Sharif Digital Repository

Using 3-D scanned data to analyze and extract pore network plays a vital role in investigation of porous media's characteristics. In this paper, a new simple method is developed to detect pores and throats for analyzing the connectivity and permeability of the network. This automated method utilizes some of the common and well-known image processing functions which are widely accessible by researchers and this has led to an easy algorithm implementation. In this method, after polishing and quality control of images, using city-block distance function and watershed segmentation algorithm, pores and throats are detected and 3-D network is produced. This method can also be applied on 2-D images... 

Synthetic aperture radar (SAR) appropriate image processing in conjunction with noise reduction is crucial in proper image segmentation. The authors present a new algorithm, logarithmic adaptive soft morphological (LASM) filter, utilising collectivity and flexibility of order-statistic soft morphological filters. This method not only reduces the speckle noise of the single-look SAR imagery considerably, but it significantly enhances the segmentation results. To verify the performance, a simulated SAR image is first created by applying an imagery method to an original noiseless image. The resulting image has characteristics identical to a real SAR image. The LASM method, as well as several... 

We present an attempt to improve the performance of multi-class image segmentation systems based on a multilevel description of segments. The multi-class image segmentation system used in this paper marks the segments in an image, describes the segments via multilevel feature vectors and passes the vectors to a multi-class object classifier. The focus of this paper is on the segment description section. We first propose a robust, scale-invariant texture feature set, named directional differences (DDs). This feature is designed by investigating the flaws of conventional texture features. The advantages of DDs are justified both analytically and experimentally. We have conducted several... 

We propose to use a compressed sensing recovery method called IMATCS for improving the resolution in microwave imaging applications. The electromagnetic inverse scattering problem is solved using the Distorted Born Iterative Method combined with the IMATCS algorithm. This method manages to recover small targets in cases where traditional DBIM approaches fail. Furthermore, by applying an L2-based approach to regularize the sparse recovery algorithm, we improve the algorithm's robustness and demonstrate its ability to image complex breast structures. Although our simulation scenarios do not fully represent experimental or clinical data, our results suggest that the proposed algorithm may be... 

Image segmentation is one of the fundamental problems in image processing and computer vision, since it is the first step in many image analysis systems. This paper presents a new perspective to image segmentation, namely, segmenting input images by applying efficient community detection algorithms common in social and complex networks. First, a common segmentation algorithm is used to fragment the image into small initial regions. A weighted network is then constructed. Each initial region is mapped to a vertex, and all these vertices are connected to each other. The similarity between two regions is calculated from colour information. This similarity is then used to assign weights to the... 

Segmentation of a scene to the part made is a challenging work. In this paper a graphical model is used for this task. The methods based on geometrical derivatives such as curvature and normal often haven't good result in segmentation of geometrically-complex architecture and lead to over-segmentation and even failure. Proposed method for segmentation contains two steps. At first region growing based on curvature, normal and color is used for growing region. This segmented cloud is used for unary potential in graphical model. Fully connected graph for Conditional Random Field with Gaussian kernel for pair wise potentials is used for correcting this segmentation. Gaussian kernels are based on... 

There has been an increasing demand for interactive video transmission over the Internet for applications such as video conferencing, video calls, and telepresence applications. These applications are increasingly moving towards providing High Definition (HD) video quality to users. A key challenge in these applications is to preserve the quality of video when it is transported over best-effort networks that do not guarantee lossless transport of video packets. In such conditions, it is important to protect the transmitted video by using intelligent and adaptive protection schemes. Applications such as HD video conferencing require live interaction among participants, which limits the... 

Digital watermarking is an efficacious technique to protect the copyright and ownership of digital information. Traditional image watermarking algorithms embed a logo in the image that reduces its visual quality. A new approach in watermarking called zero watermarking doesn’t need to embed a logo in the image. In this algorithm we find a feature from the main image and combine it with a logo to obtain a key. This key is securely kept by a trusted authority. In this paper we show that we can increase the robustness of digital zero watermarking by a new counter detection method in comparison to Canny Edge detection and morphological dilatation that is mostly used by related works. Experimental... 

This study proposes a spiking neuro-fuzzy clustering system based on a novel spike encoding scheme and a compatible learning algorithm. In this system, we utilize an analog to binary encoding scheme that properly maps the concept of "distance" in multi-dimensional analog spaces to the concept of "dissimilarity " of binary bits in the equivalent binary spaces. When this scheme is combined with a novel binary to spike encoding scheme and a proper learning algorithm is applied, a powerful clustering algorithm is produced. This algorithm creates flexible fuzzy clusters in its analog input space and modifies their shapes to different convex shapes during the learning process. This system has... 

A new method for speeding up the approximate maximum posterior marginal (MPM) inference in meanfield approximation of a fully connected graph is introduced. Weight of graph edges is measured by mixture of Gaussian kernels. This fully connected graph is used for segmentation of image data. The bottleneck of the inference in meanfield approximation is where the similar bilateral filtering is needed for updating the marginal in the message passing step. To speed up the inference, the adaptive manifold high dimensional Gaussian filter is used. As its time complexity is 0(ND), it leads to accelerating the marginal update in the message passing step. Its time complexity is linear and relative to... 

Segmentation of aerial images has been a challenging task in recent years. This paper introduces a simple and efficient method for segmentation and classification of aerial images based on a pixel-level classification. To this end, we use the Gabor texture features in HSV color space as our best experienced features for aerial images segmentation and classification. We test different classifiers including KNN, SVM and a classifier based on sparse representation. Comparison of our proposed method with a sample of segmentation pre-process based classification methods shows that our pixel-wise approach results in higher accuracy results with lower computation time  

Most of the clinically useful information in Electrocardiogram (ECG) signal can be obtained from the intervals, amplitudes and wave shapes (morphologies). The automatic detection of ECG waves is important to cardiac disease diagnosis. In this paper, we propose an efficient method for extraction of characteristic points of ECG. The method is based on a nonlinear dynamic model, previously introduced for generation of synthetic ECG signals. For estimating the parameters of model, we use an Extendend Kalman Filter (EKF). By introducing a simple AR model for each of the dynamic parameters of Gaussian functions in model and considering separate states for ECG waves, the new EKF structure was... 

The automatic detection of Electrocardiogram (ECG) waves is important to cardiac disease diagnosis. A good performance of an automatic ECG analyzing system depends heavily upon the accurate and reliable detection of QRS complex, as well as P and T waves. In this paper, we propose an efficient method for extraction of characteristic points of ECG signal. The method is based on a nonlinear dynamic model, previously introduced for generation of synthetic ECG signals. For estimating the parameters of model, we use an Extendend Kalman Filter (EKF). By introducing a simple AR model for each of the dynamic parameters of Gaussian functions in model and considering separate states for ECG waves, the... 

The configuration of landmarks is an important issue in minimizing the target registration error (TRE). In this paper the effect of different landmark configurations on the accuracy of pedicle screw placement during image guided spine surgery (IGSS) is investigated. Since the spine is deformed in intra-operative conditions compared to the preoperative situation, an accurate alignment of each vertebra is crucial to compensate for the deformation. CT compatible markers are placed over anatomical landmarks which are feasible and routinely used in surgical procedures. The TRE is obtained directly for the markers which are placed on the right and left pedicle of the vertebra. The estimated TRE... 

Methods of human semen assessment are quite wide ranging. In this paper, we use background subtraction methods in order to detect progressive sperms whose quality of movement strongly influence fertility. Robust Principal Component Analysis (RPCA) is a powerful algorithm which has been used recently for background subtraction purposes. Sperm tracking problem can also be defined as a background subtraction problem. In RPCA algorithm, data is represented by a low rank plus sparse matrix. In our approach, the foreground data is recovered through such matrix decomposition. We compare the RPCA approach with four other background subtraction methods in order to check accuracy of algorithm as a... 

Automatic extraction of the varying regions of magnetic resonance images is required as a prior step in a diagnostic intelligent system. The sparsest representation and high-dimensional feature are provided based on learned dictionary. The classification is done by employing the technique that computes the reconstruction error locally and non-locally of each pixel. The acquired results from the real and simulated images are superior to the best MRI segmentation method with regard to the stability advantages. In addition, it is segmented exactly through a formula taken from the distance and sparse factors. Also, it is done automatically taking sparse factor in unsupervised clustering methods... 

Progressive image transmission enables the receivers to reconstruct a transmitted image at various bit rates. Most of the works in this field are based on the conventional Shannon-Nyquist sampling theory. In the present work, progressive image transmission is investigated using sparse recovery of random samples. The sparse recovery methods such as Iterative Method with Adaptive Thresholding (IMAT) and Iterative IKMAX Thresholding (IKMAX) are exploited in this framework since they have the ability for successive reconstruction. The simulation results indicate that the proposed method performs well in progressive recovery. The IKMAX has better final reconstruction than IMAT at the cost of... 

Recognition and correction of inhomogeneous displacement caused by patient's movement has been recently discussed as an interesting topic in medical image processing. Considering consistency in general structure of the image during distortion, histogram could be employed as a fast implementation method in feature domain. Accordingly, attribute vectors could be defined for each pixel based on spatial features to find corresponding points in two images. Consequently a point-based and non-rigid transformation approach will be designed. A B-spline image registration has been applied to match those pairs with a defined smoothness factor. This algorithm is a step-by-step registration process... 

Finger-Knuckle-Print (FKP) is one of the newest biometrics. In this paper, a novel approach has been proposed to segment the Region of Interest (ROI) of a FKP image using the global intensity. This method upgrades the speed and accuracy of segmentation stage, as well as the pace of other steps of the procedure. This has been achieved by employing the area with maximum intensity in ROI extraction, instead of using the creases of the knuckle image. To confirm this improvement, lots of experiments have been performed and the method has been compared with the only existing schemes for ROI extraction suggested by Zhang and Kekre. At the end, the captured ROI images obtained by three methods have... 

Processing time and segmentation quality are two main factors in evaluation of image segmentation methods. Oversegmentation is one of the most challenging problems that significantly degrade the segmentation quality. This paper presents a framework for decreasing the oversegmentation rate and improving the processing time. Significant variations in both color and texture spaces are the main reasons that lead to oversegmentation. We exploit Affine Invariant Region Detectors to mark regions with high variations in both color and texture spaces. The results are then utilized to reduce the oversegmentation rate of image segmentation algorithms. The performance of the proposed framework is...