Image Processing

Open Access Article

1 - Medial-axis Enhancement of Tubular Structures and its Application in the Extraction of Portal Veins
amirhossein forouza reza aghaeizade youshi sato masa houri

I In this paper, a new filter is designed to enhance medial-axis of tubular structures. Based on a multi-scale method and using eigenvectors of Hessian matrix, the distance of a point to the edges of the tube is found. To do this, a hypothetical line with a deliberate d More

I In this paper, a new filter is designed to enhance medial-axis of tubular structures. Based on a multi-scale method and using eigenvectors of Hessian matrix, the distance of a point to the edges of the tube is found. To do this, a hypothetical line with a deliberate direction is passed through the point which cuts the tube at its edges. For points which are located on the medial-axis, this distance is symmetric with respect to any deliberate direction. We find samples of the distances in different directions and assign a measure to the points based on this symmetry property. The output of this step is an enhanced image in which noise is removed and tubes can be seen more clearly. Then, we employ the filter developed by Pock et al. to enhance medial axis. Evaluation of the proposed method is performed using 2D/3D synthetic/clinical datasets both quantitatively and qualitatively. Manuscript profile

Open Access Article

2 - On-road Vehicle detection based on hierarchical clustering using adaptive vehicle localization
Moslem Mohammadi Jenghara Hossein Ebrahimpour Komleh

10.7508/jist.2015.04.004

Vehicle detection is one of the important tasks in automatic driving. It is a hard problem that many researchers focused on it. Most commercial vehicle detection systems are based on radar. But these methods have some problems such as have problem in zigzag motions. Im More

Vehicle detection is one of the important tasks in automatic driving. It is a hard problem that many researchers focused on it. Most commercial vehicle detection systems are based on radar. But these methods have some problems such as have problem in zigzag motions. Image processing techniques can overcome these problems.This paper introduces a method based on hierarchical clustering using low-level image features for on-road vehicle detection. Each vehicle assumed as a cluster. In traditional clustering methods, the threshold distance for each cluster is fixed, but in this paper, the adaptive threshold varies according to the position of each cluster. The threshold measure is computed with bivariate normal distribution. Sampling and teammate selection for each cluster is applied by the members-based weighted average. For this purpose, unlike other methods that use only horizontal or vertical lines, a fully edge detection algorithm was utilized. Corner is an important feature of video images that commonly were used in vehicle detection systems. In this paper, Harris features are applied to detect the corners. LISA data set is used to evaluate the proposed method. Several experiments are applied to investigate the performance of proposed algorithm. Experimental results show good performance compared to other algorithms . Manuscript profile

Open Access Article

3 - Drone Detection by Neural Network Using GLCM and SURF Features
Tanzia Ahmed Tanvir Rahman Bir Ballav Roy Jia Uddin

10.52547/jist.9.33.15

This paper presents a vision-based drone detection method. There are a number of researches on object detection which includes different feature extraction methods – all of those are used distinctly for the experiments. But in the proposed model, a hybrid feature extrac More

This paper presents a vision-based drone detection method. There are a number of researches on object detection which includes different feature extraction methods – all of those are used distinctly for the experiments. But in the proposed model, a hybrid feature extraction method using SURF and GLCM is used to detect object by Neural Network which has never been experimented before. Both are very popular ways of feature extraction. Speeded-up Robust Feature (SURF) is a blob detection algorithm which extracts the points of interest from an integral image, thus converts the image into a 2D vector. The Gray-Level Co-Occurrence Matrix (GLCM) calculates the number of occurrences of consecutive pixels in same spatial relationship and represents it in a new vector- 8 × 8 matrix of best possible attributes of an image. SURF is a popular method of feature extraction and fast matching of images, whereas, GLCM method extracts the best attributes of the images. In the proposed model, the images were processed first to fit our feature extraction methods, then the SURF method was implemented to extract the features from those images into a 2D vector. Then for our next step GLCM was implemented which extracted the best possible features out of the previous vector, into a 8 × 8 matrix. Thus, image is processed in to a 2D vector and feature extracted from the combination of both SURF and GLCM methods ensures the quality of the training dataset by not just extracting features faster (with SURF) but also extracting the best of the point of interests (with GLCM). The extracted featured related to the pattern are used in the neural network for training and testing. Pattern recognition algorithm has been used as a machine learning tool for the training and testing of the model. In the experimental evaluation, the performance of proposed model is examined by cross entropy for each instance and percentage error. For the tested drone dataset, experimental results demonstrate improved performance over the state-of-art models by exhibiting less cross entropy and percentage error. Manuscript profile

Open Access Article

4 - Computer Aided Graphology for Farsi Handwriting
A. A. Bahrami Sharif E. Kabir

Graphology is the science of study and analysis of the personality of an individual from his/her style of handwriting. In western communities, the most important application of graphology is the recruitment of job applicants. In this regard, computer aided extraction an More

Graphology is the science of study and analysis of the personality of an individual from his/her style of handwriting. In western communities, the most important application of graphology is the recruitment of job applicants. In this regard, computer aided extraction and analysis of features from handwriting can be of great assistance to graphologists. The most dominant features of handwriting employed in graphology include the shape of the page margins, line spacing, line skew, word slant, size of letters, text density, writing speed and regularity. In this paper a number of methods are proposed for automated extraction of some of these features from Farsi handwriting. Experimental results on 118 test samples of different writers are presented and discussed. Manuscript profile

Open Access Article

5 - Design of Low Power High Speed Dilation Operator for Binary Images in CMOS Technology
M. hajirahimi E. Kabir

This paper describes the design of hybrid wave-pipeline architecture for implementation of real time morphological dilation. With minor changes to this architecture, it can be utilized for erosion, closing, and opening operators. The new architecture results in higher s More

This paper describes the design of hybrid wave-pipeline architecture for implementation of real time morphological dilation. With minor changes to this architecture, it can be utilized for erosion, closing, and opening operators. The new architecture results in higher speed, less hardware complexity, and lower area and power dissipation compared to conventional pipeline implementation. In addition, it is faster than the wave-pipeline structure, without the difficulty of balancing the delay of long signal paths. Using the new architecture, three ASIC chips in 0.18µm CMOS are designed for binary image processing through Verilog. These chips dilate a 1024×1024 image by a 21×21 structuring element in 256.58μ s. The maximum frequency of the operations is 5.882 GHz, 5 GHz, and 4.167 GHz. For the power supply of 1.8 V and the 4.167 GHz frequency, the power dissipation is 597mW, 478 mW, and 410 mW, and the chip area is 0.118 mm2, 0.087 mm2, and 0.075 mm2, respectively. Manuscript profile

Open Access Article

6 - Improving Age Estimation of Dental Panoramic Images Based on Image Contrast Correction by Spatial Entropy Method
Masoume Mohseni Hussain Montazery Kordy Mehdi Ezoji

In forensic dentistry, age is estimated using dental radiographs. Our goal is to automate these steps using image processing and pattern recognition techniques. With a dental radiograph, the contour is extracted and features such as apex, width and tooth length are dete More

In forensic dentistry, age is estimated using dental radiographs. Our goal is to automate these steps using image processing and pattern recognition techniques. With a dental radiograph, the contour is extracted and features such as apex, width and tooth length are determined, which are used to estimate age. Optimizing the resolution of radiographic images is an important step in contour extraction and age estimation. In this article, the aim is to improve the image resolution in order to extract the appropriate area and proper segmentation of the tooth, which makes it possible to estimate age better. In this model, due to the low resolution of radiographic images, in order to increase the accuracy of extracting the desired area of each tooth (ROI), the image resolution increases using spatial entropy based on the spatial distribution of pixel brightness, along with another increasing resolution method, like the Laplacian pyramids. Increasing the resolution of the image leads to the extraction of appropriate ROI and the removal of unwanted areas. The database used in this study is 154 adolescent panoramic radiographs, of which 73 are male and 81 are female. This database is prepared from Babol University of Medical Sciences. The results show that by using fixed tooth segmentation methods and only by applying the proposed effective method to improve image resolution, the extraction of appropriate ROI increased from 66% to 78% which shows a good improvement. The extracted ROI is then delivered to the segmented block and the contour extracted. After contour extraction, age is estimated. The age estimation using the proposed method is closer to the manual age estimate compared to the method that does not use the proposed algorithm to increase the image resolution. Manuscript profile

Open Access Article

7 - An Intelligent Vision System for Automatic Forest Fire Surveillance
Mohammad Sadegh Kayhanpanah Behrooz Koohestani

Fighting forest fires to avoid their potential dangers as well as protect natural resources is a challenge for researchers. The goal of this research is to identify the features of fire and smoke from the unmanned aerial vehicle (UAV) visual images for classification, o More

Fighting forest fires to avoid their potential dangers as well as protect natural resources is a challenge for researchers. The goal of this research is to identify the features of fire and smoke from the unmanned aerial vehicle (UAV) visual images for classification, object detection, and image segmentation. Because forests are highly complex and nonstructured environments, the use of the vision system is still having problems such as the analogues of flame characteristics to sunlight, plants, and animals, or the smoke blocking the images of the fire, which causes false alarms. The proposed method in this research is the use of convolutional neural networks (CNNs) as a deep learning method that can automatically extract or generate features in different layers. First, we collect data and increase them according to data augmentation methods, and then, the use of a 12-layer network for classification as well as transfer learning method for segmentation of images is proposed. The results show that the data augmentation method used due to resizing and processing the input images to the network to prevent the drastic reduction of the features in the original images and also the CNNs used can extract the fire and smoke features in the images well and finally detect and localize them. Manuscript profile

Open Access Article

8 - Evaluation of the Progression of Boxwood Blight Disease in the Forests of Northern Iran Using Satellite Image Processing Techniques
marzieh ghavidel Peiman Bayat Mohammadebrahim farashiani

In recent years, boxwood dieback has become one of the essential concerns of practitioners and managers of the natural resources of the country. To control the expansion of the factors contributing to the dieback of box trees, the early detection and preparation of dist More

In recent years, boxwood dieback has become one of the essential concerns of practitioners and managers of the natural resources of the country. To control the expansion of the factors contributing to the dieback of box trees, the early detection and preparation of distribution maps are required. Assessment data can play an important role in this regard. The combination of high-resolution and low-spectrum panchromatic images with low resolution is used for evaluating the extent of destruction. Also, spectral and textural features are considered simultaneously in images extracted from Landsat 8 satellite. Finally, by extracting effective features from the candidate description space with the help of genetic algorithm and using the appropriate classification in the form of simultaneous application of fuzzy clustering and maximum similarity classification of area resulted in good accuracy in 2014-2018. The coefficients obtained from the models confirm their model validation for future estimates and the possibility it usage to assess the extent of the affected areas and the evolution of progress for all regions. Manuscript profile

Open Access Article

9 - Comparing the Semantic Segmentation of High-Resolution Images Using Deep Convolutional Networks: SegNet, HRNet, CSE-HRNet and RCA-FCN
Nafiseh Sadeghi Homayoun Mahdavi-Nasab Mansoor Zeinali Hossein Pourghasem

10.61186/jist.39680.11.44.359

Semantic segmentation is a branch of computer vision, used extensively in image search engines, automated driving, intelligent agriculture, disaster management, and other machine-human interactions. Semantic segmentation aims to predict a label for each pixel from a giv More

Semantic segmentation is a branch of computer vision, used extensively in image search engines, automated driving, intelligent agriculture, disaster management, and other machine-human interactions. Semantic segmentation aims to predict a label for each pixel from a given label set, according to semantic information. Among the proposed methods and architectures, researchers have focused on deep learning algorithms due to their good feature learning results. Thus, many studies have explored the structure of deep neural networks, especially convolutional neural networks. Most of the modern semantic segmentation models are based on fully convolutional networks (FCN), which first replace the fully connected layers in common classification networks with convolutional layers, getting pixel-level prediction results. After that, a lot of methods are proposed to improve the basic FCN methods results. With the increasing complexity and variety of existing data structures, more powerful neural networks and the development of existing networks are needed. This study aims to segment a high-resolution (HR) image dataset into six separate classes. Here, an overview of some important deep learning architectures will be presented with a focus on methods producing remarkable scores in segmentation metrics such as accuracy and F1-score. Finally, their segmentation results will be discussed and we would see that the methods, which are superior in the overall accuracy and overall F1-score, are not necessarily the best in all classes. Therefore, the results of this paper lead to the point to choose the segmentation algorithm according to the application of segmentation and the importance degree of each class. Manuscript profile

Open Access Article

10 - Comparison of Faster RCNN and RetinaNet for Car Recognition in Adverse Weather
Yaser Jamshidi Raziyeh Sadat Okhovat

Vehicle detection and tracking plays an important role in self-driving cars and smart transportation systems. Adverse weather conditions, such as the heavy snow, fog, rain, dust, create dangerous limitations by reducing camera visibility and affect the performance of de More

Vehicle detection and tracking plays an important role in self-driving cars and smart transportation systems. Adverse weather conditions, such as the heavy snow, fog, rain, dust, create dangerous limitations by reducing camera visibility and affect the performance of detection algorithms used in traffic management systems and autonomous cars. In this article, Faster RCNN deep object recognition network with ResNet50 core and RetinaNet network is used and the accuracy of these two networks for vehicle recognition in adverse weather is investigated. The used dataset is the DAWN file, which contains real-world images collected with different types of adverse weather conditions. The obtained results show that the presented method has increased the detection accuracy from 0.2% to 75% in the best case, and the highest increase in accuracy is related to rainy conditions. Manuscript profile

Open Access Article

11 - Estimation of grain size curve of surface coarse sediments using imaging system designed
A.H. Tabee A. Karami – Khaniki A.A. Bidokhti K. Lari

Sediment recognition is one of the basic topics in coastal and river engineering. One of the parameters of sediment identification is their grain size. To determine the grain size, traditional methods such as sieving the sediments are usually used, which is accurate More

Sediment recognition is one of the basic topics in coastal and river engineering. One of the parameters of sediment identification is their grain size. To determine the grain size, traditional methods such as sieving the sediments are usually used, which is accurate but time consuming. Image processing provides the ability to isolate and track targets (sediment grains) in images using the smallest unit of a digital image (pixel). In this paper, a one-piece system for imaging coarse-grained field sediments and presenting a granulation curve is constructed and tested, in which sediment processing and analysis is performed with ImageJ software and the results are compared by sieving method and was validated. Image samples were taken from laboratory and natural sand and sand sediments. The results show that the distribution obtained from the images of coarse (larger than one millimeter) and uniform surface sediments has a good correlation with the distribution obtained from the sieve analysis and reduces the time to at least one tenth and the total cost. Manuscript profile

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