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Author |
David Fernandez; Simone Marinai; Josep Llados; Alicia Fornes |
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Title |
Contextual Word Spotting in Historical Manuscripts using Markov Logic Networks |
Type |
Conference Article |
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Year |
2013 |
Publication |
2nd International Workshop on Historical Document Imaging and Processing |
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36-43 |
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Natural languages can often be modelled by suitable grammars whose knowledge can improve the word spotting results. The implicit contextual information is even more useful when dealing with information that is intrinsically described as one collection of records. In this paper, we present one approach to word spotting which uses the contextual information of records to improve the results. The method relies on Markov Logic Networks to probabilistically model the relational organization of handwritten records. The performance has been evaluated on the Barcelona Marriages Dataset that contains structured handwritten records that summarize marriage information. |
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washington; USA; August 2013 |
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978-1-4503-2115-0 |
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HIP |
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DAG; 600.056; 600.045; 600.061; 602.006 |
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no |
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Admin @ si @ FML2013 |
Serial |
2308 |
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Author |
Thanh Ha Do; Salvatore Tabbone; Oriol Ramos Terrades |
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Title |
Sparse representation over learned dictionary for symbol recognition |
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Journal Article |
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Year |
2016 |
Publication |
Signal Processing |
Abbreviated Journal |
SP |
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Volume |
125 |
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36-47 |
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Keywords |
Symbol Recognition; Sparse Representation; Learned Dictionary; Shape Context; Interest Points |
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Abstract |
In this paper we propose an original sparse vector model for symbol retrieval task. More specically, we apply the K-SVD algorithm for learning a visual dictionary based on symbol descriptors locally computed around interest points. Results on benchmark datasets show that the obtained sparse representation is competitive related to state-of-the-art methods. Moreover, our sparse representation is invariant to rotation and scale transforms and also robust to degraded images and distorted symbols. Thereby, the learned visual dictionary is able to represent instances of unseen classes of symbols. |
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DAG; 600.061; 600.077 |
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no |
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Admin @ si @ DTR2016 |
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2946 |
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Author |
Patricia Suarez; Angel Sappa; Dario Carpio; Henry Velesaca; Francisca Burgos; Patricia Urdiales |
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Title |
Deep Learning Based Shrimp Classification |
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Conference Article |
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Year |
2022 |
Publication |
17th International Symposium on Visual Computing |
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Volume |
13598 |
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36–45 |
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Pigmentation; Color space; Light weight network |
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Abstract |
This work proposes a novel approach based on deep learning to address the classification of shrimp (Pennaeus vannamei) into two classes, according to their level of pigmentation accepted by shrimp commerce. The main goal of this actual study is to support the shrimp industry in terms of price and process. An efficient CNN architecture is proposed to perform image classification through a program that could be set other in mobile devices or in fixed support in the shrimp supply chain. The proposed approach is a lightweight model that uses HSV color space shrimp images. A simple pipeline shows the most important stages performed to determine a pattern that identifies the class to which they belong based on their pigmentation. For the experiments, a database acquired with mobile devices of various brands and models has been used to capture images of shrimp. The results obtained with the images in the RGB and HSV color space allow for testing the effectiveness of the proposed model. |
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ISVC |
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MSIAU; no proj |
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no |
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Admin @ si @ SAC2022 |
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3772 |
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Author |
Sergio Escalera; Xavier Baro; Jordi Vitria; Petia Radeva |
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Title |
Text Detection in Urban Scenes (video sample) |
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Conference Article |
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Year |
2009 |
Publication |
12th International Conference of the Catalan Association for Artificial Intelligence |
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Volume |
202 |
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35–44 |
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Abstract. Text detection in urban scenes is a hard task due to the high variability of text appearance: different text fonts, changes in the point of view, or partial occlusion are just a few problems. Text detection can be specially suited for georeferencing business, navigation, tourist assistance, or to help visual impaired people. In this paper, we propose a general methodology to deal with the problem of text detection in outdoor scenes. The method is based on learning spatial information of gradient based features and Census Transform images using a cascade of classifiers. The method is applied in the context of Mobile Mapping systems, where a mobile vehicle captures urban image sequences. Moreover, a cover data set is presented and tested with the new methodology. The results show high accuracy when detecting multi-linear text regions with high variability of appearance, at same time that it preserves a low false alarm rate compared to classical approaches |
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Cardona (Spain) |
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978-1-60750-061-2 |
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CCIA |
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Notes |
OR;MILAB;HuPBA;MV |
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no |
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Call Number |
BCNPCL @ bcnpcl @ EBV2009 |
Serial |
1181 |
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Author |
Sergio Escalera; Oriol Pujol; J. Mauri; Petia Radeva |
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Title |
Intravascular Ultrasound Tissue Characterization with Sub-class Error-Correcting Output Codes |
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Journal Article |
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Year |
2009 |
Publication |
Journal of Signal Processing Systems |
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Volume |
55 |
Issue |
1-3 |
Pages |
35–47 |
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Abstract |
Intravascular ultrasound (IVUS) represents a powerful imaging technique to explore coronary vessels and to study their morphology and histologic properties. In this paper, we characterize different tissues based on radial frequency, texture-based, and combined features. To deal with the classification of multiple tissues, we require the use of robust multi-class learning techniques. In this sense, error-correcting output codes (ECOC) show to robustly combine binary classifiers to solve multi-class problems. In this context, we propose a strategy to model multi-class classification tasks using sub-classes information in the ECOC framework. The new strategy splits the classes into different sub-sets according to the applied base classifier. Complex IVUS data sets containing overlapping data are learnt by splitting the original set of classes into sub-classes, and embedding the binary problems in a problem-dependent ECOC design. The method automatically characterizes different tissues, showing performance improvements over the state-of-the-art ECOC techniques for different base classifiers. Furthermore, the combination of RF and texture-based features also shows improvements over the state-of-the-art approaches. |
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1939-8018 |
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Notes |
MILAB;HuPBA |
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no |
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Call Number |
BCNPCL @ bcnpcl @ EPM2009 |
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1258 |
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Author |
Miguel Reyes; Jordi Vitria; Petia Radeva; Sergio Escalera |
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Title |
Real-time Activity Monitoring of Inpatients |
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Conference Article |
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Year |
2010 |
Publication |
Medical Image Computing in Catalunya: Graduate Student Workshop |
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35–36 |
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In this paper, we present the development of an application capable of monitoring a set of patient vital signs in real time. The application has been designed to support the medical staff of a hospital. Preliminary results show the suitability
of the system to prevent the injury produced by the agitation of the patients. |
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Girona |
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MICCAT |
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OR;MILAB;HUPBA;MV |
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no |
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Call Number |
BCNPCL @ bcnpcl @ RVR2010 |
Serial |
1477 |
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Author |
Maedeh Aghaei; Petia Radeva |
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Title |
Bag-of-Tracklets for Person Tracking in Life-Logging Data |
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Conference Article |
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Year |
2014 |
Publication |
17th International Conference of the Catalan Association for Artificial Intelligence |
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Volume |
269 |
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35-44 |
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By increasing popularity of wearable cameras, life-logging data analysis is becoming more and more important and useful to derive significant events out of this substantial collection of images. In this study, we introduce a new tracking method applied to visual life-logging, called bag-of-tracklets, which is based on detecting, localizing and tracking of people. Given the low spatial and temporal resolution of the image data, our model generates and groups tracklets in a unsupervised framework and extracts image sequences of person appearance according to a similarity score of the bag-of-tracklets. The model output is a meaningful sequence of events expressing human appearance and tracking them in life-logging data. The achieved results prove the robustness of our model in terms of efficiency and accuracy despite the low spatial and temporal resolution of the data. |
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978-1-61499-451-0 |
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CCIA |
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MILAB |
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no |
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Call Number |
Admin @ si @ AgR2015 |
Serial |
2607 |
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Author |
Michael Teutsch; Angel Sappa; Riad I. Hammoud |
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Title |
Detection, Classification, and Tracking |
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Book Chapter |
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Year |
2022 |
Publication |
Computer Vision in the Infrared Spectrum. Synthesis Lectures on Computer Vision |
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35-58 |
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Automatic image and video exploitation or content analysis is a technique to extract higher-level information from a scene such as objects, behavior, (inter-)actions, environment, or even weather conditions. The relevant information is assumed to be contained in the two-dimensional signal provided in an image (width and height in pixels) or the three-dimensional signal provided in a video (width, height, and time). But also intermediate-level information such as object classes [196], locations [197], or motion [198] can help applications to fulfill certain tasks such as intelligent compression [199], video summarization [200], or video retrieval [201]. Usually, videos with their temporal dimension are a richer source of data compared to single images [202] and thus certain video content can be extracted from videos only such as object motion or object behavior. Often, machine learning or nowadays deep learning techniques are utilized to model prior knowledge about object or scene appearance using labeled training samples [203, 204]. After a learning phase, these models are then applied in real world applications, which is called inference. |
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Springer |
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SLCV |
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978-3-031-00698-2 |
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MSIAU; MACO |
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no |
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Call Number |
Admin @ si @ TSH2022c |
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3806 |
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Author |
Simone Balocco; Mauricio Gonzalez; Ricardo Ñancule; Petia Radeva; Gabriel Thomas |
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Title |
Calcified Plaque Detection in IVUS Sequences: Preliminary Results Using Convolutional Nets |
Type |
Conference Article |
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Year |
2018 |
Publication |
International Workshop on Artificial Intelligence and Pattern Recognition |
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Volume |
11047 |
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34-42 |
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Intravascular ultrasound images; Convolutional nets; Deep learning; Medical image analysis |
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The manual inspection of intravascular ultrasound (IVUS) images to detect clinically relevant patterns is a difficult and laborious task performed routinely by physicians. In this paper, we present a framework based on convolutional nets for the quick selection of IVUS frames containing arterial calcification, a pattern whose detection plays a vital role in the diagnosis of atherosclerosis. Preliminary experiments on a dataset acquired from eighty patients show that convolutional architectures improve detections of a shallow classifier in terms of 𝐹1-measure, precision and recall. |
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Cuba; September 2018 |
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LNCS |
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IWAIPR |
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MILAB; no menciona |
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no |
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Call Number |
Admin @ si @ BGÑ2018 |
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3237 |
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Author |
Jialuo Chen; Mohamed Ali Souibgui; Alicia Fornes; Beata Megyesi |
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Title |
Unsupervised Alphabet Matching in Historical Encrypted Manuscript Images |
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Conference Article |
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2021 |
Publication |
4th International Conference on Historical Cryptology |
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34-37 |
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Historical ciphers contain a wide range ofsymbols from various symbol sets. Iden-tifying the cipher alphabet is a prerequi-site before decryption can take place andis a time-consuming process. In this workwe explore the use of image processing foridentifying the underlying alphabet in ci-pher images, and to compare alphabets be-tween ciphers. The experiments show thatciphers with similar alphabets can be suc-cessfully discovered through clustering. |
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Virtual; September 2021 |
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HistoCrypt |
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DAG; 602.230; 600.140; 600.121 |
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no |
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Admin @ si @ CSF2021 |
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3617 |
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Author |
Albert Gordo; Florent Perronnin; Yunchao Gong; Svetlana Lazebnik |
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Title |
Asymmetric Distances for Binary Embeddings |
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Journal Article |
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2014 |
Publication |
IEEE Transactions on Pattern Analysis and Machine Intelligence |
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TPAMI |
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36 |
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1 |
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33-47 |
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In large-scale query-by-example retrieval, embedding image signatures in a binary space offers two benefits: data compression and search efficiency. While most embedding algorithms binarize both query and database signatures, it has been noted that this is not strictly a requirement. Indeed, asymmetric schemes which binarize the database signatures but not the query still enjoy the same two benefits but may provide superior accuracy. In this work, we propose two general asymmetric distances which are applicable to a wide variety of embedding techniques including Locality Sensitive Hashing (LSH), Locality Sensitive Binary Codes (LSBC), Spectral Hashing (SH), PCA Embedding (PCAE), PCA Embedding with random rotations (PCAE-RR), and PCA Embedding with iterative quantization (PCAE-ITQ). We experiment on four public benchmarks containing up to 1M images and show that the proposed asymmetric distances consistently lead to large improvements over the symmetric Hamming distance for all binary embedding techniques. |
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0162-8828 |
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DAG; 600.045; 605.203; 600.077 |
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no |
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Admin @ si @ GPG2014 |
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2272 |
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Author |
Agnes Borras; Josep Llados |
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Title |
Similarity-Based Object Retrieval Using Appearance and Geometric Feature Combination |
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Book Chapter |
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2007 |
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3rd Iberian Conference on Pattern Recognition and Image Analysis (IbPRIA 2007), J. Marti et al. (Eds.) LNCS 4477:113–120 |
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LNCS |
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4478 |
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33–39 |
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This work presents a content-based image retrieval system of general purpose that deals with cluttered scenes containing a given query object. The system is flexible enough to handle with a single image of an object despite its rotation, translation and scale variations. The image content is divided in parts that are described with a combination of features based on geometrical and color properties. The idea behind the feature combination is to benefit from a fuzzy similarity computation that provides robustness and tolerance to the retrieval process. The features can be independently computed and the image parts can be easily indexed by using a table structure on every feature value. Finally a process inspired in the alignment strategies is used to check the coherence of the object parts found in a scene. Our work presents a system of easy implementation that uses an open set of features and can suit a wide variety of applications. |
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Girona (Spain) |
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978-3-540-72848-1 |
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DAG; |
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DAG @ dag @ BoL2007a; IAM @ iam @ BoL2007a |
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776 |
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Author |
Ignasi Rius; Jordi Gonzalez; Mikhail Mozerov; Xavier Roca |
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Title |
Automatic Learning of 3D Pose Variability in Walking Performances for Gait Analysis |
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2008 |
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International Journal for Computational Vision and Biomechanics |
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1 |
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1 |
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33–43 |
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ISE |
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ISE @ ise @ RGM2008 |
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1020 |
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Author |
Javier Vazquez; G. D. Finlayson; Maria Vanrell |
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Title |
A compact singularity function to predict WCS data and unique hues |
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Conference Article |
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2010 |
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5th European Conference on Colour in Graphics, Imaging and Vision and 12th International Symposium on Multispectral Colour Science |
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33–38 |
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Understanding how colour is used by the human vision system is a widely studied research field. The field, though quite advanced, still faces important unanswered questions. One of them is the explanation of the unique hues and the assignment of color names. This problem addresses the fact of different perceptual status for different colors.
Recently, Philipona and O'Regan have proposed a biological model that allows to extract the reflection properties of any surface independently of the lighting conditions. These invariant properties are the basis to compute a singularity index that predicts the asymmetries presented in unique hues and basic color categories psychophysical data, therefore is giving a further step in their explanation.
In this paper we build on their formulation and propose a new singularity index. This new formulation equally accounts for the location of the 4 peaks of the World colour survey and has two main advantages. First, it is a simple elegant numerical measure (the Philipona measurement is a rather cumbersome formula). Second, we develop a colour-based explanation for the measure. |
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Joensuu, Finland |
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9781617388897 |
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CGIV/MCS |
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CAT @ cat @ VFV2010 |
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1324 |
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Author |
Antonio Hernandez; Miguel Reyes; Sergio Escalera; Petia Radeva |
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Spatio-Temporal GrabCut human segmentation for face and pose recovery |
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2010 |
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IEEE International Workshop on Analysis and Modeling of Faces and Gestures |
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33–40 |
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In this paper, we present a full-automatic Spatio-Temporal GrabCut human segmentation methodology. GrabCut initialization is performed by a HOG-based subject detection, face detection, and skin color model for seed initialization. Spatial information is included by means of Mean Shift clustering whereas temporal coherence is considered by the historical of Gaussian Mixture Models. Moreover, human segmentation is combined with Shape and Active Appearance Models to perform full face and pose recovery. Results over public data sets as well as proper human action base show a robust segmentation and recovery of both face and pose using the presented methodology. |
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San Francisco; CA; USA; June 2010 |
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2160-7508 |
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978-1-4244-7029-7 |
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AMFG |
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MILAB;HUPBA |
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BCNPCL @ bcnpcl @ HRE2010 |
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1362 |
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