Статьи журнала - International Journal of Image, Graphics and Signal Processing
Все статьи: 1092
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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A New Offline Persian Hand Writer Recognition based on 2D-Wavelet Transforms
Статья научная
All the works on writer's handwritten letters detection was based on the western languages and partly Chinese and Hindi, and there is a little study on Persian handwritten letters detection. Accordingly, in this paper a method is proposed to distinguish scanned Persian handwritten texts with image processing techniques. The proposed method assumes that the writer's handwritten are available in separate letters. The system trains with features extraction of these separate letters and then the trained system is used to detect individual handwritten among some indistinctive handwritten texts. The characteristics of our proposed method including high-speed of training in too much number of handwritten, is the content inattention and visual features considering. The results of procedures on 100 persons also admitted that the proposed method has a very good performance on Persian handwritten texts detection.
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Статья научная
To develop a Braille recognition system, it is required to have the stored images of Braille sheets. This paper describes a method and also the challenges of building the corpora for Hindi Devanagari Braille. A few Braille databases and commercial software's are obtainable for English and Arabic Braille languages, but none for Indian Braille which is popularly known as Bharathi Braille. However, the size and scope of the English and Arabic Braille language databases are limited. Researchers frequently develop and self-evaluate their algorithm based on the same private data set and report its behavior using ad-hoc measures of performance. There is no well-defined benchmark database for comparative performance evaluation of results obtained. The developed Braille database, Bharati Braille-Bank, is a large and well characterized information of Braille documents and its related data for use by the research community working in Optical Braille Recognition (OBR) for Bharati Braille. In the present form it includes databases of embossed double sided, embossed single sided, skewed, Hand punched and images with varying resolutions of Hindi Braille. The objective of this work is to stimulate current research and new investigations in the study of Hindi OBR. Without common databases such as those provided by Braille Bank it is impossible to resolve certain contradictory research results. To overcome this problem, Braille Bank provides facilities for the comparative analysis of the data and the evaluation of proposed algorithms with the standard database. In addition, it provides free access to the developed database in the form of Compact Disc-Read Only Memory (CD-ROM). This work is a step forward in the direction of development of standards for Hindi Devanagari Braille data collection for Indian languages. The mission of the resource is to accelerate the development of OBR for Bharati Braille.
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A New Steganography Technique Using Snake Scan Ordering Strategy
Статья научная
In this paper, we propose a new steganography technique using the snake scan ordering strategy. The proposed method hides the secret data that is an image in another image, known as the cover image. In this method, the pixel values of the secret image are organized in snake scan order, which are preprocessed to reduce their size. The resultant data is embedded into the Least Significant Bits (LSBs) of the pixels of the cover image. To minimize the error/distortion, the pixel values of the stegoimage are adjusted using Optimal Pixel Adjustment Process (OPAP). The performance of the proposed method is compared with that of the simple LSB substitution method, Chang et al. method, Thein & Lin method, and Chen method in terms of Peak Signal to Noise Ratio (PSNR). Our proposed method has higher PSNR in almost all cases.
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A New Video Compression Method using DCT/DWT and SPIHT based on Accordion Representation
Статья научная
As a rule, a video signal has high temporal redundancies due to the high correlation between successive frames. This redundancy has not been deflated enough by current video compression techniques. In this paper, we present a new video compression technique which tends to hard exploit the relevant temporal redundancy in the video to improve solidity efficiency with minimum processing complexity. It includes 3D (Three Dimension) to 2D (Three Dimension) transformation of the video that allows exploring the temporal redundancy of the video using 2D transforms and avoiding the computationally demanding motion recompense step. This transformation converts the spatial and temporal correlation of the video signal into a high spatial correlation. Indeed, this technique transforms each group of pictures into one picture eventually with high spatial correlation. SPIHT (Set Partitioning in Hierarchical Trees) exploits the properties of the wavelet-transformed images to increase its efficiency. Thus, the De-correlation of the resulting pictures by the DWT (Discrete Wavelet Transform) makes efficient energy compaction, and therefore produces a high video compression ratio. Many experimental tests had been conducted to prove the technique efficiency especially in high bit rate and with slow motion video.
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A New method for Image Encryption Using Chaotic Permutation
Статья научная
With the extensive recent development of communication methods and resulting increase in data surveillance and espionage, the need for reliable data encryption methods is greater than ever. Conventional encryption calculations, for example, DES and RSA, are not beneficial in the field of picture encryption because of some inherent characteristics of pictures such as bulk data size and high redundancy, which are problematic for conventional encryption. Many researchers have proposed different image encryption schemes to overcome image encryption problems. In the last two decades, more and more studies have looked to incorporate conventional encryption methods and the complex behavior of chaotic signals. In this paper, a novel image encryption algorithm is proposed based on pixel chaotic permutation. A chaotic logistic map and Ikeda map are used to design a new pseudo-random bit generator, and a novel permutation scheme is used to modify pixel values. Then, a new permutation algorithm based on a traditional Japanese game called Amidakuji is used for pixel scrambling. Different statistical manners, such as correlation coefficient, NPCR (Number of Pixels Change Rate), UACI (Unified Average Changing Intensity), and entropy, are used to provide analysis of the effectiveness of the proposed encryption methods. Our example reveals that the proposed encryption method can obtain highly secure encrypted images using a novel chaotic permutation method based on Amidakuji.
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A Non-format Compliant Scalable RSA-based JPEG Encryption Algorithm
Статья научная
A non-format compliant JPEG encryption algorithm is proposed which is based on a modification of the RSA encryption system. Firstly, an alternate form of entropy coding is described, which is more suited to the proposed algorithm, instead of the zigzag coding scheme used in JPEG. The algorithm for the encryption and decryption process is then elaborated. A variant of the algorithm, also based on the RSA algorithm is also described, which is faster than the original algorithm, but expands the bit stream slightly. Both the algorithms are shown to be scalable and resistant to 'sketch' attacks. Finally, the encrypted file sizes for both the algorithms are compared with the unencrypted JPEG compressed image file size. The encrypted image is found to be moderately expanded, but which is justified by the high security and most importantly, the scalability of the algorithm.
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A Novel Algorithm for Color Similarity Measurement and the Application for Bleeding Detection in WCE
Статья научная
Wireless Capsule Endoscopy (WCE) generates a large number of images in one examination of a patient. It is very laborious and time-consuming to detect the WCE video, and so limits the wider application of WCE. Color similarity measurement is the key technique of color image segmentation and recognition, as well as the premise of bleeding detection in WCE images. This paper deduces two color vector similarity coefficients to measure the color similarity degree in RGB color space, and based on which, a novel method of intelligent bleeding detection in WCE image is implemented. The novel algorithm is implemented in RGB color space, and is featured with simple computation and practicability. The experiments showed that the bleeding regions in WCE images can be correctly extracted, and the sensitivity and specificity of this algorithm were 90% and 97% respectively.
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A Novel Algorithm for De-Noising Radiographic Images
Статья научная
The radiographic image has low contrast and high noise. In order to improve the image for observation and accurate analysis, various digital image processing techniques can be applied. In this research we propose Two Dimensional Left Median Filter method for de-noising radiographic images of welding. We have used the measures Peak Signal-to-Noise Ratio and the Mean Absolute Error for comparison. The accuracy of results obtained through our method is better than the Median and Mean Filter methods.
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A Novel Algorithm for Minutiae Matching
Статья научная
This paper presents a simple and novel algorithm for minutiae matching in fingerprint images. After correct detection of all minutiae in two fingerprint images, the algorithm iteratively processes each minutiae point from two images and tries to find out the number of common points on the basis of structural similarity among them. We try to find all matching pairs of minutiae between two fingerprint images with reference to a pair of chosen reference point. Once all the common minutiae points identified, the matching score can be calculated using various existing formulas.
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Статья научная
Automatic Number Plate Recognition (ANPR) is becoming very popular and topic of research for the Intelligent Transportation System (ITS). Many researchers are working in this direction, as it is the topic of interest. In proposed system, we have presented a novel approach for number plate (NP) detection, which can be useful for Indian conditions. The system works well in different illumination conditions and 24 hours manner. Experiments achieved excellent accuracy of 98.88% of overall accuracy of NP detection on 90 vehicle images with different conditions and captured at different timing during day and night. Out of these 90 images, 89 images were segmented successfully. The minimum image size was 800 X 600 pixels. The system was developed using the Microsoft .NET 3.5 framework and Visual Studio 2008 as IDE with the Intel core i3 2.13 GHz processor having 3 GB RAM. Other systems discussed in this paper reported better processing time of less than 1s, but some of these systems work under restricted conditions and accuracy is also not as good as our system.
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A Novel Approach for Early Detection of Neovascular Glaucoma Using Fractal Geometry
Статья научная
Neovascular glaucoma (NVG) is a human eye disease due to diabetes that leads to permanent vision loss. Early detection and treatment of it prevent further vision loss. Hence the development of an automated system is more essential to help the ophthalmologist in detecting NVG at an earlier stage. In this paper, a novel approach is used for detection of Neovascular glaucoma using fractal geometry concepts. Fractal geometry is a branch of mathematics. It is useful in computing fractal features of irregular, asymmetrical, and complex natural objects. In this work, fractal feature-based Neovascular glaucoma detection from fundus images has been proposed. It utilizes the image adjustment enhancement technique as a preprocessing method to improve the accuracy of NVG detection and the box-counting technique of Fractal geometry to estimate the fractal dimension. The proposed system is tested over MESSIDOR and KMC datasets and yields an average accuracy of 98%.
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Статья научная
Many diversified applications do exist in science & technology, which make use of the primary theory of a recognition phenomenon as one of its solutions. Recognition scenario is incorporated with a set of decisions and the action according to the decision purely relies on the quality of extracted information on utmost applications. Thus, the quality decision making absolutely reckons on processing momentum and precision which are entirely coupled with recognition methodology. In this article, a latest rule is formulated based on the degree of correlation to characterize the generalized recognition constraint and the application is explored with respect to image based information extraction. Machine learning based perception called feed forward architecture of Artificial Neural Network has been applied to attain the expected eminence of elucidation. The proposed method furnishes extraordinary advantages such as less memory requirements, extremely high level security for storing data, exceptional speed and gentle implementation approach.
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A Novel Approach for MRI Brain Images Segmentation
Статья научная
Segmentation of brain from magnetic resonance (MR) images has important applications in neuroimaging, in particular it facilitates in extracting different brain tissues such as cerebrospinal fluids, white matter and gray matter. That helps in determining the volume of the tissues in three-dimensional brain MR images, which yields in analyzing many neural disorders such as epilepsy and Alzheimer disease. The Fisher information is a measure of the fluctuations in the observations. In a sense, the Fisher information of an image specifies the quality of the image. In this paper, we developed a new thresholding method using the Fisher information measure and intensity contrast to segment medical images. It is the weighted sum of the Fisher information measure and intensity contrast between the object and background. This technique is a powerful method for noisy image segmentation. The method applied on a normal MR brain images and a glioma MR brain images. Experimental results show that the use of the Fisher information effectively segmented MR brain images.
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A Novel Approach to Diagnose Diabetic Retinopathy
Статья научная
Early identification of diabetic retinopathy is highly beneficial for preventing the progression of disease. Appearance of blood vessels & retinal surface is a good ophthalmological sign of diabetic retinopathy in fundus images. In this paper, a novel method involving two approaches has been proposed for diagnosis of diabetic retinopathy. The first approach deals with estimation of fractal dimension of lesions by applying power spectral fractal dimension algorithms. For healthy retinas, fractal dimensions are found to be in the range of 2.00 to 2.069, whereas for retinas with diabetic retinopathy, fractal dimensions exceed upper limit. In the second approach, Gray Level Co-occurrence Matrix method is used to analyze the extracted regions from healthy and diabetes affected fundus retinal images. Texture features such as entropy & contrast are computed for healthy and unhealthy regions. These texture features are compared with fractal dimensions. The authors observed positive correlation between entropy and fractal dimensions, whereas negative correlation with contrast and fractal dimensions. Detailed implementations of the proposed work are presented.
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A Novel Boundary Matching Algorithm for Video Temporal Error Concealment
Статья научная
With the fast growth of communication networks, the video data transmission from these networks is extremely vulnerable. Error concealment is a technique to estimate the damaged data by employing the correctly received data at the decoder. In this paper, an efficient boundary matching algorithm for estimating damaged motion vectors (MVs) is proposed. The proposed algorithm performs error concealment for each damaged macro block (MB) according to the list of identified priority of each frame. It then uses a classic boundary matching criterion or the proposed boundary matching criterion adaptively to identify matching distortion in each boundary of candidate MB. Finally, the candidate MV with minimum distortion is selected as an MV of damaged MB and the list of priorities is updated. Experimental results show that the proposed algorithm improves both objective and subjective qualities of reconstructed frames without any significant increase in computational cost. The PSNR for test sequences in some frames is increased about 4.7, 4.5, and 4.4 dB compared to the classic boundary matching, directional boundary matching, and directional temporal boundary matching algorithm, respectively.
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Статья научная
An automatic container code recognition system is of a great importance to the logistic supply chain management. Techniques have been proposed and implemented for the ISO container code region identification and recognition, however those systems have limitations on the type of container images with illumination factor and marks present on the container due to handling in the mass environmental condition. Moreover the research is not limited for differentiating between different formats of code and color of code characters. In this paper firstly an object clustering method is proposed to localize each line of the container code region. Secondly, the localizing algorithm is implemented with opencv and visual studio to perform localization and then recognition. Thus for real time application, the implemented system has added advantage of being easily integrated with other web application to increase the efficiency of the supply chain management. The experimental results and the application demonstrate the effectiveness of the proposed system for practical use.
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Статья научная
The alarming environmental concern of air pollution has a severe global impact. Accurate forecasting can help minimize its hazardous implications well in time. Air Quality forecasting is a complex problem in the domain of time series data forecasting. In this paper we propose a novel customized air quality forecaster developed using Gated Recurrent Unit network-based Encoder-Decoder model (GRUED) of Deep Learning using Inverse Distance Weighted Interpolation for forecasting air pollutant concentrations of Delhi, India. The unique composition and customization of our air quality forecaster is a more efficient and better state of the art model for pollutant concentration prediction than its counterparts. Experimental results are indicative that the proposed model outperforms the conventional Deep Learning models. The proposed model was made to forecast air pollutant concentrations of SO2, CO, NO2 and O3. Each pollutant forecast was evaluated by computing MAE and RMSE metrices. MAE values for SO2, CO, NO2 and O3 forecasts were 60.63%, 26.83%, 33.2% and 31.33% lesser for our GRUED model as compared to conventional LSTM model. RMSE values for SO2, CO, NO2 and O3 forecasts were 43.4%, 19.5%, 26.4% and 27.7% lesser for our GRUED model in comparison to LSTM model. The effectiveness and optimal performance of the suggested approach has been established experimentally.
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A Novel Image Encryption Scheme based on a Nonlinear Chaotic Map
Статья научная
Only by means of XOR operation, a novel image encryption scheme is proposed based on a nonlinear chaotic map (NCM). There are two rounds in the proposed image encryption scheme. In each round of the scheme, the pixel gray values are modified from the first pixel to the last pixel firstly, and then the modified image is encrypted from the last pixel to the first pixel in the inverse order. In order to accelerate the encryption speed, every time NCM is iterated, n (n>3) bytes random numbers which are used to mask the plain-image can be gained. And to enhance the security, a small perturbation will be given to the parameters of the NCM based on the last obtained n bytes modified elements before next iteration. Experimental results and theory analysis show that the algorithm has a high security performance and a good efficiency.
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Статья научная
Human pose detection in 2D/3D images plays a vital role in a large number of applications such as gesture recognition, video surveillance and human robot interaction. Joint human pose estimation in the 2D motion video sequence and 3D facial pose estimation is the challenging issue in computer vision due to noise, large deformation, illumination and complex background. Traditional directed and undirected graphical models such as the Bayesian Markov model, conditional random field have limitations with arbitrary pose estimation in 2D/3D images using the joint probabilistic model. To overcome these issues, we introduce an ensemble chaining graph model to estimate arbitrary human poses in 2D video sequences and facial expression evaluation in 3D images. This system has three main hybrid algorithms, namely 2D/3D human pose pre-processing algorithm, ensemble graph chaining segmented model on 2D/3D video sequence pose estimation and 3D ensemble facial expression detection algorithm. The experimental results on public benchmarks 2D/3D datasets show that our model is more efficient in solving arbitrary human pose estimation problem. Also, this model has the high true positive rate, low false detection rate compared to traditional joint human pose detection models.
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