Suchita Bhinge

Problem Statement : Dynamic functional connectivity analysis is an effective way to capture the networks that are functionally associated and continuously changing over the scanning period. However, these methods mostly analyze the dynamic associations across the activation patterns of the spatial networks while assuming that the spatial networks are stationary. Hence, a model that allows for the variability in both domains and reduces the assumptions imposed on the data provides an effective… Show more

Problem Statement : Dynamic functional connectivity analysis is an effective way to capture the networks that are functionally associated and continuously changing over the scanning period. However, these methods mostly analyze the dynamic associations across the activation patterns of the spatial networks while assuming that the spatial networks are stationary. Hence, a model that allows for the variability in both domains and reduces the assumptions imposed on the data provides an effective way for extracting spatiotemporal networks.

Method : Independent vector analysis (IVA) is a joint blind source separation technique that allows for estimation of spatial and temporal features while successfully preserving variability. However, its performance is affected for higher number of datasets. Hence, we develop an effective two-stage method to extract time-varying spatial and temporal features using IVA, mitigating the problems with higher number of datasets while preserving the variability across subjects and time. The first stage is used to extract reference signals using group-independent component analysis (GICA) that are used in a parameter-tuned constrained IVA framework to estimate time-varying representations of these signals by preserving the variability through tuning the constraint parameter.

Results : This approach effectively captures variability across time from a large-scale resting-state fMRI data acquired from healthy controls and patients with schizophrenia and identifies more functionally relevant connections that are significantly different among healthy controls and patients with schizophrenia, compared with the widely used GICA method alone. Show less

Problem Statement : The existence of complementary information across multiple sensors has driven the proliferation of multivariate datasets. Exploitation of this common information, while minimizing the assumptions imposed on the data has led to the popularity of data-driven methods. Independent vector analysis (IVA), in particular, provides a flexible and effective approach for the fusion of multivariate data. In many practical applications, important prior information about the data exists… Show more

Problem Statement : The existence of complementary information across multiple sensors has driven the proliferation of multivariate datasets. Exploitation of this common information, while minimizing the assumptions imposed on the data has led to the popularity of data-driven methods. Independent vector analysis (IVA), in particular, provides a flexible and effective approach for the fusion of multivariate data. In many practical applications, important prior information about the data exists and incorporating this information into the IVA model is expected to yield improved separation performance.

Method : This paper talks about a general formulation for non-orthogonal constrained IVA (C-IVA) framework that can incorporate prior information about either the sources or the mixing coefficients into the IVA cost function. A powerful decoupling method is the major enabling factor in this task.

Results : Obtained average performance gain of 50% on noisy simulated data and 72% classification accuracy on multitask functional MRI data Show less

Problem Statement : Due to the potential for object occlusion in crowded areas, the use of multiple cameras for video surveillance has prevailed over the use of a single camera. This has motivated the development of a number of techniques to analyze such multi-camera video sequences. However, most of these techniques require a camera calibration step, which is cumbersome and must be done for every new configuration. Additionally, these techniques fail to exploit the complementary information… Show more

Problem Statement : Due to the potential for object occlusion in crowded areas, the use of multiple cameras for video surveillance has prevailed over the use of a single camera. This has motivated the development of a number of techniques to analyze such multi-camera video sequences. However, most of these techniques require a camera calibration step, which is cumbersome and must be done for every new configuration. Additionally, these techniques fail to exploit the complementary information across these multiple datasets.

Method : This paper proposes a novel and completely data-driven solution for detection of abandoned objects by taking advantage of the inherent similarity of temporal signatures of these objects across video sequences using independent vector analysis. The new technique does not require any calibration and thus can be readily applied to any camera configuration.

Results : We obtain 74.83% accurate detection by incorporating the joint information across multiple cameras acquired from different angles Show less

Problem Statement : The success of many joint blind source separation techniques is dependent upon accurate estimation of the common signal subspace order across multiple datasets. This has stimulated the development of techniques to estimate the number of common signals across two datasets, in particular, a method that uses information theoretic criteria using the canonical correlation coefficients in the likelihood formulation and a method based upon a two stage procedure, principal component… Show more

Problem Statement : The success of many joint blind source separation techniques is dependent upon accurate estimation of the common signal subspace order across multiple datasets. This has stimulated the development of techniques to estimate the number of common signals across two datasets, in particular, a method that uses information theoretic criteria using the canonical correlation coefficients in the likelihood formulation and a method based upon a two stage procedure, principal component analysis and canonical correlation analysis. However, these methods are limited to two datasets.

Method : In this paper, we propose a method based on multiset canonical correlation analysis followed by knee point detection (MCCA-KPD) to estimate the common subspace order across more than two datasets.

Results : We present a detailed comparison of the order estimation methods using simulated examples as well as real functional magnetic resonance imaging data. We demonstrate the superior performance of MCCA-KPD in terms of estimating the true common subspace order across multiple datasets. Show less

Problem Statement : Automated detection of abandoned object (AO) is an important application in video surveillance for security purposes. Because of its importance, a number of techniques have been proposed to automatically detect abandoned objects in the past years. However, these techniques require prior knowledge on the properties of the object such as its shape and color, in order to classify foreground objects as abandoned object. In contrast, independent component analysis (ICA) does not… Show more

Problem Statement : Automated detection of abandoned object (AO) is an important application in video surveillance for security purposes. Because of its importance, a number of techniques have been proposed to automatically detect abandoned objects in the past years. However, these techniques require prior knowledge on the properties of the object such as its shape and color, in order to classify foreground objects as abandoned object. In contrast, independent component analysis (ICA) does not require such prior knowledge. However, it can only model one dataset at a time, thus limiting its usage to monochrome frames.

Method : In this paper, we propose to use independent vector analysis (IVA), a recent extension of ICA to multivariate data that takes the dependence across multiple datasets into account while retaining the independence within each dataset.

Results : We present a new framework for AO detection using IVA and show that it provides successful performance in complicated scenarios, such as for videos with crowd, illumination change, and occlusion. Show less

Problem Statement : The automated detection of abandoned objects is a quickly developing and widely researched field in video processing with specific application to automated surveillance. In the recent years, a number of approaches have been proposed to automatically detect abandoned objects. However, these techniques require prior knowledge of certain properties of the object such as its shape and color, to classify the foreground objects as abandoned object. The performance of… Show more

Problem Statement : The automated detection of abandoned objects is a quickly developing and widely researched field in video processing with specific application to automated surveillance. In the recent years, a number of approaches have been proposed to automatically detect abandoned objects. However, these techniques require prior knowledge of certain properties of the object such as its shape and color, to classify the foreground objects as abandoned object. The performance of tracking-based approaches degrades in complex scenes, i.e., when the abandoned object is occluded or in the case of crowding.

Method : In this paper, we propose a data-driven approach based on independent component analysis (ICA) for object detection.

Results : We demonstrate the success of the proposed ICA-based methodology with examples of videos with complex scenarios. We also show that algorithm choice plays an important role in performance, in particular when multiple types of diversities are taken into account and demonstrate the importance of order selection. Show less

Problem Statement : Recently, much attention has been devoted to examining time-varying changes in functional connectivity to understand the network structure in the human brain. Most studies, however, analyze the time-varying functional connectivity but ignore the time-varying spatial information.

Method : In this paper, we propose a method based on independent vector analysis (IVA) to study dynamic functional network connectivity (dFNC) as well as dynamic spatial functional network… Show more

Problem Statement : Recently, much attention has been devoted to examining time-varying changes in functional connectivity to understand the network structure in the human brain. Most studies, however, analyze the time-varying functional connectivity but ignore the time-varying spatial information.

Method : In this paper, we propose a method based on independent vector analysis (IVA) to study dynamic functional network connectivity (dFNC) as well as dynamic spatial functional network connectivity (dsFNC) in fMRI data. Though IVA allows one to effectively capture both, its performance degrades with the increase in the number of datasets. Hence, we propose an effective scheme to bypass this limitation followed by graph theoretical analysis to study both inter-network dynamics and intra-network stationarity.

Results : We observe higher dFNC fluctuations for patients with schizophrenia in the default-mode (DM)-salience network and cerebellum with associated connections. dsFNC analysis indicates higher inter-network fluctuation in patients while DM, anterior DM and frontal networks demonstrate significant intra-network fluctuation in controls. Show less