Wavelets on the Two-Sphere and Other Conic Sections

We survey the construction of the continuous wavelet transform (CWT) on the twosphere. Then we discuss the discretization of the spherical CWT, obtaining various types of discrete frames. Finally, we give some indications on the construction of a CWT on other conic section

A Variational Model for Object Segmentation Using Boundary Information and Shape Prior Driven by the Mumford-Shah Functional

In this paper, we propose a new variational model to segment an object belonging to a given shape space using the active contour method, a geometric shape prior and the Mumford-Shah functional. The core of our model is an energy functional composed by three complementary terms. The first one is based on a shape model which constrains the active contour to get a shape of interest. The second term detects object boundaries from image gradients. And the third term drives globally the shape prior and the active contour towards a homogeneous intensity region. The segmentation of the object of interest is given by the minimum of our energy functional. This minimum is computed with the calculus of variations and the gradient descent method that provide a system of evolution equations solved with the well-known level set method. We also prove the existence of this minimum in the space of functions with bounded variation. Applications of the proposed model are presented on synthetic and medical image

Multiscale Active Contours

We propose a new multiscale image segmentation model, based on the active contour/snake model and the Polyakov action. The concept of scale, general issue in physics and signal processing, is introduced in the active contour model, which is a well-known image segmentation model that consists of evolving a contour in images toward the boundaries of objects. The Polyakov action, introduced in image processing by Sochen-Kimmel-Malladi in Sochen et al. (1998), provides an efficient mathematical framework to define a multiscale segmentation model because it generalizes the concept of harmonic maps embedded in higher-dimensional Riemannian manifolds such as multiscale images. Our multiscale segmentation model, unlike classical multiscale segmentations which work scale by scale to speed up the segmentation process, uses all scales simultaneously, i.e. the whole scale space, to introduce the geometry of multiscale images in the segmentation process. The extracted multiscale structures will be useful to efficiently improve the robustness and the performance of standard shape analysis techniques such as shape recognition and shape registration. Another advantage of our method is to use not only the Gaussian scale space but also many other multiscale spaces such as the Perona-Malik scale space, the curvature scale space or the Beltrami scale space. Finally, this multiscale segmentation technique is coupled with a multiscale edge detecting function based on the gradient vector flow model, which is able to extract convex and concave object boundaries independent of the initial condition. We apply our multiscale segmentation model on a synthetic image and a medical imag

Leaf-Encapsulated Vaccines: Agroinfiltration and Transient Expression of the Antigen Staphylococcal Endotoxin B in Radish Leaves.

Transgene introgression is a major concern associated with transgenic plant-based vaccines. Agroinfiltration can be used to selectively transform nonreproductive organs and avoid introgression. Here, we introduce a new vaccine modality in which Staphylococcal enterotoxin B (SEB) genes are agroinfiltrated into radishes (Raphanw sativus L.), resulting in transient expression and accumulation of SEB in planta. This approach can simultaneously express multiple antigens in a single leaf. Furthermore, the potential of high-throughput vaccine production was demonstrated by simultaneously agroinfiltrating multiple radish leaves using a multichannel pipette. The expression of SEB was detectable in two leaf cell types (epidermal and guard cells) in agroinfiltrated leaves. ICR mice intranasally immunized with homogenized leaves agroinfiltrated with SEB elicited detectable antibody to SEB and displayed protection against SEB-induced interferon-gamma (IFN-γ) production. The concept of encapsulating antigens in leaves rather than purifying them for immunization may facilitate rapid vaccine production during an epidemic disease

Fast Global Minimization of the Active Contour/Snake Model

The active contour/snake model is one of the most successful variational models in image segmentation. It consists of evolving a contour in images toward the boundaries of objects. Its success is based on strong mathematical properties and efficient numerical schemes based on the level set method. The only drawback of this model is the existence of local minima in the active contour energy, which makes the initial guess critical to get satisfactory results. In this paper, we propose to solve this problem by determining a global minimum of the active contour model. Our approach is based on the unification of image segmentation and image denoising tasks into a global minimization framework. More precisely, we propose to unify three well-known image variational models, namely the snake model, the Rudin-Osher-Fatemi denoising model and the Mumford-Shah segmentation model. We will establish theorems with proofs to determine the existence of a global minimum of the active contour model. From a numerical point of view, we propose a new practical way to solve the active contour propagation problem toward object boundaries through a dual formulation of the minimization problem. The dual formulation, easy to implement, allows us a fast global minimization of the snake energy. It avoids the usual drawback in the level set approach that consists of initializing the active contour in a distance function and re-initializing it periodically during the evolution, which is time-consuming. We apply our segmentation algorithms on synthetic and real-world images, such as texture images and medical images, to emphasize the performances of our model compared with other segmentation model

MS-DIAL: data-independent MS/MS deconvolution for comprehensive metabolome analysis.

Data-independent acquisition (DIA) in liquid chromatography (LC) coupled to tandem mass spectrometry (MS/MS) provides comprehensive untargeted acquisition of molecular data. We provide an open-source software pipeline, which we call MS-DIAL, for DIA-based identification and quantification of small molecules by mass spectral deconvolution. For a reversed-phase LC-MS/MS analysis of nine algal strains, MS-DIAL using an enriched LipidBlast library identified 1,023 lipid compounds, highlighting the chemotaxonomic relationships between the algal strains

Matching pursuit-based shape representation and recognition using scale-space

In this paper, we propose an analytical low-level representation of images, obtained by a decomposition process, namely the matching pursuit (MP) algorithm, as a new way of describing objects through a general continuous description using an affine invariant dictionary of basis function (BFs). This description is used to recognize multiple objects in images. In the learning phase, a template object is decomposed, and the extracted subset of BFs, called meta-atom, gives the description of the object. This description is then naturally extended into the linear scale-space using the definition of our BFs, and thus providing a more general representation of the object. We use this enhanced description as a predefined dictionary of the object to conduct an MP-based shape recognition task into the linear scale-space. The introduction of the scale-space approach improves the robustness of our method: we avoid local minima issues encountered when minimizing a nonconvex energy function. We show results for the detection of complex synthetic shapes, as well as real world (aerial and medical) images. © 2007 Wiley Periodicals, Inc. Int J Imaging Syst Technol, 16, 162-180, 200

Face Authentication using Client-specific Matching Pursuit

In this paper, we address the problem of finding image decompositions that allow good compression performance, and that are also efficient for face authentication. We propose to decompose the face image using Matching Pursuit and to perform the face authentication in the compressed domain using a MLP (Multi-Layer Perceptron) classifier. We provide experimental results and comparisons with PCA and LDA systems on the multi-modal benchmark database BANCA using its associated protocol

Evaluation of High Solids Alkaline Pretreatment of Rice Straw

Fresh-harvested, air-dried rice straw was pretreated at a water content of 5 g H2O/g straw using sodium hydroxide (NaOH) and compared to pretreatment at 10 g H2O/g straw by hydrated lime (Ca(OH)2). Full factorial experiments including parallel wash-only treatments were completed with both sources of alkali. The experiments were designed to measure the effects of alkaline loading and pretreatment time on delignification and sugar yield upon enzymatic hydrolysis. Reaction temperature was held constant at 95°C for lime pretreatment and 55°C for NaOH pretreatment. The range of delignification was 13.1% to 27.0% for lime pretreatments and was 8.6% to 23.1% for NaOH pretreatments. Both alkaline loading and reaction time had significant positive effects (p < 0.001) on delignification under the design conditions, but only alkaline loading had a significant positive effect on enzymatic hydrolysis. Treatment at higher temperature also improved delignification; delignification with water alone ranged from 9.9% to 14.5% for pretreatment at 95°C, but there was little effect observed at 55°C. Post-pretreatment washing of biomass was not necessary for subsequent enzymatic hydrolysis. Maximum glucose yields were 176.3 mg/g dried biomass (48.5% conversion efficiency of total glucose) in lime-pretreated and unwashed biomass and were 142.3 mg/g dried biomass (39.2% conversion efficiency of total glucose) in NaOH-pretreated and unwashed biomass

Scale-space analysis and active contours for omnidirectional images

A new generation of optical devices that generate images covering a larger part of the field of view than conventional cameras, namely catadioptric cameras, is slowly emerging. These omnidirectional images will most probably deeply impact computer vision in the forthcoming years, providing the necessary algorithmic background stands strong. In this paper we propose a general framework that helps defining various computer vision primitives. We show that geometry, which plays a central role in the formation of omnidirectional images, must be carefully taken into account while performing such simple tasks as smoothing or edge detection. Partial Differential Equations (PDEs) offer a very versatile tool that is well suited to cope with geometrical constraints. We derive new energy functionals and PDEs for segmenting images obtained from catadioptric cameras and show that they can be implemented robustly using classical finite difference schemes. Various experimental results illustrate the potential of these new methods on both synthetic and natural images