Статьи журнала - International Journal of Image, Graphics and Signal Processing
Все статьи: 1056
Статья научная
Acoustic analysis is a proper method in vocal fold pathology diagnosis so that it can complement and in some cases replace the other invasive, based on direct vocal fold observation, methods. There are different approaches and algorithms for vocal fold pathology diagnosis. These algorithms usually have three stages which are Feature Extraction, Feature Reduction and Classification. While the third stage implies a choice of a variety of machine learning methods (Support Vector Machines, Artificial Neural Networks, etc), the first and second stages play a critical role in performance and accuracy of the classification system. In this paper we present initial study of feature extraction and feature reduction in the task of vocal fold pathology diagnosis. A new type of feature vector, based on wavelet packet decomposition and Mel-Frequency-Cepstral-Coefficients (MFCCs), is proposed. Also a new GA-based method for feature reduction stage is proposed and compared with conventional methods such as Principal Component Analysis (PCA). Support vector machine is used as a classifier for evaluating the performance of the proposed method. The results show the priority of the proposed method in comparison with the current methods.
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Статья научная
Non-rigid image registration in extracting deformation map for two satellite images of the same region before and after earthquake occurrence based on measure of intensity dissimilarity C(Ir, T(If)) can play a significant role in post hazard analysis. In this paper, we have proposed a novel image transformation and regional segmentation of the same visualized region by assigning displacement label to change in intensity using Advanced Synthetic Aperture Radar (ASAR) satellite images. We used graph cut based non rigid registraion with a data term and a smoothness term for assigning markovianity between neighboring pixels. Displacement labels has been directly assigned from this data term for small intensity difference. Secondly, our data term imposes stricter penalty for intensity mismatches and hence yields higher registration accuracy. Based on the satellite image analysis through image segmentation, it is found that the area of .997 km2 for the Honshu region was a maximum damage zone localized in the coastal belt of NE Japan fore-arc region. A further objective has been to correlate fractal analysis of seismic clustering behavior with image segmentation suggesting that increase in the fractal dimension coefficient is associated with the deviation of the pixel values that gives a metric of the devastation of the de-clustered region.
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A Model based on Deep Learning for COVID-19 X-rays Classification
Статья научная
Throughout the COVID-19 pandemic in 2019 and until now, patients overrun hospitals and health care emergency units to check up on their health status. The health care systems were burdened by the increased number of patients and there was a need to speed up the diagnoses process of detecting this disease by using computer algorithms. In this paper, an integrated model based on deep and machine learning for covid-19 x-rays classification will be presented. The integration is built-up open two phases. The first phase is features extraction using deep transfer models such as Alexnet, Resnet18, VGG16, and VGG19. The second phase is the classification using machine learning algorithms such as Support Vector Machine (SVM), Decision Trees, and Ensemble algorithm. The dataset selected consists of three classes (COVID-19, Viral pneumonia, and Normal) class and the dataset is available online under the name COVID-19 Radiography database. More than 30 experiments are conducted to select the optimal integration between machine and deep learning models. The integration of VGG19 and SVM achieved the highest accuracy possible with 98.61%. The performance indicators such as Recall, Precision, and F1 Score support this finding. The proposed model consumes less time and resources in the training process if it is compared to deep transfer models. Comparative results are con-ducted at the end of the research, and the proposed model overcomes related works which used the same dataset in terms of testing accuracy.
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Статья научная
Pixel value differencing is a steganographic technique for gray scaled images. In this paper, we propose a modified pixel value differencing image steganographic scheme with least significant bit substitution method. Our method divides the cover image into the blocks of two consecutive pixels and calculates the absolute difference between the pixels of a block similar to [1, 2]. If the difference is less than a particular threshold, i.e. 15 (in this paper) than 4 bits of secret data are taken and these bits are embedded onto the LSBs of the block's pixels through least significant bit substitution method otherwise the number of bits to be hidden are selected based on some characteristics of the block and hidden. The experimental results show that our method significantly improves the quality of stego image as compared to the [1, 3] and have sufficient payload.
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A More Robust Mean Shift Tracker on Joint Color-CLTP Histogram
Статья научная
A more robust mean shift tracker using the joint of color and Completed Local Ternary Pattern (CLTP) histogram is proposed. CLTP is a generalization of Local Binary Pattern (LBP) which can be applied to obtain texture features that are more discriminant and less sensitive to noise. The joint of color and CLTP histogram based target representation can exploit the target structural information efficiently. To reduce the interference of background in target localization, a corrected background-weighted histogram and background update mechanism are adapted to decrease the weights of both prominent background color and texture features similar to the target object. Comparative experimental results on various challenging videos demonstrate that the proposed tracker performs favorably against several variants of state-of-the-art mean shift tracker when heavy occlusions and complex background changes exist.
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A Multi-Scale Image Enhancement Model using Human Visual System Characteristics
Статья научная
Image enhancement is a fundamental pre-processing step for many automated systems and vision systems. Many enhancement algorithms have been anticipated based on different sets of criteria. One of the most widely used algorithms is the direct multi-scale image enhancement algorithm. The specialty of this algorithm is, it provides contrast enhancement, tonal rendition, dynamic range compression and accurate edge preservation of the images. It also provides these features to the individual images and/or simultaneously to the images. In this proposed method, a multi-scale image enhancement algorithm is established by using parametric contrast measure with the transform techniques such as Laplacian pyramid, discrete wavelet transform, Stationary wavelet transform and Dual-tree complex wavelet transform. The new contrast measure provides both the luminance and contrast masking characteristics of the human visual system. The proposed method is used to attain simultaneous local and global enhancements. The enhancement measures such as Entropy, Mean opinion score and Measure of enhancement gives better results than the existing methods.
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A Neural Network Based Recognition and Classification of Commonly Used Indian Non Leafy Vegetables
Статья научная
A methodology to characterize the commonly used Indian non-leafy vegetables’ images is developed. From the captured images of Indian non-leafy vegetables, color components, namely, RGB and HSV features are extracted, analyzed and classified. A feed forward backpropagation artificial neural network (BPNN) is used for the classification. The results show that it has good robustness and a very high success rate in the range of 96-100% for eight types of vegetables. The work finds usefulness in developing recognition system for super market, automatic vending, packing and grading of vegetables, food preparation and Agriculture Produce Market Committee (APMC).
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A New Adder Theory Based on Half Adder and Implementation in CMOS Gates
Статья научная
This paper proposes a new theory of adder and its basic structure. The new adder of asynchronous structure constructed by half adders, called Parallel Feedback Carry Adder (PFCA) as its carry mode is parallel feedback. In theory, the area consumption of n-bit PFCA is close to O(n) and the average length of carry chain is O(log n). A CMOS gate implementation scheme is implemented. HSPICE simulation results show that PFCA has obvious advantages over RCA, CLA, CSeA in speed and area, especially when n is bigger.
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A New Algorithm for Computationally Efficient Modified Dual Tree Complex Wavelet Transform
Статья научная
We introduce a new generation functionally distinct redundant free Modified Dual Tree Complex Wavelet structure with improved orthogonality and symmetry properties. Traditional Dual Tree Complex Wavelets Transform (DTCWT), which incorporates two operationally similar, procedurally different Discrete Wavelet Transform (DWT) trees, is inherently redundant and computationally complex. In this paper, we propose Symmetrically Modified DTCWT (SMDTCWT) to explore the close relationships between the wavelet coefficients from the real and imaginary tree of the dual-tree CWT with an advent of a Quadrature Filter. This exploitation can reduce the level of redundancy that currently exists in a dual-tree wavelet system and decrease the computational complexity .Some of the primary constraints include that the designed algorithm should be satisfying the Hilbert transform pair condition and should have high coding gain, good directional sensitivity, and sufficient degree of regularity.
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A New Approach for Texture Classification Based on Average Fuzzy Left Right Texture Unit Approach
Статья научная
Texture refers to the variation of gray level tones in a local neighbourhood. The “local” texture information for a given pixel and its neighbourhood is characterized by the corresponding texture unit. Based on the concept of texture unit, this paper describes a new statistical approach to texture analysis, based on average of the both fuzzy left and right texture unit matrix. In this method the “local” texture information for a given pixel and its neighbourhood is characterized by the corresponding fuzzy texture unit. The proposed Average Fuzzy Left and Right Texture Unit (AFLRTU) matrices overcome the disadvantage of FTU by reducing the texture unit from 2020 to 79. The proposed scheme also overcomes the disadvantage of the left and right texture unit matrix (LRTM) by considering the texture unit numbers from all the 4 different LRTM’s instead of the minimum one as in the case of LRTM. The co-occurrence features extracted from the AFLRTU matrix provide complete texture information about an image, which is useful for texture classification. Classification performance is compared with the various fuzzy based texture classification methods. The results demonstrate that superior performance is achieved by the proposed method.
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A New Approach to Region Based Image Retrieval using Shape Adaptive Discrete Wavelet Transform
Статья научная
In this paper, we present an efficient region-based image retrieval method, which uses multi-features color, texture and edge descriptors. In contrast to recent image retrieval methods, which use discrete wavelet transform (DWT), we propose using shape adaptive discrete wavelet transform (SA-DWT). The advantage of this method is that the number of coefficients after transformation is identical to the number of pixels in the original region. Since image data is often stored in compressed formats: JPEG 2000, MPEG 4…; constructing image histograms directly in the compressed domain, allows accelerating the retrieval operation time, and reducing computing complexities. Moreover, SA-DWT represents the best way to exploit the coefficients characteristics, and properties such as the correlation. Characterizing image regions without any conversion or modification is first addressed. Using edge descriptor to complement image region characterizing is then introduced. Experimental results show that the proposed method outperforms content based image retrieval methods and recent region based image retrieval methods.
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Статья научная
Multi-scale segmentation is one of the most important methods for object-oriented classification. The selection of the optimal scale segmentation parameters has become difficult and hot in current research certainly. This paper takes aerial images and IKONOS images as the experimental objects and proposes an automatic selection method of optimal segmentation scale for high resolution remote sensing image based on multi-scale MRF model. This method introduces the region feature into the object, and obtains the hierarchical structure of the image from the bottom up through the message propagation between the objects. Finally, the optimal segmentation scale is obtained automatically by computing the marginal probabilities of the objects in each scale image. Experimental results show that this method can effectively avoid the subjectivity and sidedness of the segmentation process, and improve the accuracy and efficiency of high resolution segmentation.
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A New Design Approach for Speaker Recognition Using MFCC and VAD
Статья научная
This paper presents a new approach for designing a speaker recognition system based on mel frequency cepstral coefficients (MFCCs) and voice activity detector (VAD). VAD has been employed to suppress the background noise and distinguish between silence and voice activity. MFCCs were extracted from the detected voice sample and are compared with the database for recognition of the speaker. A new criteria for detection is proposed which gives very good performance in noisy environment.
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A New Distortion Measure for Parameter Quantization Based on MELP
Статья научная
Parameter quantization is very important for the synthetic speech quality of the vocoder. A new distortion measure for pitch as well as lsf quantization in ultra low bit rate Vocoder, whose parameters for several consecutive frames are grouped into a vector and jointly quantized to obtain high coding efficiency, is proposed based on mixed excitation linear prediction(MELP) vocoder. The product of sum of band pass voicing coefficients and gain parameter is used to denote the weighting factor of pitch as well as lsf parameters of current speech frame in the consecutive frames using weighted squared Euclidean distance measure to search the vector codebook. Comparing with the traditional method for a constant weighting factor by distinguishing Voiced/Unvoiced(UV) pattern of each speech frame, objective test results show that the quantization distortion of pitch is reduced by 3.3% and the mean opinion score (MOS) is increased by almost 0.1(3.5%).
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A New Efficient Reordering Algorithm for Color Palette Image
Статья научная
Palette re-ordering is a class of pre-processing methods aiming at finding a permutation of color palette such that the resulting image of indexes is more amenable for compression. The efficiency of lossless compression algorithms for fixed-palette images (indexed images) may change if a different indexing scheme is adopted. Obtaining an optimal re-indexing scheme is suspected to be a hard problem and only approximate solutions have been provided in literature. In this paper, we explore a heuristic method to improve the performances on compression ratio. The results indicate that the proposed approach is very effective, acceptable and proved.
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A New Enhanced Semi Supervised Image Segmentation Using Marker as Prior Information
Статья научная
In Recent days Semi supervised image segmentation techniques play a noteworthy role in image processing. Semi supervised image segmentation needs both labeled data and unlabeled data. It means that a Small amount of human assistance or Prior information is given during clustering process. This paper discusses an enhanced semi supervised image segmentation method from labeled image. It uses both a background selection marker and fore ground object selection marker separately. The EM (Expectation Maximization) algorithm is used for clustering along with must link constraints. The proposed method is applied for natural images using MATLAB 7. Thus the proposed method extracts Object of Interest (OOI) from OONI (Object of Not Interest) efficiently and the experimental results are compared with Standard K Means and EM Algorithm also. The results show that the proposed system gives better results than the other two methods. It may also be suitable for object extraction from natural images and medical image analysis.
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A New Framework for Video-based Frequent Iris Movement Analysis towards Anomaly Observer Detection
Статья научная
This paper suggested a new framework for detecting abnormal behavior, specifically based on frequent iris movements. It contributed to a decision whereas an individual is dubious or unsuspected from a video. One of the key components of questionable observer detection is to detect some specific suspicious activity. According to the writer, various areas of the body movement and human behaviors may be an indicator of suspicious behavior. In this research, we considered the movement of human eyes to identify suspicious activity. This working field is also a significant aspect of machine vision and artificial intelligence, and a big part of the understanding of human behavior. The system framework comprises three parts to monitor suspicious video activities. First, we used the Multi-task Cascaded Convolutional Networks (MTCNN) classifier to detect eyes. Second, we observe irises from eye representations with the use of Circular Hough Transformation (CHT). Finally, we calculated the average distance of iris movement from eye images using a new morphological method called TRM using some properties of the iris movement. We have observed a particular phenomenon of frequent iris movement. Hence, we are making a case of someone being an abnormal person and referring it to a suspicious observer. To vouch for our work, we created our data set with 100 videos where 30 individuals volunteered to validate this research. Each video comprises 200 frames with a duration of 6-10 seconds. We’ve reached an accuracy of 94% on detecting a frequent iris movement. Rather the goal is to minimize people’s burdens so they can focus on a small range of cases for investigation in more depth. This research’s sole purpose is to indicate a person’s anomalous behavior on the basis of frequent iris movement. Our research outstrips much of the current literature on abnormal iris movement and dubious investigator identification.
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A New Heuristic Approach for DNA Sequences Alignment
Статья научная
The problem of comparing DNA sequences is one of the most significant tasks in the field of computational biology. It helps locating the similarities and differences between pairs of DNA sequences. This task can be achieved by finding the longest common substrings between DNA sequences and consequently aligning them. The complexity of this task is due to the high computational power and huge space consuming. Comparing DNA sequences leads to infer the cause of a certain disease beside many significant biological applications. This paper introduces a new Heuristic Approach for DNA Sequences Alignment between two DNA sequences. The new approach is based on three processing phases: the first phase finds the multiple common substrings in the two sequences, the second one sorts the obtained common substrings descending according to their lengths, and the last phase generates the optimal two aligned sequences. The modules of the new approach have been implemented and tested in C# language under Windows platform. The obtained results manifest a reduction in both time of processing and memory requirements.
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A New Image Quality Index and it’s Application on MRI Image
Статья научная
Image quality assessment (IQA) is a process of measurement of the image quality using the evaluations of subjective value with the model of computation. The quality of the image can be calculated by using different types of method where each method works with using isolated features of image. One very renowned method is structural similarity index (SSIM) which measured the quality of image comparing structure of image and the structure stage is obtained from pixel-based stage. FSIM (Feature Similarity Index) measured image quality using low level feature and Gradient magnitude (GM) act as primary feature of image. In this work, a novel MFSIM (Moderate Feature Similarity Index) is introduced which work with full reference IQA, HVS (Human Visual System) and low-level feature of images. In MFSIM the Phase Congruency (PC) is used as primary feature where the PC is dimensionless contrast invariant. In the moderated FSIM the Gradient Magnitude (GM) of the image is considered as the feature of secondary. For application IQA, we applied into segmented image with original image using MRI images. The distortion level of the segmented image is calculated using different image quality index measurement techniques. The image can be used in numerous purposes and the quality of image is distorted for different reason. There are lots of applications where noise less of perfect image is used for getting exact result. So it is very important to find out the distortion level of image. For instance during the segmentation of MRI image for brain tumor detection, the exactness of image need to calculate so that the brain tumor can be find out accurately. So the main purpose of this research work is to introduce a new image quality index and find out the brain tumor and the segmented image quality.
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A New Locally Adaptive Patch Variation Based CT Image Denoising
Статья научная
The main aim of image denoising is to improve the visual quality in terms of edges and textures of images. In Computed Tomography (CT), images are generated with a combination of hardware, software and radiation dose. Generally, CT images are noisy due to hardware/software fault or mathematical computation error or low radiation dose. The analysis and extraction of medical relevant information from noisy CT images are challenging tasks for diagnosing problems. This paper presents a novel edge preserving image denoising technique based on wavelet transform. The proposed scheme is divided into two phases. In first phase, input CT image is separately denoised using different patch size where denoising is performed based on thresholding and its method noise thresholding. The outcome of first phase provides more than one denoised images. In second phase, block wise variation based aggregation is performed in wavelet domain. The final outcomes of proposed scheme are excellent in terms of noise suppression and structure preservation. The proposed scheme is compared with existing methods and it is observed that performance of proposed method is superior to existing methods in terms of visual quality, PSNR and Image Quality Index (IQI).
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