Surf

Dans le cadre des activités de recherche en partenariat, entre des équipes françaises et marocaines, menées au sein du LMI MediTer (1 et 2) et du programme de l'ANR MedInnLocal, le littoral Atlantique marocain a été retenu comme un site d'intérêt pour le groupe travaillant sur le tourisme. Dans la zone autour de Taghazout, identifiée dès 2011, plusieurs missions de terrains, individuelles et collectives, donnant lieux à des enquêtes pluridis-ciplinaires se sont déroulées entre 2013 et 2017 sur diverses thématiques en liaison avec le tourisme balnéaire, rural, de niche et plus globalement sur les dynamiques et changements en cours. Les données collectées ont permis d'identifier un certain nombre de phénomènes, des ajustements, des stratégies, dont certaines sont éphémères et particulièrement difficiles à caractériser alors que d'autres semblent plus pérennes. Du fait que les changements dans la zone sont toujours en cours, et ce pour plusieurs années sans doute, et que les recherches elles aussi conti-nuent au fil des aménagements du littoral, des nouvelles infrastructures et des initiatives locales, ou nationales en réponse à ces évolutions, les premiers résultats présentés, commentés et analysés dans ce chapitre ne constituent nullement un point de vue final et définitif sur ces travaux. Ils doivent être pris comme un éclairage à un moment donné sur une situation en évolution constante. Ce chapitre est de ce fait beaucoup plus descriptif qu'analytique mais il s'insère dans une dynamique de recherche collective qui a déjà donné lieu à de nombreuses publications (Senil et al. 2014 ; Berriane 2014 ; Berriane et Michon 2016).

In this work, we compare the performances of four
different classifiers for detection of diabetic
retinopathy (DR) in retinal pathology images. The
classifiers considered are Naïve Bayes Classifier,
Decision Tree Classifier, Support Vector Machine
(SVM) Classifier, and Feed-forward Neural
Network Classifier. In the first step, the interest
points and descriptors are extracted from the fundus
images using the SURF algorithm. In the second
step, based on the extracted interest points, a Bagof-Visual-Words histogram is created for each
image. Finally in the third step, the classifiers are
first trained using the histograms of the images and
then they are employed to classify whether a retinal
image is normal or abnormal based on DR
anomalies. For conducting the experiments, two
well-known databases, namely, RetiDB and
Messidor are used.

When Salvadoran government officials signed the Peace Accords in 1992, the global surf community took note. For twelve years, civil war had ravaged the Central American country, leaving nearly 80,.000 civilians dead or missing. Once the republic re-emerged as a popular surfing destination, miles of pristine beaches and near-vacant waves were no longer accessible only to the fearless. By the turn of the century, a beach town nicknamed El Tunco became a refuge where waves beckoned the war-weary. Between 1993 and 2009, El Salvador attracted an estimated 12.5 million tourists, many of them in search of surf. El Tunco's evolution into a wavetopia raises several issues that warrant attention. This paper examines how the global surf industry affects EI Tunco's economic and cultural landscape. Grounds for the study concern tourism, property rights, capital investment, and the aftermath of neoliberal reforms. Ethnographic and field research conducted in August 2010 indicates property values in El Tunco have nearly tripled since 2005. Matters pertaining to land ownership and beach access also have aggravated social tensions. One central argument emerges: Surf tourism serves as a key sector in a depressed Salvadoran economy wherever waves are in demand. Published scholarly analyses dissecting the influence of the global surf industry on specific Central American countries are either
undeveloped or nonexistent. The qualitative data presented should fuel discussions and promote more awareness among individuals who recognize surfing as a globalized lifestyle, sport and business.

This paper proposes a novel of text document authorization based on SIFT and SURF. It mainly consists of two stages enrollment and identification. In all stages SIFT (scale invariant feature transform) descriptors are extracted the scale and orientation (SOs) of the each sentences. At the same time SURF (speed up robust feature) will extract the scale and orientation (SOs) of the same word. These feature values are stored in the code book. In the identification stage SOs of input handwriting text documents are extracted and classify the document on the basis of lib SVM classifier. Experimental result consist of different English data sets (Iam, Firemaker etc).And by measuring the classification accuracy a comparative study is done on SIFT and SURF algorithms.

Résultat d’une enquête socio-anthropologique menée sur le terrain durant trois années, sur les Côte Basque et Landaise (France), ce livre analyse la parole des surfeurs et des surfeuses en répondant à cette question : « Que font réellement les surfeurs quand ils disent qu’ils font du surf ? » . L'auteure "montre comment les pratiquants "bricolent" une identité par jeux de don et de contre-don en relation avec eux-même, les autres et l'environnement." (J.P Augustin, 2008, 108). En analysant l'histoire, dont la stigmatisation qu'a pu subir le surf, les modalités d'apprentissage, le rapport au corps, au genre, à la nature, ce livre montre quels sont les rites, les représentations, et les symboles propres à cette pratique. L'auteure propose de voir dans le surf "une thérapeutique de sociabilités détériorées ",  pouvant, d'une certaine façon, répondre à la crise de sens de nos sociétés contemporaines.



(J.P Augustin, Agora n°50, 4°trimestre, INJEP, 2008, pp. : 106-108)

With the SIFT and SURF based recognition, the paper presents the impact of salient features in object recognition. We use the two well-known image descriptors in the bag of words framework on five online available standard datasets. Experiments show that by introducing saliency in the bag of words model, state-of-the-art performance can still be retained while reducing considerable amount of data processing and thus achieving faster execution times.

Having effective methods to access the desired images is essential nowadays with the availability of a huge amount of digital images. The proposed approach is based on an analogy between content-based image retrieval and text retrieval. The aim of the approach is to build a meaningful mid-level representation of images to be used later on for matching between a query image and other images in the desired database. The approach is based firstly on constructing different visual words using local patch extraction and fusion of descriptors. Secondly, we introduce a new method using multilayer pLSA to eliminate the noisiest words generated by the vocabulary building process. Thirdly, a new spatial weighting scheme is introduced that consists of weighting visual words according to the probability of each visual word to belong to each of the n Gaussian. Finally, we construct visual phrases from groups of visual words that are involved in strong association rules. Experimental results show that our approach outperforms the results of traditional image retrieval techniques.

Weather forecasting has become an indispensable application to predict the state of the atmosphere for a future time based on cloud cover identification. But it generally needs the experience of a well-trained meteorologist. In this paper, a novel method is proposed for automatic cloud cover estimation, typical to Indian Territory Speeded Up Robust Feature Transform(SURF) is applied on the satellite images to obtain the affine corrected images. The extracted cloud regions from the affine corrected images based on Otsu threshold are superimposed on the artistic grids representing latitude and longitude over India. The segmented cloud and grid composition drive a look up table mechanism to identify the cloud cover regions. Owing to its simplicity, the proposed method processes the test images faster and provides accurate segmentation for cloud cover regions.

Chemical and hydrological characterization of nine core holes (wells) on the 4850-ft level at Sanford Underground Research Facility have indicated decreasing flow rates with time, naturally buffered water, and existence of multiple flow systems. Flow rates for the wells all decreased by 17 to 63% over approximately nine months. The primary fluid pathway was most likely through ubiquitous secondary fracture permeability. pH was neutral to slightly basic at 7.75 with little variation. Core hole temperatures also stabilized at the drift temperature, decreasing by up to 31%. Oxidation reduction potential values of the water indicated reducing conditions and strong iron-holding capacity. All wells were fitted with pressure gauges and relief valves and were shut in to allow pressure to build. Analyses were conducted utilizing Horner time functions resulting in a maximum reservoir pressure of 1200 psi (~2800 ft head). There were strong indications that three isolated flow systems existed, including one that impacted a hole that was originally dry and began flowing and building pressure over time indicating hydraulic connectivity had occurred. These and supporting data have been utilized in the design of large underground excavations for future physics experiments.

In the present world, images and videos are the main carrier of information. So that, they are usually provided as essential evidence in many different areas like ranging from mainstream media, journalism and scientific publication, criminal investigation and surveillance system, etc. However, this source of information can be easily tampered by using open source of powerful computer applications like Photoshop, Magisto Video Editor, Cyber Link Director Suite, Corel Video Studio Pro, etc. For making tampering more robust, various transformation like scaling, rotation, lighting changes, blurring etc. applied. Copy-move image forgery is frequently used to tamper the image. In copy-move forgery, a specific area is copied and then pasted into any region of the image. An efficient approach for detecting copy-move tampering is to use visual feature such as Speed Up Robust Features (SURF). This paper presents a tempering detection method based on SURF, which detects the key points and their corresponding features. From the key points, grouping is performed on the matched key points, which shows the copied and pasted regions.

The determination of Region-of-Interest has been recognised as an important means by which unimportant image content can be identified and excluded during image compression or image modelling, however existing Region-of-Interest detection methods are computationally expensive thus are mostly unsuitable for managing large number of images and the compression of images especially for real-time video applications. This paper therefore proposes an unsupervised algorithm that takes advantage of the high computation speed being offered by Speeded-Up Robust Features (SURF) and Features from Accelerated Segment Test (FAST) to achieve fast and efficient Region-of-Interest detection.

Retrieving the location of a mobile device by matching a query image to a database of geo-tagged imagery is one popu- lar application of content-based image retrieval (CBIR). Stan- dard CBIR-based approaches exploit appearance features of the environment for the matching process. Many locations, however, are characterized by distinct structural (geometric) features. We investigate whether a standard appearance-based CBIR pipeline can be adapted to perform location retrieval using a range image-based representation of the environment. The contributions are three-fold: We design a rigorous ex- perimental setup using an extensive and challenging indoor dataset. Secondly, we compare the state-of-the-art feature al- gorithm specifically designed for range images, the Normal Aligned Radial Feature (NARF) [1], against some of the most established appearance-based features. Thirdly, we combine the high key point detection rate of NARF, with the robustness of the Speeded-Up Robust Feature for range-image based location recognition. This detector-descriptor combination, which we coin NURF, leads to 15% improvement in absolute location recognition performance compared to simple NARF in our experimental setup.

Object classification in computer vision is the task of classifying a given object in an image or video sequence to one of a set of predefined object categories. There are two main factors which affect the performance of object classification. These are image ...

The determination of Region-of-Interest has been recognised as an important means by which unimportant image content can be identified and excluded during image compression or image modelling, however existing Region-of-Interest detection methods are computationally expensive thus are mostly unsuitable for managing large number of images and the compression of images especially for real-time video applications. This paper therefore proposes an unsupervised algorithm that takes advantage of the high computation speed being offered by Speeded-Up Robust Features (SURF) and Features from Accelerated Segment Test (FAST) to achieve fast and efficient Region-of-Interest detection.