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jamzad--mansour
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3D Reconstruction of Face Using Front View and Side View Images
, M.Sc. Thesis Sharif University of Technology ; Ramezanin, Rassul (Supervisor) ; Jamzad, Mansour (Co-Advisor)
Abstract
3D face modeling is currently a popular area in Computer Graphics and Computer Vision. Many techniques have been introduced for this purpose, such as using one or more cameras, 3D scanners, and many other systems of sophisticated hardware with related software. But the main goal is to find a good balance between visual reality and the cost of the system. In this thesis, reconstruction of a 3D human face from a pair of orthogonal views, front face and side face is studied. Unlike many other systems, facial feature points are obtained automatically from two photographs with the help of an Active shape model algorithm for the frontal face and an edge detection algorithm for side view of the...
Automatic Skin Cancer (Melanoma) Detection Using Visual Features
, M.Sc. Thesis Sharif University of Technology ; Jamzad, Mansour (Supervisor)
Abstract
Melanoma is a malignant skin cancer which is caused by cancerous growth of melanocytes. If not treated at its early development stages, melanoma is the deadliest form of cancer. The best way to cure melanoma is to treat it in its earliest stage of development. Since a melanoma leasion is similar to benign moles (regaring its shape and appearance) at its early stages of development, it is often mistaken for moles and left untreated. Therefore, automatic melanoma detection can increase the survival rate of patients by detecting melanoma in its early stages. In this thesis, a new method for automatic diagnosis of melanoma using segmented dermoscopic images is provided. Almost all related...
Scene Classification Based on Color and Texture Features
, M.Sc. Thesis Sharif University of Technology ; Jamzad, Mansour (Supervisor)
Abstract
Scene classification is one of the most controversial fields in computer vision. It has many applications such as robot navigation and control, content-based image retrieval (CBIR), semantic organization of image databases, depth estimation and multimedia services. In fact the outcome of any classification system depends on the ability of the feature vector defined for the problem, by means of its distinguishing strength. In this research we focus on efficient feature extraction methods. In recent years, methods based on bags of features and special pyramid approach, have shown good performance in scene classification comparison to the others. So we based our proposed method on these ideas....
Domain Adaptation Using Source Classifier for Object Detection
, Ph.D. Dissertation Sharif University of Technology ; Jamzad, Mansour (Supervisor)
Abstract
Detection degradation caused by distribution discrepancy between the training and testing domains is a common problem in object detection systems. The difference between training and testing domains’ distribution mainly happenes because of the different ways of collecting and gathering data. For instance, datasets which have images with different illumination, view point, resolution, background and are obtained by different acquisition systems, have variance in distribution. The solution toward improving the detection rate of the classifier trained on training (source) domain when it is applied on testing (target) domain is to use Domain Adaptation (DA) techniques. One of important branches...
Extracting Proper Features for Human Detection in Still Images
, M.Sc. Thesis Sharif University of Technology ; Jamzad, Mansour (Supervisor)
Abstract
Human detection in still images is one of the hardest problems in object detection area. There are several challenges like articulation, pose variation, variant clothing, none uniform illumination, cluttered background and occlusion which make this problem more sophisticated than any other object detection problem. The general solution for this kind of problems is based on supervised learning that contains two main parts: 1-extracting proper features, 2- using proper classifier. The main focus of this thesis is on the first part, extracting proper features, which should be robust to mentioned challenges. Based on the level of extraction we can divide the features to four groups: 1-low-level,...
Improving the 3D Segmentation of Nodules in Lung CT Images
, M.Sc. Thesis Sharif University of Technology ; Jamzad, Mansour (Supervisor) ; Beigy, Hamid (Co-Supervisor)
Abstract
Lung cancer is one of the most common types of cancers, and its early diagnosis can save many lives. Due to the high number of computed tomography (CT) images used to detect lung cancer, it is difficult to accurately and rapidly diagnose this disease. Doing so requires high expertise by radiologists. Therefore the demand for computer aided diagnosis systems in this area has been increased. The core of all lung cancer detection systems is the distinction between cancer and non-cancerous tissues. The main objective of this study is to present a new method based on 3D convolutional neural networks (CNN) that can perform false positives reduction operations while providing high sensitivity. In...
A Self-Tag Rectifier Model for Automatic Image Annotation
,
Ph.D. Dissertation
Sharif University of Technology
;
Jamzad, Mansour
(Supervisor)
;
Beigy, Hamid
(Co-Supervisor)
Abstract
Automatic image annotation is an image retrieval mechanism to extract relative semantic tags from visual contents. The number of digital images uploaded in the virtual world is rapidly growing every day. Most of those images are not assigned with proper tags or labels. Although automatic image annotation methods are developed to assign proper tags to images, most of these methods assign some irrelevant tags and also sometimes a few relevant tags are missing. So far, the improvements of accuracy in newly developed automatic image annotation methods have been about one or two percent in F1-score compared to the previous methods. To reach much better performance, we analyzed most of the...
Design and Implementation of a Face Model in Video-realistic Speech Animation for Farsi Language
, M.Sc. Thesis Sharif University of Technology ; Jamzad, Mansour (Supervisor)
Abstract
With increasing use of computers in everyday life, improved communication between machines and human is needed. To make a right communication and understand a humankind face which is made in a graphical environment, implementing the audio and visual projects like lip reading, audio and visual speech recognition and lip modelling needed. The main goal in this project is natural representation of strings of lip movements for Farsi language. Lack of a complete audio and visual database for this application in Farsi language made us provide a new complete Farsi database for this project that is called SFAVD. It is a unique audio and visual database which covers the most applicable words, all...
Object Tracing Based on Detection and Learning
, M.Sc. Thesis Sharif University of Technology ; Jamzad, Mansour (Supervisor)
Abstract
Tracking is one of the old and still not thoroughly solved problems in machine vision. Its importance lies on its many applications. These applications vary from security surveillance to examining the motion pattern of atomic particles. There is not a tracker which has acceptable results in all situations, yet. A tracker faces many difficulties such as change in illumination and occlusion. In past, tracking was done by using filters or optical flows. By use of the advances in machine learning and pattern recognition, many models have been proposed to accomplish tracking by using these new learning methods. In this dissertation, we proposed a new tracking method which utilizes sparse...
Face Motion Capture using a Regular Camera and Constructing face 3D Graphical Model
, M.Sc. Thesis Sharif University of Technology ; Jamzad, Mansour (Supervisor)
Abstract
In this project, using one regular color camera, a video is captured of an actor’s face. Using machine vision and without any kind of markers on the actor’s face, this video is processed to extract the locations of some desired points on the face of the actor. Then these points are mapped to corresponding points on a 3D graphical model of a face, so that a realistic animation of facial movements is achieved. The extracted points, including points on the eyebrows, eyes and lips are the most important ones for the purpose of facial animation. To extract these points, in each region several methods are implemented and studied to find the best method, finally for extracting points on the...
Content Based Image Retrieval Using Segmentation Similarity Measure
,
M.Sc. Thesis
Sharif University of Technology
;
Jamzad, Mansour
(Supervisor)
Abstract
Content Based Image Retrieval (CBIR) is a research area in computer vision. This area comprises of two main steps, low level feature extraction such as color, texture and shape extraction and also similarity measures for comparison of images. The challenge in this system is the existence semantic gap between the low level visual features and the high level image semantics. The aim of research in this field is to reduce this semantic gap. In this study the images are divided into regions using Meanshift method, for color segmentation and then moments of each region as color feature are calculated. Also for extracting texture the images are divided into regions using Jseg method, and then...
Visual Tracking of Arbitrary-Shaped Objects in Unconstrained Environments
, M.Sc. Thesis Sharif University of Technology ; Manzouri, Mohammad Taghi (Supervisor) ; Jamzad, Mansour (Co-Advisor)
Abstract
Most of current state-of-the-art methods for object tracking use adaptive tracking-by-detection. The performance of state-of-the-art methods is almost real-time with acceptable accuracy. These methods use tracking-by-detection because of its robustness. Tracking-bydetection methods use a detector as a tracker and sweep input for object of interest. They use their predictions to adapt their parameters and therefore be adaptive to appearance change in target. While suitable for cases when the object does not disappear from the scene, these methods tend to fail on occlusions. In this work, we build on a novel approach called Tracking-Learning-Detection (TLD) that overcomes this problem. In...
A Hybrid Method for Improving the Color Constancy in Images
, M.Sc. Thesis Sharif University of Technology ; Jamzad, Mansour (Supervisor)
Abstract
The ability of measuring colors of objects, independent of light source illumination, is called color constancy which is an important field in machine vision and image processing. In this thesis, we propose five new combinantional ways in color constansy fields. The first two proposed methods use neural networks to combining basic methods. The third and forth proposed methods use fuzzy measures and integrals for combining color constancy methods. And finally fifth method combines methods with indoor outdoor classification this method has the best result (3.01 median angular error) in proposed methods and past methods in color constancy fields. It is shown in this article that the proposed...
New Generation of On-purpose Attacks for Evaluating Digital Image Watermarking Methods by Preserving the Image Quality
, Ph.D. Dissertation Sharif University of Technology ; Jamzad, Mansour (Supervisor)
Abstract
Up to now, compared with the comprehensive research for developing robust watermarking algorithms, no equal attention has been devoted to the proposition of benchmarks tailored to assess the watermark robustness. In addition, almost all the state of the art benchmarks only integrate a number of common image processing operations like geometrical transformations to remove watermarks. However, the quality of the processed image is often too degraded to permit further commercial exploitation. Moreover, to the best of our knowledge, the design of these tools does not take into account the statistical properties of the images and watermarks in the design of attacks. In spite of the significant...
Images Classification with Limited Number of Labeled Data Using Domain Adaptation
, M.Sc. Thesis Sharif University of Technology ; Jamzad, Mansour (Supervisor)
Abstract
The traditional machine learning methods assume that the training data and the test data are drawn from the same distribution (or drawn from the same domain). In practice, in many computer vision applications, this assumption may not hold. Unfortunately, the performance of these methods degrades on dataset drawn from a different domain. Domain adaptation attempts to minimize this degradation caused by distribution mismatch between the training and test data. Domain adaptation tries to adapt a model trainded from one domain to another domain. We focus on supervised domain adaptation method in which limited labeled data is available from the target domain. We propose a new domain adaptation...
Image Classication for Content Based Image Retrieval
, M.Sc. Thesis Sharif University of Technology ; Jamzad, Mansour (Supervisor)
Abstract
In this project we tried to to solve the problem of clustring images of a large image database. Considering that there is no prior information regarding domain of the images, we will review unsupervised clustring methods. For this, we will discuss about image description vector and similarity measures. At last, our contribution will be about finding the optimum number of clusters with the least of user invervention. Results of runnig our method on a databse with 1000 images is reported and compared to a similar method named CLUE. Our result shows considerable improvements when user feedback taken to account.
Improving the Robustness of Image Watermarking for Publicly Copyright-Proving
, M.Sc. Thesis Sharif University of Technology ; Jamzad, Mansour (Supervisor)
Abstract
The advent of Internet and advancement of computer technologies have enabled convenient and fast exchange of multimedia so the illegal reproduction and modification of digital media has become increasingly serious. Hence, how to protect the intellectual property rights of digital multimedia is an imperative issue. Digital watermarking is one of the solutions to prevent unauthorized use of images. Traditional digital watermarking techniques embed a watermark such as logo, trademark, or copyright information into a host image so that it is not perceptible. These techniques, depending on the amount of embedded data, will distort the content of host image which results in quality degradation of...
Indoor Office Environment Mapping Using a Mobile Robot with Kinect Sensor
, M.Sc. Thesis Sharif University of Technology ; Jamzad, Mansour (Supervisor)
Abstract
In recent years with advancement of Robotics, the applications of wide scale use of robots in our homes is not as far-fetched as it was before. One of the important problems for an indoor robot is moving inside a new and unknown environment. To achieve this, the robot should, with the help of its sensors, not only calculate its location; but should also build a map of the environment for later use. Additionally, the robot must be able to explore this unknown environment. The most important drawbacks of the classical solutions to these problems are long computation times, heavy memory usage and absence of precision. In recent years, large amount of research effort has put on solving these...
Improving the Embedding Capacity of Steganography Methods
, Ph.D. Dissertation Sharif University of Technology ; Jamzad, Mansour (Supervisor)
Abstract
Providing security for digital information in a communication channel is a challenge in the recent years. This is due to the strength of high-speed processing resources that can threaten the security and correctness of communicating information. Using steganography method, the secret information can be hidden in an innocent media that does not attract the attention of third parties. This research discusses about steganography in images. The main goal of this research is presenting a universal steganography scheme with high steganography capacity. Using this scheme the steganographer can embed every large secret data in images securely. The reasons for selecting the subject of “Improving...
Localized Multiple Kernel Learning for Image Classification
, Ph.D. Dissertation Sharif University of Technology ; Jamzad, Mansour (Supervisor)
Abstract
It is not possible to compute a linear classifier to classify real world images, which are the focus of this thesis. Therefore, the space of such images is considered as a complex. In such cases, kernel trick in which data samples are implicitly mapped to a higher dimension space, leads to a more accurate classifier in such spaces. In kernel learning methods, the best kernel is trained for the classification problem in hand. Multiple Kernel Learning is a framework which uses weighted sum of multiple kernels. This framework achieves good accuracy in image classification since it allows describing images via various features. In the image input space which is composed of different extracted...