List of subject articles Image Processing


    • Open Access Article

      1 - Eye Gaze Detection Based on Learning Automata by Using SURF Descriptor
      Hassan Farsi Reza Nasiripour Sajad Mohammadzadeh
      In the last decade, eye gaze detection system is one of the most important areas in image processing and computer vision. The performance of eye gaze detection system depends on iris detection and recognition (IR). Iris recognition is very important role for person iden Full Text
      In the last decade, eye gaze detection system is one of the most important areas in image processing and computer vision. The performance of eye gaze detection system depends on iris detection and recognition (IR). Iris recognition is very important role for person identification. The aim of this paper is to achieve higher recognition rate compared to learning automata based methods. Usually, iris retrieval based systems consist of several parts as follows: pre-processing, iris detection, normalization, feature extraction and classification which are captured from eye region. In this paper, a new method without normalization step is proposed. Meanwhile, Speeded up Robust Features (SURF) descriptor is used to extract features of iris images. The descriptor of each iris image creates a vector with 64 dimensions. For classification step, learning automata classifier is applied. The proposed method is tested on three known iris databases; UBIRIS, MMU and UPOL database. The proposed method results in recognition rate of 100% for UBIRIS and UPOL databases and 99.86% for MMU iris database. Also, EER rate of the proposed method for UBIRIS, UPOL and MMU iris database are 0.00%, 0.00% and 0.008%, respectively. Experimental results show that the proposed learning automata classifier results in minimum classification error, and improves precision and computation time. Manuscript Document
    • Open Access Article

      2 - Improvement in Accuracy and Speed of Image Semantic Segmentation via Convolution Neural Network Encoder-Decoder
      هانیه زمانیان Hassan Farsi Sajad Mohammadzadeh
      Recent researches on pixel-wise semantic segmentation use deep neural networks to improve accuracy and speed of these networks in order to increase the efficiency in practical applications such as automatic driving. These approaches have used deep architecture to predic Full Text
      Recent researches on pixel-wise semantic segmentation use deep neural networks to improve accuracy and speed of these networks in order to increase the efficiency in practical applications such as automatic driving. These approaches have used deep architecture to predict pixel tags, but the obtained results seem to be undesirable. The reason for these unacceptable results is mainly due to the existence of max pooling operators, which reduces the resolution of the feature maps. In this paper, we present a convolutional neural network composed of encoder-decoder segments based on successful SegNet network. The encoder section has a depth of 2, which in the first part has 5 convolutional layers, in which each layer has 64 filters with dimensions of 3×3. In the decoding section, the dimensions of the decoding filters are adjusted according to the convolutions used at each step of the encoding. So, at each step, 64 filters with the size of 3×3 are used for coding where the weights of these filters are adjusted by network training and adapted to the educational data. Due to having the low depth of 2, and the low number of parameters in proposed network, the speed and the accuracy improve compared to the popular networks such as SegNet and DeepLab. For the CamVid dataset, after a total of 60,000 iterations, we obtain the 91% for global accuracy, which indicates improvements in the efficiency of proposed method. Manuscript Document
    • Open Access Article

      3 - A Novel Method for Image Encryption Using Modified Logistic Map
      ardalan Ghasemzadeh Omid R.B.  Speily
      With the development of the internet and social networks, the interest on multimedia data, especially digital images, has been increased among scientists. Due to their advantages such as high speed as well as high security and complexity, chaotic functions have been ext Full Text
      With the development of the internet and social networks, the interest on multimedia data, especially digital images, has been increased among scientists. Due to their advantages such as high speed as well as high security and complexity, chaotic functions have been extensively employed in images encryption. In this paper, a modified logistic map function was proposed, which resulted in higher scattering in obtained results. Confusion and diffusion functions, as the two main actions in cryptography, are not necessarily performed respectively, i.e. each of these two functions can be applied on the image in any order, provided that the sum of total functions does not exceed 10. In calculation of sum of functions, confusion has the coefficient of 1 and diffusion has the coefficient of 2. To simulate this method, a binary stack is used. Application of binary stack and pseudo-random numbers obtained from the modified chaotic function increased the complexity of the proposed encryption algorithm. The security key length, entropy value, NPCR and UICA values and correlation coefficient analysis results demonstrate the feasibility and validity of the proposed method. Analyzing the obtained results and comparing the algorithm to other investigated methods clearly verified high efficiency of proposed method. Manuscript Document
    • Open Access Article

      4 - Retinal Vessel Extraction Using Dynamic Threshold And Enhancement Image Filter From Retina Fundus
      erwin erwin Tomi Kiyatmoko
      In the diagnosis of retinal disease, Retinal vessels become an important role in determining certain diseases. Retina vessels are an important element with a variety of shapes and sizes, each human blood vessel also can determine the disease with various types, but the Full Text
      In the diagnosis of retinal disease, Retinal vessels become an important role in determining certain diseases. Retina vessels are an important element with a variety of shapes and sizes, each human blood vessel also can determine the disease with various types, but the feasibility of the pattern of retinal blood vessels is very important for the advanced diagnosis process in medical retina such as detection, identification and classification. Improvement and improvement of image quality in this case is very important by focusing on extracting or segmenting the retinal veins so that parameters such as accuracy, specifications, and sensitivity can be obtained that are better and meet the advanced system. Therefore we conducted experiments in order to develop extraction of retinal images to obtain binary images of retinal vessels in the medical world using Dynamic Threshold and Butterworth Bandpass Filter. Using a database DRIVE Accuracy of 94.77%, sensitivity of 54.48% and specificity of 98.71%. Manuscript Document
    • Open Access Article

      5 - Body Field: Structured Mean Field with Human Body Skeleton Model and Shifted Gaussian Edge Potentials
      Sara Ershadi-Nasab Shohreh Kasaei Esmaeil Sanaei Erfan Noury Hassan Hafez-kolahi
      An efficient method for simultaneous human body part segmentation and pose estimation is introduced. A conditional random field with a fully-connected graphical model is used. Possible node (image pixel) labels comprise of the human body parts and the background. In the Full Text
      An efficient method for simultaneous human body part segmentation and pose estimation is introduced. A conditional random field with a fully-connected graphical model is used. Possible node (image pixel) labels comprise of the human body parts and the background. In the human body skeleton model, the spatial dependencies among body parts are encoded in the definition of pairwise energy functions according to the conditional random fields. Proper pairwise edge potentials between image pixels are defined according to the presence or absence of human body parts that are near to each other. Various Gaussian kernels in position, color, and histogram of oriented gradients spaces are used for defining the pairwise energy terms. Shifted Gaussian kernels are defined between each two body parts that are connected to each other according to the human body skeleton model. As shifted Gaussian kernels impose a high computational cost to the inference, an efficient inference process is proposed by a mean field approximation method that uses high dimensional shifted Gaussian filtering. The experimental results evaluated on the challenging KTH Football, Leeds Sports Pose, HumanEva, and Penn-Fudan datasets show that the proposed method increases the per-pixel accuracy measure for human body part segmentation and also improves the probability of correct parts metric of human body joint locations. Manuscript Document
    • Open Access Article

      6 - A Two-Stage Multi-Objective Enhancement for Fused Magnetic Resonance Image and Computed Tomography Brain Images
      Leena Chandrashekar A Sreedevi Asundi
      Magnetic Resonance Imaging (MRI) and Computed Tomography (CT) are the imaging techniques for detection of Glioblastoma. However, a single imaging modality is never adequate to validate the presence of the tumor. Moreover, each of the imaging techniques represents a diff Full Text
      Magnetic Resonance Imaging (MRI) and Computed Tomography (CT) are the imaging techniques for detection of Glioblastoma. However, a single imaging modality is never adequate to validate the presence of the tumor. Moreover, each of the imaging techniques represents a different characteristic of the brain. Therefore, experts have to analyze each of the images independently. This requires more expertise by doctors and delays the detection and diagnosis time. Multimodal Image Fusion is a process of generating image of high visual quality, by fusing different images. However, it introduces blocking effect, noise and artifacts in the fused image. Most of the enhancement techniques deal with contrast enhancement, however enhancing the image quality in terms of edges, entropy, peak signal to noise ratio is also significant. Contrast Limited Adaptive Histogram Equalization (CLAHE) is a widely used enhancement technique. The major drawback of the technique is that it only enhances the pixel intensities and also requires selection of operational parameters like clip limit, block size and distribution function. Particle Swarm Optimization (PSO) is an optimization technique used to choose the CLAHE parameters, based on a multi objective fitness function representing entropy and edge information of the image. The proposed technique provides improvement in visual quality of the Laplacian Pyramid fused MRI and CT images. Manuscript Document
    • Open Access Article

      7 - Farsi Font Detection using the Adaptive RKEM-SURF Algorithm
      Zahra Hossein-Nejad Hamed Agahi Azar Mahmoodzadeh
      Farsi font detection is considered as the first stage in the Farsi optical character recognition (FOCR) of scanned printed texts. To this aim, this paper proposes an improved version of the speeded-up robust features (SURF) algorithm, as the feature detector in the font Full Text
      Farsi font detection is considered as the first stage in the Farsi optical character recognition (FOCR) of scanned printed texts. To this aim, this paper proposes an improved version of the speeded-up robust features (SURF) algorithm, as the feature detector in the font recognition process. The SURF algorithm suffers from creation of several redundant features during the detection phase. Thus, the presented version employs the redundant keypoint elimination method (RKEM) to enhance the matching performance of the SURF by reducing unnecessary keypoints. Although the performance of the RKEM is acceptable in this task, it exploits a fixed experimental threshold value which has a detrimental impact on the results. In this paper, an Adaptive RKEM is proposed for the SURF algorithm which considers image type and distortion, when adjusting the threshold value. Then, this improved version is applied to recognize Farsi fonts in texts. To do this, the proposed Adaptive RKEM-SURF detects the keypoints and then SURF is used as the descriptor for the features. Finally, the matching process is done using the nearest neighbor distance ratio. The proposed approach is compared with recently published algorithms for FOCR to confirm its superiority. This method has the capability to be generalized to other languages such as Arabic and English. Manuscript Document