Object-based image labeling through learning by example and multi-level segmentation

We propose a method for automatic extraction and labeling of semantically meaningful image objects using "learning by example" and threshold-free multi-level image segmentation. The proposed method scans through images, each of which is pre-segmented into a hierarchical uniformity tree, to seek and label objects that are similar to an example object presented by the user. By representing images with stacks of multi-level segmentation maps, objects can be extracted in the segmentation map level with adequate detail. Experiments have shown that the proposed multi-level image segmentation results in significant reduction in computation complexity for object extraction and labeling (compared to a single fine-level segmentation) by avoiding unnecessary tests of combinations in finer levels. The multi-level segmentation-based approach also achieves better accuracy in detection and labeling of small objects. (C) 2003 Pattern Recognition Society. Published by Elsevier Science Ltd. All rights reserved

Global Motion Model for Stereovision-Based Motion Analysis

<p/> <p>An advantage of stereovision-based motion analysis is that the depth information is available, thus motion can be estimated more precisely in <inline-formula><graphic file="1687-6180-2006-053691-i1.gif"/></inline-formula>D stereo coordinate system (SCS) constructed by the depth and the image coordinates. In this paper, <it>stereo global motion</it> in SCS, which is induced by 3D camera motion in real-world coordinate system (WCS), is parameterized by a five-parameter global motion model (GMM). Based on such model, global motion can be estimated and identified directly in SCS without knowing the physical parameters about camera motion and camera setup in WCS. The reconstructed global motion field accords with the spatial structure of the scene much better. Experiments on both synthetic data and real-world images illustrate its promising performance.</p

Recognition Of Images Degraded By Linear Motion Blur Without Restoration

The paper is devoted to the feature-based description of images degraded by linear motion blur. The proposed features are invariant with respect to motion velocity, are based on image moments and are calculated directly from the blurred image. In that way, we are able to describe the original image without the PSF identification and image restoration. In many applications (such as in image recognition against a database) our approach is much more effective than the traditional &quot;blind-restoration&quot; one. The derivation of the motion blur invariants is a major theoretical result of the paper. Numerical experiments are presented to illustrate the utilization of the invariants for blurred image description. Stability of the invariants with respect to additive random noise is also discussed and is shown to be sufficiently high. Finally, another set of features which are invariant not only to motion velocity but also to motion direction is introduced. Index Terms: Blurred image, linear imaging..

Speech-driven facial animation using manifold relevance determination

In this paper, a new approach to visual speech synthesis using a joint probabilistic model is introduced, namely the Gaussian process latent variable model trimmed with manifold relevance determination model, which explicitly models coarticulation. One talking head dataset is processed (LIPS dataset) by extracting visual and audio features from the sequences. The model can capture the structure of data with extremely high dimensionality. Distinguishable visual features can be inferred directly from the trained model by sampling from the discovered latent points. Statistical evaluation of inferred visual features against ground truth data is obtained and compared with the current state-of-the-art visual speech synthesis approach. The quantitative results demonstrate that the proposed approach outperforms the state-of-the-art technique

Web-based embodied conversational agents and older people

Within Human-Computer Interaction, there has recently been an important turn to embodied and voice-based interaction. In this chapter, we discuss our ongoing research on building online Embodied Conversational Agents (ECAs), specifically, their interactive 3D web graphics aspects. We present ECAs based on our technological pipeline, which integrates a number of free online editors, such as Adobe Fuse CC or MakeHuman, and standards, mainly BML (Behaviour Markup Language). We claim that making embodiment available for online ECAs is attainable, and advantageous over current alternatives, mostly desktop-based. In this chapter we also report on initial results of activities aimed to explore the physical appearance of ECAs for older people. A group of them (N = 14) designed female ECAs. Designing them was easy and great fun. The perspective on older-adult HCI introduced in this chapter is mostly technological, allowing for rapid online experimentations to address key issues, such as anthropomorphic aspects, in the design of ECAs with, and for, older people.This work was funded by the EU’s Horizon 2020 research and innovation programme under the Marie Sklodowska-Curie grant agreement No 675324 (ENRICH) and under the contract number H2020-645012-RIA (KRISTINA)