| Records |
| Author |
Hans Stadthagen-Gonzalez; Luis Lopez; M. Carmen Parafita; C. Alejandro Parraga |
| Title |
Using two-alternative forced choice tasks and Thurstone law of comparative judgments for code-switching research |
Type |
Book Chapter |
| Year |
2018 |
Publication  |
Linguistic Approaches to Bilingualism |
Abbreviated Journal |
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| Volume |
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Issue |
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Pages |
67-97 |
| Keywords |
two-alternative forced choice and Thurstone's law; acceptability judgment; code-switching |
| Abstract |
This article argues that 2-alternative forced choice tasks and Thurstone’s law of comparative judgments (Thurstone, 1927) are well suited to investigate code-switching competence by means of acceptability judgments. We compare this method with commonly used Likert scale judgments and find that the 2-alternative forced choice task provides granular details that remain invisible in a Likert scale experiment. In order to compare and contrast both methods, we examined the syntactic phenomenon usually referred to as the Adjacency Condition (AC) (apud Stowell, 1981), which imposes a condition of adjacency between verb and object. Our interest in the AC comes from the fact that it is a subtle feature of English grammar which is absent in Spanish, and this provides an excellent springboard to create minimal code-switched pairs that allow us to formulate a clear research question that can be tested using both methods. |
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| Notes |
NEUROBIT; no menciona |
Approved |
no |
| Call Number |
Admin @ si @ SLP2018 |
Serial |
2994 |
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| Author |
Fernando Vilariño |
| Title |
Giving Value to digital collections in the Public Library |
Type |
Conference Article |
| Year |
2016 |
Publication |
Librarian 2020 |
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Brussels; Belgium; October 2016 |
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LIB |
| Notes |
MV; 600.097;SIAI |
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no |
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Admin @ si @Vil2016a |
Serial |
2802 |
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| Author |
Anna Salvatella; Maria Vanrell; Ramon Baldrich |
| Title |
Subtexture Components for Texture Description |
Type |
Miscellaneous |
| Year |
2003 |
Publication |
Lecture Notes in Computer Science, vol 2652, pp 884–892 |
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Springer-Verlag |
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CIC |
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no |
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CAT @ cat @ SVR2003 |
Serial |
421 |
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| Author |
Agnes Borras; Francesc Tous; Josep Llados; Maria Vanrell |
| Title |
High-Level Clothes Description Based on Color-Texture and Structural Features |
Type |
Book Chapter |
| Year |
2003 |
Publication |
Lecture Notes in Computer Science |
Abbreviated Journal |
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| Volume |
2652 |
Issue |
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Pages |
108–116 |
| Keywords |
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| Abstract |
This work is a part of a surveillance system where content- based image retrieval is done in terms of people appearance. Given an image of a person, our work provides an automatic description of his clothing according to the colour, texture and structural composition of its garments. We present a two-stage process composed by image segmentation and a region-based interpretation. We segment an image by modelling it due to an attributed graph and applying a hybrid method that follows a split-and-merge strategy. We propose the interpretation of five cloth combinations that are modelled in a graph structure in terms of region features. The interpretation is viewed as a graph matching with an associated cost between the segmentation and the cloth models. Fi- nally, we have tested the process with a ground-truth of one hundred images. |
| Address |
Springer-Verlag |
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DAG;CIC |
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no |
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CAT @ cat @ BTL2003a |
Serial |
368 |
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| Author |
Anton Cervantes; Gemma Sanchez; Josep Llados; Agnes Borras; Ana Rodriguez |
| Title |
Biometric Recognition Based on Line Shape Descriptors |
Type |
Book Chapter |
| Year |
2006 |
Publication |
Lecture Notes in Computer Science |
Abbreviated Journal |
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| Volume |
3926 |
Issue |
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Pages |
346–357, |
| Keywords |
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| Abstract |
Abstract. In this paper we propose biometric descriptors inspired by shape signatures traditionally used in graphics recognition approaches. In particular several methods based on line shape descriptors used to iden- tify newborns from the biometric information of the ears are developed. The process steps are the following: image acquisition, ear segmentation, ear normalization, feature extraction and identification. Several shape signatures are defined from contour images. These are formulated in terms of zoning and contour crossings descriptors. Experimental results are presented to demonstrate the effectiveness of the used techniques. |
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Springer Link |
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DAG |
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no |
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DAG @ dag @ CSL2006 |
Serial |
685 |
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| Author |
Francesc Tous; Agnes Borras; Robert Benavente; Ramon Baldrich; Maria Vanrell; Josep Llados |
| Title |
Textual Descriptions for Browsing People by Visual Apperance. |
Type |
Book Chapter |
| Year |
2002 |
Publication |
Lecture Notes in Artificial Intelligence |
Abbreviated Journal |
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| Volume |
2504 |
Issue |
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Pages |
419-429 |
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| Abstract |
This paper presents a first approach to build colour and structural descriptors for information retrieval on a people database. Queries are formulated in terms of their appearance that allows to seek people wearing specific clothes of a given colour name or texture. Descriptors are automatically computed by following three essential steps. A colour naming labelling from pixel properties. A region seg- mentation step based on colour properties of pixels combined with edge information. And a high level step that models the region arrangements in order to build clothes structure. Results are tested on large set of images from real scenes taken at the entrance desk of a building |
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Springer Verlag |
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| Notes |
DAG;CIC |
Approved |
no |
| Call Number |
CAT @ cat @ TBB2002b |
Serial |
319 |
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| Author |
Beata Megyesi; Alicia Fornes; Nils Kopal; Benedek Lang |
| Title |
Historical Cryptology |
Type |
Book Chapter |
| Year |
2024 |
Publication |
Learning and Experiencing Cryptography with CrypTool and SageMath |
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| Abstract |
Historical cryptology studies (original) encrypted manuscripts, often handwritten sources, produced in our history. These historical sources can be found in archives, often hidden without any indexing and therefore hard to locate. Once found they need to be digitized and turned into a machine-readable text format before they can be deciphered with computational methods. The focus of historical cryptology is not primarily the development of sophisticated algorithms for decipherment, but rather the entire process of analysis of the encrypted source from collection and digitization to transcription and decryption. The process also includes the interpretation and contextualization of the message set in its historical context. There are many challenges on the way, such as mistakes made by the scribe, errors made by the transcriber, damaged pages, handwriting styles that are difficult to interpret, historical languages from various time periods, and hidden underlying language of the message. Ciphertexts vary greatly in terms of their code system and symbol sets used with more or less distinguishable symbols. Ciphertexts can be embedded in clearly written text, or shorter or longer sequences of cleartext can be embedded in the ciphertext. The ciphers used mostly in historical times are substitutions (simple, homophonic, or polyphonic), with or without nomenclatures, encoded as digits or symbol sequences, with or without spaces. So the circumstances are different from those in modern cryptography which focuses on methods (algorithms) and their strengths and assumes that the algorithm is applied correctly. For both historical and modern cryptology, attack vectors outside the algorithm are applied like implementation flaws and side-channel attacks. In this chapter, we give an introduction to the field of historical cryptology and present an overview of how researchers today process historical encrypted sources. |
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DAG |
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no |
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Admin @ si @ MFK2024 |
Serial |
4020 |
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| Author |
Hugo Berti; Angel Sappa; Osvaldo Agamennoni |
| Title |
Improved Dynamic Window Approach by Using Lyapunov Stability Criteria |
Type |
Journal |
| Year |
2008 |
Publication |
Latin American Applied Research |
Abbreviated Journal |
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| Volume |
38 |
Issue |
4 |
Pages |
289–298 |
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ADAS |
Approved |
no |
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ADAS @ adas @ BSA2008 |
Serial |
1056 |
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| Author |
Zhengying Liu; Isabelle Guyon; Julio C. S. Jacques Junior; Meysam Madadi; Sergio Escalera; Adrien Pavao; Hugo Jair Escalante; Wei-Wei Tu; Zhen Xu; Sebastien Treguer |
| Title |
AutoCV Challenge Design and Baseline Results |
Type |
Conference Article |
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2019 |
Publication |
La Conference sur l’Apprentissage Automatique |
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| Abstract |
We present the design and beta tests of a new machine learning challenge called AutoCV (for Automated Computer Vision), which is the first event in a series of challenges we are planning on the theme of Automated Deep Learning. We target applications for which Deep Learning methods have had great success in the past few years, with the aim of pushing the state of the art in fully automated methods to design the architecture of neural networks and train them without any human intervention. The tasks are restricted to multi-label image classification problems, from domains including medical, areal, people, object, and handwriting imaging. Thus the type of images will vary a lot in scales, textures, and structure. Raw data are provided (no features extracted), but all datasets are formatted in a uniform tensor manner (although images may have fixed or variable sizes within a dataset). The participants's code will be blind tested on a challenge platform in a controlled manner, with restrictions on training and test time and memory limitations. The challenge is part of the official selection of IJCNN 2019. |
| Address |
Toulouse; Francia; July 2019 |
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HUPBA; no proj |
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no |
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Admin @ si @ LGJ2019 |
Serial |
3323 |
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| Author |
J. Elder; Fadi Dornaika; Y. Hou; R. Goldstein |
| Title |
Attentive wide-field sensing for visual telepresence and surveillance |
Type |
Book Chapter |
| Year |
2005 |
Publication |
L. Itti, G. Rees and J. Tsotsos (editors), Neurobiology of Attention, Academic Press / Elsevier |
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no |
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Admin @ si @ EDH2005 |
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604 |
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| Author |
Fernando Vilariño |
| Title |
Computer Vision and Performing Arts |
Type |
Conference Article |
| Year |
2015 |
Publication |
Korean Scholars of Marketing Science |
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Seoul; Korea; October 2015 |
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KAMS |
| Notes |
MV;SIAI |
Approved |
no |
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Admin @ si @Vil2015 |
Serial |
2799 |
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| Author |
Juan Ramon Terven Salinas; Joaquin Salas; Bogdan Raducanu |
| Title |
Estado del Arte en Sistemas de Vision Artificial para Personas Invidentes |
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Journal |
| Year |
2013 |
Publication |
Komputer Sapiens |
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KS |
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1 |
Issue |
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Pages |
20-25 |
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OR;MV |
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no |
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Admin @ si @ TSR2013 |
Serial |
2231 |
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| Author |
Katerine Diaz; Jesus Martinez del Rincon; Aura Hernandez-Sabate |
| Title |
Decremental generalized discriminative common vectors applied to images classification |
Type |
Journal Article |
| Year |
2017 |
Publication |
Knowledge-Based Systems |
Abbreviated Journal |
KBS |
| Volume |
131 |
Issue |
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Pages |
46-57 |
| Keywords |
Decremental learning; Generalized Discriminative Common Vectors; Feature extraction; Linear subspace methods; Classification |
| Abstract |
In this paper, a novel decremental subspace-based learning method called Decremental Generalized Discriminative Common Vectors method (DGDCV) is presented. The method makes use of the concept of decremental learning, which we introduce in the field of supervised feature extraction and classification. By efficiently removing unnecessary data and/or classes for a knowledge base, our methodology is able to update the model without recalculating the full projection or accessing to the previously processed training data, while retaining the previously acquired knowledge. The proposed method has been validated in 6 standard face recognition datasets, showing a considerable computational gain without compromising the accuracy of the model. |
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| Notes |
ADAS; 600.118; 600.121 |
Approved |
no |
| Call Number |
Admin @ si @ DMH2017a |
Serial |
3003 |
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| Author |
Katerine Diaz; Francesc J. Ferri; Aura Hernandez-Sabate |
| Title |
An overview of incremental feature extraction methods based on linear subspaces |
Type |
Journal Article |
| Year |
2018 |
Publication |
Knowledge-Based Systems |
Abbreviated Journal |
KBS |
| Volume |
145 |
Issue |
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Pages |
219-235 |
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| Abstract |
With the massive explosion of machine learning in our day-to-day life, incremental and adaptive learning has become a major topic, crucial to keep up-to-date and improve classification models and their corresponding feature extraction processes. This paper presents a categorized overview of incremental feature extraction based on linear subspace methods which aim at incorporating new information to the already acquired knowledge without accessing previous data. Specifically, this paper focuses on those linear dimensionality reduction methods with orthogonal matrix constraints based on global loss function, due to the extensive use of their batch approaches versus other linear alternatives. Thus, we cover the approaches derived from Principal Components Analysis, Linear Discriminative Analysis and Discriminative Common Vector methods. For each basic method, its incremental approaches are differentiated according to the subspace model and matrix decomposition involved in the updating process. Besides this categorization, several updating strategies are distinguished according to the amount of data used to update and to the fact of considering a static or dynamic number of classes. Moreover, the specific role of the size/dimension ratio in each method is considered. Finally, computational complexity, experimental setup and the accuracy rates according to published results are compiled and analyzed, and an empirical evaluation is done to compare the best approach of each kind. |
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0950-7051 |
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ADAS; 600.118 |
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no |
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Admin @ si @ DFH2018 |
Serial |
3090 |
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| Author |
Yecong Wan; Yuanshuo Cheng; Miingwen Shao; Jordi Gonzalez |
| Title |
Image rain removal and illumination enhancement done in one go |
Type |
Journal Article |
| Year |
2022 |
Publication |
Knowledge-Based Systems |
Abbreviated Journal |
KBS |
| Volume |
252 |
Issue |
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Pages |
109244 |
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| Abstract |
Rain removal plays an important role in the restoration of degraded images. Recently, CNN-based methods have achieved remarkable success. However, these approaches neglect that the appearance of real-world rain is often accompanied by low light conditions, which will further degrade the image quality, thereby hindering the restoration mission. Therefore, it is very indispensable to jointly remove the rain and enhance illumination for real-world rain image restoration. To this end, we proposed a novel spatially-adaptive network, dubbed SANet, which can remove the rain and enhance illumination in one go with the guidance of degradation mask. Meanwhile, to fully utilize negative samples, a contrastive loss is proposed to preserve more natural textures and consistent illumination. In addition, we present a new synthetic dataset, named DarkRain, to boost the development of rain image restoration algorithms in practical scenarios. DarkRain not only contains different degrees of rain, but also considers different lighting conditions, and more realistically simulates real-world rainfall scenarios. SANet is extensively evaluated on the proposed dataset and attains new state-of-the-art performance against other combining methods. Moreover, after a simple transformation, our SANet surpasses existing the state-of-the-art algorithms in both rain removal and low-light image enhancement. |
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Sept 2022 |
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Elsevier |
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| Notes |
ISE; 600.157; 600.168 |
Approved |
no |
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Admin @ si @ WCS2022 |
Serial |
3744 |
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