Статьи журнала - International Journal of Image, Graphics and Signal Processing
Все статьи: 1092
Статья научная
Crankshaft is the most important and hard manufacture part in engine and diesel, its deformation contains multiple arcs, and its straightening technology is a complex NP-hard determination problem. The crankshaft straightening process was analyzed, and some effects from multiple arcs crankshaft straightening was investigated. The mathematics model of multiple arcs straightening technology determination system was built based on graph theory principle. And the Influence of Bauschinger effect was considered, the online straightening technology determination calculation algorithm based on weighted evaluation function was proposed. The experiments about straightening crankshaft were performed in YH40-160 straightening press to evaluate two different weighted set C1={2.5,1.5,1,1.2,3.5} and C2={1,1,1,1,1}, and the results expressed that the online straightening technology determination calculation algorithm was advantage to crankshaft straightening process, and C1 had better influence to qualities and efficiencies than C2.
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Crop Type Classification Based on Clonal Selection Algorithm for High Resolution Satellite Image
Статья научная
This paper presents a hierarchical clustering algorithm for crop type classification problem using multi-spectral satellite image. In unsupervised techniques, the automatic generation of clusters and its centers is not exploited to their full potential. Hence, a hierarchical clustering algorithm is proposed which uses splitting and merging techniques. Initially, the splitting method is used to search for the best possible number of clusters and its centers using non-parametric technique i.e., clonal selection method. Using these clusters, a merging method is used to group the data points based on a parametric method (K-means algorithm). The performance of the proposed hierarchical clustering algorithm is compared with two unsupervised algorithms (K-means and Self-Organizing Map) that are available in the literature. A performance comparison of the proposed algorithm with the conventional algorithms is presented. From the results obtained, we conclude that the proposed hierarchical clustering algorithm is more accurate.
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Статья научная
In this paper we propose a method for automatic detection of crowd escape behaviour. Motion features are extracted by optical flow using Lucas-Kanade derivative of Gaussian method (LKDoG) followed by robust probabilistic weighted feature pooling operation. Probabilistic feature polling chooses the most descriptive features in the sub-block and summarizes the joint representation of the selected features by Probabilistic Weighted Optical Flow Magnitude Histogram (PWOFMH) and Probabilistic Weighted Optical Flow Direction Histogram (PWOFDH). One class Extreme Learning Machine (OC-ELM) is used to train and test our proposed algorithm. The accuracy of our proposed method is evaluated on UMN, PETS 2009 and AVANUE datasets and correlations with the best in class techniques approves the upsides of our proposed method.
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Curvelet transform for efficient static texture classification and image fusion
Статья научная
Wavelet Transform (WT) has widely been used in signal processing. WT breaks a signal into its wavelets that are scaled and shifted versions of given signal. Thus wavelets are able represent graphical objects. The irregular shape and compact support of wavelets made them ideal for analyzing non-stationary signals. They are useful in analysis in both temporal and frequency domains. In contract, the Fourier transform provides information in frequency domain lacking in information in time domain. Thus wavelets became popular for signal processing and image processing applications. Nevertheless, wavelets suffer from a drawback as they cannot effectively represent images at different angles and different scales. To overcome this problem, of late, Curvelet Transform (CT) came into existence. CT is nothing but the higher dimensional generalization of WT which can effectively represent images at different angles and different scales. In this paper we proposed a CT method that is used to represent textures and classify them. The methodology used in this paper has an underlying approach that exploits statistical features of curvelets that resulted in curvelet decomposition. We built a prototype application using MATLAB to demonstrate proof of the concept.
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Curvilinear Tracing Approach for Extracting Kannada Word Sign Symbol from Sign Video
Статья научная
Gesture based communications are utilized as a primary method of correspondence, however, the differing qualities in the sign image portrayal limit its use to district bound. There is a tremendous assorted quality in the sign image portrayal from one nation to another, one state to another. In India, there is distinctive gesture-based communication watched for each state locale. It is henceforth exceptionally troublesome for one area individual to convey to other utilizing a signature image. This paper proposes a curvilinear tracing approach for the shape portrayal of Kannada communication via gestures acknowledgment. To build up this approach, a dataset is consequently made with all Swaragalu, Vyanjanagalu, Materials and Numbers in Kannada dialect. The arrangement of the dataset is framed by characterizing a vocabulary dataset for various sign images utilized as a part of regular interfacing. In the portrayal of gesture-based communication for acknowledgment, edge elements of hand areas are thought to be an ideal element portrayal of communication through signing. In the preparing of gesture-based communication, the agent includes assumes a critical part in arrangement execution. For the developed approach of sign language detection, where a single significant transformation is carried out, a word level detection is then performed. To represent the processing efficiency, a set of cue symbols is used for formulating a word. This word symbols are then processed to evaluate the performance for sign language detection. Word processing is carried out as a recursive process of a single cue symbol representation, where each frame data are processed for a curvilinear shape feature. The frame data are extracted based on the frame reading rate and multiple frames are processed in successive format to extract the region of interest. A system outline to process the video data and to give an optimal frame processing for sign recognition a word level process is performed.
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Cut off Your Arm: A Medium-Cost System for Integrating a 3D Object with a Real Actor
Статья научная
In the film industry, many tricks have been employed using the integration of a 3D object with a real actor. Usually, attaching a 3D object with a real actor is a costly process because of the usage of an expensive motion capture system. This paper presents a system using a medium-cost motion capture system and a chroma-keying technique for generating a video footage of an actor with an integrated 3D object (e.g. amputated arm). The result of the proposed system shows the attaching process of different 3D objects with a real actor who is combined with a new background scene in the same viewpoint.
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Cyclic Analysis of Phonocardiogram Signals
Статья научная
Acoustic vibrations of the heart in time domain correspond to phonocardiogram (PCG) signal. A PCG signal, in the healthy case, consists of two fundamental sounds s1 and s2 produced by the mechanical functioning of the heart. Abnormalities in the heart valves correspond to other cardiac sounds than s1 and s2. This makes PCG signal a valuable tool related to the track of heart diseases. Actually, the characterization and the analysis of PCG signals is being a fertile area of study and investigation. However, most of the topics which treated this area of research focused only on time-frequency analysis, without exploiting the periodic character of PCG signal due to the limitations of the PCG modeling. In this work, we propose a coherent mathematical model for PCG signals based on cyclostationarity and Gabor kernel. The motivation behind is to define a framework, utilizing cyclic statistic due to noise robustness, for a full description of PCG signals, which leads to an easy and efficient early identification of certain heart abnormalities. The validation of the proposed model and its capacity to reflect the heart functioning is tested over synthetic and real data sets.
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Cyclic Sparse Greedy Deconvolution
Статья научная
The purpose of this study is to introduce the concept of cyclic sparsity or cyclosparsity in deconvolution framework for signals that are jointly sparse and cyclostationary. Indeed, all related works in this area exploit only one property, either sparsity or cyclostationarity and never both properties together. Although, the key feature of the cyclosparsity concept is that it gathers both properties to better characterize this kind of signals. We show that deconvolution based on cyclic sparsity increases the performances and reduces significantly the computation cost. Finally, we use simulations to investigate the behavior in deconvolution framework of the algorithms MP, OMP and theirs respective extensions to cyclic sparsity context, Cyclo-MP and Cyclo-OMP.
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Cyclic analysis of extra heart sounds: gauss kernel based model
Статья научная
Phonocardiograms (PCG) Phonocardiograms (PCG) are recordings of the acoustic waves produced by the mechanical action of the cardiac system. This makes PCG an effective method for tracking the progress of heart diseases. A PCG signal, in the healthy case, consists of two fundamental sounds s1 and s2. These two elements are derived from the mechanical functioning of the heart. A triple rhythm in diastole is called a gallop and results from the presence of a heart sound s3, s4 or both. An Extra Heart Sound (EHS) may not be a sign of disease. However, in some situations it is an important sign of disease, which, if detected early, could save lives. The major aim of this study is to propose cyclostationary and Gabor kernel based mathematical model for extra heart sounds. The ambition behind it is to present a framework, making use of cyclic statistics for robustness to low SNR conditions, which allow the detection of EHS s3 and s4 and hence the early identification of some heart diseases. For this reason, the proposed model is compared with the one of normal PCG signal [17] in order to set up the differences allowing the early detection of EHS. Lastly, this research is proved on experimental data sets.
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Cyclone Prediction from Remote Sensing Images Using Hybrid Deep Learning Approach Based on AlexNet
Статья научная
With the world feeling the negative effects of climate change, detecting and predicting severe weather occurrences is now an extremely vital and difficult task. Cyclones, a type of extreme weather phenomenon, have increased in frequency and severity in Indian subcontinent regions over the past few years. It is estimated that around three cyclones struck the east coastal region of India, causing substantial damage to people, farms, and infrastructure. Predicting cyclones ahead of time is crucial for avoiding or significantly lowering the devastating effects. The traditional methodologies employed numerical equations that demand strong experience and greater skills to obtain satisfactory prediction accuracy. Problems with domain expertise and the probability of human mistakes can be avoided with the help of Deep Learning (DL). As a result, in this work, we sought to forecast cyclone intensity using a Convolution Neural Network (CNN), a basic DL structure. To increase the CNN model's architecture and effectiveness, hybrid models such as Convolution Neural Network & Long short-term memory (CNN-LSTM) and AlexNet & Gated recurrent units (AlexNet-GRU) are developed. Data from the INSAT 3D satellite was utilized to develop and evaluate the DL model. We processed both the training and testing dataset and increase the training dataset using augmentation. All three DL models are tested and compared, the AlexNet-GRU model outperforms on the test data, with a relatively high accuracy of 93.35% and a low mean square error (MSE) of 215.
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DNA 3D Self-assembly Algorithmic Model to Solve Maximum Clique Problem
Статья научная
Self-assembly reveals the essence of DNA computing, DNA self-assembly is thought to be the best way to make DNA computing transform into computer chip. This paper introduce a method of DNA 3D self-assembly algorithm to solve the Maximum Clique Problem. Firstly, we introduce a non-deterministic algorithm. Then, according to the algorithm we design the types of DNA tiles which the computation needs. Lastly, we demonstrate the self-assembly process and the experimental methods which could get the final result. The computation time is linear, and the number of the distinctive tile types is constant.
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Статья научная
The main disadvantage of the classical direct torque control is high torque and flux ripples. This is due to hysteresis comparators suffer from a variable switching frequency and a high torque ripple. Besides, a hybrid strategy; Direct Torque Control with Space Vector Modulation (DTC -SVM) is established using Interval Type-2 Fuzzy Logic Controller (IT2FLC) for enhancing control performance parameters to reducing torque and flux ripple. In this work, a IT2FLC is applied to the DTC-SVM of Double Stator Induction Machine (DSIM). Simulation results are shown to present the robustness and efficiency of the recommended control strategy.
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DYNAMIC JOINT LOAD TRANSFER EFFICIENCY OF RIGID PAVEMENT
Статья научная
the mechanistic analysis presented in this paper is only the beginning of new approach for understanding the real joint load transfer capability on airport and highway concrete pavements. It gives up the two major assumptions those have been popularly adopted by hundreds of published papers: the load is transferred under a wheel with zero speed and with fixed position. The real load transfer in field is always under wheels with non-zero speed and with varied position at any moment. The objective of this study focuses on quantifying the dynamic effects of a moving wheel while it is crossing a joint on a pavement. The analysis is conducted using a model of two-slab system on Kelvin foundation under a moving wheel with variable speed v, different pavement damping Cs, foundation reaction modulus k and foundation damping Ck. The dynamic joint load transfer efficiency is temporarily and empirically defined by the peak strain ratio LTE(S) on the two sides of a joint. The primary findings include: (1) The higher speed of a moving wheel leads to the higher LTE(S);(2) The larger the pavement damping Cs leads to the higher LTE(S);(3) The numerical ratio c(=LTE(S)dynamic/ LTE(S)static) varies in the range 1 to 2 mainly depending on speed v and damping Cs;(4) The LTE(S)dynamic is not sensitive to foundation reaction modulus k and foundation damping Ck. Further researches are needed for appropriate applications of the new model in practice.
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Data Content Weighing for Subjective versus Objective Picture Quality Assessment of Natural Pictures
Статья научная
Estimating the visual quality of picture is a real challenge for various picture and video frame applications. The aim is to evaluate the quality of picture automatically in both subjective (human visual frame work) and objectively. The quality of picture is evaluated by comparing precision and closeness of a picture with reference or error free picture. The quality estimation can be done to achieve consistency in desired quality of picture with help of modeling remarkable physiological, psycho visual components framework and picture fidelity measure methods. In this article, the picture quality is evaluated by analyzing loss of picture information of the distortion system using differing noise models and examine the relationship between picture data, visual quality and error metric. The quality of picture & video frame assessment is really important that, every human can judge the visual quality of natural picture. The subjective quality of picture is assessed by using structural similarity metric, objective quality of picture is computed by root means squared error, mean squared error and peak signal to noise ratio and data content in picture is weighted through entropy.
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Статья научная
Deep learning (DL) architectures are becoming increasingly popular in modern traffic systems and self-driven vehicles owing to their high efficiency and accuracy. Emerging technological advancements and the availability of large databases have made a favorable impact on such improvements. In this study, we present a traffic sign recognition system based on novel DL architectures, trained and tested on a locally collected traffic sign database. Our approach includes two stages; traffic sign identification from live video feed, and classification of each sign. The sign identification model was implemented with YOLO architecture and the classification model was implemented with Xception architecture. The input video feed for these models were collected using dashboard camera recordings. The classification model has been trained with the German Traffic Sign Recognition Benchmark dataset as well for comparison. Final accuracy of classification for the local dataset was 96.05% while the standard dataset has given an accuracy of 92.11%. The final model is a combination of the detection and classification algorithms and it is able to successfully detect and classify traffic signs from an input video feed within an average detection time of 4.5fps
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Deep-ShrimpNet fostered Lung Cancer Classification from CT Images
Статья научная
Lung cancer affects the majority of people, due to genetic changes in lung tissues. Several existing methods on lung cancer detection are utilized with machine learning, but it does not accurately classify the lung cancer and also it takes high computation time. To overwhelm these issues, Deep-ShrimpNet fostered Lung cancer classification from CT images (LCC-Deep-ShrimpNet) is proposed. Initially, the input lung CT images are taken from IQ-OTH/NCCD Lung Cancer Dataset. Then the input lung CT images are pre-processed using Kernel co-relation method. Then these pre-processed lung CT images are given to Bayesian fuzzy clustering for extracting lung nodule region. Then the extracted lung nodule region is given into Deep-ShrimpNet classifier for representing features and classifying the lung CT images as normal (Healthy), Benign, and Malignant. The proposed LCC-Deep-ShrimpNet method is activated in python. The performance of the proposed LCC-Deep-ShrimpNet method attains 26.26%, 16.9%, 12.67%, 21.52% and 24.05% high accuracy, 68.86%, 59.57%, 57%, 62.72% and 65.69% low error rate and 60.76%, 53.67%, 68.58%, 59% and 56.61% low computation time compared with the existing methods.
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Denoising Self-Distillation Masked Autoencoder for Self-Supervised Learning
Статья научная
Self-supervised learning has emerged as an effective paradigm for learning universal feature representations from vast amounts of unlabeled data. It’s remarkable success in recent years has been demonstrated in both natural language processing and computer vision domains. Serving as a cornerstone of the development of large-scale models, self-supervised learning has propelled the advancement of machine intelligence to new heights. In this paper, we draw inspiration from Siamese Networks and Masked Autoencoders to propose a denoising self-distilling Masked Autoencoder model for Self-supervised learning. The model is composed of a Masked Autoencoder and a teacher network, which work together to restore input image blocks corrupted by random Gaussian noise. Our objective function incorporates both pixel-level loss and high-level feature loss, allowing the model to extract complex semantic features. We evaluated our proposed method on three benchmark datasets, namely Cifar-10, Cifar-100, and STL-10, and compared it with classical self-supervised learning techniques. The experimental results demonstrate that our pre-trained model achieves a slightly superior fine-tuning performance on the STL-10 dataset, surpassing MAE by 0.1%. Overall, our method yields comparable experimental results when compared to other masked image modeling methods. The rationale behind our designed architecture is validated through ablation experiments. Our proposed method can serve as a complementary technique within the existing series of self-supervised learning approaches for masked image modeling, with the potential to be applied to larger datasets.
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Denoising and Enhancement of Medical Images Using Wavelets in LabVIEW
Статья научная
In this paper, we have proposed a novel image enhancement technique based on M band wavelets. The conventional image enhancement algorithms opt for contrast enhancement using equalization techniques. Contrast enhancement is one of the most important issues in image enhancement techniques. High difference in luminance reflected from two adjacent surfaces results in a good contrast image which makes the object more distinguishable from other objects in the background. Many a times owing to over contrast, minute details of the images are lost; which cannot be tolerated for biomedical images. Moreover, they don't account for the noise embedded in the images. Also denoising using conventional filters result in blurring of images. The proposed algorithm not only denoises the image by retaining the high frequency edges, but also increases the contrast and generates a high resolution image. Various parameters like MSE and PSNR are been taken into account for comparison of enhanced images generated from the proposed algorithm with that of the conventional techniques.
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