Search results for: correlation features image fusion

Authors: S. Ratna Sulistiyanti, Adhi Susanto, Thomas Sri Widodo, Gede Bayu Suparta

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In this paper, an experimentation to enhance the visibility of hot objects in a thermal image acquired with ordinary digital camera is reported, after the applications of lowpass and median filters to suppress the distracting granular noises. The common thresholding and slicing techniques were used on the histogram at different gray levels, followed by a subjective comparative evaluation. The best result came out with the threshold level 115 and the number of slices 3.

Keywords: enhance, thermal image, thresholding and slicingtechniques, granular noise, hot objects.

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Authors: Thomas Fuhrmann

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A new fast correlation algorithm for calibrating the wavelength of Optical Spectrum Analyzers (OSAs) was introduced in [1]. The minima of acetylene gas spectra were measured and correlated with saved theoretical data [2]. So it is possible to find the correct wavelength calibration data using a noisy reference spectrum. First tests showed good algorithmic performance for gas line spectra with high noise. In this article extensive performance tests were made to validate the noise resistance of this algorithm. The filter and correlation parameters of the algorithm were optimized for improved noise performance. With these parameters the performance of this wavelength calibration was simulated to predict the resulting wavelength error in real OSA systems. Long term simulations were made to evaluate the performance of the algorithm over the lifetime of a real OSA.

Keywords: correlation, gas reference, optical spectrum analyzer, wavelength calibration

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Authors: Sahila Chopra, Hemdeep, Arshdeep Kaur, Raj K. Gupta

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In recent times, we noticed an interesting and important role of non-coplanar degree-of-freedom (Φ = 00) in heavy ion reactions. Using the dynamical cluster-decay model (DCM) with Φ degree-of-freedom included, we have studied three compound systems 246Bk∗, 164Yb∗ and 105Ag∗. Here, within the DCM with pocket formula for nuclear proximity potential, we look for the effects of including compact, non-coplanar configurations (Φc = 00) on the non-compound nucleus (nCN) contribution in total fusion cross section σfus. For 246Bk∗, formed in 11B+235U and 14N+232Th reaction channels, the DCM with coplanar nuclei (Φc = 00) shows an nCN contribution for 11B+235U channel, but none for 14N+232Th channel, which on including Φ gives both reaction channels as pure compound nucleus decays. In the case of 164Yb∗, formed in 64Ni+100Mo, the small nCN effects for Φ=00 are reduced to almost zero for Φ = 00. Interestingly, however, 105Ag∗ for Φ = 00 shows a small nCN contribution, which gets strongly enhanced for Φ = 00, such that the characteristic property of PCN presents a change of behaviour, like that of a strongly fissioning superheavy element to a weakly fissioning nucleus; note that 105Ag∗ is a weakly fissioning nucleus and Psurv behaves like one for a weakly fissioning nucleus for both Φ = 00 and Φ = 00. Apparently, Φ is presenting itself like a good degree-of-freedom in the DCM.

Keywords: Dynamical cluster-decay model, fusion cross sections, non-compound nucleus effects, non-coplanarity.

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Authors: S. Usha, S. Arumugam

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Cancer affects people globally with breast cancer being a leading killer. Breast cancer is due to the uncontrollable multiplication of cells resulting in a tumour or neoplasm. Tumours are called ‘benign’ when cancerous cells do not ravage other body tissues and ‘malignant’ if they do so. As mammography is an effective breast cancer detection tool at an early stage which is the most treatable stage it is the primary imaging modality for screening and diagnosis of this cancer type. This paper presents an automatic mammogram classification technique using wavelet and Gabor filter. Correlation feature selection is used to reduce the feature set and selected features are classified using different decision trees.

Keywords: Breast Cancer, Mammogram, Symlet Wavelets, Gabor Filters, Decision Trees

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Authors: Jaqueline M. R. Vieira

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Different strategies and tools are available at the oil and gas industry for detecting and analyzing tension and possible fractures in borehole walls. Most of these techniques are based on manual observation of the captured borehole images. While this strategy may be possible and convenient with small images and few data, it may become difficult and suitable to errors when big databases of images must be treated. While the patterns may differ among the image area, depending on many characteristics (drilling strategy, rock components, rock strength, etc.). In this work we propose the inclusion of data-mining classification strategies in order to create a knowledge database of the segmented curves. These classifiers allow that, after some time using and manually pointing parts of borehole images that correspond to tension regions and breakout areas, the system will indicate and suggest automatically new candidate regions, with higher accuracy. We suggest the use of different classifiers methods, in order to achieve different knowledge dataset configurations.

Keywords: Brazil, classifiers, data-mining, Image Segmentation, oil well visualization, classifiers.

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Authors: M. B. Meenavathi, K. Rajesh

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In this paper, we propose a new class of Volterra series based filters for image enhancement and restoration. Generally the linear filters reduce the noise and cause blurring at the edges. Some nonlinear filters based on median operator or rank operator deal with only impulse noise and fail to cancel the most common Gaussian distributed noise. A class of second order Volterra filters is proposed to optimize the trade-off between noise removal and edge preservation. In this paper, we consider both the Gaussian and mixed Gaussian-impulse noise to test the robustness of the filter. Image enhancement and restoration results using the proposed Volterra filter are found to be superior to those obtained with standard linear and nonlinear filters.

Keywords: Gaussian noise, Image enhancement, Imagerestoration, Linear filters, Nonlinear filters, Volterra series.

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Authors: M. Razak, A. Jusoh, A. Zakaria

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This paper presents an on-going research work on the implementation of feature-based machining via macro programming. Repetitive machining features such as holes, slots, pockets etc can readily be encapsulated in macros. Each macro consists of methods on how to machine the shape as defined by the feature. The macro programming technique comprises of a main program and subprograms. The main program allows user to select several subprograms that contain features and define their important parameters. With macros, complex machining routines can be implemented easily and no post processor is required. A case study on machining of a part that comprised of planar face, hole and pocket features using the macro programming technique was carried out. It is envisaged that the macro programming technique can be extended to other feature-based machining fields such as the newly developed STEP-NC domain.

Keywords: Feature-based machining, CNC, Macro, STEP-NC.

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Authors: Sanghoon Park, Ki-Jin Kim, Kwang-Ho Ahn, Hyeon-Woo Lee

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It is very effective way to utilize a very wide tunable filter in co-existing multi-standards wireless communications environment. Especially, as the long term evolution (LTE) communication era has come, the multi-band coverage is one of the important features required for the RF components. In this paper, we present the frequency conversion technique, and so generate two types of RF filters which are specially designed for the superb tunable ability to support multiple wireless communication standards. With the help of a complex mixing structure, the inherent image signal is suppressed. The RF band-pass filter (BPF) and notch filter achieve 1.8dB and 1.6dB insertion losses and 18 dB and 17 dB attenuations, respectively. The quality factor show greater than 30.

Keywords: RF filters, interference, wideband, tunable, channel selection, complex mixing, balanced mixer.

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Authors: Zhen-Hui Hu, Jyh-Shong Ju, Ming-Hwei Perng

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This paper presents a useful sub-pixel image registration method using line segments and a sub-pixel edge detector. In this approach, straight line segments are first extracted from gray images at the pixel level before applying the sub-pixel edge detector. Next, all sub-pixel line edges are mapped onto the orientation-distance parameter space to solve for line correspondence between images. Finally, the registration parameters with sub-pixel accuracy are analytically solved via two linear least-square problems. The present approach can be applied to various fields where fast registration with sub-pixel accuracy is required. To illustrate, the present approach is applied to the inspection of printed circuits on a flat panel. Numerical example shows that the present approach is effective and accurate when target images contain a sufficient number of line segments, which is true in many industrial problems.

Keywords: Defect detection, Image registration, Straight line segment, Sub-pixel.

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Authors: Anežka Hamranová, Blandína Šramová, Katarína Fichnová

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We presented results of research aimed on findings influence of social - psychological training (realized with students of Constantine the Philosopher University- future teachers within their undergraduate preparation) on the choice of intrapersonal and interpersonal features. After social- psychological training using Interpersonal Check List (ICL) we found out shift of behavior to more adaptive forms in categories, which are characterized by extroversive friendly behavior, willingness to cooperation, conformity regard to social situation, responsible and regardful behavior. Using State-Trait Anxiety Inventory (STAI) we found out the cut down of state anxiety and of trait anxiety. The report was processed within grants KEGA 3/5269/07 and VEGA 1/3675/06.

Keywords: Intrapersonal and interpersonal features, social -psychological training, social competences.

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Authors: Sergei Aleinik, Mikhail Stolbov

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In this work, a method of time delay estimation for  dual-channel acoustic signals (speech, music, etc.) recorded under  reverberant conditions is investigated. Standard methods based on  cross-correlation of the signals show poor results in cases involving  strong reverberation, large distances between microphones and  asynchronous recordings. Under similar conditions, a method based  on cross-correlation of temporal envelopes of the signals delivers a  delay estimation of acceptable quality. This method and its properties  are described and investigated in detail, including its limits of  applicability. The method’s optimal parameter estimation and a  comparison with other known methods of time delay estimation are  also provided.

 

Keywords: Cross-correlation, delay estimation, signal envelope, signal processing.

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Authors: Xiaoling Dai

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Many Chinese artists have used digital photography to create works with features of Chinese landscape paintings since the 20th century. The ‘New Mountains and Water’ works created by digital techniques reflect the fusion of photographic techniques and traditional Chinese aesthetic thoughts. Borrowing from Chinese landscape paintings in the Song Dynasty, the Chinese photographer Yao Lu uses digital photography to reflect contemporary environmental construction in his series New Landscapes. By portraying a variety of natural environments brought by urbanization in the contemporary period, Lu deconstructs traditional Chinese paintings and reconstructs contemporary photographic practices. The primary object of this study is to investigate how Chinese photographer Yao Lu redefines and re-interprets the relationship between tradition and contemporaneity. In this study, Yao Lu’s series work New Landscapes is used for photo elicitation, which seeks to broaden understanding of the development of Chinese landscape photography. Furthermore, discourse analysis will be used to evaluate how Chinese social developments influence the creation of photographic practices. Through the visual and discourse analysis, this study aims to excavate the relationship between tradition and contemporaneity in Lu’s works. According to New Landscapes, the study argues that in Lu’s interpretations of landscapes, tradition and contemporaneity are seen to establish a new relationship. Traditional approaches to creation do not become obsolete over time. On the contrary, traditional notions and styles of creation can shed new light on contemporary issues or techniques.

Keywords: Chinese aesthetics, contemporaneity, New Landscapes, tradition, Yao Lu.

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Authors: Ardhendu Behera, Denis Lalanne, Rolf Ingold

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This paper proposes a method, combining color and layout features, for identifying documents captured from low-resolution handheld devices. On one hand, the document image color density surface is estimated and represented with an equivalent ellipse and on the other hand, the document shallow layout structure is computed and hierarchically represented. Our identification method first uses the color information in the documents in order to focus the search space on documents having a similar color distribution, and finally selects the document having the most similar layout structure in the remaining of the search space.

Keywords: Document color modeling, document visualsignature, kernel density estimation, document identification.

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Authors: M. Bastanfard, B. Jalaeian, S. Jafari

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Sonogram images of normal and lymphocyte thyroid tissues have considerable overlap which makes it difficult to interpret and distinguish. Classification from sonogram images of thyroid gland is tackled in semiautomatic way. While making manual diagnosis from images, some relevant information need not to be recognized by human visual system. Quantitative image analysis could be helpful to manual diagnostic process so far done by physician. Two classes are considered: normal tissue and chronic lymphocyte thyroid (Hashimoto's Thyroid). Data structure is analyzed using K-nearest-neighbors classification. This paper is mentioned that unlike the wavelet sub bands' energy, histograms and Haralick features are not appropriate to distinguish between normal tissue and Hashimoto's thyroid.

Keywords: Sonogram, thyroid, Haralick feature, wavelet.

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Authors: Abdulrahman S. Alturki, John S. Loomis

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This paper discusses a corner detection algorithm for camera calibration. Calibration is a necessary step in many computer vision and image processing applications. Robust corner detection for an image of a checkerboard is required to determine intrinsic and extrinsic parameters. In this paper, an algorithm for fully automatic and robust X-corner detection is presented. Checkerboard corner points are automatically found in each image without user interaction or any prior information regarding the number of rows or columns. The approach represents each X-corner with a quadratic fitting function. Using the fact that the X-corners are saddle points, the coefficients in the fitting function are used to identify each corner location. The automation of this process greatly simplifies calibration. Our method is robust against noise and different camera orientations. Experimental analysis shows the accuracy of our method using actual images acquired at different camera locations and orientations.

Keywords: Camera Calibration, Corner Detector, Saddle Points, X-Corners.

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Authors: Saliha Gachi, Mouloud Aissani, Fouad Boubenider

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Friction Stir Welding (FSW) is a solid-state welding technique that can join material without melting the plates to be welded. In this work, we are interested to demonstrate the potentiality of FSW for joining the heat-treatable aluminum alloy 2024-T3 which is reputed as difficult to be welded by fusion techniques. Thereafter, the FSW joint is compared with another one obtained from a conventional fusion process Tungsten Inert Gas (TIG). FSW welds are made up using an FSW tool mounted on a milling machine. Single pass welding was applied to fabricated TIG joint. The comparison between the two processes has been made on the temperature evolution, mechanical and microstructure behavior. The microstructural examination revealed that FSW weld is composed of four zones: Base metal (BM), Heat affected zone (HAZ), Thermo-mechanical affected zone (THAZ) and the nugget zone (NZ). The NZ exhibits a recrystallized equiaxed refined grains that induce better mechanical properties and good ductility compared to TIG joint where the grains have a larger size in the welded region compared with the BM due to the elevated heat input. The microhardness results show that, in FSW weld, the THAZ contains the lowest microhardness values and increase in the NZ; however, in TIG process, the lowest values are localized on the NZ.

Keywords: Friction stir welding, tungsten inert gaz, aluminum, microstructure.

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Authors: M. Shaker, S. Minaei, M. H. Khoshtaghaza, A. Banakar, A. Jafari

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In this study a system of machine vision and singulation was developed to separate paddy from rice and determine paddy husking and rice breakage percentages. The machine vision system consists of three main components including an imaging chamber, a digital camera, a computer equipped with image processing software. The singulation device consists of a kernel holding surface, a motor with vacuum fan, and a dimmer. For separation of paddy from rice (in the image), it was necessary to set a threshold. Therefore, some images of paddy and rice were sampled and the RGB values of the images were extracted using MATLAB software. Then mean and standard deviation of the data were determined. An Image processing algorithm was developed using MATLAB to determine paddy/rice separation and rice breakage and paddy husking percentages, using blue to red ratio. Tests showed that, a threshold of 0.75 is suitable for separating paddy from rice kernels. Results from the evaluation of the image processing algorithm showed that the accuracies obtained with the algorithm were 98.36% and 91.81% for paddy husking and rice breakage percentage, respectively. Analysis also showed that a suction of 45 mmHg to 50 mmHg yielding 81.3% separation efficiency is appropriate for operation of the kernel singulation system.

Keywords: Computer vision, rice kernel, husking, breakage.

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Authors: Mahdieh Khalilinezhad, Silvana Dellepiane, Gianni Vernazza

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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: Jiqing Han, Rongchun Gao

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One major source of performance decline in speaker recognition system is channel mismatch between training and testing. This paper focuses on improving channel robustness of speaker recognition system in two aspects of channel compensation technique and channel robust features. The system is text-independent speaker identification system based on two-stage recognition. In the aspect of channel compensation technique, this paper applies MAP (Maximum A Posterior Probability) channel compensation technique, which was used in speech recognition, to speaker recognition system. In the aspect of channel robust features, this paper introduces pitch-dependent features and pitch-dependent speaker model for the second stage recognition. Based on the first stage recognition to testing speech using GMM (Gaussian Mixture Model), the system uses GMM scores to decide if it needs to be recognized again. If it needs to, the system selects a few speakers from all of the speakers who participate in the first stage recognition for the second stage recognition. For each selected speaker, the system obtains 3 pitch-dependent results from his pitch-dependent speaker model, and then uses ANN (Artificial Neural Network) to unite the 3 pitch-dependent results and 1 GMM score for getting a fused result. The system makes the second stage recognition based on these fused results. The experiments show that the correct rate of two-stage recognition system based on MAP channel compensation technique and pitch-dependent features is 41.7% better than the baseline system for closed-set test.

Keywords: Channel Compensation, Channel Robustness, MAP, Speaker Identification

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Authors: Gizelle K. Vianna, Gabriel V. Cunha, Gustavo S. Oliveira

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Foliage diseases in plants can cause a reduction in both quality and quantity of agricultural production. Intelligent detection of plant diseases is an essential research topic as it may help monitoring large fields of crops by automatically detecting the symptoms of foliage diseases. This work investigates ways to recognize the late blight disease from the analysis of tomato digital images, collected directly from the field. A pair of multilayer perceptron neural network analyzes the digital images, using data from both RGB and HSL color models, and classifies each image pixel. One neural network is responsible for the identification of healthy regions of the tomato leaf, while the other identifies the injured regions. The outputs of both networks are combined to generate the final classification of each pixel from the image and the pixel classes are used to repaint the original tomato images by using a color representation that highlights the injuries on the plant. The new images will have only green, red or black pixels, if they came from healthy or injured portions of the leaf, or from the background of the image, respectively. The system presented an accuracy of 97% in detection and estimation of the level of damage on the tomato leaves caused by late blight.

Keywords: Artificial neural networks, digital image processing, pattern recognition.

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Authors: Mourad Moussa, Ali Douik, Hassani Messaoud

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In this paper, we present a comparative study between two computer vision systems for objects recognition and tracking, these algorithms describe two different approach based on regions constituted by a set of pixels which parameterized objects in shot sequences. For the image segmentation and objects detection, the FCM technique is used, the overlapping between cluster's distribution is minimized by the use of suitable color space (other that the RGB one). The first technique takes into account a priori probabilities governing the computation of various clusters to track objects. A Parzen kernel method is described and allows identifying the players in each frame, we also show the importance of standard deviation value research of the Gaussian probability density function. Region matching is carried out by an algorithm that operates on the Mahalanobis distance between region descriptors in two subsequent frames and uses singular value decomposition to compute a set of correspondences satisfying both the principle of proximity and the principle of exclusion.

Keywords: Image segmentation, objects tracking, Parzen window, singular value decomposition, target recognition.

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Authors: Helene Martin, Solmaz Boroomandi Barati, Jean-Charles Pinoli, Stephane Valette, Yann Gavet

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In the present work we developed an image processing algorithm to measure water droplets characteristics during dropwise condensation on pillared surfaces. The main problem in this process is the similarity between shape and size of water droplets and the pillars. The developed method divides droplets into four main groups based on their size and applies the corresponding algorithm to segment each group. These algorithms generate binary images of droplets based on both their geometrical and intensity properties. The information related to droplets evolution during time including mean radius and drops number per unit area are then extracted from the binary images. The developed image processing algorithm is verified using manual detection and applied to two different sets of images corresponding to two kinds of pillared surfaces.

Keywords: Dropwise condensation, textured surface, image processing, watershed.

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Authors: Waqar Shahid Qureshi, Abu-Baqar Nisar Alvi

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Vision based tracking problem is solved through a combination of optical flow, MACH filter and log r-θ mapping. Optical flow is used for detecting regions of movement in video frames acquired under variable lighting conditions. The region of movement is segmented and then searched for the target. A template is used for target recognition on the segmented regions for detecting the region of interest. The template is trained offline on a sequence of target images that are created using the MACH filter and log r-θ mapping. The template is applied on areas of movement in successive frames and strong correlation is seen for in-class targets. Correlation peaks above a certain threshold indicate the presence of target and the target is tracked over successive frames.

Keywords: Correlation filters, optical flow, log r-θ mapping.

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Authors: S. K. Khosrowshahi, E. Güler

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This study investigates the reinforcement function of geosynthetics on the shear strength and strain profile of sand. Conducting a series of simple shear tests, the shearing behavior of the samples under static and cyclic loads was evaluated. Three different types of geosynthetics including geotextile and geonets were used as the reinforcement materials. An image processing analysis based on the optical flow method was performed to measure the lateral displacements and estimate the shear strains. It is shown that besides improving the shear strength, the geosynthetic reinforcement leads a remarkable reduction on the shear strains. The improved layer reduces the required thickness of the soil layer to resist against shear stresses. Consequently, the geosynthetic reinforcement can be considered as a proper approach for the sustainable designs, especially in the projects with huge amount of geotechnical applications like subgrade of the pavements, roadways, and railways.

Keywords: Image processing, soil reinforcement, geosynthetics, simple shear test, shear strain profile.

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Authors: Permphan Dharmasaroja

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Mutations of the telomeric copy of the survival motor neuron 1 (SMN1) gene cause spinal muscular atrophy. A deletion of the Eef1a2 gene leads to lower motor neuron degeneration in wasted mice. Indirect evidences have been shown that the eEF1A protein family may interact with SMN, and our previous study showed that abnormalities of neuromuscular junctions in wasted mice were similar to those of Smn mutant mice. To determine potential colocalization between SMN and tissue-specific translation elongation factor 1A2 (eEF1A2), an immunochemical analysis of HeLa cells transfected with the plasmid pcDNA3.1(+)C-hEEF1A2- myc and a new quantitative test of colocalization by intensity correlation analysis (ICA) was used to explore the association of SMN and eEF1A2. Here the results showed that eEF1A2 redistributed from the cytoplasm to the nucleus in response to serum and epidermal growth factor. In the cytoplasm, compelling evidence showed that staining for myc-tagged eEF1A2 varied in synchrony with that for SMN, consistent with the formation of a SMN-eEF1A2 complex in the cytoplasm of HeLa cells. These findings suggest that eEF1A2 may colocalize with SMN in the cytoplasm and may be a component of the SMN complex. However, the limitation of the ICA method is an inability to resolve colocalization in components of small organelles such as the nucleus.

Keywords: Intensity correlation analysis, intensity correlation quotient.

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Authors: Jang-Hee Yoo, Doosung Hwang, Jong-Wook Han, Ki-Young Moon

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The ability to recognize humans and their activities by computer vision is a very important task, with many potential application. Study of human motion analysis is related to several research areas of computer vision such as the motion capture, detection, tracking and segmentation of people. In this paper, we describe a segmentation method for extracting human body contour in modified HLS color space. To estimate a background, the modified HLS color space is proposed, and the background features are estimated by using the HLS color components. Here, the large amount of human dataset, which was collected from DV cameras, is pre-processed. The human body and its contour is successfully extracted from the image sequences.

Keywords: Background Subtraction, Human Silhouette Extraction, HLS Color Space, and Object Segmentation

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Authors: Haji Ali Haji, Hussein Suleman, Ulrike Rivett

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This paper presents the design, development and evaluation of an application prototype developed to support tuberculosis (TB) patients’ treatment adherence. The system makes use of graphics and voice reminders as opposed to text messaging to encourage patients to follow their medication routine. To evaluate the effect of the prototype applications, participants were given mobile phones on which the reminder system was installed. Thirty-eight people, including TB health workers and patients from Zanzibar, Tanzania, participated in the evaluation exercises. The results indicate that the participants found the mobile image-based application is useful to support TB treatment. All participants understood and interpreted the intended meaning of every image correctly. The study findings revealed that the use of a mobile visualbased application may have potential benefit to support TB patients (both literate and illiterate) in their treatment processes.

Keywords: ICT4D, mobile technology, tuberculosis, visualbased reminder.

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Authors: Dariush Semnani, Hossein Ghayoor

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In this paper a novel method was presented for evaluating the fabric pills using digital image processing techniques. This work provides a novel technique for detecting pills and also measuring their heights, surfaces and volumes. Surely, measuring the intensity of defects by human vision is an inaccurate method for quality control; as a result, this problem became a motivation for employing digital image processing techniques for detection of defects of fabric surface. In the former works, the systems were just limited to measuring of the surface of defects, but in the presented method the height and the volume of defects were also measured, which leads to a more accurate quality control. An algorithm was developed to first, find pills and then measure their average intensity by using three criteria of height, surface and volume. The results showed a meaningful relation between the number of rotations and the quality of pilled fabrics.

Keywords: 3D analysis, computer vision, fabric, pile, surface evaluation

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Authors: Alessandro Preziosi, Antonio Prioletti, Luca Castangia

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Deformable part models achieve high precision in pedestrian recognition, but all publicly available implementations are too slow for real-time applications. We implemented a deformable part model algorithm fast enough for real-time use by exploiting information about the camera position and orientation. This implementation is both faster and more precise than alternative DPM implementations. These results are obtained by computing convolutions in the frequency domain and using lookup tables to speed up feature computation. This approach is almost an order of magnitude faster than the reference DPM implementation, with no loss in precision. Knowing the position of the camera with respect to horizon it is also possible prune many hypotheses based on their size and location. The range of acceptable sizes and positions is set by looking at the statistical distribution of bounding boxes in labelled images. With this approach it is not needed to compute the entire feature pyramid: for example higher resolution features are only needed near the horizon. This results in an increase in mean average precision of 5% and an increase in speed by a factor of two. Furthermore, to reduce misdetections involving small pedestrians near the horizon, input images are supersampled near the horizon. Supersampling the image at 1.5 times the original scale, results in an increase in precision of about 4%. The implementation was tested against the public KITTI dataset, obtaining an 8% improvement in mean average precision over the best performing DPM-based method. By allowing for a small loss in precision computational time can be easily brought down to our target of 100ms per image, reaching a solution that is faster and still more precise than all publicly available DPM implementations.

Keywords: Autonomous vehicles, deformable part model, dpm, pedestrian recognition.

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Authors: Songtao Wu, Yuesheng Zhu, Ziqiang Sun

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Region covariance (RC) descriptor is an effective and efficient feature for visual tracking. Current RC-based tracking algorithms use the whole RC matrix to track the target in video directly. However, there exist some issues for these whole RCbased algorithms. If some features are contaminated, the whole RC will become unreliable, which results in lost object-tracking. In addition, if some features are very discriminative to the background, other features are still processed and thus reduce the efficiency. In this paper a new robust tracking method is proposed, in which the whole RC matrix is decomposed into several low rank matrices. Those matrices are dynamically chosen and processed so as to achieve a good tradeoff between discriminability and complexity. Experimental results have shown that our method is more robust to complex environment changes, especially either when occlusion happens or when the background is similar to the target compared to other RC-based methods.

Keywords: Visual tracking, region covariance descriptor, lowrankregion covariance

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