Recursive Motion and Structure Estimation with Complete Error Characterization

We present an algorithm that perfom recursive estimation of ego-motion andambient structure from a stream of monocular Perspective images of a number of feature points. The algorithm is based on an Extended Kalman Filter (EKF) that integrates over time the instantaneous motion and structure measurements computed by a 2-perspective-views step. Key features of our filter are (I) global observability of the model, (2) complete on-line characterization of the uncertainty of the measurements provided by the two-views step. The filter is thus guaranteed to be well-behaved regardless of the particular motion undergone by the observel: Regions of motion space that do not allow recovery of structure (e.g. pure rotation) may be crossed while maintaining good estimates of structure and motion; whenever reliable measurements are available they are exploited. The algorithm works well for arbitrary motions with minimal smoothness assumptions and no ad hoc tuning. Simulations are presented that illustrate these characteristics

Least-squares multirate FIR filters

The authors propose a new least-squares design procedure for multirate FIR filters with any desired shape of the (band-limited) frequency response. The aliasing, inherent in such systems, is implicitly taken into account in the approximation criterion

Overcomplete steerable pyramid filters and rotation invariance

A given (overcomplete) discrete oriented pyramid may be converted into a steerable pyramid by interpolation. We present a technique for deriving the optimal interpolation functions (otherwise called 'steering coefficients'). The proposed scheme is demonstrated on a computationally efficient oriented pyramid, which is a variation on the Burt and Adelson (1983) pyramid. We apply the generated steerable pyramid to orientation-invariant texture analysis in order to demonstrate its excellent rotational isotropy. High classification rates and precise rotation identification are demonstrated

Classification of human actions into dynamics based primitives with application to drawing tasks

We develop the study of primitives of human motion, which we refer to as movemes. The idea is to understand human motion by decomposing it into a sequence of elementary building blocks that belong to a known alphabet of dynamical systems. How can we construct an alphabet of movemes from human data? In this paper we address this issue by introducing the notion of well-posednes. Using examples from human drawing data, we show that the well-posedness notion can be applied in practice so to establish if sets of actions, viewed as signals in time, can define movemes

Influence of Zn excess on compositional, structural and vibrational properties of Cu2ZnSn0.5Ge0.5Se4 thin films and their effect on solar cell efficiency

This Accepted Manuscript will be available for reuse under a CC BY-NC-ND licence after 24 months of embargo periodThe effect of Zn content on compositional, structural and vibrational properties of Cu2ZnSn1-xGexSe4 (CZTGSe, x ~ 0.5) thin films is studied. Kesterite layer is deposited by co-evaporation onto 5 × 5 cm2 Mo/SLG substrate followed by a thermal treatment at maximum temperature of 480 °C, obtaining areas with different composition and morphology which are due to the sample position in the co-evaporation system and to the non-uniform temperature distribution across the substrate. Kesterite layers with higher Zn amounts are characterized by lower Cu and Ge contents; however, a uniform Ge distribution through the absorber layer is detected in all cases. The excess Zn concentration leads to the formation of ZnSe secondary phase on the surface and in the bulk of the absorber as determined by Raman spectroscopy. When higher Ge content and no ZnSe are present in the absorber layer, a compact structure is formed with larger grain size of kesterite. This effect could explain the higher Voc of the solar cell. The Zn content does not affect the bandgap energy significantly (Eg near 1.3 eV), although the observed effect of Zn excess in CZTGSe results in a decreased device performance from 6.4 to 4.2%. This investigation reveals the importance of the control of the off-stoichiometric CZTGSe composition during the deposition process to enhance solar cells propertiesThis work was supported by Spanish Ministry of Science, Innovation and Universities Project WINCOST (ENE2016-80788-C5-2-R) and European Project INFINITE CELL (H2020-MSCA-RISE-2017-777968). ARP also acknowledges financial support from Community of Madrid within Youth Employment Program (PEJD-2017-PRE/IND-4062). MG acknowledges the financial support from ACCIÓ-Generalitat de Catalunya within the TECNIOspring Plus fellowship (TECSPR18-1-0048

Tourmaline records the hydrothermal events related to Zn-Pb mineralization around the Murguía diapir (Basque Cantabrian Basin, N Spain)

The chemical composition of tourmaline has been used as a host environment register as well as a potential exploration tool for mineral deposits. In this study, the textural and chemical composition of tourmalines associated with Zn-Pb mineralizations around the Murguía diapir (Basque Cantabrian Basin, N Spain) are examined to verify if they record the mineralizing events in the area. Petrographically, tourmalines have been differentiated between inherited and authigenic. Colorless authigenic tourmalines are present as halos partially around green and pleochroic detrital grains or as individual crystals. Inherited and authigenic tourmalines are also chemically distinct. Authigenic tourmalines show different X-site occupancies, a Mg/(Mg+Fe) ratio above 0.77, and are aluminum rich and plot to the right of the povondraite-oxidravite join, above the schorl-dravite join. Inherited tourmalines plot within the alkaline (Na+K) group field, and have a Mg/(Mg+Fe) ratio below 0.77. These data suggest that authigenic tourmalines grew under reducing conditions, compatible with the hydrothermal event responsible for the ore deposition and caprock formation during the diapir ascent

Learning object categories from Google's image search

Current approaches to object category recognition require datasets of training images to be manually prepared, with varying degrees of supervision. We present an approach that can learn an object category from just its name, by utilizing the raw output of image search engines available on the Internet. We develop a new model, TSI-pLSA, which extends pLSA (as applied to visual words) to include spatial information in a translation and scale invariant manner. Our approach can handle the high intra-class variability and large proportion of unrelated images returned by search engines. We evaluate the models on standard test sets, showing performance competitive with existing methods trained on hand prepared datasets