Bayesian integration of isotope ratio for geographic sourcing of castor beans

pre-printRecent years have seen an increase in the forensic interest associated with the poison ricin, which is extracted from the seeds of the Ricinus communis plant. Both light element (C, N, O, and H) and strontium (Sr) isotope ratios have previously been used to associate organic material with geographic regions of origin. We present a Bayesian integration methodology that can more accurately predict the region of origin for a castor bean than individual models developed independently for light element stable isotopes or Sr isotope ratios. Our results demonstrate a clear improvement in the ability to correctly classify regions based on the integrated model with a class accuracy of 60.9 ± 2.1% versus 55.9 ± 2.1% and 40.2 ± 1.8% for the light element and strontium (Sr) isotope ratios, respectively. In addition, we show graphically the strengths and weaknesses of each dataset in respect to class prediction and how the integration of these datasets strengthens the overall model

Multi-camera cooperative scene interpretation

In our society, video processing has become a convenient and widely used tool to assist, protect and simplify the daily life of people in areas such as surveillance and video conferencing. The growing number of cameras, the handling and analysis of these vast amounts of video data enable the development of multi-camera applications that cooperatively use multiple sensors. In many applications, bandwidth constraints, privacy issues, and difficulties in storing and analyzing large amounts of video data make applications costly and technically challenging. In this thesis, we deploy techniques ranging from low-level to high-level approaches, specifically designed for multi-camera networks. As a low-level approach, we designed a novel low-level foreground detection algorithm for real-time tracking applications, concentrating on difficult and changing illumination conditions. The main part of this dissertation focuses on a detailed analysis of two novel state-of-the-art real-time tracking approaches: a multi-camera tracking approach based on occupancy maps and a distributed multi-camera tracking approach with a feedback loop. As a high-level application we propose an approach to understand the dynamics in meetings - so called, smart meetings - using a multi-camera setup, consisting of fixed ambient and portable close-up cameras. For all method, we provided qualitative and quantitative results on several experiments, compared to state-of-the-art methods

Mechanism-based classification of PAH mixtures to predict carcinogenic potential

We have previously shown that relative potency factors and DNA adduct measurements are inadequate for predicting carcinogenicity of certain polycyclic aromatic hydrocarbons (PAHs) and PAH mixtures, particularly those that function through alternate pathways or exhibit greater promotional activity compared to benzo[a]pyrene (BaP). Therefore, we developed a pathway based approach for classification of tumor outcome after dermal exposure to PAH/mixtures. FVB/N mice were exposed to dibenzo[def,p]chrysene (DBC), BaP or environmental PAH mixtures (Mix 1-3) following a two-stage initiation/promotion skin tumor protocol. Resulting tumor incidence could be categorized by carcinogenic potency as DBC>>BaP=Mix2=Mix3>Mix1=Control, based on statistical significance. Gene expression profiles measured in skin of mice collected 12 h post-initiation were compared to tumor outcome for identification of short-term bioactivity profiles. A Bayesian integration model was utilized to identify biological pathways predictive of PAH carcinogenic potential during initiation. Integration of probability matrices from four enriched pathways (p<0.05) for DNA damage, apoptosis, response to chemical stimulus and interferon gamma signaling resulted in the highest classification accuracy with leave-one-out cross validation. This pathway-driven approach was successfully utilized to distinguish early regulatory events during initiation prognostic for tumor outcome and provides proof-of-concept for using short-term initiation studies to classify carcinogenic potential of environmental PAH mixtures. These data further provide a ‘source-to outcome’ model that could be used to predict PAH interactions during tumorigenesis and provide an example of how mode-of-action based risk assessment could be employed for environmental PAH mixtures.This is an author's peer-reviewed final manuscript, as accepted by the publisher. The published article is copyrighted by Oxford University Press and can be found at: http://toxsci.oxfordjournals.org/.Keywords: mixtures, toxicogenomics, skin cancer, polycyclic aromatic hydrocarbons, modelin

VIDEO FOREGROUND LOCALIZATION FROM TRADITIONAL METHODS TO DEEP LEARNING

These days, detection of Visual Attention Regions (VAR), such as moving objects has become an integral part of many Computer Vision applications, viz. pattern recognition, object detection and classification, video surveillance, autonomous driving, human-machine interaction (HMI), and so forth. The moving object identification using bounding boxes has matured to the level of localizing the objects along their rigid borders and the process is called foreground localization (FGL). Over the decades, many image segmentation methodologies have been well studied, devised, and extended to suit the video FGL. Despite that, still, the problem of video foreground (FG) segmentation remains an intriguing task yet appealing due to its ill-posed nature and myriad of applications. Maintaining spatial and temporal coherence, particularly at object boundaries, persists challenging, and computationally burdensome. It even gets harder when the background possesses dynamic nature, like swaying tree branches or shimmering water body, and illumination variations, shadows cast by the moving objects, or when the video sequences have jittery frames caused by vibrating or unstable camera mounts on a surveillance post or moving robot. At the same time, in the analysis of traffic flow or human activity, the performance of an intelligent system substantially depends on its robustness of localizing the VAR, i.e., the FG. To this end, the natural question arises as what is the best way to deal with these challenges? Thus, the goal of this thesis is to investigate plausible real-time performant implementations from traditional approaches to modern-day deep learning (DL) models for FGL that can be applicable to many video content-aware applications (VCAA). It focuses mainly on improving existing methodologies through harnessing multimodal spatial and temporal cues for a delineated FGL. The first part of the dissertation is dedicated for enhancing conventional sample-based and Gaussian mixture model (GMM)-based video FGL using probability mass function (PMF), temporal median filtering, and fusing CIEDE2000 color similarity, color distortion, and illumination measures, and picking an appropriate adaptive threshold to extract the FG pixels. The subjective and objective evaluations are done to show the improvements over a number of similar conventional methods. The second part of the thesis focuses on exploiting and improving deep convolutional neural networks (DCNN) for the problem as mentioned earlier. Consequently, three models akin to encoder-decoder (EnDec) network are implemented with various innovative strategies to improve the quality of the FG segmentation. The strategies are not limited to double encoding - slow decoding feature learning, multi-view receptive field feature fusion, and incorporating spatiotemporal cues through long-shortterm memory (LSTM) units both in the subsampling and upsampling subnetworks. Experimental studies are carried out thoroughly on all conditions from baselines to challenging video sequences to prove the effectiveness of the proposed DCNNs. The analysis demonstrates that the architectural efficiency over other methods while quantitative and qualitative experiments show the competitive performance of the proposed models compared to the state-of-the-art

움직이는 물체 검출 및 추적을 위한 생체 모방 모델

학위논문 (박사)-- 서울대학교 대학원 : 전기·컴퓨터공학부, 2014. 2. 최진영.In this thesis, we propose bio-mimetic models for motion detection and visual tracking to overcome the limitations of existing methods in actual environments. The models are inspired from the theory that there are four different forms of visual memory for human visual perception when representing a scenevisible persistence, informational persistence, visual short-term memory (VSTM), and visual long-term memory (VLTM). We view our problem as a problem of modeling and representing an observed scene with temporary short-term models (TSTM) and conservative long-term models (CLTM). We study on building efficient and effective models for TSTM and CLTM, and utilizing them together to obtain robust detection and tracking results under occlusions, clumsy initializations, background clutters, drifting, and non-rigid deformations encountered in actual environments. First, we propose an efficient representation of TSTM to be used for moving object detection on non-stationary cameras, which runs within 5.8 milliseconds (ms) on a PC, and real-time on mobile devices. To achieve real-time capability with robust performance, our method models the background through the proposed dual-mode kernel model (DMKM) and compensates the motion of the camera by mixing neighboring models. Modeling through DMKM prevents the background model from being contaminated by foreground pixels, while still allowing the model to be able to adapt to changes of the background. Mixing neighboring models reduces the errors arising from motion compensation and their influences are further reduced by keeping the age of the model. Also, to decrease computation load, the proposed method applies one DMKM to multiple pixels without performance degradation. Experimental results show the computational lightness and the real-time capability of our method on a smart phone with robust detection performances. Second, by using the concept from both TSTM and CLTM, a new visual tracking method using the novel tri-model is proposed. The proposed method aims to solve the problems of occlusions, background clutters, and drifting simultaneously with the new tri-model. The proposed tri-model is composed of three models, where each model learns the target object, the background, and other non-target moving objects online. The proposed scheme performs tracking by finding the best explanation of the scene with the three learned models. By utilizing the information in the background and the foreground models as well as the target object model, our method obtains robust results under occlusions and background clutters. Also, the target object model is updated in a conservative way to prevent drifting. Furthermore, our method is not restricted to bounding-boxes when representing the target object, and is able to give pixel-wise tracking results. Third, we go beyond pixel-wise modeling and propose a local feature based tracking model using both TSTM and CLTM to track objects in case of uncertain initializations and severe occlusions. To track objects accurately in such situations, the proposed scheme uses ``motion saliency'' and ``descriptor saliency'' of local features and performs tracking based on generalized Hough transform (GHT). The proposed motion saliency of a local feature utilizes instantaneous velocity of features to form TSTM and emphasizes features having distinctive motions, compared to the motions coming from local features which are not from the object. The descriptor saliency models local features as CLTM and emphasizes features which are likely to be of the object in terms of its feature descriptors. Through these saliencies, the proposed method tries to ``learn and find'' the target object rather than looking for what was given at initialization, becoming robust to initialization problems. Also, our tracking result is obtained by combining the results of each local features of the target and the surroundings, thus being robust against severe occlusions as well. The proposed method is compared against eight other methods, with nine image sequences, and hundred random initializations. The experimental results show that our method outperforms all other compared methods. Fourth and last, we focus on building robust CLTM with local patches and their neighboring structures. The proposed method is based on sequential Bayesian inference and focuses on solving both the problem of tracking under partial occlusions and the problem of non-rigid object tracking in real-time on desktop personal computers (PC). The proposed scheme is mainly composed of two parts: (1) modeling the target object using elastic structure of local patches for robust performanceand (2) efficient hierarchical diffusion method to perform the tracking process in real-time. The elastic structure of local patches allows the proposed scheme to handle partial occlusions and non-rigid deformations through the relationship among neighboring patches. The proposed hierarchical diffusion generates samples from the region where the posterior is concentrated to reduce computation time. The method is extensively tested on a number of challenging image sequences with occlusion and non-rigid deformation. The experimental results show the real-time capability and the robustness of the proposed scheme under various situations.1 Introduction 1.1 Background and Research Issues 1.1.1 Issues in Motion Detection 1.1.2 Issues in Object Tracking 1.2 The Human Visual Memory 1.2.1 Sensory Memory 1.2.2 Visual Short-Term Memory 1.2.3 Visual Long-Term Memory 1.3 Bio-mimetic Framework for Detection and Tracking 1.4 Contents of the Research 2 Detection by Pixel-wise Dual-Mode Kernel Model 2.1 Proposed Method 2.1.1 Approximated Gaussian Kernel Model 2.1.2 Dual-Mode Kernel Model (DMKM) 2.1.3 Motion Compensation by Mixing Models 2.1.4 Detection of Foreground Pixels 2.2 Experimental Results 2.2.1 Runtime Comparison 2.2.2 Qualitative Comparison 2.2.3 Quantitative Comparison 2.2.4 Effects of Dual-Mode Kernel Model 2.2.5 Effects of Motion Compensation 2.2.6 Mobile Results 2.3 Remarks and Discussion 3 Tracking by Pixel-wise Tri-Model Representation 3.1 Tri-Model Framework 3.1.1 Overall Scheme 3.1.2 Advantages 3.1.3 Practical Approximation 3.2 Tracking with the Tri-Model 3.2.1 Likelihood of the Tri-Model 3.2.2 Likelihood Maximization 3.2.3 Estimating Pixel-Wise Labels 3.3 Learning the Tri-Model 3.3.1 Target Model 3.3.2 Background Model 3.3.3 Foreground Model 3.4 Experimental Results 3.4.1 Experimental Settings 3.4.2 Tracking Accuracy: Bounding Box 3.4.3 Tracking Accuracy: Pixel-Wise 3.5 Remarks and Discussion 4 Tracking by Feature-point-wise Saliency Model 4.1 Proposed Method 4.1.1 Tracking based on GHT 4.1.2 Descriptor Saliency and Feature DB Update 4.1.3 Motion Saliency 4.2 Experimental Results 4.2.1 Tracking with Inaccurate Initializations 4.2.2 Tracking Under Occlusions 4.3 Remarks and Discussion 5 Tracking by Patch-wise Elastic Structure Model 5.1 Tracking with Elastic Structure of Local Patches 5.1.1 Sequential Bayesian Inference Framework 5.1.2 Elastic Structure of Local Patches 5.1.3 Modeling a Single Patch 5.1.4 Modeling the Relationship between Patches 5.1.5 Model Update 5.1.6 Hierarchical Diffusion 5.1.7 Summary of the Proposed Method 5.2 Experiments 5.2.1 Parameter Effects 5.2.2 Performance Evaluation 5.2.3 Discussion on Translation, Rotation, Illumination Changes 5.2.4 Discussion on Partial Occlusions 5.2.5 Discussion on Non-Rigid Deformations 5.2.6 Discussion on Additional Cases 5.2.7 Summary of Tracking Results 5.2.8 Effectiveness of Hierarchical Diffusion 5.2.9 Limitations 5.3 Remarks and Discussion 6 Concluding Remarks and Future Works Bibliography Abstract in KoreanDocto

Microparticle image processing and field profile optimisation for automated Lab-On-Chip magnetophoretic analytical systems

The work described in this thesis, concerns developments to analytical microfluidic Lab-On-Chip platform originally developed by Prof Pamme's research group at the University of Hull. This work aims to move away from traditional laboratory analysis system towards a more effective system design which is fully automated and therefore potentially deployable in applications such as point of care medical diagnosis. The microfluidic chip platform comprises an external permanent magnet and chip with multiple parallel reagent streams through which magnetic micro-particles pass in sequence. These streams may include particles, analyte, fluorescent labels and wash solutions; together they facilitate an on-chip multi-step analytical procedure. Analyte concentration is measured via florescent intensity of the exiting micro-particles. This has previously been experimentally proven for more than one analytical procedure. The work described here has addressed a couple of issues which needed improvement, specifically optimizing the magnetic field and automating the measurement process. These topics are related by the fact that an optimal field will reduce anomalies such as aggregated particles which may degrade automated measurements.For this system, the optimal magnetic field is homogeneous gradient of sufficient strength to pull the particles across the width of the device during fluid transit of its length. To optimise the magnetic field, COMSOL (a Multiphysics simulation program) was used to evaluate a number of multiple magnet configurations and demonstrate an improved field profile. The simulation approach was validated against experimental data for the original single-magnet design.To analyse the results automatically, a software tool has been developed using C++ which takes image files generated during an experiment and outputs a calibration curve or specific measurement result. The process involves detection of the particles (using image segmentation) and object tracking. The intensity measurement follows the same procedure as the original manual approach, facilitating comparison, but also includes analysis of particle motion behaviour to allow automatic rejection of data from anomalous particles (e.g. stuck particles). For image segmentation a novel texture based technique called Temporal- Adaptive Median Binary Pattern (T-AMBP) combining with Three Frame Difference method to model the background for representing the foreground was proposed. This proposed approached is based on previously developed Adaptive Median Binary Pattern (AMBP) and Gaussian Mixture Model (GMM) approach for image segmentation. The proposed method successfully detects micro-particles even when they have very low fluorescent intensity, while most of the previous approaches failed and is more robust to noise and artefacts. For tracking the micro-particles, we proposed a novel algorithm called "Hybrid Meanshift", which combines Meanshift, Histogram of oriented gradients (HOG) matching and optical flow techniques. Kalman filter was also combined with it to make the tracking robust.The processing of an experimental data set for generating a calibration curve, getting effectively the same results in less than 5 minutes was demonstrated, without needing experimental experience, compared with at least 2 hours work by an experienced experimenter using the manual approach

Object Tracking in Video with Part-Based Tracking by Feature Sampling

Visual tracking of arbitrary objects is an active research topic in computer vision, with applications across multiple disciplines including video surveillance, activity analysis, robot vision, and human computer interface. Despite great progress having been made in object tracking in recent years, it still remains a challenge to design trackers that can deal with difficult tracking scenarios, such as camera motion, object motion change, occlusion, illumination changes, and object deformation. A promising way of tackling these types of problems is to use a part-based method; one which models and tracks small regions of the object and estimates the location of the object based on the tracked part's positions. These approaches typically model parts of objects with histograms of various hand-crafted features extracted from the region in which the part is located. However, it is unclear how such relatively homogeneous regions should be represented to form an effective part-based tracker. In this thesis we present a part-based tracker that includes a model for object parts that is designed to empirically characterise the underlying colour distribution of an image region, representing it by pairs of randomly selected colour features and counts of how many pixels are similar to each feature. This novel feature representation is used to find probable locations for the part in future frames via a Bhattacharyya Distance-based metric, which is modified to prefer higher quality matches. Sets of candidate patch locations are generated by randomly generating non-shearing affine transformations of the part's previous locations and locally optimising the most likely sets of parts to allow for small intra-frame object deformations. We also present a study of model initialisation in online, model-free tracking and evaluate several techniques for selecting the regions of an image, given a target bounding box most likely to contain an object. The strengths and limitations of the combined tracker are evaluated on the VOT2016 and VOT2018 datasets using their evaluation protocol, which also allows an extensive evaluation of parameter robustness. The presented tracker is ranked first among part-based trackers on the VOT2018 dataset and is particularly robust to changes in object and camera motion, as well as object size changes