Статьи журнала - International Journal of Image, Graphics and Signal Processing
Все статьи: 1056
An Improved Local Equilibrium Contrast Enhancement Algorithm for Infrared Laser Images
Статья научная
An improved local equilibrium contrast enhancement algorithm based self-adaptive contrast enhancement algorithm is proposed for infrared laser images, in which the image pixel value histogram is divided into three parts: background and noise area, targets area, and uninterested area. The targets parts are highlighted, while the background and noise parts and the uninterested parts are restrained. A comprehensive qualitative and quantitative image enhancement performance evaluation is presented to verify the proposed theory and algorithm validity, efficiency and reasonability. The experimental results indicate that the proposed algorithm can greatly improve the global and local contrast for both near infrared images and far infrared laser images while efficiently reducing noise in the infrared laser images，and the visual quality of enhanced image is obviously better than the enhancement of the traditional histogram equalization, double plateaus histogram equalization algorithm, etc.
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Статья научная
Automatic segmentation and detection of brain tumor is a notoriously complicated issue in Magnetic Resonance Image. The similar state-of-art segmentation methods and techniques are limited for the detection of tumor in multimodal brain MRI. Thus this work deals about the accurate segmentation and detection of tumor in multimodal brain MRI and this research work is focused to improve automatic segmentation results. This work analyses the segmentation performance of existing state-of-art method improved Fuzzy C-Means Clustering (FCMC) method and marker controlled Watershed method and this research work proposed method to amalgamated segmentation results of improved Fuzzy C-Means Clustering (FCMC) method and marker controlled Watershed method to carry out accurate brain tumor detection and enhance the segmentation results. The performance of proposed method is evaluated with assorted performance metric, viz., Segmentation accuracy, Sensitivity and Specificity. The comparative performance of the Proposed Method, FCMC Method and Watershed method is demonstrated on real and benchmark multimodal brain MRI datasets, viz. FLAIR MRI, T1 MRI, MRI and T2 MRI and the experimental results of the proposed method exhibits better results for segmentation and detection of tumor in multimodal brain MR images.
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An Improved Popular Items Extraction for Covering Reduction Collaborative Filtering
Статья научная
Recommender Systems are systems that aid users in finding relevant items, products, or services, usually in an online setting. Collaborative Filtering is the most popular approach for building recommender system due to its superior performance. There are several collaborative filtering methods developed, however, all of them have an inherent problem of data sparsity. Covering Reduction Collaborative Filtering (CRCF) is a new collaborative filtering method developed to solve the problem. CRCF has a key feature called popular items extraction algorithm which produces a list of items with the most ratings, however, the algorithm fails in a denser dataset because it allows any item to be in the list. Likewise, the algorithm does not consider the rating values of items while considering the popular items. These make it to produce less accurate recommendation. This research extends CRCF by developing a new popular item extraction algorithm that removes items with low modal ratings and similarly utilizes the rating values in considering the popular items. This newly developed method is incorporated in CRCF and the new method is called Improved Popular Items Extraction for Covering Reduction Collaborative Filtering (ICRCF). Experiment was conducted on Movielens-1M and Movielens-10M datasets using precision, recall and f1-score as performance metrics. The result of the experiment shows that the new method, ICRCF provides a better recommendation than the base method CRCF in all the performance metrics. Furthermore, the new method is able to perform well both at higher and lower levels of sparsity.
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An Improved Watermarking Scheme for Tiny Tamper Detection of Color Images
Статья научная
In many applications, images are sensitive to an extent such that any modification in it could lead to serious problems. For example, hiding any portion of a medical image could lead to a misdiagnosis. Thus, detecting forgery in images is a mandatory as well as being a legal and ethical duty. The main contribution of this paper is to propose a new Content Authentication (CA) watermarking scheme, which aims at detecting any modification, forgery, or illegal manipulation of images even if it is small. Our proposed scheme is a fragile, secure, and a reversible watermarking scheme. It generates the watermark uniquely using a messy model. The generated watermark is embedded accumulatively; to obtain spreading over the whole image area, and embedded homogeneously; to obtain a high quality watermarked image. Our proposed scheme is a development of a recently proposed watermarking scheme. Our proposed scheme surpassed its counterpart in terms of capacity, quality, watermark spreading, fragility, and embedding time. The payload of the host image increased from 81.71 % to 93.82 %. The minimum obtained PSNR value increased from 27.15 dB to 31.76 dB. The watermark spreading percentage, or the percentage of the protected pixels, is noticeably increased. Our proposed scheme is very sensitive to modifications anywhere in the image even if it is tiny. Finally, our proposed CA scheme has a faster embedding time than that of its counterpart. We obtained an average reduction in time equals 0.15 second.
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Статья научная
Video analytics refers to process the videos intelligently. Video analytics has its most important usage in the field of the surveillance. Surveillance has been used in various areas and one of them is the detection of unintentional fall of patients, senior citizens and children which can cause serious injuries and health threats to children as well as to old persons. Developed countries are progressing in the Surveillance and activity monitoring. But there are limitation and facing problems under certain circumstances. Advancement in the field of computer vision and the prominent decrease in the prices of digital cameras assisted and motivated researchers to propose very useful algorithms for fall detection. The proposed research work is based on the combination of motion history images and eclipse centroid calculation to detect the fall efficiently. The proposed system provides very effective and efficient results on the video sequences of simulated falls.
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An Interactive Approach for Retrieval of Semantically Significant Images
Статья научная
Content-based image retrieval is the process of recovering the images that are based on their primitive features such as texture, color, shape etc. The main challenge in this type of retrieval is the gap between low-level primitive features and high-level semantic concepts. This is known as the semantic gap. This paper proposes an interactive approach for optimizing the semantic gap. The primitive features used are HSV histogram, local binary pattern histogram, and color coherence vector histogram. The mapping between primitive features of the image and its semantic concepts is done by involving the user in the feedback loop. Proposed primitive feature extraction method shows improved image retrieval results (Average precision 73.1%) over existing methods. We have proposed an innovative relevance feedback technique in which the concept of prominent features is introduced. On the application of the relevance feedback, only prominent features which are having maximum similarity are utilized. This method reduces the feature length and increases the efficiency. Our own interactive approach for relevance feedback is not only computationally simple and fast but also shows improvement in the retrieval of semantically meaningful relevant images as we go on increasing the iterations.
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An Object of Interest based Segmentation Approach for Selective Compression of Video Frames
Статья научная
The automatic segmentation of objects of interest is a new research area with applications in various fields. In this paper, the object segmentation method is used for content based video management and compression of video frames for video conferencing. The face region, which is the object of interest in the video frames, is identified first using a skin color based algorithm. The face regions are then extracted and encoded without loss, while the non- face regions and the non-face frames are quantized before encoding. Results show that the decompressed video has an improved quality with the proposed approach at low bit rates.
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An affine arithmetic approach to model and estimate the safety parameters of AC transmission lines
Статья научная
With an increase in population of the country day by day and with high growing speed of geographical residential plots, the demand by the public for new set up of electrical power transmission system has become a common mandate. Therefore it is the responsibility of the concerned authority to protect the interests of common man in a smart manner and develop solutions for the growth of country in an intelligent way keeping safety of public as prime importance. This paper proposes an Affine Arithmetic approach of mathematical modelling to estimate the safety parameters of AC transmission line leading to sag, like, temperature, wind loading, ice loading, weight of the conductor, stress, tension, pressure etc., taking into account the uncertainty conditions so that the solutions developed address in real time. The proposed model is executed in MATLAB integrated development environment and gives the complete behavior of sag in transmission lines with respect to each of the safety parameters individually and closely ascertains the safety threshold limits considering 50% factor of safety and 5m ground clearance. The critical safety threshold limit for wind loading is found to be about 3.02 kg/m and the typical value is about 1.51 kg/m. Similarly, the critical safety threshold limit for weight of ice is found to be about 1.45 kg/m, whereas its typical value is about 0.7 kg/m. Extending further, the critical safety threshold limit for conductor weight is found to be about 3.07 kg/m, whereas its typical value is about 1.6 kg/m, which is a close approximation to the typical weight per unit length of industry-grade conductors like Aluminum Conductor Steel Reinforced (ACSR). The critical safety threshold limit for tension in the conductor and the span lengths are found to be 1850 kg and 230m respectively.
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Статья научная
Sclerosis is a disease that triggers mainly due to damage of nerve cells in the brain and spinal cord. Various impairments are observed with this disease. Analyzing this type of images is needed for the medical research field for early stage identification. So, the present paper uses Bivariate Gaussian Mixture distribution for analyzing the noisy sclerosis images. For this, the present paper uses neural network for classification. The proposed method is evaluated with various images of brain web repository and the results show the efficiency of the proposed method.
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Статья научная
The Coronary Artery Disease (CAD) which is one among the major class of cardiovascular diseases is emerging as an epidemic in the society and has proven to be the leading cause for more number of deaths when compared to the other cardiovascular diseases. It is emerging as one of the threats to the economy. It has become very important to detect CAD in its early stage which can help society in a broader way by saving a significant number of lives. The proposed method is a novel efficient automated approach which is capable of detecting CAD among the large group of patients using Electrocardiogram (ECG) signal. The system design provides a complete model of pre-processing of ECG, finding the heart rate which is further decomposed up to 4 level sub-bands using analytic transformation based signal decomposition method. The signal decomposition method is used to analyze the low frequency components of the signal and to deal with non stationary nature of heart signals. Two Non-linear entropy estimators as K-Nearest Neighbor (K-NN) and Correlation entropy are applied to decomposed sub- bands obtained after applying Analytic wavelet transformation based flexible decomposition technique to extract non-linear dynamics. The clinical significant features from the large data set can be selected by employing wilcoxon ranking method which assigns ranks on the applied signal. Further, an entropy-based classification approach and a suitable classifier namely Linear support vector machine (L-SVM) is used to classify among CAD and normal class. The algorithm is simulated in MATLAB and it is found that the results matched closely with the available data. This computer-assisted automated system which characterizes the heart signal can serve as an aid for the cardiologists in their daily screening of a large number of patients and can be used in primary health care centers which help the physicians in the early detection of a CAD.
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An efficient object search in video using template matching
Статья научная
This research paper presents a novel approach for object instance search in video. At the inception, video is selected for which the object instance within the desired video is to be searched and given as an input to system. In preprocessing step, video is divided into key frames. In next step, features are extracted from query image and using template matching algorithm it is compared with key frames. If the object is present in frame then it will display detected object. Similarly, all the frames in video which contains the object are displayed. Max Path Search algorithm is used to remove the noise against classifier and Spatio-Temporal trajectories are used to improve object search. We encountered the fundamental challenge to detect an object from a set of key frames of a video with a partial appearance of object due to lighting, positioning, occlusion etc. from a known class such as logo and any other. The goal of proposed method is to detect all instances of object from known class.
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Статья научная
Remote sensing image classification is very much essential for many socio, economic and environmental applications in the society. They aid in agriculture monitoring, urban planning, forest monitoring, etc. Classification of a remote sensing image is still a challenging problem because of its multifold problems. A new algorithm LCDFOSCA (Linear Contact Distribution First Order Statistics Classification Algorithm) is proposed in this paper to extract the texture features from a Color remote sensing image. This algorithm uses linear contact distributions, mathematical morphology, and first-order statistics to extract the texture features. Later k-means is used to cluster these feature vectors and then classify the image. This algorithm is implemented on NRSC ‘Tirupathi’ area 2.5m, 1m color images and on Google Earth images. The algorithm is evaluated with various measures like the dice coefficient, segmentation accuracy, etc and obtained promising results.
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Статья научная
Speech is the natural mode of communication between humans. Human-to-machine interaction is gaining importance in the past few decades which demands the machine to be able to analyze, respond and perform tasks at the same speed as performed by human. This task is achieved by Automatic Speech Recognition (ASR) system which is typically a speech-to-text converter. In order to recognize the areas of further research in ASR, one must be aware of the current approaches, challenges faced by each and issues that needs to be addressed. Therefore, in this paper human speech production mechanism is discussed. The various speech recognition techniques and models are addressed in detail. The performance parameters that measure the accuracy of the system in recognizing the speech signal are described.
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An improved image compression algorithm using wavelet and fractional cosine transforms
Статья научная
The most significant parameters of image processing are image resolution and speed of processing. Compressing the multimedia datasets, which are rich in quality and volume is challenging. Wavelet based image compression techniques are the best tools for lossless image compression, however, they suffer by low compression ratio. Conversely fractional cosine transform based compression is a lossy compression technique with less image quality. In this paper, an improved compression technique is proposed by using wavelet transform and discrete fractional cosine transform to achieve high quality of reconstruction of an image at high compression rate. The algorithm uses wavelet transform to decompose image into frequency spectrum with low and high frequency sub bands. Application of quantization process for both sub bands at two levels increases the number of zeroes, however rich zeroes from high frequency sub bands are eliminated by creating the blocks and then storing only non-zero values and kill all blocks with zero values to form reduced array. The arithmetic coding method is used to encode the sub bands. The Experimental results of proposed method are compared with its primitive two dimensional fractional cosine and fractional Fourier compression algorithms and some significant improvements can be observed in peak signal to noise ratio and self-similarity index mode at high compression ratio.
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An optimized architecture of image classification using convolutional neural network
Статья научная
The convolutional neural network (CNN) is the type of deep neural networks which has been widely used in visual recognition. Over the years, CNN has gained lots of attention due to its high capability to appropriately classifying the images and feature learning. However, there are many factors such as the number of layers and their depth, number of features map, kernel size, batch size, etc. They must be analyzed to determine how they influence the performance of network. In this paper, the performance evaluation of CNN is conducted by designing a simple architecture for image classification. We evaluated the performance of our proposed network on the most famous image repository name CIFAR-10 used for the detection and classification task. The experiment results show that the proposed network yields the best classification accuracy as compared to existing techniques. Besides, this paper will help the researchers to better understand the CNN models for a variety of image classification task. Moreover, this paper provides a brief introduction to CNN, their applications in image processing, and discuss recent advances in region-based CNN for the past few years.
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Analog Document Search Using CRNN and Keyphrase Extraction
Статья научная
There seems to be a peculiar trend in the way information is now used, moving to digital media not just for the newspapers but for books as well. With advances in Optical Character Recognition (OCR), Style Transfer Mapping (STM), and efficient key phrasing, we are now able to digitalize the document to a form that can be read across multiple platforms and searched efficiently. It provides users with the ease of searching for relevant documents without the tedious process of manual searching. We propose a system that uses the CRNN model to detect English characters in the document with high accuracy. We then pair it with a hybrid keyphrasing technique, which uses Positional Rank as its Graph based rank and re-rank the key phrases using the C-Value method. This process allows us to automatically digitize the printed document and summarise it to provide high-quality keyphrases, which can be used to efficiently search and retrieve relevant printed documents.
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Analysis and Estimation of Noise in Embedded Medical Images
Статья научная
Patient information is embedded inside the medical images for the storage or transmission, or healthcare applications. In medical image processing, various types of noises corrupt the image quality. There is a need of measure specific noise for a particular image is required for the evaluation of robustness for embedding techniques used for hiding patient information in medical images. It is very important to obtain precise images to facilitate accurate analysis and estimation of noise in embedded medical image. The current work is focused towards studying the effect of specific noise which affect particular medical image. The strength of the medical image is tested by introducing several attacks to the embedded medical images. The statistical quantity measures like peak signal-to-noise ratio (PSNR), signal-to-noise ratio (SNR) and normalized root mean square error (NRMSE) are employed to measure the quality of the output medical image.
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Analysis and detection of content based video retrieval
Статья научная
Content Based Video Retrieval (CBVR) System has been investigated over past decade it’s rooted in many applications like developments and technologies. The demand for extraction of high level semantics contents as well as handling of low level contents in video retrieval systems are still in need. Hence it motivates and encourages many researchers to discover their knowledge across CBVR domain and contribute their work to make the system more effective and useful in developing the system application. This paper highlights comprehensive and extensive review of CBVR techniques for detection of region of interest in a given video. The experiment is carried out for the detection of ROI using ACF detector. The detection rate of ROI is observed competitive and satisfactory.
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Analysis of Abdominal ECG Signal for Fetal Heart Rate Estimation Using Adaptive Filtering Technique
Статья научная
This paper presents a method for fetal heart rate estimation from an abdominal electrocardiogram (ECG) signal based on adaptive filter analysis using least mean square (LMS) adaptive filtering algorithm in order to determine the health status of a baby in its mother's womb. The fetal ECG signal is extracted from abdominal ECG containing other sources of interference using the maternal ECG signal obtained from mother's chest cavity as the reference signal. Interference/noise model used for this work include the power-line noise, the white noise and the unwanted propagating maternal ECG signal. Thereafter, the heart rate is estimated using an automated peak voltage measurement algorithm at 75 percent threshold voltage. It is found that irrespective of the estimated heart rate of the baby, 100 percent estimation is achieved at signal-to-noise ratio (SNR) greater than or equal to -31dB.
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Analysis of Arithmetic and Huffman Compression Techniques by Using DWT-DCT
Статья научная
In the recent era, digital contents are exchanging over the internet and it has increased exponentially. Sometimes, we need small sizes to share the real world, because of narrow bandwidth. Hence, the data compression concept came in limelight to utilize the storage capacity and available bandwidth efficiently. This paper presents an analysis of Arithmetic and Huffman compression techniques based on a hybrid combination of the DWT-DCT techniques. The input image is decomposed up to the 3rd level by using the DWT and then Arithmetic & Huffman coding is applied separately on quantized sub-bands on 2nd as well as 3rd level coefficients from approximation sub-bands to get a high compression ratio and high peak signal-to-noise ratio values. On the third level approximation sub-band, the DCT method is applied to reduce the blocking effect. Simulation results show that the Arithmetic coding exhibits higher CR than Huffman coding, but smaller PSNR values.
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