International Journal of Image, Graphics and Signal Processing @ijigsp
Статьи журнала - International Journal of Image, Graphics and Signal Processing
Все статьи: 1110
Статья научная
This paper presents a forensic perspective way of recognizing the weapons by processing wound patterns using ensemble learning that gives an effective forensic computational approach for the distinguished weapons used in most of crime cases. This will be one of the computational and effective substitutes to investigate the weapons used in crime, the methodology uses the collective wound patterns images from the human body for the recognition. The ensemble learning used in this proposed methodology improves the accuracy of machine learning methods by combining several methods and predicting the final accuracy by meta-classifier. It has given better recognition process compared to single individual model and the traditional method. Ensemble learning is more flexible in function and is better in the wound pattern recognition and their respective weapons as it overcomes the issue to overfit training data. The result achieved for weapon recognition based on wound patterns is 98.34%, from existing database of 800 images of pattern consisting of wounds of stabbed and gunshots. The authenticated experiments out-turns the preeminence of projected method over the widespread feature extraction approach considered in the work and also compares and suggest the false positive recognition verses false negative recognition. The proposed methodology has given better results compared to traditional method and will be helpful in forensic and crime investigation.
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Ergodic Matrix and Hybrid-key Based Image Cryptosystem
Статья научная
The existing traditional cryptosystems, such as RSA, DES, IDEA, SAFER and FEAL, are not ideal for image encryption because of their slow speed and ineffectiveness in removing the correlations of the adjacent pixels. Meanwhile chaos-based cryptosystems, which have been extensively used over the past two decades, are almost all based on symmetric cryptography. Symmetric cryptography is much faster than asymmetric ciphers, but the requirements for key exchange make them hard to use. To remedy this imperfection, a hybrid-key based image encryption and authentication scheme is proposed in this paper. In particular, ergodic matrices are utilized not only as public keys throughout the encryption/decryption process, but also as essential parameters in the confusion and diffusion stages. The experimental results, statistical analysis and sensitivity-based tests confirm that, compared to the existing chaos-based cryptosystems, the proposed image encryption scheme provides a more secure means of image encryption and transmission.
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Статья научная
This paper projects, the impact & accuracy of speech compression on AER systems. The effects of various codecs like MP3, Speex, and Adaptive multi-rate(NB & WB) are compared with the uncompressed speech signal. Loudness enlistment, or a steeper-than-normal increase in perceived loudness with presentation level, is associated with sensorineural hearing loss. Amplitude compression is frequently used to compensate for this abnormality, such as in a hearing aid. As an alternative, one may enlarge these by methods of expansion as speech intelligibility has been represented as the perception of rapid energy changes, may make communication more understandable. However, even if these signal-processing methods improve speech understanding, their design and implementation may be constrained by insufficient sound quality. Therefore, syllabic compression and temporal envelope expansion were assessed for in speech intelligibility and sound quality. An adaptive technique based on brief, commonplace words either in noise or with another speaker competing was used to assess the speech intelligibility. Speech intelligibility was tested in steady-state noise with a single competing speaker using everyday sentences. The sound quality of four artistic excerpts and quiet speech was evaluated using a rating scale. With a state-of-art, spectral error, compression error ratio, and human labeling effects, The experiments are carried out using the Telugu dataset and well-known EMO-DB. The results showed that all speech compression techniques resulted in reduce of emotion recognition accuracy. It is observed that human labeling has better recognition accuracy. For high compression, it is advised to use the overall mean of the unweighted average recall for the AMR-WB and SPEEX codecs with 6.6 bit rates to provide the optimum quality for data storage.
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Estimation and Statistical Analysis of Physical Task Stress on Human Speech Signal
Статья научная
Human speech signal is an acoustic wave, which conveys the information about the words or message being spoken, identity of the speaker, language spoken, the presence and type of speech pathologies, the physical and emotional state of the speaker. Speech under physical task stress shows variations from the speech in neutral state and thus degrades the speech system performance. In this paper we have characterized the voice samples under physical stress and the acoustic parameters are compared with the neutral state voice parameters. The traditional voice measures, glottal flow parameters, mel frequency cepstrum coefficients and energy in various frequency bands are used for this characterization. T-test is performed to check the statistical significance of parameters. Significant variations are noticed in the parameters under two states. Pitch, intensity, energy values are high for the physically stressed voice; On the other hand glottal parameter values get decreased. Cepstrum coefficients shift up from the coefficients of neutral state voice samples. Energy in lower frequency bands was more sensitive to physical stress. This study improves the performance of various speech processing applications by analyzing the unwanted effect of physical stress in voice.
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Estimation of NIIRS incorporating an automated relative edge response method
Статья научная
The quality of remote sensing satellite images are expressed in terms of ground sample distance, modular transfer function, signal to noise ratio and National Imagery Interpretability Rating Scale (NIIRS) by user community. The proposed system estimates NIIRS of an image, by incorporating a new automated method to calculate the Relative Edge Response (RER). The prominent edges which contribute the most for the estimation of RER are uniquely extracted with a combined application of certain filters and morphological operators. RER is calculated from both horizontal and vertical edges separately and the geometric mean is considered as the final result. Later applying the estimated RER along with other parameters, the system returns the NIIRS value of the input image. This work has proved the possible implementation of automated techniques to estimate the NIIRS from images and specifics in the metafile contents of imagery.
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Estimation of Noise in Nonstationary Signals Using Derivative of NLMS Algorithm
Статья научная
In this paper a new Normalized Least mean square (NLMS) algorithm is proposed by modifying Error-data normalized step-size algorithm (EDNSS). The performance of proposed algorithm is tested for nonstationary signals like speech and Electroencephalogram (EEG). The simulations of above is carried by adding stationary and nonstationary Gaussian noise , with original speech taken from standard IEEE sentence (SP23) of NOIZEUS data base and EEG taken from EEG database (sccn.ucsd.edu). The output of proposed and EDNSS algorithm are measured with excess mean square error (EMSE) in both stationary and non stationary environment. The results can be appreciated that the proposed algorithm gives improved result over EDNSS algorithm and also the speed of convergence is maintained same as other NLMS algorithms.
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Evaluating Image Recognition Efficiency using a Self-Organizing Map
Статья научная
Recognition and classification of images is an extremely topical interdisciplinary area that covers image processing, computer graphics, artificial intelligence, computer vision, and pattern recognition, resulting in many applications based on contemporary mobile devices. Developing reliable recognition schemes is a difficult task to accomplish. It depends on many factors, such as illumination, acquisition quality and the database images, in particular, their diversity. In this paper we study how the data diversity affects decision making in image recognition, presenting a database driven classification-error predictor. The predictor is based on a hybrid approach that combines a self-organizing map together with a probabilistic logical assertion method. By means of a clustering approach, the model provides fast and efficient assessment of the image database heterogeneity and, as expected, indicates that such heterogeneity is of paramount importance for robust recognition. The practicality of the model is demonstrated using a set of image samples collected from a standard traffic sign database publicly available by the UK Department for Transport.
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Evaluation Compressive Sensing Recovery Algorithms in Crypto Steganography System
Статья научная
The main contribution of this paper is using compressive sensing (CS) theory for crypto steganography system to increase both the security and the capacity and preserve the cover image imperceptibility. For CS implementation, the discrete Cosine transform (DCT) as sparse domain and random sensing matrix as measurement domain are used. We consider 7 MRI images as the secret and 7 gray scale test images as cover. In addition, three sampling rates for CS are used. The performance of seven CS recovery algorithms in terms of image imperceptibility, achieved peak signal to noise ratio (PSNR), and the computation time are compared with other references. We showed that the proposed crypto steganography system based on CS works properly even though the secret image size is greater than the cover image.
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Evaluation and Comparison of Motion Estimation Algorithms for Video Compression
Статья научная
Video compression has become an essential component of broadcast and entertainment media. Motion Estimation and compensation techniques, which can eliminate temporal redundancy between adjacent frames effectively, have been widely applied to popular video compression coding standards such as MPEG-2, MPEG-4. Traditional fast block matching algorithms are easily trapped into the local minima resulting in degradation on video quality to some extent after decoding. In this paper various computing techniques are evaluated in video compression for achieving global optimal solution for motion estimation. Zero motion prejudgment is implemented for finding static macro blocks (MB) which do not need to perform remaining search thus reduces the computational cost. Adaptive Rood Pattern Search (ARPS) motion estimation algorithm is also adapted to reduce the motion vector overhead in frame prediction. The simulation results showed that the ARPS algorithm is very effective in reducing the computations overhead and achieves very good Peak Signal to Noise Ratio (PSNR) values. This method significantly reduces the computational complexity involved in the frame prediction and also least prediction error in all video sequences. Thus ARPS technique is more efficient than the conventional searching algorithms in video compression.
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Статья научная
This paper presents the evaluation of three machine learning algorithms applied to colour recognition. The “primary” colour palette is defined in accordance with the results from social sciences. Decision Trees, Support Vector Machines and k-Nearest Neighbours classifiers are being tested on various data sets created for this purpose. One of the distance measures for the k-Nearest Neighbour classifier considered is DeltaE2000 - the standard colour difference formula, designed in conformance with human perception. Additionally, we compare these algorithms to various colour recognition applications available.
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Evaluation of Image Segmentation Algorithms for Plant Disease Detection
Статья научная
Processing images efficiently may be influenced by some important factors which are the techniques chosen, the field of study and the quality of images. In this work, we study the field of agriculture with the focus on the early detection of plant diseases through image processing. To detect plant diseases such bacterial diseases, fungal diseases and virus, two main techniques exist: The traditional techniques provided by agricultural experts during visit on the field and the artificial techniques based on images processing algorithms. Since plantations are usually distant from the cities where experts are not easy to find, the artificial techniques incorporated in computer programs become suitable. The modern techniques used to analyse images rely on existing algorithms such as k-nearest neighbor, k-means clustering, fuzzy logic, genetic algorithm, neural networks, etc. Five main phases characterise the process of images analysis: image acquisition, pre-treatment, segmentation, feature extraction and classification. Amongst these phases, we particularly focus on the segmentation which allows to locate portions of leaf that are affected by a disease. Doing so, in this paper we propose a method to evaluate segmentation algorithms (k-means clustering, canny edge and k-nearest neighbor) on the diagnostic of diseases of three of the most cultivated plants (corn, potato, tomato) in the region of study. We study and compare performance values using the ROC-AUC of disease classification using the Support Vector Machine (SVM) algorithm. The obtained results show that the canny edge algorithm produces very poor performances on the family of solanaceae plants including potato. The k-nearest neighbour algorithm produces very poor performance due to the difficulty of choosing the k-value. Finally, the k-means algorithm makes it possible to obtain good prediction rates on all the chosen plants.
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Evaluation of Shape and Color Features for Classification of Four Paddy Varieties
Статья научная
This research is aimed at evaluating the shape and color features using the most commonly used neural network architectures for cereal grain classification. An evaluation of the classification accuracy of shape and color features and neural network was done to classify four Paddy (Rice) grains, viz. Karjat-6, Ratnagiri-2, Ratnagiri-4 and Ratnagiri-24. Algorithms were written to extract the features from the high-resolution images of kernels of four grain types and use them as input features for classification. Different feature models were tested for their ability to classify these cereal grains. Effect of using different parameters on the accuracy of classification was studied. The most suitable feature set from the features was identified for accurate classification. The Shape-n-Color feature set outperformed in almost all the instances of classification.
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Evaluation of a New Integrated Fog Removal Algorithm IDCP with Airlight
Статья научная
This paper has proposed a new fog removal technique IDCP which will integrate dark channel prior with CLAHE and adaptive gamma correction to remove the fog from digital images. Fog in image reduces the visibility of the digital images. Poor visibility not only degrades the perceptual image quality but it also affects the performance of computer vision algorithms such as object detection, tracking, surveillance and segmentation.Various factors such as fog, mist and haze caused by the water droplets present in the air during bad weather leads to poor visibility. The proposed algorithm is designed and implemented in MATLAB using image processing toolbox. The comparison among Air-light and the proposed algorithm is also drawn based upon certain performance parameters. The comparison analysis has shown that the proposed algorithm has shown quite effective results.
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Evaluation of reconstructed radio images techniques of CLEAN de-convolution methods
Статья научная
In Modern Radio Interferometry Various Techniques have been developed for the Reconstruction of the high-dimensional Data scalability Radio Images. CLEAN Variants are widely used in Radio Astronomy because of its computationally efficiency and easiness to understand. CLEAN deconvolves different polarization component images independently and nonlinearly from the point source response by removing the dirty beam pattern form the images. CLEAN Algorithms have been evaluated in this paper for both single field "Deconvolution" (Hogbom, Clark, Clark Stokes, and Cotton Schwab) and multi-field "Deconvolution" (Multi Scale, Multi Frequency and Multi Scale Multi frequency). Based upon simulation results, it is clear that more updated techniques are needed for Large radio telescopes to face big data, extended sources emissions and fast imaging issues which are using dimensionality reduction from the perspective of the compressed sensing theory and to study its interplay with imaging algorithms which are designed in the context of convex optimization combined with sparse representations.
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Evolutionary Image Enhancement Using Multi-Objective Genetic Algorithm
Статья научная
Image Processing is the art of examining, identifying and judging the significances of the Images. Image enhancement refers to attenuation, or sharpening, of image features such as edgels, boundaries, or contrast to make the processed image more useful for analysis. Image enhancement procedures utilize the computers to provide good and improved images for study by the human interpreters. In this paper we proposed a novel method that uses the Genetic Algorithm with Multi-objective criteria to find more enhance version of images. The proposed method has been verified with benchmark images in Image Enhancement. The simple Genetic Algorithm may not explore much enough to find out more enhanced image. In the proposed method three objectives are taken in to consideration. They are intensity, entropy and number of edgels. Proposed algorithm achieved automatic image enhancement criteria by incorporating the objectives (intensity, entropy, edges). We review some of the existing Image Enhancement technique. We also compared the results of our algorithms with another Genetic Algorithm based techniques. We expect that further improvements can be achieved by incorporating linear relationship between some other techniques.
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Exploring the Effect of Imaging Techniques Extension to PSO on Neural Networks
Статья научная
In this paper we go through some very recent imaging techniques that are inspired from space exploration. The advantages of these techniques are to help in searching space. To explore the effectiveness of these imaging techniques on search spaces, we consider the Particle Swarm Optimization algorithm and extend it using the imaging techniques to train multiple neural networks using several datasets for the purpose of classification. The techniques were used during the population initialization stage and during the main search. The performance of the techniques has been measured based on various experiments, these techniques have been evaluated against each other, and against the particle swarm optimization algorithm alone taking into account the classification accuracy and training runtime. The results show that the use of imaging techniques produces better results.
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Extraction and Analysis of Mural Diseases Information Based on Digital Orthophoto Map
Статья научная
Currently, edge detection is an effective means of collecting and analyzing various diseases information from mural collections by using this and data mining based on digital orthophoto map (DOM). But it is hard to extract better edges of mural diseases with traditional edge detection algorithms. Therefore, a new K-means Sobel algorithm is proposed and two evaluation factors are given to judge the extracting effect. Experiment results demonstrate that we can get a better effect by using new method than traditional algorithms. At last, vectorizing detected results, we can gain diseases areas. On that basis, a decision tree about mural diseases severities is established to provide useful information for mural diseases investigation and repair.
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Extraction of Scene Text Information from Video
Статья научная
In this paper, we present an approach for scene text extraction from natural scene video frames. We assumed that the planar surface contains text information in the natural scene, based on this assumption, we detect planar surface within the disparity map obtained from a pair of video frames using stereo vision technique. It is followed by extraction of planar surface using Markov Random Field (MRF) with Graph cuts algorithm where planar surface is segmented from other regions. The text information is extracted from reduced reference i.e. extracted planar surface through filtering using Fourier-Laplacian algorithm. The experiments are carried out using our dataset and the experimental results indicate outstanding improvement in areas with complex background where conventional methods fail.
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Face Detection and Auto Positioning for Robotic Vision System
Статья научная
Robotic vision system has taken a great leap in the field of robotics. Vision system is an essential tool to be implemented in a robot for visual communication between robot and human especially in the application of Tele-Diagnostic Robot. The robot vision system must always be in the field of view. The ability for the vision system to automatically track the person in communication is crucial for the remote medical specialist. To circumvent this problem, a face detection technique is implemented and it is performed using skin color segmentation with two color space which are YCbCr and HSV. Besides that, morphological operations are also done to detect the face region accurately. Two DOF servo mechanism were designed to ensure that the servo motor rotates to centralize the detected face region. A real-time testing were conducted and it was found that this system results a good performance.
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Face Mask Recognition by the Viola-Jones Method Using Fuzzy Logic
Статья научная
In the work, the software implementation of the face mask recognition system using the Viola-Jones method and fuzzy logic is performed. The initial images are read from digital video cameras or from graphic files. Detection of face, eye and mouth positions in images is performed using appropriate Haar cascades. The confidence of detecting a face and its features is determined based on the set parameters of Haar cascades. Face recognition in the image is performed based on the results of face and eye detection by means of fuzzy logic using the Mamdani knowledge base. Fuzzy sets are described by triangular membership functions. Face mask recognition is performed based on the results of face recognition and mouth detection by means of fuzzy logic using the Mamdani knowledge base. Comprehensive consideration of the results of different Haar cascades in the detection of face, eyes and mouth allowed to increase the accuracy of recognition face and face mask. The software implementation of the system was made in Python using the OpenCV, Scikit-Fuzzy libraries and Google Colab cloud platform. The developed recognition system will allow monitoring the presence of people without masks in vehicles, in the premises of educational institutions, shopping centers, etc. In educational institutions, a face mask recognition system can be useful for determining the number of people in the premises and for analyzing their behavior.
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