Semantic domain and grammatical class effects in the picture-word interference paradigm

The role of grammatical class in lexical access and representation is still not well understood. Grammatical effects obtained in picture-word interference experiments have been argued to show the operation of grammatical constraints during lexicalization when syntactic integration is required by the task. Alternative views hold that the ostensibly grammatical effects actually derive from the coincidence of semantic and grammatical differences between lexical candidates. We present three picture-word interference experiments conducted in Spanish. In the first two, the semantic relatedness (related or unrelated) and the grammatical class (nouns or verbs) of the target and the distracter were manipulated in an infinitive form action naming task in order to disentangle their contributions to verb lexical access. In the third experiment, a possible confound between grammatical class and semantic domain (objects or actions) was eliminated by using action-nouns as distracters. A condition in which participants were asked to name the action pictures using an inflected form of the verb was also included to explore whether the need of syntactic integration modulated the appearance of grammatical effects. Whereas action-words (nouns or verbs), but not object-nouns, produced longer reaction times irrespective of their grammatical class in the infinitive condition, only verbs slowed latencies in the inflected form condition. Our results suggest that speech production relies on the exclusion of candidate responses that do not fulfil task-pertinent criteria like membership in the appropriate semantic domain or grammatical class. Taken together, these findings are explained by a response-exclusion account of speech output. This and alternative hypotheses are discussed.

A new approach to segmentation based on fusing circumscribed contours, region growing and clustering

One of the major problems in machine vision is the segmentation of images of natural scenes. This paper presents a new proposal for the image segmentation problem which has been based on the integration of edge and region information. The main contours of the scene are detected and used to guide the posterior region growing process. The algorithm places a number of seeds at both sides of a contour allowing stating a set of concurrent growing processes. A previous analysis of the seeds permits to adjust the homogeneity criterion to the regions's characteristics. A new homogeneity criterion based on clustering analysis and convex hull construction is proposed

Active regions for colour texture segmentation integrating region and boundary information

In this paper a colour texture segmentation method, which unifies region and boundary information, is proposed. The algorithm uses a coarse detection of the perceptual (colour and texture) edges of the image to adequately place and initialise a set of active regions. Colour texture of regions is modelled by the conjunction of non-parametric techniques of kernel density estimation (which allow to estimate the colour behaviour) and classical co-occurrence matrix based texture features. Therefore, region information is defined and accurate boundary information can be extracted to guide the segmentation process. Regions concurrently compete for the image pixels in order to segment the whole image taking both information sources into account. Furthermore, experimental results are shown which prove the performance of the proposed method

Unsupervised active regions for multiresolution image segmentation

An unsupervised approach to image segmentation which fuses region and boundary information is presented. The proposed approach takes advantage of the combined use of 3 different strategies: the guidance of seed placement, the control of decision criterion, and the boundary refinement. The new algorithm uses the boundary information to initialize a set of active regions which compete for the pixels in order to segment the whole image. The method is implemented on a multiresolution representation which ensures noise robustness as well as computation efficiency. The accuracy of the segmentation results has been proven through an objective comparative evaluation of the method

Image Segmentation Using Information Bottleneck Method

In image processing, segmentation algorithms constitute one of the main focuses of research. In this paper, new image segmentation algorithms based on a hard version of the information bottleneck method are presented. The objective of this method is to extract a compact representation of a variable, considered the input, with minimal loss of mutual information with respect to another variable, considered the output. First, we introduce a split-and-merge algorithm based on the definition of an information channel between a set of regions (input) of the image and the intensity histogram bins (output). From this channel, the maximization of the mutual information gain is used to optimize the image partitioning. Then, the merging process of the regions obtained in the previous phase is carried out by minimizing the loss of mutual information. From the inversion of the above channel, we also present a new histogram clustering algorithm based on the minimization of the mutual information loss, where now the input variable represents the histogram bins and the output is given by the set of regions obtained from the above split-and-merge algorithm. Finally, we introduce two new clustering algorithms which show how the information bottleneck method can be applied to the registration channel obtained when two multimodal images are correctly aligned. Different experiments on 2-D and 3-D images show the behavior of the proposed algorithms

Analysis of Satellite Data for Forest Segmentation

In this work we study the classification of forest types using mathematics based image analysis on satellite data. We are interested in improving classification of forest segments when a combination of information from two or more different satellites is used. The experimental part is based on real satellite data originating from Canada. This thesis gives summary of the mathematics basics of the image analysis and supervised learning , methods that are used in the classification algorithm. Three data sets and four feature sets were investigated in this thesis. The considered feature sets were 1) histograms (quantiles) 2) variance 3) skewness and 4) kurtosis. Good overall performances were achieved when a combination of ASTERBAND and RADARSAT2 data sets was used.

Memoria de la elaboración de subtítulos para "Babel", primer capítulo de la serie documental Fry's Planet Word

Este trabajo pretende identificar algunas de las habilidades que un traductor audiovisual debe desarrollar, desde un punto de vista práctico, para ejercer la profesión, haciendo hincapié en el dominio del software específico para subtituladores. Esta memoria describe el proceso de ensayo y error llevado a cabo durante la elaboración de los subtítulos de un documental e identifica algunas de las dificultades con las que podemos encontrarnos al realizar un encargo de este tipo si trabajamos con programas de licencia gratuita, además de intentar aportar las soluciones correspondientes.

Segmentation of Industrial Customers – Case: Finnish Mechanical Engineering Companies

Segmentointi on perinteisesti ollut erityisesti kuluttajamarkkinoinnin työkalu, mutta siirtymä tuotteista palveluihin on lisännyt segmentointitarvetta myös teollisilla markkinoilla. Tämän tutkimuksen tavoite on löytää selkeästi toisistaan erottuvia asiakasryhmiä suomalaisen liikkeenjohdon konsultointiyritys Synocus Groupin tarjoaman case-materiaalin pohjalta. K-means-klusteroinnin avulla löydetään kolme potentiaalista markkinasegmenttiä perustuen siihen, mitkä tarjoamaelementit 105 valikoitua suomalaisen kone- ja metallituoteteollisuuden asiakasta ovat maininneet tärkeimmiksi. Ensimmäinen klusteri on hintatietoiset asiakkaat, jotka laskevat yksikkökohtaisia hintoja. Toinen klusteri koostuu huolto-orientoituneista asiakkaista, jotka laskevat tuntikustannuksia ja maksimoivat konekannan käyttötunteja. Tälle kohderyhmälle kannattaisi ehkä markkinoida teknisiä palveluja ja huoltosopimuksia. Kolmas klusteri on tuottavuussuuntautuneet asiakkaat, jotka ovat kiinnostuneita suorituskyvyn kehittämisestä ja laskevat tonnikohtaisia kustannuksia. He tavoittelevat alempia kokonaiskustannuksia lisääntyneen suorituskyvyn, pidemmän käyttöiän ja alempien huoltokustannusten kautta.

Speaker diarization: Segmentation and clustering of speeches

Speaker diarization is the process of sorting speeches according to the speaker. Diarization helps to search and retrieve what a certain speaker uttered in a meeting. Applications of diarization systemsextend to other domains than meetings, for example, lectures, telephone, television, and radio. Besides, diarization enhances the performance of several speech technologies such as speaker recognition, automatic transcription, and speaker tracking. Methodologies previously used in developing diarization systems are discussed. Prior results and techniques are studied and compared. Methods such as Hidden Markov Models and Gaussian Mixture Models that are used in speaker recognition and other speech technologies are also used in speaker diarization. The objective of this thesis is to develop a speaker diarization system in meeting domain. Experimental part of this work indicates that zero-crossing rate can be used effectively in breaking down the audio stream into segments, and adaptive Gaussian Models fit adequately short audio segments. Results show that 35 Gaussian Models and one second as average length of each segment are optimum values to build a diarization system for the tested data. Uniting the segments which are uttered by same speaker is done in a bottom-up clustering by a newapproach of categorizing the mixture weights.

Esiopetuskirjojen harjoitukset kielellisen tietoisuuden kehittäjinä

Tutkimus oli kasvatustieteellinen varhaiskasvatuksen alan tutkimus, jossa hyödynnettiin kielitieteellistä käsitteistöä. Tutkimuksen kohteena olivat esiopetuskirjojen kielellisen tietoisuuden harjoitukset. Aineistona oli 12 esiopetuksen harjoituskirjaa ja opettajan opasta. Kirjoissa oli yhteensä yli 2000 sivua ja yli 1300 harjoitusta. Tarkemmassa analyysissä olleiden harjoitusten määrä oli noin 460. Analyysimenetelmänä käytettiin sisällönanalyysiä, jonka avulla tutkittiin, millaisia kielellisen tietoisuuden harjoituksia esiopetuskirjoissa oli ja miten harjoitukset etenivät. Kielellisen tietoisuuden harjoitukset luokiteltiin fonologisen, morfologisen ja syntaktisen tietoisuuden harjoituksiin. Fonologinen tietoisuus käsitettiin lapsen kyvyksi havaita ja käsitellä kielen äännerakennetta. Morfologisella tietoisuudella tarkoitettiin kykyä havaita ja käsitellä kielen morfeemeja eli pienimpiä merkityksellisiä osia. Syntaktisella tietoisuudella tarkoitettiin lapsen tietoisuutta siitä, miten lauseet rakentuvat. Olennaisiksi piirteiksi fonologisen tietoisuuden harjoituksissa identifioituivat harjoitusten edellyttämä tai harjoittama tietoisuuden taso, harjoitusten kohteena olevien lingvististen yksiköiden taso ja harjoitustyyppi sekä morfologisen ja syntaktisen tietoisuuden harjoituksissa tietoisuuden taso ja harjoitustyyppi. Tutkimuksessa luotiin aikaisempien tutkimusten pohjalta mallit siitä, miten eri harjoitukset etenisivät optimaalisesti lapsen kielellisen tietoisuuden kehittymisen kannalta. Eri harjoituskirjojen harjoitusten etenemistä verrattiin näihin malleihin. Fonologisen tietoisuuden harjoituksista voitiin tunnistaa viisi päätyyppiä, joista useat jakautuivat vielä alatyypeiksi. Harjoitusten alatyypit ilmaisivat yleensä harjoituksissa käytettävän kognitiivisen operaation – käytettiinkö harjoituksessa tunnistamista, yhdistämistä vai osiin jakamista. Tämä samoin kuin harjoituksessa käytetty lingvistinen yksikkö (sana, loppusointu, tavu tai äänne) ja tietoisuuden taso olivat yhteydessä harjoituksen vaikeuteen. Tutkimuksissa ja interventioissa esiopetusikäisilläkin käytettyjä muunteluharjoituksia, joissa sanasta tai tavusta poistetaan osa, siihen lisätään osa tai osaa siirretään, harjoituskirjoissa ei ollut. Morfologisen tietoisuuden harjoitukset kohdistuivat yhdyssanoihin, taivutuspäätteisiin tai johdoksiin. Syntaktisen tietoisuuden harjoituksista identifioitui yhdentoista harjoituslajin alatyypin kautta kuusi päätyyppiä. Tutkimusta voidaan hyödyntää esiopetuskirjojen arvioinnissa ja laatimisessa sekä varhaiskasvatus- ja esiopetuspedagogiikassa.

Word Equations and Related Topics. Independence, Decidability and Characterizations

The three main topics of this work are independent systems and chains of word equations, parametric solutions of word equations on three unknowns, and unique decipherability in the monoid of regular languages. The most important result about independent systems is a new method giving an upper bound for their sizes in the case of three unknowns. The bound depends on the length of the shortest equation. This result has generalizations for decreasing chains and for more than three unknowns. The method also leads to shorter proofs and generalizations of some old results. Hmelevksii’s theorem states that every word equation on three unknowns has a parametric solution. We give a significantly simplified proof for this theorem. As a new result we estimate the lengths of parametric solutions and get a bound for the length of the minimal nontrivial solution and for the complexity of deciding whether such a solution exists. The unique decipherability problem asks whether given elements of some monoid form a code, that is, whether they satisfy a nontrivial equation. We give characterizations for when a collection of unary regular languages is a code. We also prove that it is undecidable whether a collection of binary regular languages is a code.