Статьи журнала - International Journal of Image, Graphics and Signal Processing
Все статьи: 1056
Статья научная
Concrete crane beam on a rock wall on a new structure used in underground building has become more common in recent year. But the concrete beam cracking problem always perplexes scientists and engineers. In order to solve this, the construction information inversion and feedback analysis method is applied. A beam section was taken as a prototype experiment. The temperature and construction data was collected to inverse some necessary thermal parameters. According to the characteristics of concrete temperature field, the basic accelerating genetic algorithm was improved. The improved accelerating genetic algorithm has the merits of high precision and fast calculation. With this algorithm, the calculation temperature and measured value are very close, which shows the method is efficiency. Then inversed parameters were applied in the feedback simulation. According to the simulation results, the proper temperature control method was suggested. By this way, the concrete temperature was controlled well and the beams appear no crack in recent two year. The successful application shows that the inversion and feedback analysis of concrete temperature field can reflect the factual performance of concrete and give important direction to engineering construction.
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Investigation of Wavelets for Representation and Compression of Skin Cancer Images
Статья научная
Wavelets play a key role in many applications like image representations and compression, which is a main issue in the process of reducing the size in bytes of a digital image file to store it in the memory and as well as to transmit. This paper presents image representation using various wavelet transforms. In the proposed method, the comparison between wavelets applied on an image are considered by counting the number of approximation coefficients retained for the representation of images and comparative analysis of the standard wavelets available is presented. This paper mainly aims at the type of the wavelet which retains less number of approximation coefficients for representing skin cancer image and gives the reduced compressed file size by considering the various parameters like Peak Signal to Noise Ratio (PSNR), Mean Square Error (MSE), Structural Similarity Index Measure (SSIM) and Compression Efficiency.
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Iris Biometric Authentication used for Security Systems
Статья научная
Pupil detection and iris localisation using scanning method and feature extraction is performed with five level decomposition techniques, with these two proposed algorithm we could achieve efficient and fast person authentication in biometric security systems. Statistical performance evaluation is also performed using parameters False acceptance rate (FAR), False rejection rate (FRR), Correct recognition rate (CRR), Equal error rate (EER), Match ratio etc, using CASIA database.
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Статья научная
Classification task on the human facial attribute is hard because of the similarities in between classes. For example, emotion classification and age estimation are two important applications. There are very little changes between the different emotions of a person and a different person has a different way of expressing the same emotion. Same for age classification. There is little difference between consecutive ages. Another problem is the image resolution. Small images contain less information and large image requires a large model and lots of data to train properly. To solve both of these problems this work proposes using transfer learning on a pre-trained model combining a custom loss function called Island Loss to reduce the intra-class variation and increase the inter-class variation. The experiments have shown impressive results on both of the application with this method and achieved higher accuracies compared to previous methods on several benchmark datasets.
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JPEG Image Steganography based on Coefficients Selection and Partition
Статья научная
In this paper, we propose a novel JPEG image Steganography algorithm based on partition schemes on image coefficient values. Our method selects the AC and DC coefficients of a JPEG image according to a channel selection method and then identifies appropriate coefficients to store the secret data-bits. As opposed to other reported works, in our algorithm each selected coefficient can store a variable number of data-bits that are decided using the concept called ‘Partition Scheme’. Experimental results indicate the suitability of the proposed algorithm as compared to other existing methods.
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Kannada Language Parameters for Speaker Identification with The Constraint of Limited Data
Статья научная
In this paper we demonstrate the impact of language parameter variability on mono, cross and multi-lingual speaker identification under limited data condition. The languages considered for the study are English, Hindi and Kannada. The speaker specific features are extracted using multi-taper mel-frequency cepstral coefficients (MFCC) and speaker models are built using Gaussian mixture model (GMM)-universal background model (UBM). The sine-weighted cepstrum estimators (SWCE) with 6 tapers are considered for multi-taper MFCC feature extraction. The mono and cross-lingual experimental results show that the performance of speaker identification trained and/or tested with Kannada language is decreased as compared to other languages. It was observed that a database free from ottakshara, arka and anukaranavyayagalu results a good performance and is almost equal to other languages.
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Lattice Boltzmann implementation for Fluids Flow Simulation in Porous Media
Статья научная
In this paper, the lattice-Boltzmann method is developed to investigate the behavior of isothermal two-phase fluid flow in porous media. The method is based on the Shan–Chen multiphase model of nonideal fluids that allow coexistence of two phases of a single substance. We reproduce some different idealized situations (phase separation, surface tension, contact angle, pipe flow, and fluid droplet motion, et al) in which the results are already known from theory or laboratory measurements and show the validity of the implementation for the physical two-phase flow in porous media. Application of the method to fluid intrusion in porous media is discussed and shows the effect of wettability on the fluid flow. The capability of reproducing critical flooding phenomena under strong wettability conditions is also proved.
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Leaf Vein Extraction Based on Gray-scale Morphology
Статья научная
Leaf features play an important role in plant species identification and plant taxonomy. The type of the leaf vein is an important morphological feature of the leaf in botany. Leaf vein should be extracted from the leaf in the image before discriminating its type. In this paper a new method of leaf vein extraction has been proposed based on gray-scale morphology. Firstly, the color image of the plant leaf is transformed to the gray image according to the hue and intensity information. Secondly, the gray-scale morphology processing is applied to the image to eliminate the color overlap in the whole leaf vein and the whole background. Thirdly, the linear intensity adjustment is adopted to enlarge the gray value difference between the leaf vein and its background. Fourthly, calculate a threshold with OSTU method to segment the leaf vein from its background. Finally, the leaf vein can be got after some processing on details. Experiments have been conducted with several images. The results show the effectiveness of the method. The idea of the method is also applicable to other linear objects extraction.
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Learning Semantic Image Attributes Using Image Recognition and Knowledge Graph Embeddings
Статья научная
Extracting structured knowledge from texts has traditionally been used for knowledge base generation. However, other sources of information, such as images can be leveraged into this process to build more complete and richer knowledge bases. Structured semantic representation of the content of an image and knowledge graph embeddings can provide a unique representation of semantic relationships between image entities. Linking known entities in knowledge graphs and learning open-world images using language models has attracted lots of interest over the years. In this paper, we propose a shared learning approach to learn semantic attributes of images by combining a knowledge graph embedding model with the recognized attributes of images. The proposed model premises to help us understand the semantic relationship between the entities of an image and implicitly provide a link for the extracted entities through a knowledge graph embedding model. Under the limitation of using a custom user-defined knowledge base with limited data, the proposed model presents significant accuracy and provides a new alternative to the earlier approaches. The proposed approach is a step towards bridging the gap between frameworks which learn from large amounts of data and frameworks which use a limited set of predicates to infer new knowledge.
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Статья научная
In this paper, a color face recognition system is developed to identify human faces using Back propagation neural network. The architecture we adopt is All-Class-in-One-Network, where all the classes are placed in a single network. To accelerate the learning process we propose the use of Bhattacharyya distance as total error to train the network. In the experimental section we compare how the algorithm converge using the mean square error and the Bhattacharyya distance. Experimental results indicated that the image faces can be recognized by the proposed system effectively and swiftly.
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Статья научная
Steganography is the science that deals with conveying secret information by embedding into the cover object invisibly. In steganography, only the authorized party is aware of the existence of the hidden message to achieve secret communication. The image file is mostly used cover medium amongst various digital files such as image, text, audio and video. The proposed idea of this research work is to develop the robust image steganography. It is implemented using Least Significant Bit and Discrete Wavelet Transform techniques for digital image signal to improve the robustness & evaluate the performance of these algorithms. The parameters such as mean square error (MSE), bit error rate (BER), peak signal to noise ratio (PSNR) and processing time are considered here to evaluate the performance of the proposed work. In the proposed system, PSNR and MSE value ranges from 42 to 46 dB and 1.5 to 3.5 for LSB method respectively. For DWT method these results are further improved as it gives higher PSNR values between 49 to 57 dB and lower MSE values 0.2 to 0.7.
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Left Ventricle Segmentation in Magnetic Resonance Images with Modified Active Contour Method
Статья научная
Desired segmentation of the image is a pivotal problem in image processing. Segmenting the left ventricle (LV) in magnetic resonance images (MRIs) is essential for evaluation of cardiac function. For the segmentation of cardiac MRI several methods have been proposed and implemented. Each of them has advantages and restrictions. A modified region-based active contour model was applied for segmentation of LV chamber. A new semi-automatic algorithm was suggested calculating the appropriate Balloon force according to mean intensity of the region of interest for each image. The database is included of 2,039 MR images collected from 18 children under 18. The results were compared with previous literatures according to two standards: Dice Metric (DM) and Point to Curve (P2C). The obtained segmentation results are better than previously reported values in several literatures. In this study different points were used in cardiac cycle and several slice levels and classified into three levels: Base, Mid. and Apex. The best results were obtained at end diastole (ED) in comparison with end systole (ES), and on base slice than other slices, because of LV bigger size in ED phase and base slice. With segmentation of LV MRI based on novel active contour and application of the suggested algorithm for balloon force calculation, the mean improvement of DM compared to Grosgeorge et al. is 19.6% in ED and 49.5% in ES phase. The mean improvement of P2C compared with the same literature respectively for ED and ES phase is 43.8% and 39.6%.
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Статья научная
Image segmentation is one of the most important steps in computer vision and image processing. Image segmentation is dividing the image into meaningful regions based on similarity pixels. We propose a new segmentation algorithm based on de-noising of images, good segmentation results depends on the noisy free images. This means that, we may not get the proper segmentation results in the presence of noise. For this, image pre-processing stage is necessary to denoise the image. An image segmentation result depends on the pre-processing results. In this paper, proposed a new integrating approach based on de-noising and segmentation which is called Level Set Segmentation of Images using Block Matching Local SVD Operator Based Sparsity and TV Regularization (BMLSVD-TV). The proposed method is dividing into two stages, in the first stage images are de-noised based on BMLSVDTV algorithm. De-noising images is a crucial aspect of image processing, there are a few factors to keep in mind during image de-noising such as smoothing the flat areas, safeguarding the edges without blurring, and keeping the textures and new artifacts should not be created. Block Matching, Updating of basis vector, Sparsity regularization, and TV regularization. This method searches for blocks that are comparable to each other in block matching. The data in the array demonstrates a high level of correlation after the matching blocks are grouped together. The sparse coefficients will be gathered after adequate modification. Most of the noise in the image will be minimized through the sparsity regularization step by employing different de-noising algorithms such as Block matching 3D using fixed basis vectors. The edge information will be retained and the piecewise smoothness of the image will be produced using the TV regularization step. Later, in the second state create a contour on the de-noised image and evolve the contour based on level Set function (LSF) defined. This combined approach gives better performance for segmenting the image regions over existing level set methods. When compared our proposed level set method over state of art level set methods. The proposed segmentation method is superior in terms of no.of iterations, CPU time and area covered over the existing level set methods. By this model, we obtained a good quality of restored image from noisy image and the performance of the image quality assessed by the two important parameters such as PSNR and Mean Square Error (MSE). The higher value of PSNR and lower value of MSE leads to good quality of image. In this research work, the proposed denoising method got higher PSNR values over existing methods. Where recovering the original image content is essential for effective performance, image denoising is a key component. It is used in a variety of applications, including image restoration, visual tracking, image registration, image segmentation, and image classification. This model is the best segmentation method for accurate segmentation of objects based on denoising images when compared with the other models in the field.
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Level Sets based Directed Surface Extraction
Статья научная
Directed surface extraction from CT images is the first task in the design of medical equipment. In this paper a new approach based on level set method is proposed to extract the directed surface from CT images. Two level set functions with corresponding speed functions are involved in this study. One is used to cut the desired bone from the input CT model in which the directed surface, usually the outermost surface, and the complex inner surface are both contained. The other is used to remove the complex inner surface. The experimental results show the feasible of the proposed method.
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Статья научная
Automatic Recognition of Diseased Cotton Plant and Leaves (ARDCPL) using Deep Learning (DL) carries a greater significance in agricultural research. The cotton plant and leaves are severely infected by a disease named Bacterial Blight-affected by bacterium, Xanthomonas axonopodis pv. Malvacearum and a new rolling leaf disease affected by an unorthodox leaf roll dwarf virus. Existing research in ARDCPL requires various complicated image preprocessing, feature extraction approaches and cannot ensure higher accuracy in their detection rates. This work suggests a Deep Convolutional Neural Network (CNN) based DCPLD-CNN model that achieves a higher accuracy by leveraging the DL models ability to extract features from images automatically. Due to the enormous success of numerous pre-trained architectures regarding several image classification task, this study also explores eight CNN based pre-trained architectures: DenseNet121, NasNetLarge, VGG16, VGG19, ResNet50, InceptionV3, InceptionResNetV2, and Xception models by Fine-Tuning them using Transfer Learning (TL) to recognize diseased cotton plant and leaves. This study utilizes those pre-trained architectures by adding extra dense layers in the last layers of those models. Several Image Data Augmentation (IDA) methods were used to expand the training data to increase the model's generalization capability and reduce overfitting. The proposed DCPLD-CNN model achieves an accuracy of 98.77% in recognizing disease in cotton plant and leaves. The customized DenseNet121 model achieved the highest accuracy of 98.60% amongst all the pre-trained architectures. The proposed method's feasibility and practicality were exhibited by several simulated experimental results for this classification task.
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Статья научная
With aiming to design a novel image watermarking technique, this paper presents a novel method of image watermarking using lifting wavelet transform, discrete wavelet transform, and one-dimensional linear discriminate analysis. In this blind watermarking technique, statistical features of the watermarked image have been incorporated for preparing the training set and testing set. After that, the principal component analysis is applied to reduce the obtained feature set, so the training time is reduced to the desired level and accuracy is enhanced. The one-dimensional linear discriminate analysis is used for binary classification as it has the ability to classify with good accuracy. This technique applies discrete wavelet transform and lifting wavelet transform in two different watermarking schemes for the image transformation. Both transformations give higher tolerance against image distortion than other conventional transformation methods. One of the significant challenges of a watermarking technique is maintaining the proper balance between robustness and imperceptibility. The proposed blind watermarking technique exhibits the imperceptibility of 43.70 dB for Lena image in case of no attack for the first scheme (using LWT) and 44.71 dB for the second scheme (using DWT+LWT). The first watermarking scheme is tested for robustness, and it is seen that the given scheme is performing well against most of the image attacks in terms of robustness. This technique is compared using some existing similar watermarking methods, and it is found to be robust against most image attacks. It also maintains the excellent quality of the watermarked image.
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Local Content Based Image Authentication for Tamper Localization
Статья научная
Digital images make up a large component in the multimedia information. Hence Image authentication has attained a great importance and lead to the development of several image authentication algorithms. This paper proposes a block based watermarking scheme for image authentication based on the edge information extracted from each block. A signature is calculated from each edge block of the image using simple hash function and inserted in the same block. The proposed local edge based content hash (LECH) scheme extracts the original image without any distortion from the marked image after the hidden data have been extracted. It can also detect and localize tampered areas of the watermarked image. Experimental results demonstrate the validity of the proposed scheme.
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Local binary pattern family descriptors for texture classification
Статья научная
Texture classification is widely employed in many computer vision and pattern recognition applications. Texture classification is performed in two phases namely feature extraction and classification. Several feature extraction methods and feature descriptors have been proposed and local binary pattern (LBP) has attained much attraction due to their simplicity and ease of computation. Several variants of LBP have been proposed in literature. This paper presents a performance evaluation of LBP based feature descriptors namely LBP, uniform LBP (ULBP), LBP variance (LBPV), LBP Fourier histogram, rotated LBP (RLBP) and dominant rotation invariant LBP (DRLBP). For performance evaluation, nearest neighbor classifier is employed. The benchmark OUTEX texture database is used for performance evaluation in terms of classification accuracy and runtime.
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Lossless Image Compression Using A Simplified MED Algorithm with Integer Wavelet Transform
Статья научная
In this paper, we propose a lossless (LS) image compression technique combining a prediction step with the integer wavelet transform. The prediction step proposed in this technique is a simplified version of the median edge detector algorithm used with JPEG-LS. First, the image is transformed using the prediction step and a difference image is obtained. The difference image goes through an integer wavelet transform and the transform coefficients are used in the lossless codeword assignment. The algorithm is simple and test results show that it yields higher compression ratios than competing techniques. Computational cost is also kept close to competing techniques.
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Lossy Compression Color Medical Image Using CDF Wavelet Lifting Scheme
Статья научная
As the coming era is that of digitized medical information, an important challenge to deal with is the storage and transmission requirements of enormous data, including color medical images. Compression is one of the indispensable techniques to solve this problem. In this work, we propose an algorithm for color medical image compression based on a biorthogonal wavelet transform CDF 9/7 coupled with SPIHT coding algorithm, of which we applied the lifting structure to improve the drawbacks of wavelet transform. In order to enhance the compression by our algorithm, we have compared the results obtained with wavelet based filters bank. Experimental results show that the proposed algorithm is superior to traditional methods in both lossy and lossless compression for all tested color images. Our algorithm provides very important PSNR and MSSIM values for color medical images.
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