3D Motion Estimation of Human Head by Using Optical Flow

The paper deals with the new algorithm of estimation of large 3D motion of the human head by using the optical flow and the model Candide. In the algorithm prediction of 3D motion parameters in a feedback loop and with multiple iterations was applied. The prediction of 3D motion parameters does not require creating of the synthesized frames but directly uses the frames of input videosequence. Next the algorithm does not need extracting of feature points inside the frames because they are given by the vertices of the used calibrated model Candide. As achieved experimental results show, the iteration process in prediction of 3D motion parameters increased the accuracy of estimation above all the large 3D motion. Such a way the estimation error is decreased without its accumulation in long videosequence. Finally the experimental results show that for 3 iterations a state of saturation was achieved what means that by next increasing of the number of iterations practically no significant increasing of the accuracy of estimation of 3D motion parameters is occurred

Text Mining for Information Systems Researchers: An Annotated Topic Modeling Tutorial

Analysts have estimated that more than 80 percent of today’s data is stored in unstructured form (e.g., text, audio, image, video)—much of it expressed in rich and ambiguous natural language. Traditionally, to analyze natural language, one has used qualitative data-analysis approaches, such as manual coding. Yet, the size of text data sets obtained from the Internet makes manual analysis virtually impossible. In this tutorial, we discuss the challenges encountered when applying automated text-mining techniques in information systems research. In particular, we showcase how to use probabilistic topic modeling via Latent Dirichlet allocation, an unsupervised text-mining technique, with a LASSO multinomial logistic regression to explain user satisfaction with an IT artifact by automatically analyzing more than 12,000 online customer reviews. For fellow information systems researchers, this tutorial provides guidance for conducting text-mining studies on their own and for evaluating the quality of others

Interaction between High-Level and Low-Level Image Analysis for Semantic Video Object Extraction

The task of extracting a semantic video object is split into two subproblems, namely, object segmentation and region segmentation. Object segmentation relies on a priori assumptions, whereas region segmentation is data-driven and can be solved in an automatic manner. These two subproblems are not mutually independent, and they can benefit from interactions with each other. In this paper, a framework for such interaction is formulated. This representation scheme based on region segmentation and semantic segmentation is compatible with the view that image analysis and scene understanding problems can be decomposed into low-level and high-level tasks. Low-level tasks pertain to region-oriented processing, whereas the high-level tasks are closely related to object-level processing. This approach emulates the human visual system: what one “sees” in a scene depends on the scene itself (region segmentation) as well as on the cognitive task (semantic segmentation) at hand. The higher-level segmentation results in a partition corresponding to semantic video objects. Semantic video objects do not usually have invariant physical properties and the definition depends on the application. Hence, the definition incorporates complex domain-specific knowledge and is not easy to generalize. For the specific implementation used in this paper, motion is used as a clue to semantic information. In this framework, an automatic algorithm is presented for computing the semantic partition based on color change detection. The change detection strategy is designed to be immune to the sensor noise and local illumination variations. The lower-level segmentation identifies the partition corresponding to perceptually uniform regions. These regions are derived by clustering in an N-dimensional feature space, composed of static as well as dynamic image attributes. We propose an interaction mechanism between the semantic and the region partitions which allows to cope with multiple simultaneous objects. Experimental results show that the proposed method extracts semantic video objects with high spatial accuracy and temporal coherence

Automatic registration of 3D models to laparoscopic video images for guidance during liver surgery

Laparoscopic liver interventions offer significant advantages over open surgery, such as less pain and trauma, and shorter recovery time for the patient. However, they also bring challenges for the surgeons such as the lack of tactile feedback, limited field of view and occluded anatomy. Augmented reality (AR) can potentially help during laparoscopic liver interventions by displaying sub-surface structures (such as tumours or vasculature). The initial registration between the 3D model extracted from the CT scan and the laparoscopic video feed is essential for an AR system which should be efficient, robust, intuitive to use and with minimal disruption to the surgical procedure. Several challenges of registration methods in laparoscopic interventions include the deformation of the liver due to gas insufflation in the abdomen, partial visibility of the organ and lack of prominent geometrical or texture-wise landmarks. These challenges are discussed in detail and an overview of the state of the art is provided. This research project aims to provide the tools to move towards a completely automatic registration. Firstly, the importance of pre-operative planning is discussed along with the characteristics of the liver that can be used in order to constrain a registration method. Secondly, maximising the amount of information obtained before the surgery, a semi-automatic surface based method is proposed to recover the initial rigid registration irrespective of the position of the shapes. Finally, a fully automatic 3D-2D rigid global registration is proposed which estimates a global alignment of the pre-operative 3D model using a single intra-operative image. Moving towards incorporating the different liver contours can help constrain the registration, especially for partial surfaces. Having a robust, efficient AR system which requires no manual interaction from the surgeon will aid in the translation of such approaches to the clinics

Animation of 3D Model of Human Head

The paper deals with the new algorithm of animation of 3D model of the human head in combination with its global motion. The designed algorithm is very fast and with low calculation requirements, because it does not need the synthesis of the input videosequence for estimation of the animation parameters as well as the parameters of global motion. The used 3D model Candide generates different expressions using its animation units which are controlled by the animation parameters. These ones are estimated on the basis of optical flow without the need of extracting of the feature points in the frames of the input videosequence because they are given by the selected vertices of the animation units of the calibrated 3D model Candide. The established multiple iterations inside the designed animation algorithm of 3D model of the human head between two successive frames significantly improved its accuracy above all for the large motion

Three-dimensional facial motion and structure estimation in video coding

Ankara : Department of Electrical and Electronics Engineering and the Institute of Engineering and Science of Bilkent Univ., 1994.Thesis (Ph.D.) -- Bilkent University, 1994.Includes bibliographical references leaves 81-89.We propose a novel formulation where 3-D global and local motion estimation and the adaptation of a generic wire-frame model to a particular speaker are considered simultaneously within an optical flow based framework including the photometric effects of the motion. We use a flexible wire-frame model whose local structure is characterized by the normal vectors of the patches which are related to the coordinates of the nodes. Geometric constraints that describe the propagation of the movement of the nodes are introduced, which are then efficiently utilized to reduce the number of independent structure parameters. A stochastic relaxation algorithm has been used to determine optimum global motion estimates and the parameters describing the structure of the wire-frame model. For the initialization of the motion and structure parameters, a modified feature based algorithm is used whose performance has also been compared with the existing methods. Results with both simulated and real facial image sequences are provided.Bozdağı, GözdePh.D