Статьи журнала - International Journal of Image, Graphics and Signal Processing
Все статьи: 1092
Comparative Study of SMART and FMCDM Methods in Smartphone Selection Decision Support System
Статья научная
Smartphones are one of the communication technology tools currently used by both children and the elderly, so that interest in shopping for smartphones in Indonesia is increasing. The variety of smartphone brands makes buyers confused about which smartphone to buy. This research can help buyers to choose a smartphone to buy and help sellers provide recommendations. This study applies the Simple Multi Attribute Rating Technique (SMART) and Fuzzy Multi Criteria Decision Making (FMCDM) methods for the decision making process for smartphone selection. The purpose of this study is to apply and analyze the comparison of the SMART method and the FMCDM method in the Smartphone Selection Decision Support System. The study compared: the differences and similarities between the two methods, the results of the selection process for the two methods, and calculating the value of the sensitivity analysis of the selection results so that the best method could be determined. The criteria used: price, screen size, battery capacity, operating system, RAM, camera, and smartphone brand. The comparison results show that there are differences between the standard for determining the results, while the similarities in the calculation results, the smartphone recommended to buy is the same, namely the Asus Zenfone 2 Laser ZE500KG (16 GB) smartphone. Measurement of the accuracy of the results of the two methods uses sensitivity analysis values. It can be concluded that the better method is the FMCDM method because it has a smaller average sensitivity value than the SMART method, namely 0.2795 <0.3906.
Бесплатно
Comparative account of robust h∞ techniques for missile autopilot design
Статья научная
H∞ control techniques are prominently used as solutions for flight control problems. From the literature, a variety of techniques is reported in the last three decades with specific merits and demerits, which, when applied to multiple flight control scenarios, showing trade off in terms of performance and robustness. However, all these methods possess superior performance when compared with that of classical approaches. In this paper an attempt is made to provide an insight into the requirements and criticalities in the design of missile autopilot. This paper introduces some of the significant H∞ control techniques like H∞ mixed sensitivity, H∞ loop shaping and μ synthesis, with specific emphasis on analysis of autopilot design. A comparative account of modern control methods is presented on the basis of system performance and robustness, which will be helpful in the selection of the appropriate design method for specific application.
Бесплатно
Статья научная
An enormous amount of information in the form of image and video are dispersed all over the world like any other data therefore, retrieval of a query image from a large database of images is an important undertaking in the area of computer vision and image processing. The traditional text-based approaches for searching images are slow and inefficient. Content-based image retrieval (CBIR) provides the solution for efficient retrieval of the image from these image databases. In this paper, an efficient CBIR system is proposed using various colour and texture features. Colour features such as Colour Moments and HSV Histogram and Texture Features like Local Binary Patterns (LBP) are used. Various distance metrics are analysed for retrieval and their performance is compared to get the best distance metric for better retrieval performance. From the experimental analyses on benchmark (WANG) database, it is observed that the City block distance performs consistently encouraging from other measures. Also this paper has introduced the combination of HSV and LBP histogram and evaluated the retrieval performance. The obtained results are very promising than other variants of colour and texture features.
Бесплатно
Comparative analysis of univariate forecasting techniques for industrial natural gas consumption
Статья научная
This paper seeks to evaluate the appropriateness of various univariate forecasting techniques for providing accurate and statistically significant forecasts for manufacturing industries using natural gas. The term "univariate time series" refers to a time series that consists of single observation recorded sequentially over an equal time interval. A forecasting technique to predict natural gas requirement is an important aspect of an organization that uses natural gas in form of input fuel as it will help to predict future consumption of organization.We report the results from the seven most competitive techniques. Special consideration is given to the ability of these techniques to provide forecasts which outperforms the Naive method. Naïve method, Drift method, Simple Exponential Smoothing (SES), Holt method, ETS(Error, trend, seasonal) method, ARIMA, and Neural Network (NN) have been studied and compared.Forecasting accuracy measures used for performance checking are MSE, RMSE, and MAPE. Comparison of forecasting performance shows that ARIMA model gives a better performance.
Бесплатно
Статья научная
The proposed work gives a comparative study of three different classifiers, namely, decision tree (DT), support vector machine (SVM) and artificial neural network (ANN) for variety classification of certain thin and thick fabric images. The textural features are used in the work. The overall classification rates of 85%, 86% and 94% are obtained for DT, SVM and ANN classifiers respectively. Better results for varieties of thick fabric images are obtained compared to the varieties of thin fabric images. Further, the ANN classifier has given good classification rate than DT and SVM classifiers. But, it is also observed that, DT classifier gives better results in case of varieties of thick fabric images. The work finds applications in apparel industry, cost estimation, setting the washing time, fashion design etc.
Бесплатно
Comparing Nonsubsampled Wavelet, Contourlet and Shearlet Transforms for Ultrasound Image Despeckling
Статья научная
Ultrasound images suffer of multiplicative noise named speckle. Bayesian shrinkage in transform domain is a well-known method based on finding threshold value to suppress the speckle noise. The main problem of applying Bayesian shrinkage is finding the optimum threshold value in appropriate transform domain. In this paper, we compare the performance of adaptive Bayesian thresholding when nonsubsampled Wavelet, Contourlet and Shearlet transforms are used. We processed two synthetic test images and three original ultrasound images as well to demonstrate the efficiency of the designed filters. In order to compare the performance of Bayesian shrinkage when employing the three mentioned transform domain, we used peak signal to noise ratio (PSNR), mean square error (MSE), and structural similarity (SSIM) as the full-reference (FR) objective criteria parameters and noise variance (NV), mean square difference (MSD), and equivalent number of looks (ENL) as the no-reference (NR) objective criteria parameters.
Бесплатно
Comparison based Analysis of Different FFT Architectures
Статья научная
A time-domain sequence is converted into an equivalent frequency-domain sequence using discrete Fourier transform. The reverse operation converts a frequency-domain sequence into an equivalent time-domain sequence using inverse discrete Fourier transform. Based on the discrete Fourier transform. Fast Fourier transform (FFT) is an effective algorithm with few computations. FFT is used in everything from broadband to 3G and Digital TV to radio LAN's. To improve its architecture different efficient algorithms are developed. This paper gives an overview of the work done by a different FFT processor previously. The comparison of different architecture is also discussed.
Бесплатно
Статья научная
This article considers the peculiarities of using circular orthogonal polarization basis for measuring the parameters of an electromagnetic wave. In particular, the angle of inclination of the major axis of the polarization ellipse and the ellipticity coefficient are among measuring parameters. The main expressions for calculation of field parameters in circular and linear orthogonal polarization basis are developed and analyzed. The advantages of using the ring as a measuring antenna in comparison with symmetrical vibrators of the turnstile antenna are substantiated. The expressions obtained in the article for calculating the measurement errors of polarization parameters in a linear orthogonal polarization basis illustrate the multifactorial dependence of the measurement accuracy on the angular and amplitude parameters. In contrast to the linear polarization basis, in case of circular basis, the inclination angle of the polarization ellipse axis can be found by direct measurements of the phase shift, and the accuracy of measuring the ellipticity coefficient is affected only by the error of measuring the ratio of voltage amplitudes, which are proportional to the modules of the field strength vectors of the left and right directions of the circular polarization rotation. This provides better potential accuracy of measurement for the electromagnetic wave parameters when using circular polarization antennas and, correspondingly, more reasonable analysis in the circular orthogonal polarization basis.
Бесплатно
Comparison of Mamdani Fuzzy Inference System for Multiple Membership Functions
Статья научная
Contrast enhancement is an emerging method for image enhancement of specific application to analyze the images clearer for interpretation and analysis in the spatial domain. The goal of Contrast enhancement is to serve an input image so that resultant image is more suited to the particular application. Images with good steps of grays between black and white are commonly the best images for the aim of human perception, a novel approach is proposed in this paper based on fuzzy logic. Mamdani fuzzy inference system models are developed to enhance the contrast of images based on different membership functions (MFs).
Бесплатно
Comparison of Wireless MIMO System Under Alamouti's Scheme and Maximum Ratio Combining Technique
Статья научная
In wireless communication fading of channels is the serious cause of the received degraded signals. The effect of fading can be minimized by using various time and space domain techniques. However, space domain techniques are preferred over the others due to its advantages. In this paper, comparison of the wireless MIMO system under Almouti's and maximum ratio combining schemes is presented. Basic idea in these schemes is to transmit and receive more than one copy of the original signals. Using two transmitter antennas and one receiver antenna, the scheme provides the nearly same diversity order as the maximal-ratio receiver combining (MRRC) with one transmitter antenna, and two receiver antennas. Results for one transmitter and four receivers under MRRC is also presented and compared. Finally, results are presented while varying the average transmitted power.
Бесплатно
Статья научная
There are many methods proposed for Back-ground Subtraction algorithm in past years. Background subtraction algorithm is widely used for real time moving object detection in video surveillance system. In this paper we have studied and implemented different types of meth-ods used for segmentation in Background subtraction algo-rithm with static camera. This paper gives good under-standing about procedure to obtain foreground using exist-ing common methods of Background Subtraction, their complexity, utility and also provide basics which will useful to improve performance in the future . First, we have explained the basic steps and procedure used in vision based moving object detection. Then, we have debriefed the common methods of background subtraction like Sim-ple method, statistical methods like Mean and Median filter, Frame Differencing and W4 System method, Running Gaussian Average and Gaussian Mixture Model and last is Eigenbackground Model. After that we have implemented all the above techniques on MATLAB software and show some experimental results for the same and compare them in terms of speed and complexity criteria.
Бесплатно
Compressive Sensing Based Multiple Watermarking Technique for Biometric Template Protection
Статья научная
Biometric authentication system is having several security issues. Two security issues are template protection at system database and at communication channel between system database and matcher subsystem of biometric system. In this paper, two level watermarking technique based on CS Theory framework in wavelet domain is proposed for security and authentication of biometric template at these two vulnerable points. In the proposed technique, generate sparse measurement information of fingerprint and iris biometric template using CS theory framework. This sparse measurement information is used as secure watermark information which is embedding into a face image of same individual for generation of multimodal biometric template. Sparse watermark information is computed using Discrete Wavelet transform (DWT) and random seed. The proposed watermarking technique not only provide protection to biometric templates, it also gives computational security against spoofing attack because of it is difficult for imposter to get three secure biometric template information where two encoded biometric template is embed in term of sparse measurement information into third biometric template. Similarity value between original watermark image and reconstructed watermark image is the measuring factor for identification and authentication. The experimental results show that the technique is robust against various attacks.
Бесплатно
Статья научная
Reconstruction of a sparse signal from fewer observations require compressive sensing based recovery algorithm for saving memory storage. Various sparse recovery techniques including l_1 minimization, greedy pursuit approaches and non-convex optimization requires sparsity to be known in advance. This article presents the generalized adaptive orthogonal matching pursuit with forward-backward movement under the cumulative coherence property; which removes the need of knowledge of sparsity prior to implementation. In this technique, the forward step increases the size of support set and backward step eliminates the misidentified elements. It selects multiple indices on the basis of maximum correlation by forward-backward movement. The size of backward step is kept smaller than the forward one. These forward-backward steps then iterate and increment through the algorithm adaptively and terminate with stopping condition to ensure the identification of significant components. Recovery performance of proposed algorithm is demonstrated via simulation results including reconstruction of sparse signals in noisy and noise free environment. The algorithm has major advantage that it does not require the knowledge of sparsity in advance in contrast to the earlier reconstruction techniques. The evaluation and comparative analysis of result shows that algorithm leads to the increment in recovery performance and efficiency considerably.
Бесплатно
Computer A ided Detection of Tumours in Mammograms
Статья научная
Mammography is a special CT scan technique, which uses X-rays and high-resolution film to detect breast tumors efficiently. Mammography is used only in breast tumor detection, and images help physicians to detect diseases due to cells normal growth. Mammography is an effective imaging modality for early breast cancer abnormalities detection. Computer aided diagnosis helps the radiologists to detect abnormalities earlier than traditional procedures. In this paper, an automated mammogram classification method is presented. Symlet, singular value decomposition and weighted histograms are used for feature extraction in mammograms. The extracted features are classified using naïve bayes, random forest and neural network algorithms.
Бесплатно
Статья научная
In medical diagnosis, Artificial Intelligence (AI) has offered significant revolution, especially for cancers. Acute Myeloid Leukemia (AML) is a deadly blood cancer caused by the rapid growth of abnormal White Blood Cells (WBCs) in humans. Although AML classification is a popular area of research, existing detection methods utilize manual examination of microscopic blood samples, which includes high complexity and tedious. Therefore, this work presented a computerized deep learning model-based AML classification from peripheral blood stain images, which helps in earlier AML diagnosis. The processing steps followed in AML classification are Image Pre-processing, Localization of RoI (Region of Interest), Fusion-based Feature Extraction and Classification. First, the input image is pre-processed, which includes noise filtering, image resizing, and colour conversion. The noise in the image is filtered using normalized Gaussian filtering (NGF). Next, the image is resized into a standard size, and the RGB image is converted into CMYK colour space. Then, the RoI is identified using the Image Moment Localization (IML) technique. Next, the valuable multi-level dense features are extracted using DenseSqueeze Network, and multi-scale features are extracted using Dilated Convolution Spatial Pyramid Pooling (Dilated CSPP). Both these extracted features are fused using the element-wise summation. Finally, the Softmax classifier is used in the last layer to classify the classes of AML and the loss in the network is optimized using the Improved Artificial Fish Swarm (Improved AFS) algorithm. The proposed work results in 99% of accuracy, 98.5% of precision and 98.9% of F-score by using the AML-Cytomorphology LMU dataset.
Бесплатно
Contact distribution function based clustering technique with self-organizing maps
Статья научная
Currently clustering techniques play a vital role in object recognition process. The clustering techniques are found to be efficient with neural networks. So, the present paper proposed a novel method for clustering the input objects with Self-Organizing Map (SOM). The proposed method considers the input object as a random closed set. The random set can be efficiently described with various features viz., volume fractions, co-variance and contact distributions etc. In the proposed method, the input object is described efficiently with spherical contact distribution. The proposed method is experimented with the leaf data set with 795 images. The performance of the proposed method is evaluated with various topologies of SOM and is measured with four measures viz., FNR, FPR, TPR and TNR. The results indicate the efficiency of the proposed method.
Бесплатно
Content based image retrieval using multi motif co-occurrence matrix
Статья научная
In this paper, two extended versions of motif co-occurrence matrices (MCM) are derived and concatenated for efficient content-based image retrieval (CBIR). This paper divides the image into 2 x 2 grids. Each 2 x 2 grid is replaced with two different Peano scan motif (PSM) indexes, one is initiated from top left most pixel and the other is initiated from bottom right most pixel. This transforms the entire image into two different images and co-occurrence matrices are derived on these two transformed images: the first one is named as “motif co-occurrence matrix initiated from top left most pixel (MCMTL)” and second one is named as “motif co-occurrence matrix initiated from bottom right most pixel (MCMBR)”. The proposed method concatenates the feature vectors of MCMTL and MCMBR and derives multi motif co-occurrence matrix (MMCM) features. This paper carried out investigation on image databases i.e. Corel-1k, Corel-10k, MIT-VisTex, Brodtaz, and CMU-PIE and the results are compared with other well-known CBIR methods. The results indicate the efficacy of the proposed MMCM than the other methods and especially on MCM [19] method.
Бесплатно
Content-based Search for Image Retrieval
Статья научная
In this paper, a content-based image retrieval approach is presented for effective searching. The proposed approach uses two or more types of query for accessing images, textual annotation associated with images and visual appearance, such as colour, texture and positional features of objects in sample images. One can first place a keyword-based query, and then the desired images are retrieved by visual content-based query. The proposed retrieval approach shows clear improvements over competing approaches in terms of retrieval accuracy and visual inspection using Corel gallery and WWW images.
Бесплатно
Contour Based Retrieval for Plant Species
Статья научная
Recognizing a plant in any huge vegetation is a tedious work for us. We recognize a plant on the basis of its size, leaves, flowers, fruits, etc. Leaf is a part of the plant which can be found on plants almost in all seasons and most of the time we have to recognize plants on the basis of its leaf. But when dealing with leaf of plant, it is important to consider the finer details of the contour representing the shape of the leaf. We are trying to build a system which has a database of leaves of different plants and given a leaf, we find out the plant to which it may belong. In this paper, we present the results of tangential angle approach used for retrieval. A database of around one thousand leaves of different plants has been created. Each leaf image is preprocessed to extract its boundary. Then tangential angle approach is applied which captures the angular details of the boundary of shape. We have done the testing for around 1000 leaves and on the basis of that recall, precision and error rate have been calculated to measure the effectiveness of the proposed method.
Бесплатно
Contrast Enhancement of Images through Skewness and Mode Based Bi-Histogram Equalization
Статья научная
In this paper, skewness and mode-based histogram equalization algorithm have been proposed for contrast enhancement of digital images. The present method gives a novel idea for histogram clipping and histogram bifurcation. The prior is done with the skewness value and the latter is done with help of mode values of the intensity level random data set. The pixel intensity levels are random and thus a stochastic approach has been used and found to yield improved figure of merits. The image histogram has been clipped with the help of a pre-assigned threshold value computed from skewness value to restrict the rate of over enhancement. The clipped histogram is subdivided into two parts, using the histogram subdivision limit which is calculated on the basis of the mode value of the image. Histogram of individual sub-image is equalized independently and then integrated to form the final enhanced image. The simulation results have shown that the proposed skewness and mode based bi-histogram equalization algorithm enhances the contrast of the image in a better manner compared with the other histogram equalization methods in terms of FSIM, PSIM, SFF, VSI, HaarPSI, and GMSD.
Бесплатно
