Sharif Digital Repository

Motivated by the bus escape routing problem in printed circuit boards, we revisit the following problem: given a set of n axis-parallel rectangles inside a rectangular region R, find the maximum number of rectangles that can be extended toward the boundary of R, without overlapping each other. We provide an efficient algorithm for solving this problem in O(n2 log3 n log log n) time, improving over the current best O(n3)-time algorithm available for the problem  

Motivated by the bus escape routing problem in printed circuit boards, we study the following rectangle escape problem: given a set S of n axis-aligned rectangles inside an axis-aligned rectangular region R, extend each rectangle in S toward one of the four borders of R so that the maximum density over the region R is minimized. The density of each point p∈R is defined as the number of extended rectangles containing p. We show that the problem is hard to approximate to within a factor better than 3/2 in general. When the optimal density is sufficiently large, we provide a randomized algorithm that achieves an approximation factor of 1+ε with high probability improving over the current best... 

We explore the dictionary learning problem for sparse representation when the signals are dependent. In this paper, a first-order Markovian model is considered for dependency of the signals, that has many applications especially in medical signals. It is shown that the considered dependency among the signals can degrade the performance of the existing dictionary learning algorithms. Hence, we propose a method using the Maximum Log-likelihood Estimator (MLE) and the Expectation Minimization (EM) algorithm to learn the dictionary from the signals generated under the first-order Markovian model. Simulation results show the efficiency of the proposed method in comparison with the... 

Data are presented on the solidification of aluminum alloys and their macro segregation behavior. Three alloys with different solidification ranges were prepared in two temperatures. Two types of molds were also prepared by sand and metallic materials. The solidification of Al alloys were studied and the effects of four parameters were determined, including cooling rate, casting temperature, degassing and nucleation effects on solidification and a comparison was conducted to Scheil model performance. Three types of aluminum alloys (Al-12.1%Si, Al-6.9%Si and Al-4.4% Cu) with short, medium and wide solidification ranges were studied and the results showed that degassing, nucleation, increasing... 

We revisit the problem of finding (Formula presented.) paths with a minimum number of shared edges between two vertices of a graph. An edge is called shared if it is used in more than one of the (Formula presented.) paths. We provide a (Formula presented.)-approximation algorithm for this problem, improving the best previous approximation factor of (Formula presented.). We also provide the first approximation algorithm for the problem with a sublinear approximation factor of (Formula presented.), where (Formula presented.) is the number of vertices in the input graph. For sparse graphs, such as bounded-degree and planar graphs, we show that the approximation factor of our algorithm can be... 

We revisit the problem of finding k paths with a minimum number of shared edges between two vertices of a graph. An edge is called shared if it is used in more than one of the k paths. We provide a ⌊k/2⌋-approximation algorithm for this problem, improving the best previous approximation factor of k - 1. We also provide the first approximation algorithm for the problem with a sublinear approximation factor of O(n3/4), where n is the number of vertices in the input graph. For sparse graphs, such as bounded-degree and planar graphs, we show that the approximation factor of our algorithm can be improved to O(√n). While the problem is NP-hard, and even hard to approximate to within an O(log n)... 

Error concealment is a useful method for improving the damaged video quality in the decoder side. In this paper, a dynamic method with low computational complexity is presented to improve the visual quality of videos when up to 50% of the frames are damaged. In the proposed method, temporal replacement and the improved outer boundary matching algorithm are used for dynamical error concealment in inter-frames of videos. With the use of motion vectors (MVs) which are close to the damaged macroblock (MB) the method can determine whether the motion in specific areas is either regular, irregular, or zero. Then, based on this knowledge, different methods are performed. It adaptively selects a set... 

Nowadays, the use of multimedia services such as video sequences is constantly growing. Unfortunately, due to the lack of reliable communication channels and video data sensitivity to transmission errors, the quality of received video might decrease. Therefore, decoder error concealment methods have been developed to retrieve the damaged or lost data. In this paper, a novel temporal error concealment (TEC) algorithm based on moment invariants is presented. It includes three main stages of: designation of candidate motion vectors (MVs) set, adaptive determination of block size in the current and reference frames for feature extraction, and error function calculation based on moment... 

While visual target tracking is one of the noteworthy and the most active research areas in computer vision and machine learning, many challenges are still unresolved. In this paper, an adaptive generic target tracker is proposed that includes the adaptive determination of learning parameters from spatio-temporal context model, analysis of prior targets and confidence map for accurate target localization, and modified scale estimation scheme based on confidence map. According to spatio-temporal context model, the learning parameters are adaptively determined for achieving confidence map and target scale robustly. Moreover, analysis of the confidence map helps our tracker to change context... 

In recent years, visual tracking methods that are based on discriminative correlation filters (DCFs) have been very promising. However, most of these methods suffer from a lack of robust scale estimation skills. Although a wide range of recent DCF-based methods exploit the features that are extracted from deep convolutional neural networks (CNNs) in their translation model, the scale of the visual target is still estimated by hand-crafted features. Whereas the exploitation of CNNs imposes a high computational burden, this paper exploits pre-trained lightweight CNNs models to propose two efficient scale estimation methods, which not only improve the visual tracking performance but also... 

Smoothing, while preserving edges, has always been a major challenge in image processing. In this paper, we propose a new approach that uses segmentation in order to avoid inter-region smoothing thus preserving the edges. It is common to smooth the image prior to region growing. The opposite procedure does not work properly in the presence of noise since region growing is very noise sensitive. To overcome this difficulty we adapted a robust region growing algorithm. Since region growing is very resource consuming, we do not perform it for every pixel. Instead, we divide the image into a number of overlapping blocks for which we carry out the segmentation. Then, we use the results for some of... 

Shape analysis plays an important role in many medical imaging studies. One of the recent shape analysis methods uses the Laplace Beltrami eigenvalues which is also used in this paper for global shape comparison of hippocampus of normal subjects and epileptic patients. Popularity of the Laplace Beltrami operator in this field is due to its isometry-invariance which avoids pre-processing steps like mapping, registration, and alignment. In addition, it is capable of revealing fine details in shapes that makes this method a good choice for deformation detecting purposes like epilepsy diagnosis. To examine capability of the proposed method, statistical analysis and two ways of classification,... 

Given a set of points in the plane, the unit clustering problem asks for finding a minimum-size set of unit disks that cover the whole input set. We study the unit clustering problem in a distributed setting, where input data is partitioned among several machines. We present a (3 + ϵ)-approximation algorithm for the problem in the Euclidean plane, and a (4 + ϵ)-approximation algorithm for the problem under general Lp metric (p1). We also study the capacitated version of the problem, where each cluster has a limited capacity for covering the points. We present a distributed algorithm for the capacitated version of the problem that achieves an approximation factor of 4+" in the L2 plane, and a... 

Due to the automatic feature extraction procedure via multi-layer nonlinear transformations, the deep learning-based visual trackers have recently achieved a great success in challenging scenarios for visual tracking purposes. Although many of those trackers utilize the feature maps from pre-trained convolutional neural networks (CNNs), the effects of selecting different models and exploiting various combinations of their feature maps are still not compared completely. To the best of our knowledge, all those methods use a fixed number of convolutional feature maps without considering the scene attributes (e.g., occlusion, deformation, and fast motion) that might occur during tracking. As a... 

Visual target tracking is one of the most sought-after yet challenging research topics in computer vision. Given the ill-posed nature of the problem and its popularity in a broad range of real-world scenarios, a number of large-scale benchmark datasets have been established, on which considerable methods have been developed and demonstrated with significant progress in recent years - predominantly by recent deep learning (DL)-based methods. This survey aims to systematically investigate the current DL-based visual tracking methods, benchmark datasets, and evaluation metrics. It also extensively evaluates and analyzes the leading visual tracking methods. First, the fundamental... 

Visual target tracking is one of the most sought-after yet challenging research topics in computer vision. Given the ill-posed nature of the problem and its popularity in a broad range of real-world scenarios, a number of large-scale benchmark datasets have been established, on which considerable methods have been developed and demonstrated with significant progress in recent years - predominantly by recent deep learning (DL)-based methods. This survey aims to systematically investigate the current DL-based visual tracking methods, benchmark datasets, and evaluation metrics. It also extensively evaluates and analyzes the leading visual tracking methods. First, the fundamental... 

Packing problems arise in a wide variety of application areas. The basic problem is that of determining an efficient arrangement of different objects in a region without any overlap and with minimal wasted gap between shapes. This paper presents a novel population based approach for optimizing arrangement of irregular shapes. In this approach, each shape is coded as an agent and the agents' reproductions and grouping policies results in arrangements of the objects in positions with least wasted area between them. The approach is implemented in an application for cutting sheets and test results on several problems from literature are presented. COPYRIGHT © ENFORMATIKA  

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Shape analysis plays an important role in many medical imaging studies. One of the recent shape analysis methods uses the Laplace Beltrami operator which is also used in this paper for hippocampal shape comparison. We proposed a feature vector which consists of size measures and shape descriptors based on Laplace Beltrami eigenvalues and eigenfunctions. The aforementioned feature space is utilised for automatic differentiating normal subjects from epileptic patients as well as distinguishing epileptic patients with left temporal lobe epilepsy (LTLE) from patients with right temporal lobe epilepsy (RTLE). Achieved results are diagnostic accuracy of 93% with 95% sensitivity and lateralization... 

A passive image steganalysis method is proposed to universally detect spatial-domain steganography schemes. It is shown to have better performance than universal steganalyzers known to be powerful in spatial domain, including the WFLogSv and the WAM methods. This level of accuracy is the result of improving the WFLogSv steganalyzer by considering a more comprehensive relationship between the singular values of each image block and the linear correlation of the rows and the columns. That is, instead of the closeness of the lower singular values to zero, the energy distribution of the singular values is investigated. An innovative measure is proposed for this investigation, which is inspired...