Edge detection by selection of pieces of level lines

We propose an edge detector based on the selection of wellcontrasted pieces of level lines, following the proposal ofDesolneux-Moisan-Morel (DMM) [1]. The DMM edge detectorhas the problem of over-representation, that is, everyedge is detected several times in slightly different positions.In this paper we propose two modifications of the originalDMM edge detector in order to solve this problem. The firstmodification is a post-processing of the output using a generalmethod to select the best representative of a bundle of curves.The second modification is the use of Canny’s edge detectorinstead of the norm of the gradient to build the statistics. Thetwo modifications are independent and can be applied separately.Elementary reasoning and some experiments showthat the best results are obtained when both modifications areapplied together.

Geometry-based demosaicking

Demosaicking is a particular case of interpolation problems where, from a scalar image in which each pixel has either the red, the green or the blue component, we want to interpolate the full-color image. State-of-the-art demosaicking algorithms perform interpolation along edges, but these edges are estimated locally. We propose a level-set-based geometric method to estimate image edges, inspired by the image in-painting literature. This method has a time complexity of O(S) , where S is the number of pixels in the image, and compares favorably with the state-of-the-art algorithms both visually and in most relevant image quality measures.

Towards a lightweight adaptative multicomponential affective recognition system

As part of the Affective Computing research field, the development of automatic affective recognition systems can enhance human-computer interactions by allowing the creation of interfaces that react to the user's emotional state. To that end, this Master Thesis brings affect recognition to nowadays most used human computer interface, mobile devices, by developing a facial expression recognition system able to perform detection under the difficult conditions of viewing angle and illumination that entails the interaction with a mobile device. Moreover, this Master Thesis proposes to combine emotional features detected from expression with contextual information of the current situation, to infer a complex and extensive emotional state of the user. Thus, a cognitive computational model of emotion is defined that provides a multicomponential affective state of the user through the integration of the detected emotional features into appraisal processes. In order to account for individual differences in the emotional experience, these processes can be adapted to the culture and personality of the user.

Scene classifier for real time road detection

L’objectiu principal del projecte és el de classificar escenes de carretera en funció del contingut de les imatges per així poder fer un desglossament sobre quin tipus de situació tenim en el moment. És important que fixem els paràmetres necessaris en funció de l’escenari en què ens trobem per tal de treure el màxim rendiment possible a cada un dels algoritmes. La seva funcionalitat doncs, ha de ser la d’avís i suport davant els diferents escenaris de conducció. És a dir, el resultat final ha de contenir un algoritme o aplicació capaç de classificar les imatges d’entrada en diferents tipus amb la màxima eficiència espacial i temporal possible. L’algoritme haurà de classificar les imatges en diferents escenaris. Els algoritmes hauran de ser parametritzables i fàcilment manejables per l’usuari. L’eina utilitzada per aconseguir aquests objectius serà el MATLAB amb les toolboxs de visió i xarxes neuronals instal·lades.

Intestinal content detection in capsule endoscopy using robust features

This work covers two aspects. First, it generally compares and summarizes the similarities and differences of state of the art feature detector and descriptor and second it presents a novel approach of detecting intestinal content (in particular bubbles) in capsule endoscopy images. Feature detectors and descriptors providing invariance to change of perspective, scale, signal-noise-ratio and lighting conditions are important and interesting topics in current research and the number of possible applications seems to be numberless. After analysing a selection of in the literature presented approaches, this work investigates in their suitability for applications information extraction in capsule endoscopy images. Eventually, a very good performing detector of intestinal content in capsule endoscopy images is presented. A accurate detection of intestinal content is crucial for all kinds of machine learning approaches and other analysis on capsule endoscopy studies because they occlude the field of view of the capsule camera and therefore those frames need to be excluded from analysis. As a so called “byproduct” of this investigation a graphical user interface supported Feature Analysis Tool is presented to execute and compare the discussed feature detectors and descriptor on arbitrary images, with configurable parameters and visualized their output. As well the presented bubble classifier is part of this tool and if a ground truth is available (or can also be generated using this tool) a detailed visualization of the validation result will be performed.

Evaluation of detection methods for Virus, Viroids and Phytoplasmas affecting pear and apple

The RT-PCR technique for the detection of apple stem grooving virus (ASGV), apple stem pitting virus (ASPV), apple chlorotic leaf spot virus (ACLSV), apple mosaic virus (ApMV) and pear blister canker viroid (PBCV) was evaluated for health control of fruit plants from nurseries. The technique was evaluated in purified RNA and crude extracts and also in phloem collected in autumn and from young spring shoots. The results obtained for phytoplasma detection with ribosomal and non-ribosomal primers are also presented.

Admission Control for TCP Flows Using Packet Classes and Edge-to-edge Measurements of Aggregates

TCP flows from applications such as the web or ftp are well supported by a Guaranteed Minimum Throughput Service (GMTS), which provides a minimum network throughput to the flow and, if possible, an extra throughput. We propose a scheme for a GMTS using Admission Control (AC) that is able to provide different minimum throughput to different users and that is suitable for "standard" TCP flows. Moreover, we consider a multidomain scenario where the scheme is used in one of the domains, and we propose some mechanisms for the interconnection with neighbor domains. The whole scheme uses a small set of packet classes in a core-stateless network where each class has a different discarding priority in queues assigned to it. The AC method involves only edge nodes and uses a special probing packet flow (marked as the highest discarding priority class) that is sent continuously from ingress to egress through a path. The available throughput in the path is obtained at the egress using measurements of flow aggregates, and then it is sent back to the ingress. At the ingress each flow is detected using an implicit way and then it is admission controlled. If it is accepted, it receives the GMTS and its packets are marked as the lowest discarding priority classes; otherwise, it receives a best-effort service. The scheme is evaluated through simulation in a simple "bottleneck" topology using different traffic loads consisting of "standard" TCP flows that carry files of varying sizes

Detection of matchings in a sequence of underwater images through texture analysis

This paper presents an approach to ameliorate the reliability of the correspondence points relating two consecutive images of a sequence. The images are especially difficult to handle, since they have been acquired by a camera looking at the sea floor while carried by an underwater robot. Underwater images are usually difficult to process due to light absorption, changing image radiance and lack of well-defined features. A new approach based on gray-level region matching and selective texture analysis significantly improves the matching reliability

Proposal of a parallel architecture for a motion detection algorithm

This paper proposes a parallel architecture for estimation of the motion of an underwater robot. It is well known that image processing requires a huge amount of computation, mainly at low-level processing where the algorithms are dealing with a great number of data. In a motion estimation algorithm, correspondences between two images have to be solved at the low level. In the underwater imaging, normalised correlation can be a solution in the presence of non-uniform illumination. Due to its regular processing scheme, parallel implementation of the correspondence problem can be an adequate approach to reduce the computation time. Taking into consideration the complexity of the normalised correlation criteria, a new approach using parallel organisation of every processor from the architecture is proposed

Dam wall detection and tracking using a Mechanically Scanned Imaging Sonar

In dam inspection tasks, an underwater robot has to grab images while surveying the wall meanwhile maintaining a certain distance and relative orientation. This paper proposes the use of an MSIS (mechanically scanned imaging sonar) for relative positioning of a robot with respect to the wall. An imaging sonar gathers polar image scans from which depth images (range & bearing) are generated. Depth scans are first processed to extract a line corresponding to the wall (with the Hough transform), which is then tracked by means of an EKF (Extended Kalman Filter) using a static motion model and an implicit measurement equation associating the sensed points to the candidate line. The line estimate is referenced to the robot fixed frame and represented in polar coordinates (rho&thetas) which directly corresponds to the actual distance and relative orientation of the robot with respect to the wall. The proposed system has been tested in simulation as well as in water tank conditions

GNSS signal detection based on first-order absolute statistics

Aquest projecte es centra principalment en el detector no coherent d’un GPS. Per tal de caracteritzar el procés de detecció d’un receptor, es necessita conèixer l’estadística implicada. Pel cas dels detectors no coherents convencionals, l’estadística de segon ordre intervé plenament. Les prestacions que ens dóna l’estadística de segon ordre, plasmada en la ROC, són prou bons tot i que en diferents situacions poden no ser els millors. Aquest projecte intenta reproduir el procés de detecció mitjançant l’estadística de primer ordre com a alternativa a la ja coneguda i implementada estadística de segon ordre. Per tal d’aconseguir-ho, s’usen expressions basades en el Teorema Central del Límit i de les sèries Edgeworth com a bones aproximacions. Finalment, tant l’estadística convencional com l’estadística proposada són comparades, en termes de la ROC, per tal de determinar quin detector no coherent ofereix millor prestacions en cada situació.

Unusual voltage sag event detection in power systems

Three multivariate statistical tools (principal component analysis, factor analysis, analysis discriminant) have been tested to characterize and model the sags registered in distribution substations. Those models use several features to represent the magnitude, duration and unbalanced grade of sags. They have been obtained from voltage and current waveforms. The techniques are tested and compared using 69 registers of sags. The advantages and drawbacks of each technique are listed

Modelling of Mercury’s surface composition and remote detection from the orbit with the BepiColombo Mercury Planetary Orbiter

It can be assumed that the composition of Mercury’s thin gas envelope (exosphere) is related to thecomposition of the planets crustal materials. If this relationship is true, then inferences regarding the bulkchemistry of the planet might be made from a thorough exospheric study. The most vexing of allunsolved problems is the uncertainty in the source of each component. Historically, it has been believedthat H and He come primarily from the solar wind, while Na and K originate from volatilized materialspartitioned between Mercury’s crust and meteoritic impactors. The processes that eject atoms andmolecules into the exosphere of Mercury are generally considered to be thermal vaporization, photonstimulateddesorption (PSD), impact vaporization, and ion sputtering. Each of these processes has its owntemporal and spatial dependence. The exosphere is strongly influenced by Mercury’s highly ellipticalorbit and rapid orbital speed. As a consequence the surface undergoes large fluctuations in temperatureand experiences differences of insolation with longitude. We will discuss these processes but focus moreon the expected surface composition and solar wind particle sputtering which releases material like Caand other elements from the surface minerals and discuss the relevance of composition modelling

alr approach for replacing values below the detection limit

All of the imputation techniques usually applied for replacing values below thedetection limit in compositional data sets have adverse effects on the variability. In thiswork we propose a modification of the EM algorithm that is applied using the additivelog-ratio transformation. This new strategy is applied to a compositional data set and theresults are compared with the usual imputation techniques

Multicast algorithms evaluation using an adaptative routing in ATM networks

This paper proposes a multicast implementation based on adaptive routing with anticipated calculation. Three different cost measures for a point-to-multipoint connection: bandwidth cost, connection establishment cost and switching cost can be considered. The application of the method based on pre-evaluated routing tables makes possible the reduction of bandwidth cost and connection establishment cost individually