Selecting surface features for accurate multi-camera surface reconstruction

This paper proposes a novel feature detector for selecting local textures that are suitable for accurate multi-camera surface reconstruction, and in particular planar patch fitting techniques. This approach is in contrast to conventional feature detectors, which focus on repeatability under scale and affine transformations rather than suitability for multi-camera reconstruction techniques. The proposed detector selects local textures that are sensitive to affine transformations, which is a fundamental requirement for accurate patch fitting. The proposed detector is evaluated against the SIFT detector on a synthetic dataset and the fitted patches are compared against ground truth. The experiments show that patches originating from the proposed detector are fitted more accurately to the visible surfaces than those originating from SIFT keypoints. In addition, the detector is evaluated on a performance capture studio dataset to show the real-world application of the proposed detector

Seeing things

This paper is concerned with the problem of attaching meaningful symbols to aspects of the visible environment in machine and biological vision. It begins with a review of some of the arguments commonly used to support either the 'symbolic' or the 'behaviourist' approach to vision. Having explored these avenues without arriving at a satisfactory conclusion, we then present a novel argument, which starts from the question : given a functional description of a vision system, when could it be said to support a symbolic interpretation? We argue that to attach symbols to a system, its behaviour must exhibit certain well defined regularities in its response to its visual input and these are best described in terms of invariance and equivariance to transformations which act in the world and induce corresponding changes of the vision system state. This approach is illustrated with a brief exploration of the problem of identifying and acquiring visual representations having these symmetry properties, which also highlights the advantages of using an 'active' model of vision

Unsupervised learning and clustering using a random field approach

In this work we propose a random field approach to unsupervised machine learning, classifier training and pattern classification. The proposed method treats each sample as a random field and attempts to assign an optimal cluster label to it so as to partition the samples into clusters without a priori knowledge about the number of clusters and the initial centroids. To start with, the algorithm assigns each sample a unique cluster label, making it a singleton cluster. Subsequently, to update the cluster label, the similarity between the sample in question and the samples in a voting pool and their labels are involved. The clusters progressively form without the user specifying their initial centroids, as interaction among the samples continues. Due to its flexibility and adaptability, the proposed algorithm can be easily adjusted for on-line learning and is able to cope with the stability-plasticity dilemma

Partial mixture model for tight clustering of gene expression time-course

Background: Tight clustering arose recently from a desire to obtain tighter and potentially more informative clusters in gene expression studies. Scattered genes with relatively loose correlations should be excluded from the clusters. However, in the literature there is little work dedicated to this area of research. On the other hand, there has been extensive use of maximum likelihood techniques for model parameter estimation. By contrast, the minimum distance estimator has been largely ignored. Results: In this paper we show the inherent robustness of the minimum distance estimator that makes it a powerful tool for parameter estimation in model-based time-course clustering. To apply minimum distance estimation, a partial mixture model that can naturally incorporate replicate information and allow scattered genes is formulated. We provide experimental results of simulated data fitting, where the minimum distance estimator demonstrates superior performance to the maximum likelihood estimator. Both biological and statistical validations are conducted on a simulated dataset and two real gene expression datasets. Our proposed partial regression clustering algorithm scores top in Gene Ontology driven evaluation, in comparison with four other popular clustering algorithms. Conclusion: For the first time partial mixture model is successfully extended to time-course data analysis. The robustness of our partial regression clustering algorithm proves the suitability of the ombination of both partial mixture model and minimum distance estimator in this field. We show that tight clustering not only is capable to generate more profound understanding of the dataset under study well in accordance to established biological knowledge, but also presents interesting new hypotheses during interpretation of clustering results. In particular, we provide biological evidences that scattered genes can be relevant and are interesting subjects for study, in contrast to prevailing opinion

Multi-frame scene-flow estimation using a patch model and smooth motion prior

This paper addresses the problem of estimating the dense 3D motion of a scene over several frames using a set of calibrated cameras. Most current 3D motion estimation techniques are limited to estimating the motion over a single frame, unless a strong prior model of the scene (such as a skeleton) is introduced. Estimating the 3D motion of a general scene is difficult due to untextured surfaces, complex movements and occlusions. In this paper, we show that it is possible to track the surfaces of a scene over several frames, by introducing an effective prior on the scene motion. Experimental results show that the proposed method estimates the dense scene-flow over multiple frames, without the need for multiple-view reconstructions at every frame. Furthermore, the accuracy of the proposed method is demonstrated by comparing the estimated motion against a ground truth

Estimation of a 3D motion field from a multi-camera array using a multiresolution Gaussian mixture model

The problem of modelling geometry for video based rendering has been much studied in recent years, due to the growing interest in 'free viewpoint' video and similar applications. Common approaches fall into two categories: those which approximate surfaces from dense depth maps obtained by generalisations of stereopsis and those which employ an explicit geometric representation such as a mesh. While the former have generality with respect to geometry, they are limited in terms of viewpoint; the latter, on the other hand, sacrifice generality of geometry for freedom to pick an arbitary viewpoint. The purpose of the work reported here is to bridge this gap in object representation, by employing a stochastic model of object structure: a multiresolution Gaussian mixture. Estimation of the model and tracking it through time from multiple cameras is achieved by a multiresolution stochastic simulation. After a brief outline of the method, its use in modelling human motion using data from local and other sources is presented to illustrate its effectiveness compared to the current state of the art

The nymph of Zyginella pulchra Löw, 1885: (Hemiptera, Cicadellidae, Typhlocybinae)

Das fünfte Larvenstadium von Zyginella pulchra Löw (Cicadellidae, Typhlocybinae) wird beschrieben und mit anderen Laubgehölze besiedelnden Typhlocybinae verglichen.The 5th instar nymph of Zyginella pulchra Löw (Cicadellidae, Typhlocybinae) is described and compared with other tree-associated typhlocybine species