Beyond Gauss: Image-set matching on the Riemannian manifold of PDFs

State-of-the-art image-set matching techniques typically implicitly model each image-set with a Gaussian distribution. Here, we propose to go beyond these representations and model image-sets as probability distribution functions (PDFs) using kernel density estimators. To compare and match image-sets, we exploit Csiszar´ f-divergences, which bear strong connections to the geodesic distance defined on the space of PDFs, i.e., the statistical manifold. Furthermore, we introduce valid positive definite kernels on the statistical manifold, which let us make use of more powerful classification schemes to match image-sets. Finally, we introduce a supervised dimensionality reduction technique that learns a latent space where f-divergences reflect the class labels of the data. Our experiments on diverse problems, such as video-based face recognition and dynamic texture classification, evidence the benefits of our approach over the state-of-the-art image-set matching methods.

A discrete wavelet transform approach to multiresolution complex SAR image generation

We present a signal processing approach using discrete wavelet transform (DWT) for the generation of complex synthetic aperture radar (SAR) images at an arbitrary number of dyadic scales of resolution. The method is computationally efficient and is free from significant system-imposed limitations present in traditional subaperture-based multiresolution image formation. Problems due to aliasing associated with biorthogonal decomposition of the complex signals are addressed. The lifting scheme of DWT is adapted to handle complex signal approximations and employed to further enhance the computational efficiency. Multiresolution SAR images formed by the proposed method are presented.

Eye-view: A retinal image acquisition system

This paper presents a low cost but high resolution retinal image acquisition system of the human eye. The images acquired by a CMOS image sensor are communicated through the Universal Serial Bus (USB) interface to a personal computer for viewing and further processing. The image acquisition time was estimated to be 2.5 seconds. This system can also be used in telemedicine applications.

Memory-efficient spatial prediction image compression scheme

In prediction phase, the hierarchical tree structure obtained from the test image is used to predict every central pixel of an image by its four neighboring pixels. The prediction scheme generates the predicted error image, to which the wavelet/sub-band coding algorithm can be applied to obtain efficient compression. In quantization phase, we used a modified SPIHT algorithm to achieve efficiency in memory requirements. The memory constraint plays a vital role in wireless and bandwidth-limited applications. A single reusable list is used instead of three continuously growing linked lists as in case of SPIHT. This method is error resilient. The performance is measured in terms of PSNR and memory requirements. The algorithm shows good compression performance and significant savings in memory. (C) 2006 Elsevier B.V. All rights reserved.

Urban satellite image classification using biologically inspired techniques

This paper focuses on optimisation algorithms inspired by swarm intelligence for satellite image classification from high resolution satellite multi- spectral images. Amongst the multiple benefits and uses of remote sensing, one of the most important has been its use in solving the problem of land cover mapping. As the frontiers of space technology advance, the knowledge derived from the satellite data has also grown in sophistication. Image classification forms the core of the solution to the land cover mapping problem. No single classifier can prove to satisfactorily classify all the basic land cover classes of an urban region. In both supervised and unsupervised classification methods, the evolutionary algorithms are not exploited to their full potential. This work tackles the land map covering by Ant Colony Optimisation (ACO) and Particle Swarm Optimisation (PSO) which are arguably the most popular algorithms in this category. We present the results of classification techniques using swarm intelligence for the problem of land cover mapping for an urban region. The high resolution Quick-bird data has been used for the experiments.

Extending visual perception with haptic exploration for improved scene understanding

Scene understanding has been investigated from a mainly visual information point of view. Recently depth has been provided an extra wealth of information, allowing more geometric knowledge to fuse into scene understanding. Yet to form a holistic view, especially in robotic applications, one can create even more data by interacting with the world. In fact humans, when growing up, seem to heavily investigate the world around them by haptic exploration. We show an application of haptic exploration on a humanoid robot in cooperation with a learning method for object segmentation. The actions performed consecutively improve the segmentation of objects in the scene.

Automatic Road Extraction using High Resolution Satellite Image Based on Texture Progressive Analysis and Normalized Cut Method

In this paper the approach for automatic road extraction for an urban region using structural, spectral and geometric characteristics of roads has been presented. Roads have been extracted based on two levels: Pre-processing and road extraction methods. Initially, the image is pre-processed to improve the tolerance by reducing the clutter (that mostly represents the buildings, parking lots, vegetation regions and other open spaces). The road segments are then extracted using Texture Progressive Analysis (TPA) and Normalized cut algorithm. The TPA technique uses binary segmentation based on three levels of texture statistical evaluation to extract road segments where as, Normalizedcut method for road extraction is a graph based method that generates optimal partition of road segments. The performance evaluation (quality measures) for road extraction using TPA and normalized cut method is compared. Thus the experimental result show that normalized cut method is efficient in extracting road segments in urban region from high resolution satellite image.

Efficient content-based image retrieval based on multi-feature fusion

The increased availability of image capturing devices has enabled collections of digital images to rapidly expand in both size and diversity. This has created a constantly growing need for efficient and effective image browsing, searching, and retrieval tools. Pseudo-relevance feedback (PRF) has proven to be an effective mechanism for improving retrieval accuracy. An original, simple yet effective rank-based PRF mechanism (RB-PRF) that takes into account the initial rank order of each image to improve retrieval accuracy is proposed. This RB-PRF mechanism innovates by making use of binary image signatures to improve retrieval precision by promoting images similar to highly ranked images and demoting images similar to lower ranked images. Empirical evaluations based on standard benchmarks, namely Wang, Oliva & Torralba, and Corel datasets demonstrate the effectiveness of the proposed RB-PRF mechanism in image retrieval.

Seismic Tomography and Earthquake Mechanism Beneath the Central Fennoscandian Shield

The aim of this thesis was to study the seismic tomography structure of the earth s crust together with earthquake distribution and mechanism beneath the central Fennoscandian Shield, mainly in southern and central Finland. The earthquake foci and some fault plane solutions are correlated with 3-D images of the velocity tomography. The results are discussed in relation to the stress field of the Shield and with other geophysical, e.g. geomagnetic, gravimetric, tectonic, and anisotropy studies of the Shield. The earthquake data of the Fennoscandian Shield has been extracted from the Nordic earthquake parameter data base which was founded at the time of inception of the earthquake catalogue for northern Europe. Eight earlier earthquake source mechanisms are included in a pilot study on creating a novel technique for calculating an earthquake fault plane solution. Altogether, eleven source mechanisms of shallow, weak earthquakes are related in the 3-D tomography model to trace stresses of the crust in southern and central Finland. The earthquakes in the eastern part of the Fennoscandian Shield represent low-active, intraplate seismicity. Earthquake mechanisms with NW-SE oriented horizontal compression confirm that the dominant stress field originates from the ridge-push force in the North Atlantic Ocean. Earthquakes accumulate in coastal areas, in intersections of tectonic lineaments, in main fault zones or are bordered by fault lines. The majority of Fennoscandian earthquakes concentrate on the south-western Shield in southern Norway and Sweden. Onwards, epicentres spread via the ridge of the Shield along the west-coast of the Gulf of Bothnia northwards along the Tornio River - Finnmark fault system to the Barents Sea, and branch out north-eastwards via the Kuusamo region to the White Sea Kola Peninsula faults. The local seismic tomographic method was applied to find the terrane distribution within the central parts of the Shield the Svecofennian Orogen. From 300 local explosions a total of 19765 crustal Pg- and Sg-wave arrival times were inverted to create independent 3-D Vp and Vs tomographic models, from which the Vp/Vs ratio was calculated. The 3-D structure of the crust is presented as a P-wave and for the first time as an S-wave velocity model, and also as a Vp/Vs-ratio model of the SVEKALAPKO area that covers 700x800 km2 in southern and central Finland. Also, some P-wave Moho-reflection data was interpolated to image the relief of the crust-mantle boundary (i.e. Moho). In the tomography model, the seismic velocities vary smoothly. The lateral variations are larger for Vp (dVp =0.7 km/s) than for Vs (dVs =0.4 km/s). The Vp/Vs ratio varies spatially more distinctly than P- and S-wave velocities, usually from 1.70 to 1.74 in the upper crust and from 1.72 to 1.78 in the lower crust. Schist belts and their continuations at depth are associated with lower velocities and lower Vp/Vs ratios than in the granitoid areas. The tomography modelling suggests that the Svecofennian Orogen was accreted from crustal blocks ranging in size from 100x100 km2 to 200x200 km2 in cross-sectional area. The intervening sedimentary belts have ca. 0.2 km/s lower P- and S-wave velocities and ca. 0.04 lower Vp/Vs ratios. Thus, the tomographic model supports the concept that the thick Svecofennian crust was accreted from several crustal terranes, some hidden, and that the crust was later modified by intra- and underplating. In conclusion, as a novel approach the earthquake focal mechanism and focal depth distribution is discussed in relation to the 3-D tomography model. The schist belts and the transformation zones between the high- and low-velocity anomaly blocks are characterized by deeper earthquakes than the granitoid areas where shallow events dominate. Although only a few focal mechanisms were solved for southern Finland, there is a trend towards strike-slip and oblique strike-slip movements inside schist areas. The normal dip-slip type earthquakes are typical in the seismically active Kuusamo district in the NE edge of the SVEKALAPKO area, where the Archean crust is ca. 15-20 km thinner than the Proterozoic Svecofennian crust. Two near vertical dip-slip mechanism earthquakes occurred in the NE-SW junction between the Central Finland Granitoid Complex and the Vyborg rapakivi batholith, where high Vp/Vs-ratio deep-set intrusion splits the southern Finland schist belt into two parts in the tomography model.

Deeper and wider fully convolutional network coupled with conditional random fields for scene labeling

Deep convolutional neural networks (DCNNs) have been employed in many computer vision tasks with great success due to their robustness in feature learning. One of the advantages of DCNNs is their representation robustness to object locations, which is useful for object recognition tasks. However, this also discards spatial information, which is useful when dealing with topological information of the image (e.g. scene labeling, face recognition). In this paper, we propose a deeper and wider network architecture to tackle the scene labeling task. The depth is achieved by incorporating predictions from multiple early layers of the DCNN. The width is achieved by combining multiple outputs of the network. We then further refine the parsing task by adopting graphical models (GMs) as a post-processing step to incorporate spatial and contextual information into the network. The new strategy for a deeper, wider convolutional network coupled with graphical models has shown promising results on the PASCAL-Context dataset.

The dynamics of solvation of an electron in the image potential state by a layer of polar adsorbates

Recently, ultrafast two-photon photoemission has been used to study electron solvation at a two-dimensional metal/polar adsorbate interfaces [A. Miller , Science 297, 1163 (2002)]. The electron is bound to the surface by the image interaction. Earlier we have suggested a theoretical description of the states of the electron interacting with a two-dimensional layer of the polar adsorbate [K. L. Sebastian , J. Chem. Phys. 119, 10350 (2003)]. In this paper we have analyzed the dynamics of electron solvation, assuming a trial wave function for the electron and the solvent polarization and then using the Dirac-Frenkel variational method to determine it. The electron is initially photoexcited to a delocalized state, which has a finite but large size, and causes the polar molecules to reorient. This reorientation acts back on the electron and causes its wave function to shrink, which will cause further reorientation of the polar molecules, and the process continues until the electron gets self-trapped. For reasonable values for the parameters, we are able to obtain fair agreement with the experimental observations. (c) 2005 American Institute of Physics.

Inhibitory activity of Image -asparaginase from Mycobcacterium tuberculosis on Yoshida ascites sarcoma in rats

The antitumor activity of Image -asparagine amidohydrolases (EC 3.5.1.1) from Mycobacterium tuberculosis H37Rv and H37Ra strains has been tested on Yoshida ascites sarcoma in rats. The enzyme specific to M. tuberculosis H37Ra but not to H37Rv has proved to be effective in inhibiting the growth of the sarcoma. Comparative studies on the activity of this enzyme with that of similar enzyme from Escherichia coli B, has shown that at the same levels the former is more effective than the latter. Long-lived immunity to this tumor in A/IISc Wistar rats following treatment of tumor bearing animals with M. tuberculosis H37Ra, pH 9.6 Image -asparaginase has been observed. Immunity in these rats was demonstrated by tumor rejection and detection of humoral antibodies in the sera to the antigen of the cell-free extract of the tumor. The enzyme was ineffective in inhibiting fibrosarcoma in mice at the dose levels tested.

On the design of quadratic weirs

This paper considers the problem of the design of the quadratic weir notch, which finds application in the proportionate method of flow measurement in a by-pass, such that the discharge through it is proportional to the square root of the head measured above a certain datum. The weir notch consists of a bottom in the form of a rectangular weir of width 2W and depth a over which a designed curve is fitted. A theorem concerning the flow through compound weirs called the “slope discharge continuity theorem” is discussed and proved. Using this, the problem is reduced to the determination of an exact solution to Volterra's integral equation in Abel's form. It is shown that in the case of a quadratic weir notch, the discharge is proportional to the square root of the head measured above a datum Image a above the crest of the weir. Further, it is observed that the function defining the shape of the weir is rapidly convergent and its value almost approximates to zero at distances of 3a and above from the crest of the weir. This interesting and significant behaviour of the function incidentally provides a very good approximate solution to a particular Fredholm integral equation of the first kind, transforming the notch into a device called a “proportional-orifice”. A new concept of a “notch-orifice” capable of passing a discharge proportional to the square root of the head (above a particular datum) while acting both as a notch, and as an orifice, is given. A typical experiment with one such notch-orifice, having A = 4 in., and W = 6 in., shows a remarkable agreement with the theory and is found to have a constant coefficient of discharge of 0.61 in the ranges of both notch and orifice.

Applicability of Statistical Learning Algorithms for Spatial Variability of Rock Depth

Two algorithms are outlined, each of which has interesting features for modeling of spatial variability of rock depth. In this paper, reduced level of rock at Bangalore, India, is arrived from the 652 boreholes data in the area covering 220 sqa <.km. Support vector machine (SVM) and relevance vector machine (RVM) have been utilized to predict the reduced level of rock in the subsurface of Bangalore and to study the spatial variability of the rock depth. The support vector machine (SVM) that is firmly based on the theory of statistical learning theory uses regression technique by introducing epsilon-insensitive loss function has been adopted. RVM is a probabilistic model similar to the widespread SVM, but where the training takes place in a Bayesian framework. Prediction results show the ability of learning machine to build accurate models for spatial variability of rock depth with strong predictive capabilities. The paper also highlights the capability ofRVM over the SVM model.

Image -asparaginases from Mycobacterium tuberculosis strains H37Rv and H37Ra

M. tuberculosis H37Ra possesses two Image -asparaginases while the H37Rv strain possesses only a single enzyme. These enzymes have been purified and their properties studied. The two Image -asparaginases in H37Ra strain differ from each other in pH optima, heat inactivation, Michaelis constant and effects of inhibitors, while one of them resembles the single Image -asparaginase present in the H37Rv strain. Image -Cysteine inhibits both Image -asparaginases in an allosteric manner probably because it is one of the end-products in Image -asparagine metabolism. This is the first time that a qualitative difference has been reported in the enzyme pattern between the avirulent and virulent strains of M. tuberculosis.