Content based image pose manipulation

This thesis proposes the application of space-frequency transformations to the domain of pose estimation in images. This idea is explored using the Wavelet Transform with illustrative applications in pose estimation for face images, and images of planar scenes. The approach is based on examining the spatial frequency components in an image, to allow the inherent scene symmetry balance to be recovered. For face images with restricted pose variation (looking left or right), an algorithm is proposed to maximise this symmetry in order to transform the image into a fronto-parallel pose. This scheme is further employed to identify the optimal frontal facial pose from a video sequence to automate facial capture processes. These features are an important pre-requisite in facial recognition and expression classification systems. The under lying principles of this spatial-frequency approach are examined with respect to images with planar scenes. Using the Continuous Wavelet Transform, full perspective planar transformations are estimated within a featureless framework. Restoring central symmetry to the wavelet transformed images in an iterative optimisation scheme removes this perspective pose. This advances upon existing spatial approaches that require segmentation and feature matching, and frequency only techniques that are limited to affine transformation recovery. To evaluate the proposed techniques, the pose of a database of subjects portraying varying yaw orientations is estimated and the accuracy is measured against the captured ground truth information. Additionally, full perspective homographies for synthesised and imaged textured planes are estimated. Experimental results are presented for both situations that compare favourably with existing techniques in the literature

Развитие вербализации и опознания эмоций как основа эмоционального интеллекта : этап 6

Заключительный этап научно-поискового исследования «Развитие вербализации и опознания эмоций как основа эмоционального интеллекта» был направлен согласно Плану экспериментальных исследований по Госконтракту № П1216 на обобщение результатов трехлетнего лонгитюдного экспериментального плана, выполняемого с целью получения новых данных о влиянии развития способности вербализировать и осознавать эмоции по лицевой экспрессии на показатели эмоционального интеллекта, а также сравнение полученных результатов с предварительными данными предыдущих этапов работы. Основная цель VI-го этапа исследования: описать основные тенденции развития эмоционального интеллекта на протяжение возрастного отрезка 14-35 лет, опираясь на данные трехлетнего лонгитюдного эксперимента и срезовых исследований, осуществленных в ходе выполнения НИР.ФЦП «Научные и научно-педагогические кадры инновационной России на 2009-2013 годы

Vision for Social Robots: Human Perception and Pose Estimation

In order to extract the underlying meaning from a scene captured from the surrounding world in a single still image, social robots will need to learn the human ability to detect different objects, understand their arrangement and relationships relative both to their own parts and to each other, and infer the dynamics under which they are evolving. Furthermore, they will need to develop and hold a notion of context to allow assigning different meanings (semantics) to the same visual configuration (syntax) of a scene. The underlying thread of this Thesis is the investigation of new ways for enabling interactions between social robots and humans, by advancing the visual perception capabilities of robots when they process images and videos in which humans are the main focus of attention. First, we analyze the general problem of scene understanding, as social robots moving through the world need to be able to interpret scenes without having been assigned a specific preset goal. Throughout this line of research, i) we observe that human actions and interactions which can be visually discriminated from an image follow a very heavy-tailed distribution; ii) we develop an algorithm that can obtain a spatial understanding of a scene by only using cues arising from the effect of perspective on a picture of a person’s face; and iii) we define a novel taxonomy of errors for the task of estimating the 2D body pose of people in images to better explain the behavior of algorithms and highlight their underlying causes of error. Second, we focus on the specific task of 3D human pose and motion estimation from monocular 2D images using weakly supervised training data, as accurately predicting human pose will open up the possibility of richer interactions between humans and social robots. We show that when 3D ground-truth data is only available in small quantities, or not at all, it is possible to leverage knowledge about the physical properties of the human body, along with additional constraints related to alternative types of supervisory signals, to learn models that can regress the full 3D pose of the human body and predict its motions from monocular 2D images. Taken in its entirety, the intent of this Thesis is to highlight the importance of, and provide novel methodologies for, social robots' ability to interpret their surrounding environment, learn in a way that is robust to low data availability, and generalize previously observed behaviors to unknown situations in a similar way to humans.</p

Active modelling of virtual humans

This thesis provides a complete framework that enables the creation of photorealistic 3D human models in real-world environments. The approach allows a non-expert user to use any digital capture device to obtain four images of an individual and create a personalised 3D model, for multimedia applications. To achieve this, it is necessary that the system is automatic and that the reconstruction process is flexible to account for information that is not available or incorrectly captured. In this approach the individual is automatically extracted from the environment using constrained active B-spline templates that are scaled and automatically initialised using only image information. These templates incorporate the energy minimising framework for Active Contour Models, providing a suitable and flexible method to deal with the adjustments in pose an individual can adopt. The final states of the templates describe the individual’s shape. The contours in each view are combined to form a 3D B-spline surface that characterises an individual’s maximal silhouette equivalent. The surface provides a mould that contains sufficient information to allow for the active deformation of an underlying generic human model. This modelling approach is performed using a novel technique that evolves active-meshes to 3D for deforming the underlying human model, while adaptively constraining it to preserve its existing structure. The active-mesh approach incorporates internal constraints that maintain the structural relationship of the vertices of the human model, while external forces deform the model congruous to the 3D surface mould. The strength of the internal constraints can be reduced to allow the model to adopt the exact shape of the bounding volume or strengthened to preserve the internal structure, particularly in areas of high detail. This novel implementation provides a uniform framework that can be simply and automatically applied to the entire human model

Change blindness: eradication of gestalt strategies

Arrays of eight, texture-defined rectangles were used as stimuli in a one-shot change blindness (CB) task where there was a 50% chance that one rectangle would change orientation between two successive presentations separated by an interval. CB was eliminated by cueing the target rectangle in the first stimulus, reduced by cueing in the interval and unaffected by cueing in the second presentation. This supports the idea that a representation was formed that persisted through the interval before being 'overwritten' by the second presentation (Landman et al, 2003 Vision Research 43149–164]. Another possibility is that participants used some kind of grouping or Gestalt strategy. To test this we changed the spatial position of the rectangles in the second presentation by shifting them along imaginary spokes (by ±1 degree) emanating from the central fixation point. There was no significant difference seen in performance between this and the standard task [F(1,4)=2.565, p=0.185]. This may suggest two things: (i) Gestalt grouping is not used as a strategy in these tasks, and (ii) it gives further weight to the argument that objects may be stored and retrieved from a pre-attentional store during this task

Vision-assisted modeling for model-based video representations

Thesis (Ph. D.)--Massachusetts Institute of Technology, Program in Media Arts & Sciences, 1997.Includes bibliographical references (leaves 134-145).by Shawn C. Becker.Ph.D

Applying image processing techniques to pose estimation and view synthesis.

Fung Yiu-fai Phineas.Thesis (M.Phil.)--Chinese University of Hong Kong, 1999.Includes bibliographical references (leaves 142-148).Abstracts in English and Chinese.Chapter 1 --- Introduction --- p.1Chapter 1.1 --- Model-based Pose Estimation --- p.3Chapter 1.1.1 --- Application - 3D Motion Tracking --- p.4Chapter 1.2 --- Image-based View Synthesis --- p.4Chapter 1.3 --- Thesis Contribution --- p.7Chapter 1.4 --- Thesis Outline --- p.8Chapter 2 --- General Background --- p.9Chapter 2.1 --- Notations --- p.9Chapter 2.2 --- Camera Models --- p.10Chapter 2.2.1 --- Generic Camera Model --- p.10Chapter 2.2.2 --- Full-perspective Camera Model --- p.11Chapter 2.2.3 --- Affine Camera Model --- p.12Chapter 2.2.4 --- Weak-perspective Camera Model --- p.13Chapter 2.2.5 --- Paraperspective Camera Model --- p.14Chapter 2.3 --- Model-based Motion Analysis --- p.15Chapter 2.3.1 --- Point Correspondences --- p.16Chapter 2.3.2 --- Line Correspondences --- p.18Chapter 2.3.3 --- Angle Correspondences --- p.19Chapter 2.4 --- Panoramic Representation --- p.20Chapter 2.4.1 --- Static Mosaic --- p.21Chapter 2.4.2 --- Dynamic Mosaic --- p.22Chapter 2.4.3 --- Temporal Pyramid --- p.23Chapter 2.4.4 --- Spatial Pyramid --- p.23Chapter 2.5 --- Image Pre-processing --- p.24Chapter 2.5.1 --- Feature Extraction --- p.24Chapter 2.5.2 --- Spatial Filtering --- p.27Chapter 2.5.3 --- Local Enhancement --- p.31Chapter 2.5.4 --- Dynamic Range Stretching or Compression --- p.32Chapter 2.5.5 --- YIQ Color Model --- p.33Chapter 3 --- Model-based Pose Estimation --- p.35Chapter 3.1 --- Previous Work --- p.35Chapter 3.1.1 --- Estimation from Established Correspondences --- p.36Chapter 3.1.2 --- Direct Estimation from Image Intensities --- p.49Chapter 3.1.3 --- Perspective-3-Point Problem --- p.51Chapter 3.2 --- Our Iterative P3P Algorithm --- p.58Chapter 3.2.1 --- Gauss-Newton Method --- p.60Chapter 3.2.2 --- Dealing with Ambiguity --- p.61Chapter 3.2.3 --- 3D-to-3D Motion Estimation --- p.66Chapter 3.3 --- Experimental Results --- p.68Chapter 3.3.1 --- Synthetic Data --- p.68Chapter 3.3.2 --- Real Images --- p.72Chapter 3.4 --- Discussions --- p.73Chapter 4 --- Panoramic View Analysis --- p.76Chapter 4.1 --- Advanced Mosaic Representation --- p.76Chapter 4.1.1 --- Frame Alignment Policy --- p.77Chapter 4.1.2 --- Multi-resolution Representation --- p.77Chapter 4.1.3 --- Parallax-based Representation --- p.78Chapter 4.1.4 --- Multiple Moving Objects --- p.79Chapter 4.1.5 --- Layers and Tiles --- p.79Chapter 4.2 --- Panorama Construction --- p.79Chapter 4.2.1 --- Image Acquisition --- p.80Chapter 4.2.2 --- Image Alignment --- p.82Chapter 4.2.3 --- Image Integration --- p.88Chapter 4.2.4 --- Significant Residual Estimation --- p.89Chapter 4.3 --- Advanced Alignment Algorithms --- p.90Chapter 4.3.1 --- Patch-based Alignment --- p.91Chapter 4.3.2 --- Global Alignment (Block Adjustment) --- p.92Chapter 4.3.3 --- Local Alignment (Deghosting) --- p.93Chapter 4.4 --- Mosaic Application --- p.94Chapter 4.4.1 --- Visualization Tool --- p.94Chapter 4.4.2 --- Video Manipulation --- p.95Chapter 4.5 --- Experimental Results --- p.96Chapter 5 --- Panoramic Walkthrough --- p.99Chapter 5.1 --- Problem Statement and Notations --- p.100Chapter 5.2 --- Previous Work --- p.101Chapter 5.2.1 --- 3D Modeling and Rendering --- p.102Chapter 5.2.2 --- Branching Movies --- p.103Chapter 5.2.3 --- Texture Window Scaling --- p.104Chapter 5.2.4 --- Problems with Simple Texture Window Scaling --- p.105Chapter 5.3 --- Our Walkthrough Approach --- p.106Chapter 5.3.1 --- Cylindrical Projection onto Image Plane --- p.106Chapter 5.3.2 --- Generating Intermediate Frames --- p.108Chapter 5.3.3 --- Occlusion Handling --- p.114Chapter 5.4 --- Experimental Results --- p.116Chapter 5.5 --- Discussions --- p.116Chapter 6 --- Conclusion --- p.121Chapter A --- Formulation of Fischler and Bolles' Method for P3P Problems --- p.123Chapter B --- Derivation of z1 and z3 in terms of z2 --- p.127Chapter C --- Derivation of e1 and e2 --- p.129Chapter D --- Derivation of the Update Rule for Gauss-Newton Method --- p.130Chapter E --- Proof of (λ1λ2-λ 4）>〉0 --- p.132Chapter F --- Derivation of φ and hi --- p.133Chapter G --- Derivation of w1j to w4j --- p.134Chapter H --- More Experimental Results on Panoramic Stitching Algorithms --- p.138Bibliography --- p.14

Constructing 3D faces from natural language interface

This thesis presents a system by which 3D images of human faces can be constructed using a natural language interface. The driving force behind the project was the need to create a system whereby a machine could produce artistic images from verbal or composed descriptions. This research is the first to look at constructing and modifying facial image artwork using a natural language interface. Specialised modules have been developed to control geometry of 3D polygonal head models in a commercial modeller from natural language descriptions. These modules were produced from research on human physiognomy, 3D modelling techniques and tools, facial modelling and natural language processing. [Continues.

MPEG-4 content creation: integration of MPEG-4 content creation tools into an existing animation tool

This thesis provides a complete framework that enables the creation of photorealistic 3D human models in real-world environments. The approach allows a non-expert user to use any digital capture device to obtain four images of an individual and create a personalised 3D model, for multimedia applications. To achieve this, it is necessary that the system is automatic and that the reconstruction process is flexible to account for information that is not available or incorrectly captured. In this approach the individual is automatically extracted from the environment using constrained active B-spline templates that are scaled and automatically initialised using only image information. These templates incorporate the energy minimising framework for Active Contour Models, providing a suitable and flexible method to deal with the adjustments in pose an individual can adopt. The final states o f the templates describe the individual’s shape. The contours in each view are combined to form a 3D B-spline surface that characterises an individual’s maximal silhouette equivalent. The surface provides a mould that contains sufficient information to allow for the active deformation of an underlying generic human model. This modelling approach is performed using a novel technique that evolves active-meshes to 3D for deforming the underlying human model, while adaptively constraining it to preserve its existing structure. The active-mesh approach incorporates internal constraints that maintain the structural relationship of the vertices of the human model, while external forces deform the model congruous to the 3D surface mould. The strength of the internal constraints can be reduced to allow the model to adopt the exact shape o f the bounding volume or strengthened to preserve the internal structure, particularly in areas of high detail. This novel implementation provides a uniform framework that can be simply and automatically applied to the entire human model