International Journal of Image, Graphics and Signal Processing @ijigsp
Статьи журнала - International Journal of Image, Graphics and Signal Processing
Все статьи: 1128
Статья научная
Image segmentation generally refers to the process that partitions an image into mutually exclusive regions that cover the image. Among the various image segmentation techniques, traditional image segmentation methods like edge detection, region based, watershed transformation etc. are widely used but have certain drawbacks, which cannot be used for the accurate result. In this paper clustering based techniques is employed on images which results into segmentation of images. The performance of Fuzzy C-means (FCM) integrated with the Particle Swarm optimization (PSO) technique and its variations are analyzed in different application fields. To analyze and grade the performance, computational and time complexity of techniques in different fields several metrics are used namely global consistency error, probabilistic rand index and variation of information are used. This experimental performance analysis shows that FCM along with fractional order Darwinian PSO give better performance in terms of classification accuracy, as compared to other variation of other techniques used. The integrated algorithm tested on images proves to give better results visually as well as objectively. Finally, it is concluded that fractional order Darwinian PSO along with neighborhood Fuzzy C-means and partial differential equation based level set method is an effective image segmentation technique to study the intricate contours provided the time complexity should be as small as possible to make it more real time compatible.
Бесплатно
Intelligent Geometric Classification of Irregular Patterns via Probabilistic Neural Network
Статья научная
This paper deals with interpretation of patterns via neural networks under organization and classification approaches. Fifty different groups of images including geometric shapes, mechanical instruments, machines, animals, fruits, and other classes of samples are classified here in two successive steps. Each primary category is divided into three different sub-groups. The purpose is identifying the class and sub-class of each input sample. Nowadays, industry and manufacturing are moving towards automation; hence accurate description of photos results in a myriad of industrial, security, and medical applications and takes a pressing part in artificial intelligence's progression. Intelligent interpretation of structure's design in CNC machine eventuates in autonomous selection of cutting tools by which any structure can easily be manufactured. Anyhow, this paper comes up with a pattern interpretation method to be applied in submarine detection purposes. Remotely operated vehicles (ROV) are used to detect and survey oil pipelines and underwater marine structures, so mentioned neural network classification is a practicable tool for detection mechanism and avoiding obstacles in ROVs.
Бесплатно
Intelligent Infective Endocarditis Diagnostic System Based on Echocardiography
Статья научная
In this paper, we developed a new approach to solve the problem of infective endocarditis (IE) diagnostics based on intelligent analysis of patients’ echocardiography images. The approach is based on echocardiography segmentation results and detection of valvular anomalies (namely vegetations). In this article for the first time investigates CNNs and Visual Transformers (ViT) based segmentation methods within the framework of the vegetation segmentation task on echocardiography images. Additionally, ensemble methods for combining segmentation models using a new method of models competition for data points were proposed. Furthermore, we investigated methods for aggregating the results of the ensemble based on a new meta-model, pointwise weighted aggregation, which weighs the results of each model pixel by pixel. The last proposed step was to automatically calculate the volume of segmented vegetation to determine the degree of disease and the need for urgent surgical intervention. For the studied and proposed methods, the following ensemble segmentation accuracy was achieved on the test dataset: iou 0.7822, dice score 0.886. The proposed empirical algorithm for calculating the volume of vegetations provided the basis for further improvements of the studied approach. The results obtained indicate the great potential of the developed approaches in clinical practice.
Бесплатно
Статья научная
This project aims to enhance online experiences quality by giving users greater control over the content they encounter daily. The proposed solution is particularly valuable for parents seeking to safeguard their children, educational institutions striving to foster a more conducive learning environment, and individuals prioritising ethical internet usage. It also supports users who wish to limit their exposure to misinformation, including fake news, propaganda, and disinformation. Through the implementation of a browser extension, this system will contribute to a safer internet, reducing users' vulnerability to harmful content and promoting a more positive and productive online environment. The primary objective of this work is to develop a browser extension that automatically detects and censors inappropriate text and images on web pages using artificial intelligence (AI) technologies. The extension will enable users to personalise censorship settings, including the ability to define custom prohibited words and toggle the filtering of text and images. Accuracy estimates for various classifiers such as Random Forest (0.879), Logistic Regression (0.904), Decision Tree (0.878), Naive Bayes (0.315), and KNN (0.832) were performed.
Бесплатно
Interfacing the Analog Camera with FPGA Board for Real-time Video Acquisition
Статья научная
Advances in FPGA technology have dramatically increased the use of FPGAs for computer vision applications. The primary task for development of such FPGAs based systems is the interfacing of the analog camera with FPGA board. This paper describes the design and implementation of camera interface module required for connecting analog camera with Xilinx ML510 (Virtex–5 FXT) FPGA board having no video input port. Digilent VDEC1 video daughter card is used for digitizing the analog video into digital form. The necessary control logics for video acquisition and video display are designed using VHDL and Verilog, simulated in ModelSim, and synthesized using Xilinx ISE 12.1. Designed and implemented interfaces provide the real-time video acquisition and display.
Бесплатно
Статья научная
The subject of the research in this scientific paper is the text on the Web pages, with special emphasis on the interpretation of the text fonts chosen by the Web designer, along with its typographic features, on computers of various users. In addition, users can have different operating systems, different browsers, and different preferences in terms of their computers settings. An overall direction of the choice of fonts and their characteristics when designing Web pages, as well as some advice and opinions on the same topic are presented here. After that, several problems which arise from the interpretation of the text on the Web pages of the users are analyzed, for which a few solutions for the problems, as well as recommendations on which solution when to be chosen are also given in this text. The problem of having no fonts, chosen by the designer, on the user's computer is studied as well. Then, the possibility of the users to change the default font, given by the designer, on their computers, and the possibility to change the typographic features of the default font is also analyzed. Finally, the problem with incompatibility with different operating systems and Web browsers in visualizing the fonts is also considered.
Бесплатно
Intravascular Ultrasound Image Segmentation Using Morphological Snakes
Статья научная
From the first use of the technics of intravascular ultrasound (IVUS) as an imaging technique for the coronary artery system at the 70th century until now , the segmentation of the arterial wall boundaries still an important problem . Much research has been done to give better segmentation result for better diagnostics , evaluation and therapy planning. In this paper we present a new segmentation technics based on Morphological Snakes which developed by Luis Álvarez used for the first time for IVUS segmentation. It is a simple , fast and stable approach of snakes evolution algorithm. Results are presented and discussed in order to demonstrate the effectiveness of this approach in IVUS segmentation.
Бесплатно
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.
Бесплатно
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.
Бесплатно
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.
Бесплатно
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.
Бесплатно
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.
Бесплатно
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.
Бесплатно
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.
Бесплатно
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.
Бесплатно
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.
Бесплатно
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.
Бесплатно
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.
Бесплатно
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%.
Бесплатно
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.
Бесплатно
