arXiv subjects

Hadi Moradi

Publications and source records attributed to Hadi Moradi.

28 records · Page 2Linked to original sources

arXiv2021

Solving Viewing Graph Optimization for Simultaneous Position and Rotation Registration

A viewing graph is a set of unknown camera poses, as the vertices, and the observed relative motions, as the edges. Solving the viewing graph is an essential step in a Structure-from-Motion procedure, where a set of relative motions is obtained from a collection of 2D images. Almost all methods in the literature solve for the rotations separately, through rotation averaging process, and use them for solving the positions. Obtaining positions is the challenging part because the translation observations only tell the direction of the motions. It becomes more challenging when the set of edges comprises pairwise translation observations between either near and far cameras. In this paper an iterative method is proposed that overcomes these issues. Also a method is proposed which obtains the rotations and positions simultaneously. Experimental results show the-state-of-the-art performance of the proposed methods.

cs.CV↗

arXiv2021

A Framework for Multi-View Classification of Features

One of the most important problems in the field of pattern recognition is data classification. Due to the increasing development of technologies introduced in the field of data classification, some of the solutions are still open and need more research. One of the challenging problems in this area is the curse of dimensionality of the feature set of the data classification problem. In solving the data classification problems, when the feature set is too large, typical approaches will not be able to solve the problem. In this case, an approach can be used to partition the feature set into multiple feature sub-sets so that the data classification problem is solved for each of the feature subsets and finally using the ensemble classification, the classification is applied to the entire feature set. In the above-mentioned approach, the partitioning of feature set into feature sub-sets is still an interesting area in the literature of this field. In this research, an innovative framework for multi-view ensemble classification, inspired by the problem of object recognition in the multiple views theory of humans, is proposed. In this method, at first, the collaboration values between the features is calculated using a criterion called the features collaboration criterion. Then, the collaboration graph is formed based on the calculated collaboration values. In the next step, using the community detection method, graph communities are found. The communities are considered as the problem views and the different base classifiers are trained for different views using the views corresponding training data. The multi-view ensemble classifier is then formed by a combination of base classifiers based on the AdaBoost algorithm. The simulation results of the proposed method on the real and synthetic datasets show that the proposed method increases the classification accuracy.

cs.LG↗

arXiv2021

Optimal Triangulation Method is Not Really Optimal

Triangulation refers to the problem of finding a 3D point from its 2D projections on multiple camera images. For solving this problem, it is the common practice to use so-called optimal triangulation method, which we call the L2 method in this paper. But, the method can be optimal only if we assume no uncertainty in the camera parameters. Through extensive comparison on synthetic and real data, we observed that the L2 method is actually not the best choice when there is uncertainty in the camera parameters. Interestingly, it can be observed that the simple mid-point method outperforms other methods. Apart from its high performance, the mid-point method has a simple closed formed solution for multiple camera images while the L2 method is hard to be used for more than two camera images. Therefore, in contrast to the common practice, we argue that the simple mid-point method should be used in structure-from-motion applications where there is uncertainty in camera parameters.

cs.CV↗

arXiv2021

Learning with partially separable data

There are partially separable data types that make classification tasks very hard. In other words, only parts of the data are informative meaning that looking at the rest of the data would not give any distinguishable hint for classification. In this situation, the typical assumption of having the whole labeled data as an informative unit set for classification does not work. Consequently, typical classification methods with the mentioned assumption fail in such a situation. In this study, we propose a framework for the classification of partially separable data types that are not classifiable using typical methods. An algorithm based on the framework is proposed that tries to detect separable subgroups of the data using an iterative clustering approach. Then the detected subgroups are used in the classification process. The proposed approach was tested on a real dataset for autism screening and showed its capability by distinguishing children with autism from normal ones, while the other methods failed to do so.

cs.LG↗

arXiv2021

Simple online and real-time tracking with occlusion handling

Multiple object tracking is a challenging problem in computer vision due to difficulty in dealing with motion prediction, occlusion handling, and object re-identification. Many recent algorithms use motion and appearance cues to overcome these challenges. But using appearance cues increases the computation cost notably and therefore the speed of the algorithm decreases significantly which makes them inappropriate for online applications. In contrast, there are algorithms that only use motion cues to increase speed, especially for online applications. But these algorithms cannot handle occlusions and re-identify lost objects. In this paper, a novel online multiple object tracking algorithm is presented that only uses geometric cues of objects to tackle the occlusion and reidentification challenges simultaneously. As a result, it decreases the identity switch and fragmentation metrics. Experimental results show that the proposed algorithm could decrease identity switch by 40% and fragmentation by 28% compared to the state of the art online tracking algorithms. The code is also publicly available.

cs.CV↗

arXiv2021

Teaching Turn-Taking Skills to Children with Autism using a Parrot-Like Robot

Robot Assisted Therapy is a new paradigm in many therapies such as the therapy of children with autism spectrum disorder. In this paper we present the use of a parrot-like robot as an assistive tool in turn taking therapy. The therapy is designed in the form of a card game between a child with autism and a therapist or the robot. The intervention was implemented in a single subject study format and the effect sizes for different turn taking variables are calculated. The results show that the child robot interaction had larger effect size than the child trainer effect size in most of the turn taking variables. Furthermore the therapist point of view on the proposed Robot Assisted Therapy is evaluated using a questionnaire. The therapist believes that the robot is appealing to children which may ease the therapy process. The therapist suggested to add other functionalities and games to let children with autism to learn more turn taking tasks and better generalize the learned tasks

cs.RO↗

arXiv2021

Crossbreeding in Random Forest

Ensemble learning methods are designed to benefit from multiple learning algorithms for better predictive performance. The tradeoff of this improved performance is slower speed and larger size of ensemble learning systems compared to single learning systems. In this paper, we present a novel approach to deal with this problem in Random Forest (RF) as one of the most powerful ensemble methods. The method is based on crossbreeding of the best tree branches to increase the performance of RF in space and speed while keeping the performance in the classification measures. The proposed approach has been tested on a group of synthetic and real datasets and compared to the standard RF approach. Several evaluations have been conducted to determine the effects of the Crossbred RF (CRF) on the accuracy and the number of trees in a forest. The results show better performance of CRF compared to RF.

cs.LG↗

arXiv2019

SLTR: Simultaneous Localization of Target and Reflector in NLOS Condition Using Beacons

When the direct view between the target and the observer is not available, due to obstacles with non-zero sizes, the observation is received after reflection from a reflector, this is the indirect view or Non-Line-Of Sight condition. Localization of a target in NLOS condition still one of the open problems yet. In this paper, we address this problem by localizing the reflector and the target simultaneously using a single stationary receiver, and a determined number of beacons, in which their placements are also analyzed in an unknown map. The work is done in mirror space, when the receiver is a camera, and the reflector is a planar mirror. Furthermore, the distance from the observer to the target is estimated by size constancy concept, and the angle of coming signal is the same as the orientation of the camera, with respect to a global frame. The results show the validation of the proposed work and the simulation results are matched with the theoretical results.

eess.SP↗

arXiv2018

A Comprehensive Review of Technologies Used for Screening, Assessment, and Rehabilitation of Autism Spectrum Disorder

Autism Spectrum Disorder (ASD) is an umbrella term for a wide range of developmental disorders. For the past two decades, researchers proposed the use of various technologies in order to tackle specific symptoms of the disorder. Although there exist many literature reviews about screening, assessment, and rehabilitation of ASD, no comprehensive survey of types of technologies in all defined symptoms of ASD has been presented. Therefore, in this paper a comprehensive survey of previous studies has been presented in which the studies are classified into three main categories, and several sub-categories, and three main technologies. An analysis of the number of studies in each category and sub-category is given to help researchers decide on areas which need further investigation. The analysis show that the majority of studies fall into the software-based systems technology category. Finally, a brief review of studies in each category of ASD is presented for each type of technology. As a result, this paper also helps researchers to obtain an overview of the typical methods of using a specific technology in ASD screening, assessment, and rehabilitation.

cs.HC↗

arXiv2018

A Recursive Least Square Method for 3D Pose Graph Optimization Problem

Pose Graph Optimization (PGO) is an important non-convex optimization problem and is the state-of-the-art formulation for SLAM in robotics. It also has applications like camera motion estimation, structure from motion and 3D reconstruction in machine vision. Recent researches have shown the importance of good initialization to bootstrap well-known iterative PGO solvers to converge to good solutions. The state-of-the-art initialization methods, however, works in low noise or eventually moderate noise problems, and they fail in challenging problems with high measurement noise. Consequently, iterative methods may get entangled in local minima in high noise scenarios. In this paper we present an initialization method which uses orientation measurements and then present a convergence analysis of our iterative algorithm. We show how the algorithm converges to global optima in noise-free cases and also obtain a bound for the difference between our result and the optimum solution in scenarios with noisy measurements. We then present our second algorithm that uses both relative orientation and position measurements to obtain a more accurate least squares approximation of the problem that is again solved iteratively. In the convergence proof, a structural coefficient arises that has important influence on the basin of convergence. Interestingly, simulation results show that this coefficient also affects the performance of other solvers and so it can indicate the complexity of the problem. Experimental results show the excellent performance of the proposed initialization algorithm, specially in high noise scenarios.

cs.RO↗