusion method to distinguish between the normal and (AD) MRIs. In this combined method around 27 MRIs collected from Jordanian Hospitals are analyzed based on the use of Low pass -morphological filters to get the extracted statistical outputs through intensity histogram to be employed by the descriptive box plot. Also, the artificial neural network (ANN) is applied to test the performance of this approach. Finally, the obtained result of t-test with confidence accuracy (95%) has compared with classification accuracy of ANN (100 %). The robust of the developed method can be considered effectively to diagnose and determine the type of AD image.

Keywords: Alzheimer disease, Brain MRI analysis, Morphological filter, Box plot, Intensity histogram, ANN.

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Authors: Xinyue Zhao, Yutaka Satoh, Hidenori Takauji, Shun'ichi Kaneko

Abstract:

A hybrid feature based adaptive particle filter algorithm is presented for object tracking in real scenarios with static camera. The hybrid feature is combined by two effective features: the Grayscale Arranging Pairs (GAP) feature and the color histogram feature. The GAP feature has high discriminative ability even under conditions of severe illumination variation and dynamic background elements, while the color histogram feature has high reliability to identify the detected objects. The combination of two features covers the shortage of single feature. Furthermore, we adopt an updating target model so that some external problems such as visual angles can be overcame well. An automatic initialization algorithm is introduced which provides precise initial positions of objects. The experimental results show the good performance of the proposed method.

Keywords: Hybrid feature, adaptive Particle Filter, robust Object Tracking, Grayscale Arranging Pairs (GAP) feature.

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Authors: Irshad Ahmad, Abdul Muhamin Naeem, Muhammad Islam

Abstract:

Information on weed distribution within the field is necessary to implement spatially variable herbicide application. Since hand labor is costly, an automated weed control system could be feasible. This paper deals with the development of an algorithm for real time specific weed recognition system based on Histogram Analysis of an image that is used for the weed classification. This algorithm is specifically developed to classify images into broad and narrow class for real-time selective herbicide application. The developed system has been tested on weeds in the lab, which have shown that the system to be very effectiveness in weed identification. Further the results show a very reliable performance on images of weeds taken under varying field conditions. The analysis of the results shows over 95 percent classification accuracy over 140 sample images (broad and narrow) with 70 samples from each category of weeds.

Keywords: Image Processing, real-time recognition, Weeddetection.

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Authors: Irshad Ahmad, Abdul Muhamin Naeem, Muhammad Islam, Shahid Nawaz

Abstract:

Information on weed distribution within the field is necessary to implement spatially variable herbicide application. Since hand labor is costly, an automated weed control system could be feasible. This paper deals with the development of an algorithm for real time specific weed recognition system based on Histogram Maxima with threshold of an image that is used for the weed classification. This algorithm is specifically developed to classify images into broad and narrow class for real-time selective herbicide application. The developed system has been tested on weeds in the lab, which have shown that the system to be very effectiveness in weed identification. Further the results show a very reliable performance on images of weeds taken under varying field conditions. The analysis of the results shows over 95 percent classification accuracy over 140 sample images (broad and narrow) with 70 samples from each category of weeds.

Keywords: Image processing, real-time recognition, weeddetection.

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Authors: Rachid Ahdid, Said Safi, Bouzid Manaut

Abstract:

In this paper, we present a comparative study of three methods of 2D face recognition system such as: Iso-Geodesic Curves (IGC), Geodesic Distance (GD) and Geodesic-Intensity Histogram (GIH). These approaches are based on computing of geodesic distance between points of facial surface and between facial curves. In this study we represented the image at gray level as a 2D surface in a 3D space, with the third coordinate proportional to the intensity values of pixels. In the classifying step, we use: Neural Networks (NN), K-Nearest Neighbor (KNN) and Support Vector Machines (SVM). The images used in our experiments are from two wellknown databases of face images ORL and YaleB. ORL data base was used to evaluate the performance of methods under conditions where the pose and sample size are varied, and the database YaleB was used to examine the performance of the systems when the facial expressions and lighting are varied.

Keywords: 2D face recognition, Geodesic distance, Iso-Geodesic Curves, Geodesic-Intensity Histogram, facial surface, Neural Networks, K-Nearest Neighbor, Support Vector Machines.

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Authors: Mayowa Philips Ibitola, Abe Oluwaseun Banji, Olorunfemi Akinade-Solomon

Abstract:

A total of 20 bottom sediment samples were collected from the Lekki Lagoon during the wet and dry season. The study was carried out to determine the textural characteristics, sediment distribution pattern and energy of transportation within the lagoon system. The sediment grain sizes and depth profiling was analyzed using dry sieving method and MATLAB algorithm for processing. The granulometric reveals fine grained sand both for the wet and dry season with an average mean value of 2.03 ϕ and -2.88 ϕ, respectively. Sediments were moderately sorted with an average inclusive standard deviation of 0.77 ϕ and -0.82 ϕ. Skewness varied from strongly coarse and near symmetrical 0.34- ϕ and 0.09 ϕ. The kurtosis average value was 0.87 ϕ and -1.4 ϕ (platykurtic and leptokurtic). Entirely, the bathymetry shows an average depth of 4.0 m. The deepest and shallowest area has a depth of 11.2 m and 0.5 m, respectively. High concentration of fine sand was observed at deep areas compared to the shallow areas during wet and dry season. Statistical parameter results show that the overall sediments are sorted, and deposited under low energy condition over a long distance. However, sediment distribution and sediment transport pattern of Lekki Lagoon is controlled by a low energy current and the down slope configuration of the bathymetry enhances the sorting and the deposition rate in the Lekki Lagoon.

Keywords: Lekki Lagoon, marine sediment, bathymetry, grain size distribution.

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Authors: Badarch Tuyatsetseg

Abstract:

This paper presents parametric probability density models for call holding times (CHTs) into emergency call center based on the actual data collected for over a week in the public Emergency Information Network (EIN) in Mongolia. When the set of chosen candidates of Gamma distribution family is fitted to the call holding time data, it is observed that the whole area in the CHT empirical histogram is underestimated due to spikes of higher probability and long tails of lower probability in the histogram. Therefore, we provide the Gaussian parametric model of a mixture of lognormal distributions with explicit analytical expressions for the modeling of CHTs of PSNs. Finally, we show that the CHTs for PSNs are fitted reasonably by a mixture of lognormal distributions via the simulation of expectation maximization algorithm. This result is significant as it expresses a useful mathematical tool in an explicit manner of a mixture of lognormal distributions.

Keywords: A mixture of lognormal distributions, modeling call holding times, public safety network.

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Authors: Roza Afarin, Saeed Mozaffari

Abstract:

The aim of this paper is image encryption using Genetic Algorithm (GA). The proposed encryption method consists of two phases. In modification phase, pixels locations are altered to reduce correlation among adjacent pixels. Then, pixels values are changed in the diffusion phase to encrypt the input image. Both phases are performed by GA with binary chromosomes. For modification phase, these binary patterns are generated by Local Binary Pattern (LBP) operator while for diffusion phase binary chromosomes are obtained by Bit Plane Slicing (BPS). Initial population in GA includes rows and columns of the input image. Instead of subjective selection of parents from this initial population, a random generator with predefined key is utilized. It is necessary to decrypt the coded image and reconstruct the initial input image. Fitness function is defined as average of transition from 0 to 1 in LBP image and histogram uniformity in modification and diffusion phases, respectively. Randomness of the encrypted image is measured by entropy, correlation coefficients and histogram analysis. Experimental results show that the proposed method is fast enough and can be used effectively for image encryption.

Keywords: Correlation coefficients, Genetic algorithm, Image encryption, Image entropy.

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Authors: Mahdieh Khalilinezhad, Silvana Dellepiane, Gianni Vernazza

Abstract:

The aim of this work is to build a model based on tissue characterization that is able to discriminate pathological and non-pathological regions from three-phasic CT images. With our research and based on a feature selection in different phases, we are trying to design a neural network system with an optimal neuron number in a hidden layer. Our approach consists of three steps: feature selection, feature reduction, and classification. For each region of interest (ROI), 6 distinct sets of texture features are extracted such as: first order histogram parameters, absolute gradient, run-length matrix, co-occurrence matrix, autoregressive model, and wavelet, for a total of 270 texture features. When analyzing more phases, we show that the injection of liquid cause changes to the high relevant features in each region. Our results demonstrate that for detecting HCC tumor phase 3 is the best one in most of the features that we apply to the classification algorithm. The percentage of detection between pathology and healthy classes, according to our method, relates to first order histogram parameters with accuracy of 85% in phase 1, 95% in phase 2, and 95% in phase 3.

Keywords: Feature selection, Multi-phasic liver images, Neural network, Texture analysis.

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Authors: Hussin K. Ragb, Vijayan K. Asari

Abstract:

In this paper, we present a pedestrian detection descriptor called Fused Structure and Texture (FST) features based on the combination of the local phase information with the texture features. Since the phase of the signal conveys more structural information than the magnitude, the phase congruency concept is used to capture the structural features. On the other hand, the Center-Symmetric Local Binary Pattern (CSLBP) approach is used to capture the texture information of the image. The dimension less quantity of the phase congruency and the robustness of the CSLBP operator on the flat images, as well as the blur and illumination changes, lead the proposed descriptor to be more robust and less sensitive to the light variations. The proposed descriptor can be formed by extracting the phase congruency and the CSLBP values of each pixel of the image with respect to its neighborhood. The histogram of the oriented phase and the histogram of the CSLBP values for the local regions in the image are computed and concatenated to construct the FST descriptor. Several experiments were conducted on INRIA and the low resolution DaimlerChrysler datasets to evaluate the detection performance of the pedestrian detection system that is based on the FST descriptor. A linear Support Vector Machine (SVM) is used to train the pedestrian classifier. These experiments showed that the proposed FST descriptor has better detection performance over a set of state of the art feature extraction methodologies.

Keywords: Pedestrian detection, phase congruency, local phase, LBP features, CSLBP features, FST descriptor.

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Authors: S. Zith Dey Babu, S. Kour, S. Verma, C. Verma, V. Pathania, A. Agrawal, V. Chaudhary, A. Manoj Puthur, R. Goyal, A. Pal, T. Danti Dey, A. Kumar, K. Wadhwa, O. Ved

Abstract:

Background: Skin cancer is now is the buzzing button in the field of medical science. The cyst's pandemic is drastically calibrating the body and well-being of the global village. Methods: The extracted image of the skin tumor cannot be used in one way for diagnosis. The stored image contains anarchies like the center. This approach will locate the forepart of an extracted appearance of skin. Partitioning image models has been presented to sort out the disturbance in the picture. Results: After completing partitioning, feature extraction has been formed by using genetic algorithm and finally, classification can be performed between the trained and test data to evaluate a large scale of an image that helps the doctors for the right prediction. To bring the improvisation of the existing system, we have set our objectives with an analysis. The efficiency of the natural selection process and the enriching histogram is essential in that respect. To reduce the false-positive rate or output, GA is performed with its accuracy. Conclusions: The objective of this task is to bring improvisation of effectiveness. GA is accomplishing its task with perfection to bring down the invalid-positive rate or outcome. The paper's mergeable portion conflicts with the composition of deep learning and medical image processing, which provides superior accuracy. Proportional types of handling create the reusability without any errors.

Keywords: Computer-aided system, detection, image segmentation, morphology.

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Authors: Yesubai Rubavathi Charles, Ravi Ramraj

Abstract:

In order to retrieve images efficiently from a large database, a unique method integrating color and texture features using genetic programming has been proposed. Opponent color histogram which gives shadow, shade, and light intensity invariant property is employed in the proposed framework for extracting color features. For texture feature extraction, fast discrete curvelet transform which captures more orientation information at different scales is incorporated to represent curved like edges. The recent scenario in the issues of image retrieval is to reduce the semantic gap between user’s preference and low level features. To address this concern, genetic algorithm combined with relevance feedback is embedded to reduce semantic gap and retrieve user’s preference images. Extensive and comparative experiments have been conducted to evaluate proposed framework for content based image retrieval on two databases, i.e., COIL-100 and Corel-1000. Experimental results clearly show that the proposed system surpassed other existing systems in terms of precision and recall. The proposed work achieves highest performance with average precision of 88.2% on COIL-100 and 76.3% on Corel, the average recall of 69.9% on COIL and 76.3% on Corel. Thus, the experimental results confirm that the proposed content based image retrieval system architecture attains better solution for image retrieval.

Keywords: Content based image retrieval, Curvelet transform, Genetic algorithm, Opponent color histogram, Relevance feedback.

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Authors: Krishnamurthy G N, V Ramaswamy

Abstract:

This paper demonstrates the results when either Shiftrows stage or Mixcolumns stage and when both the stages are omitted in the well known block cipher Advanced Encryption Standard(AES) and its modified version AES with Key Dependent S-box(AES-KDS), using avalanche criterion and other tests namely encryption quality, correlation coefficient, histogram analysis and key sensitivity tests.

Keywords: Encryption, Decryption, Avalanche, keysensitivity.

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Authors: Munehiro Iwami

Abstract:

A property is called persistent if for any many-sorted term rewriting system , has the property if and only if term rewriting system , which results from by omitting its sort information, has the property. In this paper,we show that termination is persistent for non-overlapping term rewriting systems and we give the example as application of this result. Furthermore we obtain that completeness is persistent for non-overlapping term rewriting systems.

Keywords: Theory of computing, Model-based reasoning, termrewriting system, termination

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Authors: Rawan I. Zaghloul, Enas F. Al-Rawashdeh

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In this paper a simple watermarking method for color images is proposed. The proposed method is based on watermark embedding for the histograms of the HSV planes using visual cryptography watermarking. The method has been proved to be robust for various image processing operations such as filtering, compression, additive noise, and various geometrical attacks such as rotation, scaling, cropping, flipping, and shearing.

Keywords: Histogram, HSV image, Visual Cryptography, Watermark.

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Authors: A. El Harraj, N. Raissouni

Abstract:

The detection of moving objects from a video image sequences is very important for object tracking, activity recognition, and behavior understanding in video surveillance. The most used approach for moving objects detection / tracking is background subtraction algorithms. Many approaches have been suggested for background subtraction. But, these are illumination change sensitive and the solutions proposed to bypass this problem are time consuming. In this paper, we propose a robust yet computationally efficient background subtraction approach and, mainly, focus on the ability to detect moving objects on dynamic scenes, for possible applications in complex and restricted access areas monitoring, where moving and motionless persons must be reliably detected. It consists of three main phases, establishing illumination changes invariance, background/foreground modeling and morphological analysis for noise removing. We handle illumination changes using Contrast Limited Histogram Equalization (CLAHE), which limits the intensity of each pixel to user determined maximum. Thus, it mitigates the degradation due to scene illumination changes and improves the visibility of the video signal. Initially, the background and foreground images are extracted from the video sequence. Then, the background and foreground images are separately enhanced by applying CLAHE. In order to form multi-modal backgrounds we model each channel of a pixel as a mixture of K Gaussians (K=5) using Gaussian Mixture Model (GMM). Finally, we post process the resulting binary foreground mask using morphological erosion and dilation transformations to remove possible noise. For experimental test, we used a standard dataset to challenge the efficiency and accuracy of the proposed method on a diverse set of dynamic scenes.

Keywords: Video surveillance, background subtraction, Contrast Limited Histogram Equalization, illumination invariance, object tracking, object detection, behavior understanding, dynamic scenes.

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Authors: Vandana Sharma, Parvinder S. Sandhu, Satwinder Singh, Baljit Saini

Abstract:

In field of Computer Science and Mathematics, sorting algorithm is an algorithm that puts elements of a list in a certain order i.e. ascending or descending. Sorting is perhaps the most widely studied problem in computer science and is frequently used as a benchmark of a system-s performance. This paper presented the comparative performance study of four sorting algorithms on different platform. For each machine, it is found that the algorithm depends upon the number of elements to be sorted. In addition, as expected, results show that the relative performance of the algorithms differed on the various machines. So, algorithm performance is dependent on data size and there exists impact of hardware also.

Keywords: Algorithm, Analysis, Complexity, Sorting.

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Authors: Munehiro Iwami

Abstract:

A property is persistent if for any many-sorted term rewriting system , has the property if and only if term rewriting system , which results from by omitting its sort information, has the property. Zantema showed that termination is persistent for term rewriting systems without collapsing or duplicating rules. In this paper, we show that the Zantema's result can be extended to term rewriting systems on ordered sorts, i.e., termination is persistent for term rewriting systems on ordered sorts without collapsing, decreasing or duplicating rules. Furthermore we give the example as application of this result. Also we obtain that completeness is persistent for this class of term rewriting systems.

Keywords: Theory of computing, Model-based reasoning, term rewriting system, termination

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Authors: Jaedo Kim, Youngjoon Han, Hernsoo Hahn

Abstract:

The purpose of this paper is to detect human in images. This paper proposes a method for extracting human body feature descriptors consisting of projected edge component series. The feature descriptor can express appearances and shapes of human with local and global distribution of edges. Our method evaluated with a linear SVM classifier on Daimler-Chrysler pedestrian dataset, and test with various sub-region size. The result shows that the accuracy level of proposed method similar to Histogram of Oriented Gradients(HOG) feature descriptor and feature extraction process is simple and faster than existing methods.

Keywords: Human detection, Projected edge descriptor, Linear SVM, Local appearance feature

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Authors: Lai Lai Win Kyi, Nay Min Tun

Abstract:

Sorting appears the most attention among all computational tasks over the past years because sorted data is at the heart of many computations. Sorting is of additional importance to parallel computing because of its close relation to the task of routing data among processes, which is an essential part of many parallel algorithms. Many parallel sorting algorithms have been investigated for a variety of parallel computer architectures. In this paper, three parallel sorting algorithms have been implemented and compared in terms of their overall execution time. The algorithms implemented are the odd-even transposition sort, parallel merge sort and parallel rank sort. Cluster of Workstations or Windows Compute Cluster has been used to compare the algorithms implemented. The C# programming language is used to develop the sorting algorithms. The MPI (Message Passing Interface) library has been selected to establish the communication and synchronization between processors. The time complexity for each parallel sorting algorithm will also be mentioned and analyzed.

Keywords: Cluster of Workstations, Parallel sorting algorithms, performance analysis, parallel computing and MPI.

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Authors: Unnop Panpuang

Abstract:

The objectives of this project are to study on the work efficiency of the employees, sorted by their profiles, and to study on the relation between job attributes and work efficiency of employees of Suan Sunandha Rajabhat University. The samples used for this study are 292 employees. The statistics used in this study are frequencies, standard deviations, One-way ANOVA and Pearson’s correlation coefficient. Majority of respondent were male with an undergraduate degree, married and lives together. The average age of respondents was between 31-41 years old, married and the educational background are higher than bachelor’s degree. The job attribute is correlated to the work efficiency with the statistical significance level of.o1. This concurs with the predetermined hypothesis. The correlation between the two main factors is in the moderate level. All the categories of job attributes such as the variety of skills, job clarity, job importance, freedom to do work are considered separately.

Keywords: Employees, Job Attributes, Work Efficiency.

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Authors: Somtop Keawchuer

Abstract:

The objectives of this research are to compare the satisfaction of students, towards the happiness learning, sorted by their personal profiles, and to figure out the factors that affect the students’ happiness learning. This paper used survey method to collect data from 362 students. The survey was mainly conducted in the Faculty of Management Science, Suan Sunandha Rajabhat University, including 3,443 students. The statistics used for interpreting the results included the frequencies, percentages, standard deviations and One-way ANOVA. The findings revealed that the students are aware and satisfaction that all the factors in 3 categories (knowledge, skill and attitude) influence the happiness learning at the highest levels. The comparison of the satisfaction levels of the students toward their happiness learning leads to the results that the students with different genders, ages, years of study, and majors of the study have the similar satisfaction at the high level.

Keywords: Happiness Learning, Satisfaction, Students.

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Authors: Abhinav Kumar, Raja Sekhar Bandaru, B Madhusudan Rao, Saket Kulkarni, Nilesh Ghatpande

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This paper proposes an algorithm which automatically aligns and stitches the component medical images (fluoroscopic) with varying degrees of overlap into a single composite image. The alignment method is based on similarity measure between the component images. As applied here the technique is intensity based rather than feature based. It works well in domains where feature based methods have difficulty, yet more robust than traditional correlation. Component images are stitched together using the new triangular averaging based blending algorithm. The quality of the resultant image is tested for photometric inconsistencies and geometric misalignments. This method cannot correct rotational, scale and perspective artifacts.

Keywords: Histogram Matching, Image Alignment, ImageStitching, Medical Imaging.

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Authors: Xingyu Pan, Laure Tougne

Abstract:

For recognizing coins, the graved release date is important information to identify precisely its monetary type. However, reading characters in coins meets much more obstacles than traditional character recognition tasks in the other fields, such as reading scanned documents or license plates. To address this challenging issue in a numismatic context, we propose a training-free approach dedicated to detection and recognition of the release date of the coin. In the first step, the date zone is detected by comparing histogram features; in the second step, a topology-based algorithm is introduced to recognize coin numbers with various font types represented by binary gradient map. Our method obtained a recognition rate of 92% on synthetic data and of 44% on real noised data.

Keywords: Coin, detection, character recognition, topology.

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Authors: Sang Hoon Lee, Jong Sue Bae, Taewan Kim, Jin Mo Song, Jong Ju Kim

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Image target detection and tracking methods based on target information such as intensity, shape model, histogram and target dynamics have been proven to be robust to target model variations and background clutters as shown by recent researches. However, no definitive answer has been given to occluded target by counter measure or limited field of view(FOV). In this paper, we will present a novel tracking method using filtering and computational geometry. This paper has two central goals: 1) to deal with vulnerable target measurements; and 2) to maintain target tracking out of FOV using non-target-originated information. The experimental results, obtained with airborne images, show a robust tracking ability with respect to the existing approaches. In exploring the questions of target tracking, this paper will be limited to consideration of airborne image.

Keywords: Tracking, Computational geometry, Homography, Filter

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Authors: Minami Ito, Akihiro Iijima

Abstract:

On March 11, 2011, the Great East Japan Earthquake occurred off the coast of Sanriku, Japan. It is important to build a sustainable society through the reconstruction process rather than simply restoring the infrastructure. To compare the goals of reconstruction plans of quake-stricken municipalities, Japanese language morphological analysis was performed by using text mining techniques. Frequently-used nouns were sorted into four main categories of “life”, “disaster prevention”, “economy”, and “harmony with environment”. Because Soma City is affected by nuclear accident, sentences tagged to “harmony with environment” tended to be frequent compared to the other municipalities. Results from cluster analysis and principle component analysis clearly indicated that the local government reinforces the efforts to reduce risks from radiation exposure as a top priority.

Keywords: Eco-friendly reconstruction, harmony with environment, decontamination, nuclear disaster.

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Authors: Ying Yuken, Pengfei Wang, Zhang Qilin, Xiao Ben

Abstract:

Based on the main entrance and garden area (ME&G) project of Shanghai Disneyland, this paper introduces the application of BIM technology in this kind of low-rise comprehensive building with complex facade system, electromechanical system and decoration system. BIM technology is applied to the whole process of design, construction and completion of the whole project. With the construction of BIM application framework of the whole project, the key points of BIM modeling methods of different systems and the integration and coordination of BIM models are elaborated in detail. The specific application methods of BIM technology in similar complex low-rise building projects are sorted out. Finally, the paper summarizes the benefits of BIM technology application, and puts forward some suggestions for BIM management mode and practical application of similar projects in the future.

Keywords: BIM, complex low-rise building, BIM modeling, model integration and coordination, 3D scanning.

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Authors: Nada Farhani, Naim Terbeh, Mounir Zrigui

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The recognition of the objects contained in images has always presented a challenge in the field of research because of several difficulties that the researcher can envisage because of the variability of shape, position, contrast of objects, etc. In this paper, we will be interested in the recognition of objects. The classical Hough Transform (HT) presented a tool for detecting straight line segments in images. The technique of HT has been generalized (GHT) for the detection of arbitrary forms. With GHT, the forms sought are not necessarily defined analytically but rather by a particular silhouette. For more precision, we proposed to combine the results from the GHT with the results from a calculation of similarity between the histograms and the spatiograms of the images. The main purpose of our work is to use the concepts from recognition to generate sentences in Arabic that summarize the content of the image.

Keywords: Recognition of shape, generalized hough transformation, histogram, Spatiogram, learning.

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Authors: Sunhyoung Lee, Askar Akshabayev, Beisenbek Baisakov, Youngjoon Han, Hernsoo Hahn

Abstract:

It is important to give input information without other device in AR system. One solution is using hand for augmented reality application. Many researchers have proposed different solutions for hand interface in augmented reality. Analyze Histogram and connecting factor is can be example for that. Various Direction searching is one of robust way to recognition hand but it takes too much calculating time. And background should be distinguished with skin color. This paper proposes a hand tracking method to control the 3D object in augmented reality using depth device and skin color. Also in this work discussed relationship between several markers, which is based on relationship between camera and marker. One marker used for displaying virtual object and three markers for detecting hand gesture and manipulating the virtual object.

Keywords: Augmented Reality, depth map, hand recognition, kinect, marker, YCbCr color model.

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