A new approach for the glotis segmentation using Snakes

The present work describes a new methodology for the automatic detection of the glottal space from laryngeal images based on active contour models (snakes). In order to obtain an appropriate image for the use of snakes based techniques, the proposed algorithm combines a pre-processing stage including some traditional techniques (thresholding and median filter) with more sophisticated ones such as anisotropic filtering. The value selected for the thresholding was fixed to the 85% of the maximum peak of the image histogram, and the anisotropic filter permits to distinguish two intensity levels, one corresponding to the background and the other one to the foreground (glottis). The initialization carried out is based on the magnitude obtained using the Gradient Vector Flow field, ensuring an automatic process for the selection of the initial contour. The performance of the algorithm is tested using the Pratt coefficient and compared against a manual segmentation. The results obtained suggest that this method provided results comparable with other techniques such as the proposed in (Osma-Ruiz et al., 2008).

A comparison of open-source segmentation architectures for dealing with imperfect data from the media in speech synthesis

Traditional Text-To-Speech (TTS) systems have been developed using especially-designed non-expressive scripted recordings. In order to develop a new generation of expressive TTS systems in the Simple4All project, real recordings from the media should be used for training new voices with a whole new range of speaking styles. However, for processing this more spontaneous material, the new systems must be able to deal with imperfect data (multi-speaker recordings, background and foreground music and noise), filtering out low-quality audio segments and creating mono-speaker clusters. In this paper we compare several architectures for combining speaker diarization and music and noise detection which improve the precision and overall quality of the segmentation.

Background and foreground knowledge in dynamic ontology construction

Ontologies have become a key component in the Semantic Web and Knowledge management. One accepted goal is to construct ontologies from a domain specific set of texts. An ontology reflects the background knowledge used in writing and reading a text. However, a text is an act of knowledge maintenance, in that it re-enforces the background assumptions, alters links and associations in the ontology, and adds new concepts. This means that background knowledge is rarely expressed in a machine interpretable manner. When it is, it is usually in the conceptual boundaries of the domain, e.g. in textbooks or when ideas are borrowed into other domains. We argue that a partial solution to this lies in searching external resources such as specialized glossaries and the internet. We show that a random selection of concept pairs from the Gene Ontology do not occur in a relevant corpus of texts from the journal Nature. In contrast, a significant proportion can be found on the internet. Thus, we conclude that sources external to the domain corpus are necessary for the automatic construction of ontologies.

Előtérben a háttér - Az önkéntesség Magyarországon (The background in the foreground – Volunteering in Hungary)

Az önkéntesség témája talán sosem lehetett olyan aktuális, mint 2011-ben, az Önkéntesség Európai Évében. Az e téma köré szerveződött rendezvények talán népszerűbbé tehették az önkéntességet Magyarországon is. Az embereket legjobban a sport szeretete ösztönzi önkéntességre, pl. egy nagy sportrendezvény megrendezésekor a szervezők nagyban támaszkodnak az önkéntes munkaerőre. Azonban fontos lenne, hogy más területeken is felismerjék az emberek az effajta munkavégzés előnyeit. ______ The issue of volunteering may never be so actual, as in 2011, the European Year of Volunteering. The events which are organized around this theme might make volunteering more popular in Hungary. People are mostly encouraged by sport love for volunteering for example when arranging a great sport event the organizers can rely largely on the volunteers. However it would be important that people recognize the benefits of this kind of work in other fields as well.

Active Foreground Region Extraction and Tracking for Sports Video Annotation

Automatic video segmentation plays a vital role in sports videos annotation. This paper presents a fully automatic and computationally efficient algorithm for analysis of sports videos. Various methods of automatic shot boundary detection have been proposed to perform automatic video segmentation. These investigations mainly concentrate on detecting fades and dissolves for fast processing of the entire video scene without providing any additional feedback on object relativity within the shots. The goal of the proposed method is to identify regions that perform certain activities in a scene. The model uses some low-level feature video processing algorithms to extract the shot boundaries from a video scene and to identify dominant colours within these boundaries. An object classification method is used for clustering the seed distributions of the dominant colours to homogeneous regions. Using a simple tracking method a classification of these regions to active or static is performed. The efficiency of the proposed framework is demonstrated over a standard video benchmark with numerous types of sport events and the experimental results show that our algorithm can be used with high accuracy for automatic annotation of active regions for sport videos.

A fully-automatic caudate nucleus segmentation of brain MRI: application in volumetric analysis of pediatric attention-deficit/hyperactivity disorder

Background Accurate automatic segmentation of the caudate nucleus in magnetic resonance images (MRI) of the brain is of great interest in the analysis of developmental disorders. Segmentation methods based on a single atlas or on multiple atlases have been shown to suitably localize caudate structure. However, the atlas prior information may not represent the structure of interest correctly. It may therefore be useful to introduce a more flexible technique for accurate segmentations. Method We present Cau-dateCut: a new fully-automatic method of segmenting the caudate nucleus in MRI. CaudateCut combines an atlas-based segmentation strategy with the Graph Cut energy-minimization framework. We adapt the Graph Cut model to make it suitable for segmenting small, low-contrast structures, such as the caudate nucleus, by defining new energy function data and boundary potentials. In particular, we exploit information concerning the intensity and geometry, and we add supervised energies based on contextual brain structures. Furthermore, we reinforce boundary detection using a new multi-scale edgeness measure. Results We apply the novel CaudateCut method to the segmentation of the caudate nucleus to a new set of 39 pediatric attention-deficit/hyperactivity disorder (ADHD) patients and 40 control children, as well as to a public database of 18 subjects. We evaluate the quality of the segmentation using several volumetric and voxel by voxel measures. Our results show improved performance in terms of segmentation compared to state-of-the-art approaches, obtaining a mean overlap of 80.75%. Moreover, we present a quantitative volumetric analysis of caudate abnormalities in pediatric ADHD, the results of which show strong correlation with expert manual analysis. Conclusion CaudateCut generates segmentation results that are comparable to gold-standard segmentations and which are reliable in the analysis of differentiating neuroanatomical abnormalities between healthy controls and pediatric ADHD.

A Segmentation Model for Hi-tech Business Markets

Segmentointi on strateginen työkalu, joka tehostaa yrityksen resurssien käyttöä ja siten vaikuttaa kaikkiin asiakkuuksiin liittyviin liiketoimintaprosesseihin. Työn tavoitteena oli muodostaa segmentointimalli (sisältää sekä segmentointiprosessin että kriteerit) yritysinternetmarkkinoille. Työn tuloksia voidaan kuitenkin tulkita ja soveltaa laajemmin korkean teknologian yrityspalvelumarkkinoille. Tämä tutkielma lisää tietämystämme ja tarjoaa uudenlaisen näkemyksen segmentointiin korkean teknologian yrityspalvelumarkkinoilla. Työssä kuvataan korkean teknologian ja yritys- sekä palvelumarkkinoinnin erityispiirteitä ja kuinka nämä tekijät vaikuttavat segmentointimallin. Tutkimuksessa selvitettiin kohdeyrityksen nykyiset segmentointikäytännöt henkilökohtaisin asiantuntijahaastatteluin. Haastatteluiden avulla luotiin kuva nykyisistä lähestymistavoista sekä niiden lähtökohdista, vahvuuksista ja haasteista. Haastatteluiden analysoinnin jälkeen perustettiin projekti segmentoinnin kehittämiseksi. Työ tuloksena luotiin segmentointimalli, joka tarjoaa vankan perustan segmentoinnin kehittämiselle jatkuvana prosessina. Työssä esitetään segmentoinnin integroimista yrityksen asiakkuuksiin liittyviin liiketoimintaprosesseihin, joka usein puuttuu aiemmista töistä, sekä informaationkulun tehostamista segmentoinnin hyödyntämiseksi tehokkaammin. Segmentointi on strateginen työkalu ja vaatii siksi ylemmän johdon tuen ja sitoutumisen. Oikein sovellettuna segmentointi tarjoaa liiketoiminnalle mahdollisuuden merkittäviin etuihin kuten asiakastyytyväisyyden ja kannattavuuden kehittämiseen.

Bag-of-Features Approach to Unsupervised Visual Object Categorisation

The large and growing number of digital images is making manual image search laborious. Only a fraction of the images contain metadata that can be used to search for a particular type of image. Thus, the main research question of this thesis is whether it is possible to learn visual object categories directly from images. Computers process images as long lists of pixels that do not have a clear connection to high-level semantics which could be used in the image search. There are various methods introduced in the literature to extract low-level image features and also approaches to connect these low-level features with high-level semantics. One of these approaches is called Bag-of-Features which is studied in the thesis. In the Bag-of-Features approach, the images are described using a visual codebook. The codebook is built from the descriptions of the image patches using clustering. The images are described by matching descriptions of image patches with the visual codebook and computing the number of matches for each code. In this thesis, unsupervised visual object categorisation using the Bag-of-Features approach is studied. The goal is to find groups of similar images, e.g., images that contain an object from the same category. The standard Bag-of-Features approach is improved by using spatial information and visual saliency. It was found that the performance of the visual object categorisation can be improved by using spatial information of local features to verify the matches. However, this process is computationally heavy, and thus, the number of images must be limited in the spatial matching, for example, by using the Bag-of-Features method as in this study. Different approaches for saliency detection are studied and a new method based on the Hessian-Affine local feature detector is proposed. The new method achieves comparable results with current state-of-the-art. The visual object categorisation performance was improved by using foreground segmentation based on saliency information, especially when the background could be considered as clutter.

Motion Segmentation And Meanshift Assisted Contour Refinement For Airborne Video

This paper presents methods for moving object detection in airborne video surveillance. The motion segmentation in the above scenario is usually difficult because of small size of the object, motion of camera, and inconsistency in detected object shape etc. Here we present a motion segmentation system for moving camera video, based on background subtraction. An adaptive background building is used to take advantage of creation of background based on most recent frame. Our proposed system suggests CPU efficient alternative for conventional batch processing based background subtraction systems. We further refine the segmented motion by meanshift based mode association.

Hybrid Background Subtraction in Video using Bi-level CodeBook Model

Detection of Objects in Video is a highly demanding area of research. The Background Subtraction Algorithms can yield better results in Foreground Object Detection. This work presents a Hybrid CodeBook based Background Subtraction to extract the foreground ROI from the background. Codebooks are used to store compressed information by demanding lesser memory usage and high speedy processing. This Hybrid method which uses Block-Based and Pixel-Based Codebooks provide efficient detection results; the high speed processing capability of block based background subtraction as well as high Precision Rate of pixel based background subtraction are exploited to yield an efficient Background Subtraction System. The Block stage produces a coarse foreground area, which is then refined by the Pixel stage. The system’s performance is evaluated with different block sizes and with different block descriptors like 2D-DCT, FFT etc. The Experimental analysis based on statistical measurements yields precision, recall, similarity and F measure of the hybrid system as 88.74%, 91.09%, 81.66% and 89.90% respectively, and thus proves the efficiency of the novel system.

Tourist Profiles and Characteristics vis-à-vis Market Segmentation of Ecotourism Destinations in Kerala

Kerala, a classic ecotourism destination in India, provides significant opportunities for livelihood options to the people who depend on the resources from the forest and those who live in difficult terrains. This article analyses the socio-demographic, psychographic and travel behavior patterns and its sub-characteristics in the background of foreign and domestic tourists. The data source for the article has been obtained from a primary survey of 350 randomly chosen tourists, 175 each from domestic and foreign tourists, visiting Kerala’s ecotourists destinations during August-December 2010-11. Several socio-demographic, psychographic and life style factors have been identified based on the inference from field survey. There is considerable divergence in most of the factors identified in the case of domestic and international tourists. Post-trip attributes like satisfaction and intentions to return show that the ecotourism destinations in Kerala have significant potential that can help communities in the region.