The rotation and coma profiles of comet C/2004 Q2 (Machholz)

Aims. Rotation periods of cometary nuclei are scarce, though important when studying the nature and origin of these objects. Our aim is to derive a rotation period for the nucleus of comet C/2004 Q2 (Machholz). Methods. C/2004 Q2 (Machholz) was monitored using the Merope CCD camera on the Mercator telescope at La Palma, Spain, in January 2005, during its closest approach to Earth, implying a high spatial resolution (50km per pixel). One hundred seventy images were recorded in three different photometric broadband filters, two blue ones (Geneva U and B) and one red (Cousins I). Magnitudes for the comet's optocentre were derived with very small apertures to isolate the contribution of the nucleus to the bright coma, including correction for the seeing. Our CCD photometry also permitted us to study the coma profile of the inner coma in the different bands. Results. A rotation period for the nucleus of P = 9.1 +/- 0.2 h was derived. The period is on the short side compared to published periods of other comets, but still shorter periods are known. Nevertheless, comparing our results with images obtained in the narrowband CN filter, the possibility that our method sampled P/2 instead of P cannot be excluded. Coma profiles are also presented, and a terminal ejection velocity of the grains v_gr = 1609 +/- 48 m/s is found from the continuum profile in the I band.Comment: 11 pages, 9 figures, accepted by A&

The Multiscale Morphology Filter: Identifying and Extracting Spatial Patterns in the Galaxy Distribution

We present here a new method, MMF, for automatically segmenting cosmic structure into its basic components: clusters, filaments, and walls. Importantly, the segmentation is scale independent, so all structures are identified without prejudice as to their size or shape. The method is ideally suited for extracting catalogues of clusters, walls, and filaments from samples of galaxies in redshift surveys or from particles in cosmological N-body simulations: it makes no prior assumptions about the scale or shape of the structures.}Comment: Replacement with higher resolution figures. 28 pages, 17 figures. For Full Resolution Version see: http://www.astro.rug.nl/~weygaert/tim1publication/miguelmmf.pd

Improved line/edge detection and visual reconstruction

Lines and edges provide important information for object categorization and recognition. In addition, one brightness model is based on a symbolic interpretation of the cortical multi-scale line/edge representation. In this paper we present an improved scheme for line/edge extraction from simple and complex cells and we illustrate the multi-scale representation. This representation can be used for visual reconstruction, but also for nonphotorealistic rendering. Together with keypoints and a new model of disparity estimation, a 3D wireframe representation of e.g. faces can be obtained in the future

Learning to Extract Motion from Videos in Convolutional Neural Networks

This paper shows how to extract dense optical flow from videos with a convolutional neural network (CNN). The proposed model constitutes a potential building block for deeper architectures to allow using motion without resorting to an external algorithm, \eg for recognition in videos. We derive our network architecture from signal processing principles to provide desired invariances to image contrast, phase and texture. We constrain weights within the network to enforce strict rotation invariance and substantially reduce the number of parameters to learn. We demonstrate end-to-end training on only 8 sequences of the Middlebury dataset, orders of magnitude less than competing CNN-based motion estimation methods, and obtain comparable performance to classical methods on the Middlebury benchmark. Importantly, our method outputs a distributed representation of motion that allows representing multiple, transparent motions, and dynamic textures. Our contributions on network design and rotation invariance offer insights nonspecific to motion estimation

The visual representation of texture

This research is concerned with texture: a source of visual information, that has motivated a huge amount of psychophysical and computational research. This thesis questions how useful the accepted view of texture perception is. From a theoretical point of view, work to date has largely avoided two critical aspects of a computational theory of texture perception. Firstly, what is texture? Secondly, what is an appropriate representation for texture? This thesis argues that a task dependent definition of texture is necessary, and proposes a multi-local, statistical scheme for representing texture orientation. Human performance on a series of psychophysical orientation discrimination tasks are compared to specific predictions from the scheme. The first set of experiments investigate observers' ability to directly derive statistical estimates from texture. An analogy is reported between the way texture statistics are derived, and the visual processing of spatio-luminance features. The second set of experiments are concerned with the way texture elements are extracted from images (an example of the generic grouping problem in vision). The use of highly constrained experimental tasks, typically texture orientation discriminations, allows for the formulation of simple statistical criteria for setting critical parameters of the model (such as the spatial scale of analysis). It is shown that schemes based on isotropic filtering and symbolic matching do not suffice for performing this grouping, but that the scheme proposed, base on oriented mechanisms, does. Taken together these results suggest a view of visual texture processing, not as a disparate collection of processes, but as a general strategy for deriving statistical representations of images common to a range of visual tasks