Browsing by Subject "Temporal"

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Open Access
An index structure for moving objects in video databases
(1999) Yavuz, Tuba
Modeling moving objects and Iiandling various types of motion queries are interesting topics to investigate in the area of video databases. In one type of motion queries, motion of multiple objects is specified by the changes in relative spatial positions of objects. Answering such kind of queries, that involve motion of multiple objects whose identifications cire not specified, requires some type of indexing because the time complexity of processing such a query in the absence of an index structure is 0{N \l{N — n)!), where N is the number of objects in the database and n is the number of objects in the query. In this work, we propose a spatio-temporal index structure, which we call ,S'M/A7-index, and compare its performance against a similar scheme proposed in [18]. The scheme presented in [18] consists of a constraint satisfaction algorithm, which is called Join Window Reduction (JW R ), combined with a spatial index structure (R*- tree). Experimental results indicate thcit SMIST-'mdex outperforms the JW R algorithm. Also, SMIST-'mdex is shown to be scalable to increasing number of frames and objects.
Open Access
Spatio-temporal assessment of pain intensity through facial transformation-based representation learning
(2021-09) Erekat, Diyala Nabeel Ata
The nature of pain makes it di cult to assess due to its subjectivity and multidimensional characteristics that include intensity, duration, and location. However, the ability to assess pain in an objective and reliable manner is crucial for adequate pain management intervention as well as the diagnosis of the underlying medical cause. To this end, in this thesis, we propose a video-based approach for the automatic measurement of self-reported pain. The proposed method aims to learn an e cient facial representation by exploiting the transformation of one subject's facial expression to that of another subject's within a similar pain group. We also explore the e ect of leveraging self-reported pain scales i.e., the Visual Analog Scale (VAS), the Sensory Scale (SEN), and the A ective Motivational Scale (AFF), as well as the Observer Pain Intensity (OPI) on the reliable assessment of pain intensity. To this end, a convolutional autoencoder network is proposed to learn the facial transformation between subjects. The autoencoder's optimized weights are then used to initialize the spatio-temporal network architecture, which is further optimized by minimizing the mean absolute error of estimations in terms of each of these scales while maximizing the consistency between them. The reliability of the proposed method is evaluated on the benchmark database for pain measurement from videos, namely, the UNBC-McMaster Pain Archive. Despite the challenging nature of this problem, the obtained results show that the proposed method improves the state of the art, and the automated assessment of pain severity is feasible and applicable to be used as a supportive tool to provide a quantitative assessment of pain in clinical settings.