Search for: computer-vision
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    A real-time and robust algorithm for calculation of time to lane crossing

    , Article IFAC/EURON Symposium on Intelligent Autonomous Vehicles, 5 July 2004 through 7 July 2004 ; Volume 37, Issue 8 , 2004 , Pages 132-135 ; 14746670 (ISSN) Saniee, M ; Jamzad, M ; Habibi, J ; Sharif University of Technology
    IFAC Secretariat  2004
    A real-time collision warning system based on TLC1 computation is proposed. Multi-frame lane detection and analysis are adopted to make the TLC calculation algorithm more reliable. The proposed system is robust because of its capability to calculate the TLC in different brightness. The presented system can be used in an intelligent vehicle for warning or lateral control. Experimental results show that the proposed algorithm, calculates TLC acceptably. © 2004 IFAC  

    GMWASC: Graph matching with weighted affine and sparse constraints

    , Article CSSE 2015 - 20th International Symposium on Computer Science and Software Engineering, 18 August 2015 ; 2015 ; 9781467391818 (ISBN) Taheri Dezaki , F ; Ghaffari, A ; Fatemizadeh, E ; Sharif University of Technology
    Institute of Electrical and Electronics Engineers Inc  2015
    Graph Matching (GM) plays an essential role in computer vision and machine learning. The ability of using pairwise agreement in GM makes it a powerful approach in feature matching. In this paper, a new formulation is proposed which is more robust when it faces with outlier points. We add weights to the one-to-one constraints, and modify them in the process of optimization in order to diminish the effect of outlier points in the matching procedure. We execute our proposed method on different real and synthetic databases to show both robustness and accuracy in contrast to several conventional GM methods  

    Genetic algorithm-optimised structure of convolutional neural network for face recognition applications

    , Article IET Computer Vision ; Volume 10, Issue 6 , 2016 , Pages 559-566 ; 17519632 (ISSN) Rikhtegar, A ; Pooyan, M ; Manzuri Shalmani, M. T ; Sharif University of Technology
    Institution of Engineering and Technology  2016
    Proposing a proper method for face recognition is still a challenging subject in biometric and computer vision applications. Although some reliable systems were introduced under relatively controlled conditions, their recognition rate is not satisfactory in the general settings. This is especially true when there are variations in pose, illumination, and facial expression. To alleviate these problems, a hybrid face recognition system is proposed which benefits from the superiority of both convolutional neural network (CNN) and support vector machine (SVM). To this end, first a genetic algorithm is employed to find the optimum structure of CNN. Then, the performance of the system is improved... 

    Principal color and its application to color image segmentation

    , Article Scientia Iranica ; Volume 15, Issue 2 , 2008 , Pages 238-245 ; 10263098 (ISSN) Abadpour, A ; Kasaei, S ; Sharif University of Technology
    Sharif University of Technology  2008
    Color image segmentation is a primitive operation in many image processing and computer vision applications. Accordingly, there exist numerous segmentation approaches in the literature, which might be misleading for a researcher who is looking for a practical algorithm. While many researchers are still using the tools which belong to the old color space paradigm, there is evidence in the research established in the eighties that a proper descriptor of color vectors should act locally in the color domain. In this paper, these results are used to propose a new color image segmentation method. The proposed method searches for the principal colors, defined as the intersections of the cylindrical... 

    Fall Detection Using Depth Videos

    , M.Sc. Thesis Sharif University of Technology Hosseinzadeh, Matin (Author) ; Vosoughi-Vahdat, Bijan (Supervisor)
    Falls are one of the major causes leading to injury of elderly people. Using wearable devices for fall detection has a high cost and may cause inconvenience to the daily lives of the elderly. In this project, we present an automated fall detection approach that requires only a low-cost depth camera. Our approach combines two computer vision techniques, spatio-temporal fall characterization and a learning-based classifier to distinguish falls from other daily actions. A dense set of spatio-temporal feature vectors are computed from video to provide a localized description of the action, and subsequently aggregated in an empirical covariance matrix to compactly represent the action. Then, we... 

    Deep Learning for Action Recognition

    , M.Sc. Thesis Sharif University of Technology Aslan Beigi, Fatemeh (Author) ; Vosoughi Vahdat, Bijan (Supervisor) ; Mohammadzadeh, Narjesolhoda (Supervisor)
    Computers, laptops, tablets and even cell phones are capable of recording, producing, storing and sharing videos. With the increasing availability of movies and more and easier access to them, the need for understanding videos has increased. Due to the limited human ability in analyzing videos, there is an increasing demand for intelligent systems to analyze videos and recognize the actions in them.Action recognition is the classification of the action performed by the individual in the video, and there are different types of action recognition depending on the nature of the data and the way it will be processed. Vision-based human action recognition is affected by several challenges due to... 

    Scale invariant feature transform using oriented pattern

    , Article Canadian Conference on Electrical and Computer Engineering ; 2014 Daneshvar, M. B ; Babaie-Zadeh, M ; Ghorshi, S ; Sharif University of Technology
    Image matching plays an important role in many aspects of computer vision. Our proposed method is based on Scale Invariant Feature Transform (SIFT) which is one of the popular image matching methods. The main ideas behind our method are removing the excess keypoints, adding oriented patterns to descriptor, and decreasing the size of the descriptors. By doing these changes to SIFT, we would have oriented patterns of keypoints. In addition, the numbers of keypoints have been reduced and the places of keypoints would be selected more accurately, and also the size of the descriptors has been reduced  

    SM3D studio: A 3D model constructor

    , Article Iranian Conference on Machine Vision and Image Processing, MVIP ; 2013 , Pages 10-15 ; 21666776 (ISSN); 9781467361842 (ISBN) Soleimani, V ; Vincheh, F. H ; Zare, E ; Engineers (IEEE) Antennas and Propagation Society; The Institute of Electrical and Electronics ; Sharif University of Technology
    IEEE Computer Society  2013
    In this paper we describe designing and implementation of a powerful, fast and compact simple 3D modeler (SM3D). In addition to saving cost and time (due to high processing speed), 3D objects can be created with minimum system resources by using this application. Easy learning and using are other strengths of this application. Modularity using classification and applying Dynamic-Link Library files are noted aspects that are regarded in writing the source code and this causes separation of main part and user interface, so the application can be easily expanded in the future. Ability to create primary objects and also applying advanced transformations and modifiers have been considered.... 

    Sparse based similarity measure for mono-modal image registration

    , Article Iranian Conference on Machine Vision and Image Processing, MVIP ; Sept , 2013 , Pages 462-466 ; 21666776 (ISSN) ; 9781467361842 (ISBN) Ghaffari, A ; Fatemizadeh, E ; Sharif University of Technology
    IEEE Computer Society  2013
    Similarity measure is an important key in image registration. Most traditional intensity-based similarity measures (e.g., SSD, CC, MI, and CR) assume stationary image and pixel by pixel independence. Hence, perfect image registration cannot be achieved especially in presence of spatially-varying intensity distortions and outlier objects that appear in one image but not in the other. Here, we suppose that non stationary intensity distortion (such as Bias field or Outlier) has sparse representation in transformation domain. Based on this as-sumption, the zero norm (ℓ0)of the residual image between two registered images in transform domain is introduced as a new similarity measure in presence... 

    Pazesh: A graph-based approach to increase readability of automatic text summaries

    , Article Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), 25 May 2011 through 27 May 2011, St. John's, NL ; Volume 6657 LNAI , 2011 , Pages 313-318 ; 03029743 (ISSN) ; 9783642210426 (ISBN) Mostafazadeh, N ; Mirroshandel, S. A ; Ghassem-Sani, G ; Bakhshandeh Babarsad, O ; Sharif University of Technology
    Today, research on automatic text summarization challenges on readability factor as one of the most important aspects of summarizers' performance. In this paper, we present Pazesh: a language-independent graph-based approach for increasing the readability of summaries while preserving the most important content. Pazesh accomplishes this task by constructing a special path of salient sentences which passes through topic centroid sentences. The results show that Pazesh compares approvingly with previously published results on benchmark datasets  

    Multi-modal distance metric learning: A bayesian non-parametric approach

    , Article Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), 6 September 2014 through 12 September 2014 ; Volume 8927 , September , 2015 , Pages 63-77 ; 03029743 (ISSN) ; 9783319161983 (ISBN) Babagholami Mohamadabadi, B ; Roostaiyan, S. M ; Zarghami, A ; Baghshah, M. S ; Rother, C ; Agapito, L ; Bronstein, M. M ; Sharif University of Technology
    Springer Verlag  2015
    In many real-world applications (e.g. social media application), data usually consists of diverse input modalities that originates from various heterogeneous sources. Learning a similarity measure for such data is of great importance for vast number of applications such as classification, clustering, retrieval, etc. Defining an appropriate distance metric between data points with multiple modalities is a key challenge that has a great impact on the performance of many multimedia applications. Existing approaches for multi-modal distance metric learning only offer point estimation of the distance matrix and/or latent features, and can therefore be unreliable when the number of training... 

    Automatic access control based on face and hand biometrics in a non-cooperative context

    , Article Proceedings - 2018 IEEE Winter Conference on Applications of Computer Vision Workshops, WACVW 2018 ; Volume 2018-January , 2018 , Pages 28-36 ; 9781538651889 (ISBN) Sabet Jahromi, M. N ; Bonderup, M. B ; Asadi Aghbolaghi, M ; Avots, E ; Nasrollahi, K ; Escalera, S ; Kasaei, S ; Moeslund, T. B ; Anbarjafari, G ; Sharif University of Technology
    Institute of Electrical and Electronics Engineers Inc  2018
    Automatic access control systems (ACS) based on the human biometrics or physical tokens are widely employed in public and private areas. Yet these systems, in their conventional forms, are restricted to active interaction from the users. In scenarios where users are not cooperating with the system, these systems are challenged. Failure in cooperation with the biometric systems might be intentional or because the users are incapable of handling the interaction procedure with the biometric system or simply forget to cooperate with it, due to for example, illness like dementia. This work introduces a challenging bimodal database, including face and hand information of the users when they... 

    Malignancy determination of tumors using perfusion MRI

    , Article 2009 International Conference on Image Processing, Computer Vision, and Pattern Recognition, IPCV 2009, Las Vegas, NV, 13 July 2009 through 16 July 2009 ; Volume 2 , 2009 , Pages 906-909 ; 9781601321190 (ISBN) Tavakol, A ; Soltanian Zadeh, H ; Akhlaghpour, S ; Fatemi Zadeh, E ; United States Military Academy, Network Science Center; HST Harvard Univ. MIT, Biomed. Cybern. Lab.; Argonne's Leadersh. Comput. Facil. Argonne Natl. Lab.; Univ. Illinois Urbana-Champaign, Funct. Genomics Lab.; University of Minnesota, Minnesota Supercomputing Institute ; Sharif University of Technology
    Our purpose was to determine whether perfusion MR imaging can be used for malignancy determination of tumors. Relative cerebral blood flow (rCBF) is a commonly used perfusion magnetic resonance imaging (MRI) technique for the evaluation of malignancy. The goal of our study was to determine the usefulness of this parameter in malignancy determination of tumors using Independent Component Analysis (ICA)  

    Design and application of industrial machine vision systems

    , Article Robotics and Computer-Integrated Manufacturing ; Volume 23, Issue 6 , December , 2007 , Pages 630-637 ; 07365845 (ISSN) Golnabi, H ; Asadpour, A ; Sharif University of Technology
    In this paper, the role and importance of the machine vision systems in the industrial applications are described. First understanding of the vision in terms of a universal concept is explained. System design methodology is discussed and a generic machine vision model is reported. Such a machine includes systems and sub-systems, which of course depend on the type of applications and required tasks. In general, expected functions from a vision machine are the exploitation and imposition of the environmental constraint of a scene, the capturing of the images, analysis of those captured images, recognition of certain objects and features within each image, and the initiation of subsequent... 

    3D-Reconstruction Using Static and Mobile Stereo-Camera for 3D-Reconstruction

    , M.Sc. Thesis Sharif University of Technology Boomari, Hossein (Author) ; Zarei, Alireza (Supervisor)
    3D-object modeling and its representation in computers are one of the interested fields in computer science and engineering and problems like object and environment modeling, representation, storage and physical interactions are some of the important problems in this field. Increasing the applications of the technologies like localization, machine vision and virtual reality made the 3D-object modeling and its related problems, like 3D-model extraction and reconstruction, a nowadays interested challenges and a variety of solutions such as time of flight sensors,
    structured light, sonar sensors and multi-camera reconstruction are presented for it. Multi-camera solutions, just like the... 

    Synchronization of Multiple Videos of a Scene with Drop Possibility Submitted in Partial Fulfillment of the Requirement

    , M.Sc. Thesis Sharif University of Technology Bagheri, Mohammad Reza (Author) ; Kasaei, Shohreh (Supervisor)
    Today many videos in an interval of a particular scene in a different view angle to each other are filmed. Use these videos are used in numerous fields. Time synchronization between the video, is the basis for their use. In order to provide an automated process for creating time synchronization between the video, numerous studies have been conducted that each requires specific conditions are to achieve the desired result. The biggest challenge of this problem, which appears when the camera recorded video viewing angles, high and low, and when filming the shared vision between them, jump frames is taken. Players are coordinated. In experiments conducted on different data sets, the average... 

    Fine-grained Image Classification

    , M.Sc. Thesis Sharif University of Technology Souri, Yaser (Author) ; Kasaei, Shohreh (Supervisor)
    Fine-grained image classification is image classification where the considered classes are all sub-classes of a certain, more general class. In this setting of the problem, the classes are visually very similar to each other, such that an unskilled human cannot discriminate between them. In this case, proposed methods for the ordinary image classification problem do not obtain good classification accuracy. So proposing new methods for solving this problem is necessary. In this thesis two new methods, based on recent advances in deep learning are proposed for solving the fine-grained image classification problem. First by improving several parts of one of the recent proposed methods for this... 

    The Application of Machine Vision to Identify the Images Underwater

    , M.Sc. Thesis Sharif University of Technology Kaboli, Ali (Author) ; Sayyadi, Hassan (Supervisor)
    Independent robots are equipped with various sound, inertia and visual sensors for decision making. Vision is an attractive sensor due to its non-invasive nature, passivity, and high information content. In natural environments, visual noises such as snow, rain, and dust distort images. in underwater environments, factors such as refraction and absorption of light suspended particles in the water, and color distortion affects the quality of visual data, resulting in noisy and distorted images. As a result, the autonomous underwater vehicles that rely on vision (AUVs) are challenged, resulting in poor performance. To improve the input to the visual algorithm for tracking the pipeline, in... 

    Self-Supervised Image Representation Learning

    , M.Sc. Thesis Sharif University of Technology Aghababazadeh, Arash (Author) ; Kasaei, Shohreh (Supervisor)
    Self-supervied learning is a method to reduce the need for large labeled datasets in supervised learning. In self-supervised learning, the goal is to design a pretext task that can be trained without any labels. This pretext task results in learning a representation of data that can reduce the need for labels when used for different tasks. In the domain of images, data augmenting transformations which are often a composition of simple transformations such as random cropping and color jitter have been used for the design of pretext tasks. These simple transformations can cause information loss in some datasets which limits the usage of the learned representations for various downstream tasks.... 

    Face Recognition Networks Review and Analysis

    , Ph.D. Dissertation Sharif University of Technology Mahjouri, Mehran (Author) ; Razvan, Mohammad Reza (Supervisor) ; Moghadasi, Reza (Supervisor) ; Kamali Tabrizi, Mostafa (Co-Supervisor)
    Face recognition, which is one of the most important biometrics, has always been one of the main challenges in many security issues, such as verifying the identity of customers of financial institutions and passengers at the airport, and such issues have many applications in daily life. Face recognition has always been an important issue in computer vision and pattern recognition. Currently, several methods based on deep networks have shown great results in face recognition, among which the following can be mentioned.1.The deep face was introduced by Facebook in 2014; 2.Face-net was presented by Google in 2015 ;3.VGGFace was presented by Oxford University in 2015; 4.Openface was presented by...