جستجوی مقالات مرتبط با کلیدواژه « پردازش تصویر » در نشریات گروه « فناوری اطلاعات »

Document images segmentation is one of the recent activities that have attracted researchers' attention. Unfortunately, there is no report on a benchmark dataset for Farsi document images understanding and segmentations applications that be available in the web. In the current article, a benchmark image dataset for the sake of the Farsi document images segmentation is presented, which includes 5598 images. The provided images are taken from the newspapers, textbooks and academic articles. Objects in the images are categorized and labeled into six different groups to be used easily in the subsequent applications. The object groups used in the dataset are paragraph(text), figure, table, logo, mathematical equation and header. To asset the effectiveness of the proposed document image dataset, three existing well-known methods based on deep learning are implemented on it and the results are presented.

Recently, the growth of technology-oriented mechanisms has led to the rapid acceleration of progress and increased need for image processing tools and related mechanisms. Therefore, the demand of experts to learn and develop these technologies is increasing at a fast pace and it shows the need of different fields for this technology. Image processing has always been considered as an important achievement in the field of artificial intelligence because image processing has been able to provide us with many services and data. In this article, an attempt has been made to examine the important applications of image processing. Also, due to the importance of the subject, this article examines the achievements and challenges of image processing. The application of image processing in each of the fields discussed in the article is very wide. The importance of image processing has multiplied in recent years and is predicted to become more important in the next years. It is predicted that image processing will change the relationship between humans and computers in a surprising way.

The aim of the present study is to evaluate the effect of image normalization and iteration number of the linear despeckle filtering on the consecutive ultrasound image quality of the carotid artery and to select the optimum iteration number of ultrasound despeckle filtering. 750 consecutive ultrasonic images over three cardiac cycles of the common carotid artery of three healthy male volunteers (32±9Yr) and 250 consecutive ultrasonic images over three cardiac cycles of the common carotid artery of a male volunteers (65 Yr) having atherosclerotic stenosis were recorded. Using a custom-written program in MATLAb software, the images were first normalized based on gray scale level of the blood and adventitia. Then a linear despeckle filter was applied in 10 iteration to the normalized images. The quality of the images processed with different iterations were evaluated via metrics including mean, variance, signal to noise ratio, relative contrast, noise speckle index, contrast to speckle ratio and structural similarity.Results of the present study shows that among all evaluated metrics, structural similarity is the only metric which is not monotone with iteration number so that by increasing the iteration, initially it increases and then decreases. The optimum iteration of the despeckling filter is that of the maximum structural similarity. According to the results of the present study it seems that 2 to 5 iterations of linear filtering of size 5×5 is required to obtain the maximum structural similarity and further increasing the iteration number results in image texture loss while more computational cost.

The purpose of design and building autonomous cars is to eliminate the human factor in order to reduce losses and costs and also increase safety by replacing smart equipment. Todays, using artificial intelligence and machine learning, we are witnessing significant advances in the intelligent transportations, especially fully automated vehicles, which are able to analyze environmental information using advanced sensors and machine vision techniques. One of the challenges in designing such systems is a correct identification of other vehicles around the route of the vehicle. In this paper, a deep learning-based method for identifying emergency vehicles is presented in which feature extraction and classification processes are performed simultaneously. The deep network used in this research is a convolutional network. In Convolutional Neural Networks (CNN), achieving acceptable results and proper performance requires having a huge amount of data for network training. Due to the limited number of images in the data set used in this study and in order to increase the identification accuracy, transfer learning process and VGG16 pre-trained network have been used. Two new datasets were created for this study and furthermore two other known datasets were also examined. The proposed method was compared with four other known methods from the literature, where the final results showed supremacy of the proposed approach.

In the traditional methods of analyzing minerals in thin sections, the boundaries of the minerals were manually separated and each section was labeled. This approach is expensive and requires high expertise and experience. Therefore, an automatic identification system is essential in this field. Such a system can increase the accuracy and reduce human error, cost and time of mineral identification. The aim of this study is to propose an automated identification system which uses image processing to identify and classify existing minerals.The main steps of the proposed method include collecting images from thin sections, segmentation, feature extraction and classification. After creating the image database, the JSEG algorithm is applied for segmentation. Then, the color and texture features in both RGB and HSI color spaces are extracted from each region and are sent to the classifier for classification, which labels each segment as a mineral. In this study, the efficiency of six different classifiers has been evaluated. According to the results, the Bagged Tree classifier has the highest accuracy of 95.52% and the lowest Mean Absolute Error of 0.04. Also, all classifiers have accuracies of over 93%, which indicates that the proposed feature extraction method is able to properly identify minerals.

Image noise removal is very important in most image processing analysis. Among various noises that can damage the image, Gaussian noise is one of the most notable. There are several ways to reduce image noises, including transformation-based methods, filter-based methods, and non-local methods.In most of the methods, only one criterion (i.e. signal-to-noise ratio) is used to reduce the image noise, and even some methods do not retain image quality and structural details of the image such as lines and edges.In this paper, the TOPSIS method was used to rank different filters of Gaussian noise filtering based on various criteria that influence the evaluation of image quality.In fact, regarding the standard deviation degree of the Gaussian noise, it was intended to know what kind of noise removal filter which contains the considered criteria should be used. The computational results of the proposed method were evaluated on different images with various standard deviation of Gaussian noise, and consequently, the filter has been specified corresponding to the degree of Gaussian noise for the application.

The Internet of Things provides instant access to information about the physical world and the objects within it, leading to new services and increasing efficiency and productivity. The wireless sensor network is an important network infrastructure in the Industrial Internet of Things and user authentication is used as a basic security mechanism to authenticate users to wireless sensor networks. In this article, we intend to provide a new way to improve the security of authentication using image processing. The analysis was performed by MATLAB software. The results presented in this article, which includes seven steps, have a 93% correct detection rate in pupil identification. These seven steps are: 1. Noise reduction, 2. Finding the outer border of the pupil, 3. Separating the eyelashes, 4. Finding the border of the eyelids, 5. Finding the outer border of the iris, 6. Separating the iris area and 7. Extracting the feature and encoding the pixels by the elliptic curve cryptography (ECC) method. With the measurements and experiments performed, it was found that the proposed method in identifying the eyelid border by the quadratic equation method is more efficient and faster in terms of time than the third-degree equation and the Huff parabolic conversion method. In order to extract the feature, the effective parameters in the SAIF feature extraction algorithm were examined and measured, and the optimal parameters were selected. The Sigma parameter with a value of 2.5 and the Octave parameter with a value of 4 should be considered as the best values. Also, in order to evaluate the resistance of the proposed method to error factors such as the angle, brightness and scale, the proposed method was tested and it was proved that the method has the appropriate resistance resolution in different conditions.

Image denoising is a well explored topic. Generally, image denoising approaches can be categorized as spatial domain and transform domain methods according to the image representation. Transform domain methods can be divided into two main groups according to their basis functions. Transform domain methods with data adaptive basis functions and transform domain methods with fixed basis functions. Fixed basis functions transform methods, in which, wavelet transform is the most popular, have been widely used for noise reduction applications due to their features and properties, such as frequency / space separation. Also, due to the non-static nature of natural images and the addition of noise to them, statistical methods have received a lot of attention among transform methods. In the present paper, after a brief introduction of denoising methods, the most important statistical models in the fixed basis transform domain are studied. The experimental results are discussed and analyzed to determine the advantages and disadvantages of these methods. The comprehensive study in this paper is a good reference for new research ideas in image denoising.

Electronic Warfare Support Systems (ESM/ELINT) receive, process, analyze and direction finding signals sent by radars. Direction Finding and determining the Direction of Arrival (DOA) is one of the most important radar parameters that plays an important role in the operations of processing, de-interleaving, Clustering, classification and location of radars. Various techniques are used to direction finding (DF) and estimate the angle of Arrival. Among the methods, amplitude comparison direction finding (ADF) is one of the most common techniques due to its high speed and low computational complexity. Multipath fading degrades the performance of Direction Finding systems. In hilly and suburban environments, the direction finding accuracy of ELINT systems can change under the influence of various factors. One of the factors influencing the accuracy of DF is the Multipath factor. In this paper, a new method is presented to reduce the effect of multipath on the pulses received from the LFM radars and improve DF accuracy in ESM/ELINT systems. The amplitude of the received pulses fluctuates and distorts due to the existence of multipath effects. The proposed method is to first convert the signal into a two-dimensional Short-Time Fourier Transform (STFT) and wavelet and using the SOBEL edge finding algorithm, two Time of Arrivals (TOA) are detected. The first TOA corresponds to the start of the main pulse (raise time of the pulse received from the direct path) and the second TOA is related to the reflected pulse (multi-path effect). Then the time interval between the first and second TOA is considered as a signal without a multi-path effect which this time, the pulse has not fluctuated or distorted. In other words, the main pulse is not merged with the reflected pulses. Therefore, the amplitude detecting is implemented for main pulse after eliminated the multi-path effect. The average value of the signal amplitude is measured in this time range. Then Direction finding is performed using the mono-pulse Direction finding technique for an array of 8 antennas. Therefore, the proposed method improves the Amplitude Direction Finding (ADF) accuracy in multi-path conditions.

Given that breast cancer has become commonplace, Early computer detection to reduce mortality has become a necessity and a challenge. In this study, a method based on image processing techniques to improve the quality of mammographic images is presented. This research is a medical system that has two stages of preprocessing (Histogram equalization and Size equalization image) and feature extraction (The use of contourlet and Curvelet transformations in mammographic images received from patients include three main categories of morphological and histological features, statistical and frequency.) for improvement and increases diagnostic accuracy. To simulate the proposed method, the MIAS digital mammography screening digital image dataset was used and the extracted feature subset is selected for the classifier input, Finally, appropriate classifications and criteria are used to evaluate the proposed method. In the last part of the simulation, the proposed method based on different classifications was evaluated. The best result on the data set was related to the proposed method. The accuracy of the proposed method was 86.3 and it had better results than other methods.

In this research, the weight of the poultry farms and the feed conversion ratio are intelligently calculated and sent to the user by SMS. Weight measurement is done by processing the captured images. The feed conversion factor or FCR is an important parameter in the herd breeding process that is directly related to weight and is obtained with high accuracy by the system by calculating the weight. Images captured by a 50-Mpixel camera connected to the processor are recorded and processed in the Raspberry Pi, which is the processor, by a program written in QT software. Information is sent by the GSM SIM800C module, which communicates with the processor via Arduino. Image processing speed is very fast, and the processing operation calculates the weight by calculating the number of target pixels in the image, which is linearly related to the weight. The error rate of 7.5% indicates the high accuracy of this system.

The content based image retrieval searches digital images in a large image database and uses visual content of images instead of metadata. This approach has many usages in security and authentication for example scanning the iris, fingerprint or finger knuckle print. This paper contributes a new method for personal authentication using finger knuckle print based on a new local binary pattern and image segmentation. The capabilities of quantum science lead to take its advantage in different areas of image processing. The main idea is inspired by the theory of quantum estimation and is applied to the feature extraction phase, in addition, the quantum circuit of the proposed feature is also designed. In order to measure the efficiency and accuracy of the proposed method, the EER (Equal Error Rate) is calculated. After implementing the proposed algorithm on the POLYU dataset, which contains 7920 images, the EER = 0.67 and accuracy =99% are obtained which indicate that the new method has more efficiency and accuracy than the similar approaches.

Most intelligent underwater vehicles and remote control marine vehicles are usually equipped with optical cameras for underwater photography. However, due to water properties and its impurity, the quality of the images taken by these imaging equipment is not desirable, enough. Because, water reduces the light and by increasing the deep of water, the further light will diminish, which will result in the absorption of colors by water. Hence, image processing operations are crucial for underwater images .In this paper, a new method has been proposed to improve the contrast and quality of underwater images. In the proposed method, the two methods of contrast stretching and histogram equalization have been employed. In the histogram equalization phase, new methods have been introduced for thresholding and histogram clipping. The proposed method has been applied on a benchmark images dataset and the results have been compared with the results of common methods. The analysis of the proposed method results, compared to the Sathya et al., which is the best method compares to other rival methods, shows 40.16% increase in the contrast of the images, in the best case

Three dimensional Automatic Breast UltraSound (ABUS) is a modern and effective imaging system which can be used as an adjunct to mammography for women with dense breasts. In this paper, the ABUS imaging system is introduced and its advantages over current handheld ultrasound and other modalities are compared. Then, we emphasize on the benefits of the computerized systems to detect masses in 3D ultrasound images of whole breasts. Consequently, different state-of-the-art computer aided mass detection systems for this type of images are described in details and their limitations are discussed. Finally, potential solutions to overcome these limitations are presented.

Nuclear medicine planar imaging is the most important medical imaging methods in detecting of lesions and abnormality of tissues and their functions. Analysis and interpretation of the nuclear medicine images plays an important role in diagnostic medicines. The images usually have low contrast, high noise and small sizes in the injury region. Abnormal region identifications are depended to images quality and resolution. In this research, the dual domain method is used and tried to improve the quality of nuclear medicine planar images. In nuclear medicine, noise usually has a high-frequency component and it seems that removing the frequency components with other contrast enhancement algorithms can be useful in noise removal. For 46 chosen images from kidney and other part of body, the dual domain method was applied. The images were very noisy that the contrast was improved by the method. Comparisons between the images show that the dual domain method by eliminating high frequency component of image can be considered as one of the most important methods for noise removal of nuclear medicine planar images. Also, the contrast enhancement method is effective for some images. For evaluation, the opinions of nuclear medicine physicians and medical physics were used. The experts’ opinions show that the quality and contrast of images have been improved significantly.

Industrial radiography (RT) is one of the most important non-destructive testing (NDT) methods for the welding defects (e.g. cracks) identification. In the radiography film interpretation, the precise detection of the cracks depends on the interpreter person’ capabilities and skills as well as the image quality of the films.Industrial radiography films often suffer from the low image quality. Therefore, there is a need for accuracy increasing of the defect detection in these type of the radiography images. Cracks have the specific frequency domain feature due to the shape, i.e. very narrow width in comparison with its length. Different frequency and time based image processing techniques can be implemented for the analyzing of the cracks defects. In this research, a wavelet based method and an empirical mode decomposition (EMD) method were used and compared for the detection and analyzing of the welding crack zone. In both methods final image was created as a combination of the original image decomposition components. Then the crack defects have been surveyed in the processed images. The EMD method showed the better results in the crack detection in the comparison of the proposed wavelet method. Although the contrast of the processed images were decreased with EMD method, but also the location and the shape of the cracks clearly appeared.

Studies show that 25% to 47% of elderly will at least once experience falls and this figure is approximately 50% among the elderly living in nursing home. In this paper, based on the Gaussian Mixture Model (GMM) and estimating their parameters by Expectation Maximization (EM) algorithm, a new method has been proposed that firstly, the binary movement of the elderly is segmented from video sequences. Next, the occurrence of falls in older persons is done relying on anatomic body changes and Motion History Images (MHI). Elevation of the system performance was set up on a set of video frames received from the elderly residing in Mother Health Care Center in Sabzevar city and CAVIAR database containing the actual occurrence the of falling. Then, based on the standard deviation and the C-motion coefficient of the walking, suspected incident falls and actual falls are accurately segregated and finally, the sensitivity of 92.68% and the specificity of 96% were obtained which represent a desired capability of the output system. In overall, appropriate simulation of algorithms on the data set due to low error rate in which is less than 6% and meanwhile a careful monitoring of the elderly’s falls will be provided by implementing this system in elderly nursing and residential homes.

In this paper, new visual features for personality identification based on Farsi handwriting images are proposed. The purpose of this study is to establish a computer aided system to automatically extract some attributes that are used by graphologists for the analysis of handwriting. Therefore, these attributes should be presented in the forms that be extractable from the handwriting images. To do this, first the handwriting images are preprocessed and the features such as pen width, spaces between lines, heights of sub-words, etc. are extracted. In the next step, some features including spaces between words, the size of letters and sub-words, the deviation of letters and the height of vertical letters are calculated. These features are employed to recognize the type of handwriting. Eventually, the extracted features from handwriting images are examined on a set of images which have been commented on by graphologist. The experimental results confirm the effectiveness of the proposed features.

In different steps of steel production، various defects appear on the surface of the sheet. Putting aside the causes of defects، precise identification of their kinds helps classify steel sheet correctly، thereby it allocates a high percentage of quality control process. QC of steel sheet for promotion of product quality and maintaining the competitive market is of great importance. In this paper، in addition to quick review of image process techniques used، using image process by means of Gabor wavelet، a fast and precise solution for detection of texture defects in steel sheet is presented. In first step، the approach extracts considerable texture specification from image by using Gabor wavelet. The specification includes both different directions and different frequencies. Then using statistical methods، images are selected that have more obvious defects، and location of defects is determined. Offering the experimental samples، the accuracy and speed of the method is indicated.