IU Indianapolis ScholarWorks :: Browsing by Author "Tsechpenakis, Gavriil"

Browsing by Author "Tsechpenakis, Gavriil"

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Active geometric model : multi-compartment model-based segmentation & registration
(2014-08-26) Mukherjee, Prateep; Tsechpenakis, Gavriil; Raje, Rajeev; Tuceryan, Mihran
We present a novel, variational and statistical approach for model-based segmentation. Our model generalizes the Chan-Vese model, proposed for concurrent segmentation of multiple objects embedded in the same image domain. We also propose a novel shape descriptor, namely the Multi-Compartment Distance Functions or mcdf. Our proposed framework for segmentation is two-fold: first, several training samples distributed across various classes are registered onto a common frame of reference; then, we use a variational method similar to Active Shape Models (or ASMs) to generate an average shape model and hence use the latter to partition new images. The key advantages of such a framework is: (i) landmark-free automated shape training; (ii) strict shape constrained model to fit test data. Our model can naturally deal with shapes of arbitrary dimension and topology(closed/open curves). We term our model Active Geometric Model, since it focuses on segmentation of geometric shapes. We demonstrate the power of the proposed framework in two important medical applications: one for morphology estimation of 3D Motor Neuron compartments, another for thickness estimation of Henle's Fiber Layer in the retina. We also compare the qualitative and quantitative performance of our method with that of several other state-of-the-art segmentation methods.
Adversarial autoencoders for anomalous event detection in images
(2017) Dimokranitou, Asimenia; Tsechpenakis, Gavriil; Zheng, Jiang Yu; Tuceryan, Mihran
Detection of anomalous events in image sequences is a problem in computer vision with various applications, such as public security, health monitoring and intrusion detection. Despite the various applications, anomaly detection remains an ill-defined problem. Several definitions exist, the most commonly used defines an anomaly as a low probability event. Anomaly detection is a challenging problem mainly because of the lack of abnormal observations in the data. Thus, usually it is considered an unsupervised learning problem. Our approach is based on autoencoders in combination with Generative Adversarial Networks. The method is called Adversarial Autoencoders [1], and it is a probabilistic autoencoder, that attempts to match the aggregated posterior of the hidden code vector of the autoencoder, with an arbitrary prior distribution. The adversarial error of the learned autoencoder is low for regular events and high for irregular events. We compare our approach with state of the art methods and describe our results with respect to accuracy and efficiency.
Automated Methods To Detect And Quantify Histological Features In Liver Biopsy Images To Aid In The Diagnosis Of Non-Alcoholic Fatty Liver Disease
(2016-03-31) Morusu, Siripriya; Tuceryan, Mihran; Zheng, Jiang; Tsechpenakis, Gavriil; Fang, Shiaofen
The ultimate goal of this study is to build a decision support system to aid the pathologists in diagnosing Non-Alcoholic Fatty Liver Disease (NAFLD) in both adults and children. The disease is caused by accumulation of excess fat in liver cells. It is prevalent in approximately 30% of the general population in United States, Europe and Asian countries. The growing prevalence of the disease is directly related to the obesity epidemic in developed countries. We built computational methods to detect and quantify the histological features of a liver biopsy which aid in staging and phenotyping NAFLD. Image processing and supervised machine learning techniques are predominantly used to develop a robust and reliable system. The contributions of this study include development of a rich web interface for acquiring annotated data from expert pathologists, identifying and quantifying macrosteatosis in rodent liver biopsies as well as lobular inflammation and portal inflammation in human liver biopsies. Our work on detection of macrosteatosis in mouse liver shows 94.2% precision and 95% sensitivity. The model developed for lobular inflammation detection performs with precision and sensitivity of 79.3% and 81.3% respectively. We also present the first study on portal inflammation identification with 82.1% precision and 88.3% sensitivity. The thesis also presents results obtained for correlation between model computed scores for each of these lesions and expert pathologists' grades.
The clash between two worlds in human action recognition: supervised feature training vs Recurrent ConvNet
(2016-11-28) Raptis, Konstantinos; Tsechpenakis, Gavriil
Action recognition has been an active research topic for over three decades. There are various applications of action recognition, such as surveillance, human-computer interaction, and content-based retrieval. Recently, research focuses on movies, web videos, and TV shows datasets. The nature of these datasets make action recognition very challenging due to scene variability and complexity, namely background clutter, occlusions, viewpoint changes, fast irregular motion, and large spatio-temporal search space (articulation configurations and motions). The use of local space and time image features shows promising results, avoiding the cumbersome and often inaccurate frame-by-frame segmentation (boundary estimation). We focus on two state of the art methods for the action classification problem: dense trajectories and recurrent neural networks (RNN). Dense trajectories use typical supervised training (e.g., with Support Vector Machines) of features such as 3D-SIFT, extended SURF, HOG3D, and local trinary patterns; the main idea is to densely sample these features in each frame and track them in the sequence based on optical flow. On the other hand, the deep neural network uses the input frames to detect action and produce part proposals, i.e., estimate information on body parts (shapes and locations). We compare qualitatively and numerically these two approaches, indicative to what is used today, and describe our conclusions with respect to accuracy and efficiency.
Crime Detection from Pre-crime Video Analysis
(2024-05) Kilic, Sedat; Tuceryan, Mihran; Zheng, Jiang Yu; Tsechpenakis, Gavriil; Durresi, Arjan
This research investigates the detection of pre-crime events, specifically targeting behaviors indicative of shoplifting, through the advanced analysis of CCTV video data. The study introduces an innovative approach that leverages augmented human pose and emotion information within individual frames, combined with the extraction of activity information across subsequent frames, to enhance the identification of potential shoplifting actions before they occur. Utilizing a diverse set of models including 3D Convolutional Neural Networks (CNNs), Graph Neural Networks (GNNs), Recurrent Neural Networks (RNNs), and a specially developed transformer architecture, the research systematically explores the impact of integrating additional contextual information into video analysis. By augmenting frame-level video data with detailed pose and emotion insights, and focusing on the temporal dynamics between frames, our methodology aims to capture the nuanced behavioral patterns that precede shoplifting events. The comprehensive experimental evaluation of our models across different configurations reveals a significant improvement in the accuracy of pre-crime detection. The findings underscore the crucial role of combining visual features with augmented data and the importance of analyzing activity patterns over time for a deeper understanding of pre-shoplifting behaviors. The study’s contributions are multifaceted, including a detailed examination of pre-crime frames, strategic augmentation of video data with added contextual information, the creation of a novel transformer architecture customized for pre-crime analysis, and an extensive evaluation of various computational models to improve predictive accuracy.
Direction Selectivity in Drosophila Proprioceptors Requires the Mechanosensory Channel Tmc
(Elsevier, 2019-03-18) He, Liping; Gulyanon, Sarun; Mihovilovic Skanata, Mirna; Karagyozov, Doycho; Heckscher, Ellie S.; Krieg, Michael; Tsechpenakis, Gavriil; Gershow, Marc; Tracey, W. Daniel; Department of Computer and Information sciences, School of Science
Summary Drosophila Transmembrane channel-like (Tmc) is a protein that functions in larval proprioception. The closely related TMC1 protein is required for mammalian hearing and is a pore-forming subunit of the hair cell mechanotransduction channel. In hair cells, TMC1 is gated by small deflections of microvilli that produce tension on extracellular tip-links that connect adjacent villi. How Tmc might be gated in larval proprioceptors, which are neurons having a morphology that is completely distinct from hair cells, is unknown. Here, we have used high-speed confocal microscopy both to measure displacements of proprioceptive sensory dendrites during larval movement and to optically measure neural activity of the moving proprioceptors. Unexpectedly, the pattern of dendrite deformation for distinct neurons was unique and differed depending on the direction of locomotion: ddaE neuron dendrites were strongly curved by forward locomotion, while the dendrites of ddaD were more strongly deformed by backward locomotion. Furthermore, GCaMP6f calcium signals recorded in the proprioceptive neurons during locomotion indicated tuning to the direction of movement. ddaE showed strong activation during forward locomotion, while ddaD showed responses that were strongest during backward locomotion. Peripheral proprioceptive neurons in animals mutant for Tmc showed a near-complete loss of movement related calcium signals. As the strength of the responses of wild-type animals was correlated with dendrite curvature, we propose that Tmc channels may be activated by membrane curvature in dendrites that are exposed to strain. Our findings begin to explain how distinct cellular systems rely on a common molecular pathway for mechanosensory responses.
E-scooter Rider Detection System in Driving Environments
(2021-08) Apurv, Kumar; Zheng, Jiang; Tian, Renran; Tsechpenakis, Gavriil
E-scooters are ubiquitous and their number keeps escalating, increasing their interactions with other vehicles on the road. E-scooter riders have an atypical behavior that varies enormously from other vulnerable road users, creating new challenges for vehicle active safety systems and automated driving functionalities. The detection of e-scooter riders by other vehicles is the first step in taking care of the risks. This research presents a novel vision-based system to differentiate between e-scooter riders and regular pedestrians and a benchmark dataset for e-scooter riders in natural environments. An efficient system pipeline built using two existing state-of-the-art convolutional neural networks (CNN), You Only Look Once (YOLOv3) and MobileNetV2, performs detection of these vulnerable e-scooter riders.
FKBP51 modulates hippocampal size and function in post-translational regulation of Parkin
(Springer, 2022-03-04) Qiu, Bin; Zhong, Zhaohui; Righter, Shawn; Xu, Yuxue; Wang, Jun; Deng, Ran; Wang, Chao; Williams, Kent E.; Ma, Yao-ying; Tsechpenakis, Gavriil; Liang, Tiebing; Yong, Weidong; Surgery, School of Medicine
FK506-binding protein 51 (encoded by Fkpb51, also known as Fkbp5) has been associated with stress-related mental illness. To investigate its function, we studied the morphological consequences of Fkbp51 deletion. Artificial Intelligence-assisted morphological analysis revealed that male Fkbp51 knock-out (KO) mice possess more elongated dentate gyrus (DG) but shorter hippocampal height in coronal sections when compared to WT. Primary cultured Fkbp51 KO hippocampal neurons were shown to exhibit larger dendritic outgrowth than wild-type (WT) controls and pharmacological manipulation experiments suggest that this may occur through the regulation of microtubule-associated protein. Both in vitro primary culture and in vivo labeling support a role for FKBP51 in the regulation of microtubule-associated protein expression. Furthermore, Fkbp51 KO hippocampi exhibited decreases in βIII-tubulin, MAP2, and Tau protein levels, but a greater than 2.5-fold increase in Parkin protein. Overexpression and knock-down FKBP51 demonstrated that FKBP51 negatively regulates Parkin in a dose-dependent and ubiquitin-mediated manner. These results indicate a potential novel post-translational regulatory mechanism of Parkin by FKBP51 and the significance of their interaction on disease onset.
Foreground discovery in streaming videos with dynamic construction of content graphs
(Elsevier, 2023-01) Farhand, Sepehr; Tsechpenakis, Gavriil; Computer and Information Science, School of Science
We study the problem of unknown foreground discovery in image streaming scenarios, where no prior information about the dynamic scene is assumed. Contrary to existing co-segmentation principles where the entire dataset is given, in streams new information emerges as content appears and disappears continually. Any object classes to be observed in the scene are unknown, therefore no detection model can be trained for the specific class set. We also assume there is no available repository of trained features from convolutional neural nets, i.e., transfer learning is not applicable. We focus on the progressive discovery of foreground, which may or may not correspond to contextual objects of interest, depending on the camera trajectory, or, in general, the perceived motion. Without any form of supervision, we construct in a bottom up fashion dynamic graphs that capture region saliency and relative topology. Such graphs are continually updated over time, and along with occlusion information, as fundamental property of the foreground–background relationship, foreground is computed for each frame of the stream. We validate our method using indoor and outdoor scenes of varying complexity with respect to content, objects motion, camera trajectory, and occlusions.
Machine Vision Assisted In Situ Ichthyoplankton Imaging System
(2013-07-12) Iyer, Neeraj; Tsechpenakis, Gavriil; Raje, Rajeev; Tuceryan, Mihran; Fang, Shiaofen
Recently there has been a lot of effort in developing systems for sampling and automatically classifying plankton from the oceans. Existing methods assume the specimens have already been precisely segmented, or aim at analyzing images containing single specimen (extraction of their features and/or recognition of specimens as single targets in-focus in small images). The resolution in the existing systems is limiting. Our goal is to develop automated, very high resolution image sensing of critically important, yet under-sampled, components of the planktonic community by addressing both the physical sensing system (e.g. camera, lighting, depth of field), as well as crucial image extraction and recognition routines. The objective of this thesis is to develop a framework that aims at (i) the detection and segmentation of all organisms of interest automatically, directly from the raw data, while filtering out the noise and out-of-focus instances, (ii) extract the best features from images and (iii) identify and classify the plankton species. Our approach focusses on utilizing the full computational power of a multicore system by implementing a parallel programming approach that can process large volumes of high resolution plankton images obtained from our newly designed imaging system (In Situ Ichthyoplankton Imaging System (ISIIS)). We compare some of the widely used segmentation methods with emphasis on accuracy and speed to find the one that works best on our data. We design a robust, scalable, fully automated system for high-throughput processing of the ISIIS imagery.

Browsing by Author "Tsechpenakis, Gavriil"

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