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Your search keyword '"Elmezain, Mahmoud"' showing total 5 results
Start Over Author "Elmezain, Mahmoud" Topic computer vision
5 results on '"Elmezain, Mahmoud"'

2. Poisonous Plants Species Prediction Using a Convolutional Neural Network and Support Vector Machine Hybrid Model.

Author

Noor, Talal H., Noor, Ayman, and Elmezain, Mahmoud

Subjects
CONVOLUTIONAL neural networks, SUPPORT vector machines, PLANT species, POISONOUS plants, FEATURE extraction
Abstract

The total number of discovered plant species is increasing yearly worldwide. Plant species differ from one region to another. Some of these discovered plant species are beneficial while others might be poisonous. Computer vision techniques can be an effective way to classify plant species and predict their poisonous status. However, the lack of comprehensive datasets that include not only plant images but also plant species' scientific names, description, poisonous status, and local name make the issue of poisonous plants species prediction a very challenging issue. In this paper, we propose a hybrid model relying on transformers models in conjunction with support vector machine for plant species classification and poisonous status prediction. First, six different Convolutional Neural Network (CNN) architectures are used to determine which produces the best results. Second, the features are extracted using six different CNNs and then optimized and employed to Support Vector Machine (SVM) for testing. To prove the feasibility and benefits of our proposed approach, we used a real case study namely, plant species discovered in the Arabian Peninsula. We have gathered a dataset that contains 2500 images of 50 different Arabic plant species and includes plants images, plant species scientific name, description, local name, and poisonous status. This study on the types of Arabic plants species will help in the reduction of the number of poisonous plants victims and their negative impact on the individual and society. The results of our experiments for the CNN approach in conjunction SVM are favorable where the classifier scored 0.92, 0.94, and 0.95 in accuracy, precision, and F1-Score respectively. [ABSTRACT FROM AUTHOR]

Published
2022
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3. LDCRFs-based hand gesture recognition.

Author

Elmezain, Mahmoud and Al-Hamadi, Ayoub

Abstract

This paper proposes a system to recognize isolated American Sign Language and numbers in real-time from Bumblebee stereo camera using Latent-Dynamic Conditional Random Fields (LDCRFs). Our system is based on three main stages: preprocessing, feature extraction and classification. In preprocessing stage, color and 3D depth map are used to detect and track the hand. The second stage, combining features of location, orientation and velocity with respected to Polar systems are used. The depth information is to identify the region of interest and consequently reduces the cost of searching and increases the processing speed. In the final stage, the hand gesture path is recognized using LDCRFs, which are more restricted to the number of hidden states owned by each class label to make training and inferencing processes tractable. Experimental results demonstrate that, our system can successfully recognize gestures with 96.14% recognition rate. Such results have the potential to compare very favorably to those of other investigators published in the literature. [ABSTRACT FROM PUBLISHER]

Published
2012
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4. A novel system for automatic hand gesture spotting and recognition in stereo color image sequences

Author

Elmezain, Mahmoud, Al-Hamadi, Ayoub, Michaelis, Bernd, and Skala, Václav

Subjects
InformationSystems_INFORMATIONINTERFACESANDPRESENTATION(e.g.,HCI), gesture recognition, pattern recognition, rozpoznávání vzorů, rozpoznávání gest, počítačové vidění, computer vision
Abstract

Automatic gesture spotting and recognition is a challenging task for locating the start and end points that correspond to a gesture of interest in Human-Computer Interaction. This paper proposes a novel gesture spotting system that is suitable for real-time implementation. The system executes gesture segmentation and recognition simultaneously without any time delay based on Hidden Markov Models. In the segmentation module, the hand of the user is tracked using mean-shift algorithm, which is a non-parametric density estimator that optimizes the smooth similarity function to find the direction of hand gesture path. In order to spot key gesture accurately, a sophisticated method for designing a non-gesture model is proposed, which is constructed by collecting the states of all gesture models in the system. The non-gesture model is a weak model compared to all trained gesture models. Therefore, it provides a good confirmation for rejecting the non-gesture pattern. To reduce the states of the non-gesture model, similar probability distributions states are merged based on relative entropy measure. Experimental results show that the proposed system can automatically recognize isolated gestures with 97.78% and key gestures with 93.31% reliability for Arabic numbers from 0 to 9.

5. Feature-supported multi-hypothesis framework for multi-object tracking using Kalman filter

Author

Pathan, Saira Saleem, Al-Hamadi, Ayoub, Elmezain, Mahmoud, Michaelis, Bernd, Chen, Min, and Skala, Václav

Subjects
multi-object tracking, dopravní dohled, ComputingMethodologies_IMAGEPROCESSINGANDCOMPUTERVISION, Kalman filter, Kalmanův filtr, zpracování obrazu, počítačové vidění, sledování multi-objektů, computer vision, traffic surveillance, image processing
Abstract

A Kalman filter is a recursive estimator and has widely been used for tracking objects. However, unsatisfying tracking of moving objects is observed under complex situations (i.e. inter-object merge and split) which are challenging for classical Kalman filter. This paper describes a multi-hypothesis framework based on multiple features for tracking the moving objects under complex situations using Kalman Tracker. In this framework, a hypothesis (i.e. merge, split, new) is generated on the basis of contextual association probability which identifies the status of the moving objects in the respective occurrences. The association among the moving objects is computed by multi-featured similarity criteria which include spatial size, color and trajectory. Color similarity probability is computed by the correlation-weighted histogram intersection (CWHI). The similarity probabilities of the size and the trajectory are computed and combined with the fused color correlation. The accumulated association probability results in online hypothesis generation. This hypothesis assists Kalman tracker when complex situations appear in real-time tracking (i.e. traffic surveillance, pedestrian tracking). Our algorithm achieves robust tracking with 97.3% accuracy, and 0.07% covariance error in different real-time scenarios.

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