Metwally Rashad

Metwally Rashad
Faculty of Computers & Artificial Intelligence, Benha University

PhD from University of Pannonia, Hungary

About

19
Publications
1,849
Reads
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54
Citations
Citations since 2017
13 Research Items
48 Citations
2017201820192020202120222023024681012
2017201820192020202120222023024681012
2017201820192020202120222023024681012
2017201820192020202120222023024681012

Publications

Publications (19)
Article
We introduce a new object retrieval approach where besides cameras, Inertial Measurement Unit (IMU) sensors are used for the retrieval of 3D objects. Contrary to computationally intensive deep learning recognition and retrieval solutions we focus on lightweight methods which could be utilized in handheld devices and autonomous systems equipped with...
Conference Paper
Full-text available
In this paper we introduce a new 3D object retrieval model inspired by some well-known mechanisms of the human brain: viewer-centric recognition, Markovian estimations, and fusion of information originating from the visual and vestibular subsystems. We have built a Hidden Markov Model (HMM) framework where 2D object views correspond to states, obse...
Article
Full-text available
String similarity join is a basic and essential operation in many applications. In this paper, we investigate the problem of string similarity join with edit distance constraints. A trie-based edit similarity join framework has been proposed recently. The main advantage of existing trie-based algorithms is support for similarity join on short strin...
Conference Paper
Full-text available
Video-based object recognition faces the problem of multi-view object variance, noisy conditions, and limited computational resources. In our previous work, we introduced a multi-view recognition approach with a compact global image descriptor coupled with orientation sensor data. Since our purpose is to run all computations in a handheld device, c...
Conference Paper
Full-text available
While there are several promising approaches for visual object recognition the application of lightweight devices under varying image conditions and using low quality images still causes lots of problems to be solved. We introduce a new retrieval mechanism including standard orientation sensors helping the visual recognition process. We apply a vie...
Article
Full-text available
Systems for retrieving and managing content-based medical images are becoming more important, especially as medical imaging technology advances and the medical image database grows. In addition, these systems can also use medical images to better grasp and gain a deeper understanding of the causes and treatments of different diseases, not just for...
Article
The advancement in medical imaging has resulted in a rapid and large increase in medical images inside repositories. These medical images contain very important information that can be used in many things, including diagnosing diseases. This implies that a precise, efficient way of indexing and retrieving biomedical images is necessary to obtain me...
Article
Full-text available
Our paper deals with active multiview object recognition focusing on the directional support of sequential multiple shots. Since inertial sensors are easily available nowadays, we propose the use of them to estimate the orientation change of the camera and thus to estimate the probability of relative poses. With the help of relative orientation cha...
Conference Paper
Full-text available
This paper describes the methods and experiments that have been used in the development of our model submitted to Irony Detection for Arabic Tweets shared task. We submitted three runs based on our model using Support Vector Machines (SVM), Linear and Ensemble classifiers. Bag-of-Words with range of n-grams model have been used for feature extracti...
Conference Paper
In this paper we introduce a new 3D object retrieval model inspired by some well-known mechanisms of the human brain: viewer-centric recognition, Markovian estimations and fusion of information originating from the visual and vestibular subsystems. We have built a Hidden Markov Model (HMM) framework where 2D object views correspond to states, obser...
Article
Full-text available
In the last few years, there has been a steadily growing interest in autonomous vehicles and robotic systems. While many of these agents are expected to have limited resources, these systems should be able to dynamically interact with other objects in their environment. We present an approach where lightweight sensory and processing techniques, req...
Conference Paper
The purpose of our demo is to show the application and performance of some low-complexity image descriptors in object recognition under realistic circumstances. We built a client-server system where several image retrieval methods and image segmentation approaches can be tested with the help of a network connected Android device (mobile phone, tabl...
Data
The database included 16 objects (fully 3Dshaped) like some types of cars, headset, books, coffee cups, stapler, plastic bags, computer mouse, pens. Between 44-73 views per object were captured from the same elevation but from different azimuth leading to approximately 900 images. Objects were centered and a bounding box was manually defined for ea...
Conference Paper
Full-text available
A string similarity join finds all similar pairs between two collections of strings. It is an essential operation in many applications, such as data integration and cleaning, and has attracted significant attention recently. In this paper, we study string similarity joins with edit-distance constraints. Recently, a Trie-based similarity Join framew...

Questions

Question (1)
Question
what is the maximum number of states in hidden markov model

Network

Cited By

Projects

Project (1)
Archived project
a new 3D object recognition model by building Hidden Markov Model M=(A,B,p) for all candidate views and proposed a new search method based on this model to calculate the most likely sequence of hidden states that produced observation sequence (CEDD descriptor) for object recognition.