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Articles 1 - 7 of 7
Full-Text Articles in Electrical and Computer Engineering
Pixel-Level Deep Multi-Dimensional Embeddings For Homogeneous Multiple Object Tracking, Mateusz Mittek
Pixel-Level Deep Multi-Dimensional Embeddings For Homogeneous Multiple Object Tracking, Mateusz Mittek
Department of Electrical and Computer Engineering: Dissertations, Theses, and Student Research
The goal of Multiple Object Tracking (MOT) is to locate multiple objects and keep track of their individual identities and trajectories given a sequence of (video) frames. A popular approach to MOT is tracking by detection consisting of two processing components: detection (identification of objects of interest in individual frames) and data association (connecting data from multiple frames). This work addresses the detection component by introducing a method based on semantic instance segmentation, i.e., assigning labels to all visible pixels such that they are unique among different instances. Modern tracking methods often built around Convolutional Neural Networks (CNNs) and additional, …
Kernels And Beyond For Data Similarity Learning In Data Mining, Akshay Malhotra
Kernels And Beyond For Data Similarity Learning In Data Mining, Akshay Malhotra
Electrical Engineering Dissertations
This work discusses the problem of unsupervised clustering of signals/data vectors based on their information content. A correlation based perspective to the clustering problem has been considered, thus relying on the high correlation between data vectors from the same class rather than on the position of the vectors in the data space. In the past, correlation based clustering has been formulated using a canonical correlation framework or as a matrix factorization problem and has been solved with different variants of gradient descent. This work focuses on improving the clustering performance by modifying the framework to utilize non-linear associations or correlations. …
Genotype Combinations Linked To Phenotype Subgroups In Autism Spectrum Disorders, Junya Zhao, Thy Nguyen, Jonathan Kopel, Perry B. Koob, Donald A. Adieroh, Tayo Obafemi-Ajayi
Genotype Combinations Linked To Phenotype Subgroups In Autism Spectrum Disorders, Junya Zhao, Thy Nguyen, Jonathan Kopel, Perry B. Koob, Donald A. Adieroh, Tayo Obafemi-Ajayi
Electrical and Computer Engineering Faculty Research & Creative Works
This paper investigates a computational model that allows for systematic comparison of phenotype data with genotype (Single Nucleotide Polymorphisms (SNPs)) data based on machine learning techniques to identify discriminant genotype markers associated with the phenotypic subgroups. The proposed discriminant SNP identifier model is empirically evaluated using Autism Spectrum Disorder (ASD) simplex sample. Six phenotype markers were selected to cluster the sample in a hexagonal lattice format yielding five multidimensional subgroups based on extremities of the phenotype markers. The SNP selection model includes random subspace selection of SNPs in conjunction with feature selection algorithms to determine which set of SNPs were …
A New Model To Determine The Hierarchical Structure Of The Wireless Sensor Networks, Resmi̇ye Nasi̇boğlu, Zülküf Teki̇n Erten
A New Model To Determine The Hierarchical Structure Of The Wireless Sensor Networks, Resmi̇ye Nasi̇boğlu, Zülküf Teki̇n Erten
Turkish Journal of Electrical Engineering and Computer Sciences
Wireless sensor networks are one of the rising areas of scientific research. Common purpose of these investigations is usually constructing optimal structure of the network by prolonging its lifetime. In this study, a new model has been proposed to construct a hierarchical structure of wireless sensor networks. Methods used in the model to determine clusters and appropriate cluster heads are k-means clustering and fuzzy inference system (FIS), respectively. The weighted averaging based on levels (WABL) defuzzification method is used to calculate crisp outputs of the FIS. A new theorem for calculation of WABL values has been proved in order to …
Evaluating The Attributes Of Remote Sensing Image Pixels For Fast K-Means Clustering, Ali̇ Sağlam, Nurdan Baykan
Evaluating The Attributes Of Remote Sensing Image Pixels For Fast K-Means Clustering, Ali̇ Sağlam, Nurdan Baykan
Turkish Journal of Electrical Engineering and Computer Sciences
Clustering process is an important stage for many data mining applications. In this process, data elements are grouped according to their similarities. One of the most known clustering algorithms is the k-means algorithm. The algorithm initially requires the number of clusters as a parameter and runs iteratively. Many remote sensing image processing applications usually need the clustering stage like many image processing applications. Remote sensing images provide more information about the environments with the development of the multispectral sensor and laser technologies. In the dataset used in this paper, the infrared (IR) and the digital surface maps (DSM) are also …
Exploring Bigram Character Features For Arabic Text Clustering, Dia Eddin Abuzeina
Exploring Bigram Character Features For Arabic Text Clustering, Dia Eddin Abuzeina
Turkish Journal of Electrical Engineering and Computer Sciences
The vector space model (VSM) is an algebraic model that is widely used for data representation in text mining applications. However, the VSM poses a critical challenge, as it requires a high-dimensional feature space. Therefore, many feature selection techniques, such as employing roots or stems (i.e. words without infixes and prefixes, and/or suffixes) instead of using complete word forms, are proposed to tackle this space challenge problem. Recently, the literature shows that one more basic unit feature can be used to handle the textual features, which is the twoneighboring character form that we call microword. To evaluate this feature type, …
Efficient Hierarchical Temporal Segmentation Method For Facial Expression Sequences, Jiali Bian, Xue Mei, Yu Xue, Liang Wu, Yao Ding
Efficient Hierarchical Temporal Segmentation Method For Facial Expression Sequences, Jiali Bian, Xue Mei, Yu Xue, Liang Wu, Yao Ding
Turkish Journal of Electrical Engineering and Computer Sciences
Temporal segmentation of facial expression sequences is important to understand and analyze human facial expressions. It is, however, challenging to deal with the complexity of facial muscle movements by finding a suitable metric to distinguish among different expressions and to deal with the uncontrolled environmental factors in the real world. This paper presents a two-step unsupervised segmentation method composed of rough segmentation and fine segmentation stages to compute the optimal segmentation positions in video sequences to facilitate the segmentation of different facial expressions. The proposed method performs localization of facial expression patches to aid in recognition and extraction of specific …