Object Tracking

Object tracking consists in estimation of trajectory of moving objects in the sequence of images. Automation of the computer object tracking is a difficult task. Dynamics of multiple parameters changes representing features and motion of the objects, and temporary partial or full occlusion of the tracked objects have to be considered. This monograph presents the development of object tracking algorithms, methods and systems. Both, state of the art of object tracking methods and also the new trends in research are described in this book. Fourteen chapters are split into two sections. Section 1 presents new theoretical ideas whereas Section 2 presents real-life applications. Despite the variety of topics contained in this monograph it constitutes a consisted knowledge in the field of computer object tracking. The intention of editor was to follow up the very quick progress in the developing of methods as well as extension of the application

Abnormal ECG search in long-term electrocardiographic recordings from an animal model of heart failure

Heart failure is one of the leading causes of death in the United States. Five million Americans suffer from heart failure. Advances in portable electrocardiogram (ECG) monitoring systems and large data storage space allow the ECG to be recorded continuously for long periods. Long-term monitoring could potentially lead to better diagnosis and treatment if the progression of heart failure could be followed. The challenge is to analyze the sheer mass of data. Manual analysis using the classical methods is impossible. In this dissertation, a framework for analysis of long-term ECG recording and methods for searching an abnormal ECG are presented.;The data used in this research were collected from an animal model of heart failure. Chronic heart failure was gradually induced in rats by aldosterone infusion and a high Na and low Mg diet. The ECG was continuously recorded during the experimental period of 11-12 weeks through radiotelemetry. The ECG leads were placed subcutaneously in lead-II configuration. In the end, there were 80 GB of data from five animals. Besides the massive amount of data, noise and artifacts also caused problems in the analysis.;The framework includes data preparation, ECG beat detection, EMG noise detection, baseline fluctuation removal, ECG template generation, feature extraction, and abnormal ECG search. The raw data was converted from its original format and stored in a database for data retrieval. The beat detection technique was improved from the original algorithm so that it was less sensitive to signal baseline jump and more sensitive to beat size variation. A method for estimating a parameter required for baseline fluctuation removal is proposed. It provides a good result on test signals. A new algorithm for EMG noise detection was developed using morphological filters and moving variance. The resulting sensitivity and specificity are 94% and 100%, respectively. A procedure for ECG template generation was proposed to capture gradual change in ECG morphology and manage the matching process if numerous ECG templates are created. RR intervals and heart rate variability parameters are extracted and plotted to display progressive changes as heart failure develops. In the abnormal ECG search, premature ventricular complexes, elevated ST segment, and split-R-wave ECG are considered. New features are extracted from ECG morphology. The Fisher linear discriminant analysis is used to classify the normal and abnormal ECG. The results provide classification rate, sensitivity, and specificity of 97.35%, 96.02%, and 98.91%, respectively

Feature-driven Volume Visualization of Medical Imaging Data

Direct volume rendering (DVR) is a volume visualization technique that has been proved to be a very powerful tool in many scientific visualization domains. Diagnostic medical imaging is one such domain in which DVR provides new capabilities for the analysis of complex cases and improves the efficiency of image interpretation workflows. However, the full potential of DVR in the medical domain has not yet been realized. A major obstacle for a better integration of DVR in the medical domain is the time-consuming process to optimize the rendering parameters that are needed to generate diagnostically relevant visualizations in which the important features that are hidden in image volumes are clearly displayed, such as shape and spatial localization of tumors, its relationship with adjacent structures, and temporal changes in the tumors. In current workflows, clinicians must manually specify the transfer function (TF), view-point (camera), clipping planes, and other visual parameters. Another obstacle for the adoption of DVR to the medical domain is the ever increasing volume of imaging data. The advancement of imaging acquisition techniques has led to a rapid expansion in the size of the data, in the forms of higher resolutions, temporal imaging acquisition to track treatment responses over time, and an increase in the number of imaging modalities that are used for a single procedure. The manual specification of the rendering parameters under these circumstances is very challenging. This thesis proposes a set of innovative methods that visualize important features in multi-dimensional and multi-modality medical images by automatically or semi-automatically optimizing the rendering parameters. Our methods enable visualizations necessary for the diagnostic procedure in which 2D slice of interest (SOI) can be augmented with 3D anatomical contextual information to provide accurate spatial localization of 2D features in the SOI; the rendering parameters are automatically computed to guarantee the visibility of 3D features; and changes in 3D features can be tracked in temporal data under the constraint of consistent contextual information. We also present a method for the efficient computation of visibility histograms (VHs) using adaptive binning, which allows our optimal DVR to be automated and visualized in real-time. We evaluated our methods by producing visualizations for a variety of clinically relevant scenarios and imaging data sets. We also examined the computational performance of our methods for these scenarios

Feature-driven Volume Visualization of Medical Imaging Data

Direct volume rendering (DVR) is a volume visualization technique that has been proved to be a very powerful tool in many scientific visualization domains. Diagnostic medical imaging is one such domain in which DVR provides new capabilities for the analysis of complex cases and improves the efficiency of image interpretation workflows. However, the full potential of DVR in the medical domain has not yet been realized. A major obstacle for a better integration of DVR in the medical domain is the time-consuming process to optimize the rendering parameters that are needed to generate diagnostically relevant visualizations in which the important features that are hidden in image volumes are clearly displayed, such as shape and spatial localization of tumors, its relationship with adjacent structures, and temporal changes in the tumors. In current workflows, clinicians must manually specify the transfer function (TF), view-point (camera), clipping planes, and other visual parameters. Another obstacle for the adoption of DVR to the medical domain is the ever increasing volume of imaging data. The advancement of imaging acquisition techniques has led to a rapid expansion in the size of the data, in the forms of higher resolutions, temporal imaging acquisition to track treatment responses over time, and an increase in the number of imaging modalities that are used for a single procedure. The manual specification of the rendering parameters under these circumstances is very challenging. This thesis proposes a set of innovative methods that visualize important features in multi-dimensional and multi-modality medical images by automatically or semi-automatically optimizing the rendering parameters. Our methods enable visualizations necessary for the diagnostic procedure in which 2D slice of interest (SOI) can be augmented with 3D anatomical contextual information to provide accurate spatial localization of 2D features in the SOI; the rendering parameters are automatically computed to guarantee the visibility of 3D features; and changes in 3D features can be tracked in temporal data under the constraint of consistent contextual information. We also present a method for the efficient computation of visibility histograms (VHs) using adaptive binning, which allows our optimal DVR to be automated and visualized in real-time. We evaluated our methods by producing visualizations for a variety of clinically relevant scenarios and imaging data sets. We also examined the computational performance of our methods for these scenarios

Multiple instance fuzzy inference.

A novel fuzzy learning framework that employs fuzzy inference to solve the problem of multiple instance learning (MIL) is presented. The framework introduces a new class of fuzzy inference systems called Multiple Instance Fuzzy Inference Systems (MI-FIS). Fuzzy inference is a powerful modeling framework that can handle computing with knowledge uncertainty and measurement imprecision effectively. Fuzzy Inference performs a non-linear mapping from an input space to an output space by deriving conclusions from a set of fuzzy if-then rules and known facts. Rules can be identified from expert knowledge, or learned from data. In multiple instance problems, the training data is ambiguously labeled. Instances are grouped into bags, labels of bags are known but not those of individual instances. MIL deals with learning a classifier at the bag level. Over the years, many solutions to this problem have been proposed. However, no MIL formulation employing fuzzy inference exists in the literature. In this dissertation, we introduce multiple instance fuzzy logic that enables fuzzy reasoning with bags of instances. Accordingly, different multiple instance fuzzy inference styles are proposed. The Multiple Instance Mamdani style fuzzy inference (MI-Mamdani) extends the standard Mamdani style inference to compute with multiple instances. The Multiple Instance Sugeno style fuzzy inference (MI-Sugeno) is an extension of the standard Sugeno style inference to handle reasoning with multiple instances. In addition to the MI-FIS inference styles, one of the main contributions of this work is an adaptive neuro-fuzzy architecture designed to handle bags of instances as input and capable of learning from ambiguously labeled data. The proposed architecture, called Multiple Instance-ANFIS (MI-ANFIS), extends the standard Adaptive Neuro Fuzzy Inference System (ANFIS). We also propose different methods to identify and learn fuzzy if-then rules in the context of MIL. In particular, a novel learning algorithm for MI-ANFIS is derived. The learning is achieved by using the backpropagation algorithm to identify the premise parameters and consequent parameters of the network. The proposed framework is tested and validated using synthetic and benchmark datasets suitable for MIL problems. Additionally, we apply the proposed Multiple Instance Inference to the problem of region-based image categorization as well as to fuse the output of multiple discrimination algorithms for the purpose of landmine detection using Ground Penetrating Radar

Development of a New Local Adaptive Thresholding Method and Classification Algorithms for X-ray Machine Vision Inspection of Pecans

This study evaluated selected local adaptive thresholding methods for pecan defect segmentation and proposed a new method: Reverse Water Flow. Good pecan nuts and fabricated defective pecan nuts were used for comparison, in addition to images from published research articles. For detailed comparison, defective and good pecans, 100 each, were collect from a mechanical sorter operating at Pecan Research Farm, Oklahoma State University. To improve classification accuracy and reduce the decision time AdaBoost and support vector machine classifiers were applied and compared with Bayesian classifier. The data set was randomly divided into training and validation sets and 300 such runs were made. A new local adaptive thresholding method with a new hypothesis: reversing the water flow and a simpler thresholding criterion is proposed. The new hypothesis, reversing the simulated water flow, reduced the computational time by 40-60% as compared to the existing fastest Oh method. The proposed method could segment both larBiosystems and Agricultural Engineerin

Computational intelligence approaches to robotics, automation, and control [Volume guest editors]

No abstract available

Pattern Recognition

A wealth of advanced pattern recognition algorithms are emerging from the interdiscipline between technologies of effective visual features and the human-brain cognition process. Effective visual features are made possible through the rapid developments in appropriate sensor equipments, novel filter designs, and viable information processing architectures. While the understanding of human-brain cognition process broadens the way in which the computer can perform pattern recognition tasks. The present book is intended to collect representative researches around the globe focusing on low-level vision, filter design, features and image descriptors, data mining and analysis, and biologically inspired algorithms. The 27 chapters coved in this book disclose recent advances and new ideas in promoting the techniques, technology and applications of pattern recognition

Vision-based representation and recognition of human activities in image sequences

Magdeburg, Univ., Fak. für Elektrotechnik und Informationstechnik, Diss., 2013von Samy Sadek Mohamed Bakhee

Advances in Character Recognition

This book presents advances in character recognition, and it consists of 12 chapters that cover wide range of topics on different aspects of character recognition. Hopefully, this book will serve as a reference source for academic research, for professionals working in the character recognition field and for all interested in the subject