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Md.Mostafa Kamal Sarker; Mohammed Jabreel; , Hatem A. Rashwan; Syeda Furruka Banu; Petia Radeva; Domenec Puig |
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Title  |
CuisineNet: Food Attributes Classification using Multi-scale Convolution Network |
Type |
Conference Article |
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Year |
2018 |
Publication |
21st International Conference of the Catalan Association for Artificial Intelligence |
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Pages |
365-372 |
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Abstract |
Diversity of food and its attributes represents the culinary habits of peoples from different countries. Thus, this paper addresses the problem of identifying food culture of people around the world and its flavor by classifying two main food attributes, cuisine and flavor. A deep learning model based on multi-scale convotuional networks is proposed for extracting more accurate features from input images. The aggregation of multi-scale convolution layers with different kernel size is also used for weighting the features results from different scales. In addition, a joint loss function based on Negative Log Likelihood (NLL) is used to fit the model probability to multi labeled classes for multi-modal classification task. Furthermore, this work provides a new dataset for food attributes, so-called Yummly48K, extracted from the popular food website, Yummly. Our model is assessed on the constructed Yummly48K dataset. The experimental results show that our proposed method yields 65% and 62% average F1 score on validation and test set which outperforming the state-of-the-art models. |
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Roses; catalonia; October 2018 |
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CCIA |
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MILAB; no menciona |
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no |
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Admin @ si @ SJR2018 |
Serial |
3113 |
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Author |
Md.Mostafa Kamal Sarker; Mohammed Jabreel; Hatem A. Rashwan; Syeda Furruka Banu; Antonio Moreno; Petia Radeva; Domenec Puig |
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Title |
CuisineNet: Food Attributes Classification using Multi-scale Convolution Network. |
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Miscellaneous |
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Year |
2018 |
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Arxiv |
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Abstract |
Diversity of food and its attributes represents the culinary habits of peoples from different countries. Thus, this paper addresses the problem of identifying food culture of people around the world and its flavor by classifying two main food attributes, cuisine and flavor. A deep learning model based on multi-scale convotuional networks is proposed for extracting more accurate features from input images. The aggregation of multi-scale convolution layers with different kernel size is also used for weighting the features results from different scales. In addition, a joint loss function based on Negative Log Likelihood (NLL) is used to fit the model probability to multi labeled classes for multi-modal classification task. Furthermore, this work provides a new dataset for food attributes, so-called Yummly48K, extracted from the popular food website, Yummly. Our model is assessed on the constructed Yummly48K dataset. The experimental results show that our proposed method yields 65% and 62% average F1 score on validation and test set which outperforming the state-of-the-art models. |
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MILAB; no proj |
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Admin @ si @ KJR2018 |
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3235 |
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Author |
Robert Benavente; Laura Igual; Fernando Vilariño |
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Title |
Current Challenges in Computer Vision |
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2008 |
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Proccedings of the Third Internal Workshop |
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978-84-936529-0-6 |
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CVCRD |
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MILAB;CIC;SIAI |
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no |
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BCNPCL @ bcnpcl @ BIV2008 |
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1110 |
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Author |
Jorge Bernal; F. Javier Sanchez; Fernando Vilariño |
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Title |
Current Challenges on Polyp Detection in Colonoscopy Videos: From Region Segmentation to Region Classification. a Pattern Recognition-based Approach.ased Approach |
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Conference Article |
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Year |
2011 |
Publication |
2nd International Workshop on Medical Image Analysis and Descriptionfor Diagnosis Systems |
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62-71 |
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Medical Imaging, Colonoscopy, Pattern Recognition, Segmentation, Polyp Detection, Region Description, Machine Learning, Real-time. |
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Abstract |
In this paper we present our approach on real-time polyp detection in colonoscopy videos. Our method consists of three stages: Image Segmentation, Region Description and Image Classification. Taking into account the constraints of our project, we introduce our segmentation system that is based on the model of appearance of the polyp that we have defined after observing real videos from colonoscopy processes. The output of this stage will ideally be a low number of regions of which one of them should cover the whole polyp region (if there is one in the image). This regions will be described in terms of features and, as a result of a machine learning schema, classified based on the values that they have for the several features that we will use on their description. Although we are still on the early stages of the project, we present some preliminary segmentation results that indicates that we are going in a good direction. |
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Rome, Italy |
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SciTePress |
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Djemal, Khalifa |
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800 |
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MIAD |
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MV;SIAI |
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no |
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Call Number |
IAM @ iam @ BSV2011a |
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1695 |
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Author |
Iban Berganzo-Besga; Hector A. Orengo; Felipe Lumbreras; Aftab Alam; Rosie Campbell; Petrus J Gerrits; Jonas Gregorio de Souza; Afifa Khan; Maria Suarez Moreno; Jack Tomaney; Rebecca C Roberts; Cameron A Petrie |
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Title |
Curriculum learning-based strategy for low-density archaeological mound detection from historical maps in India and Pakistan |
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Journal Article |
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Year |
2023 |
Publication |
Scientific Reports |
Abbreviated Journal |
ScR |
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Volume |
13 |
Issue |
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Pages |
11257 |
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Abstract |
This paper presents two algorithms for the large-scale automatic detection and instance segmentation of potential archaeological mounds on historical maps. Historical maps present a unique source of information for the reconstruction of ancient landscapes. The last 100 years have seen unprecedented landscape modifications with the introduction and large-scale implementation of mechanised agriculture, channel-based irrigation schemes, and urban expansion to name but a few. Historical maps offer a window onto disappearing landscapes where many historical and archaeological elements that no longer exist today are depicted. The algorithms focus on the detection and shape extraction of mound features with high probability of being archaeological settlements, mounds being one of the most commonly documented archaeological features to be found in the Survey of India historical map series, although not necessarily recognised as such at the time of surveying. Mound features with high archaeological potential are most commonly depicted through hachures or contour-equivalent form-lines, therefore, an algorithm has been designed to detect each of those features. Our proposed approach addresses two of the most common issues in archaeological automated survey, the low-density of archaeological features to be detected, and the small amount of training data available. It has been applied to all types of maps available of the historic 1″ to 1-mile series, thus increasing the complexity of the detection. Moreover, the inclusion of synthetic data, along with a Curriculum Learning strategy, has allowed the algorithm to better understand what the mound features look like. Likewise, a series of filters based on topographic setting, form, and size have been applied to improve the accuracy of the models. The resulting algorithms have a recall value of 52.61% and a precision of 82.31% for the hachure mounds, and a recall value of 70.80% and a precision of 70.29% for the form-line mounds, which allowed the detection of nearly 6000 mound features over an area of 470,500 km2, the largest such approach to have ever been applied. If we restrict our focus to the maps most similar to those used in the algorithm training, we reach recall values greater than 60% and precision values greater than 90%. This approach has shown the potential to implement an adaptive algorithm that allows, after a small amount of retraining with data detected from a new map, a better general mound feature detection in the same map. |
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MSIAU |
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no |
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Call Number |
Admin @ si @ BOL2023 |
Serial |
3976 |
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Author |
Debora Gil; Petia Radeva |
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Title |
Curvature based Distance Maps |
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Report |
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2003 |
Publication |
CVC Technical Report |
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70 |
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Computer Vision Center |
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IAM;MILAB |
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no |
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Call Number |
IAM @ iam @ GIR2003a |
Serial |
1534 |
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Author |
Debora Gil; Petia Radeva |
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Title |
Curvature Vector Flow to Assure Convergent Deformable Models for Shape Modelling |
Type |
Book Chapter |
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Year |
2003 |
Publication |
Energy Minimization Methods In Computer Vision And Pattern Recognition |
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LNCS |
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2683 |
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357-372 |
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Keywords |
Initial condition; Convex shape; Non convex analysis; Increase; Segmentation; Gradient; Standard; Standards; Concave shape; Flow models; Tracking; Edge detection; Curvature |
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Abstract |
Poor convergence to concave shapes is a main limitation of snakes as a standard segmentation and shape modelling technique. The gradient of the external energy of the snake represents a force that pushes the snake into concave regions, as its internal energy increases when new inexion points are created. In spite of the improvement of the external energy by the gradient vector ow technique, highly non convex shapes can not be obtained, yet. In the present paper, we develop a new external energy based on the geometry of the curve to be modelled. By tracking back the deformation of a curve that evolves by minimum curvature ow, we construct a distance map that encapsulates the natural way of adapting to non convex shapes. The gradient of this map, which we call curvature vector ow (CVF), is capable of attracting a snake towards any contour, whatever its geometry. Our experiments show that, any initial snake condition converges to the curve to be modelled in optimal time. |
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Springer, Berlin |
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Lisbon, PORTUGAL |
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Springer, B. |
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Lecture Notes in Computer Science |
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LNCS |
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0302-9743 |
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3-540-40498-8 |
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IAM;MILAB |
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no |
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IAM @ iam @ GIR2003b |
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1535 |
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Author |
Lluis Gomez; Marçal Rusiñol; Dimosthenis Karatzas |
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Title |
Cutting Sayre's Knot: Reading Scene Text without Segmentation. Application to Utility Meters |
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Conference Article |
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2018 |
Publication |
13th IAPR International Workshop on Document Analysis Systems |
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97-102 |
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Robust Reading; End-to-end Systems; CNN; Utility Meters |
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Abstract |
In this paper we present a segmentation-free system for reading text in natural scenes. A CNN architecture is trained in an end-to-end manner, and is able to directly output readings without any explicit text localization step. In order to validate our proposal, we focus on the specific case of reading utility meters. We present our results in a large dataset of images acquired by different users and devices, so text appears in any location, with different sizes, fonts and lengths, and the images present several distortions such as
dirt, illumination highlights or blur. |
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Viena; Austria; April 2018 |
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DAS |
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DAG; 600.084; 600.121; 600.129 |
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Call Number |
Admin @ si @ GRK2018 |
Serial |
3102 |
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Author |
Lluis Pere de las Heras; Oriol Ramos Terrades; Sergi Robles; Gemma Sanchez |
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Title |
CVC-FP and SGT: a new database for structural floor plan analysis and its groundtruthing tool |
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Journal Article |
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Year |
2015 |
Publication |
International Journal on Document Analysis and Recognition |
Abbreviated Journal |
IJDAR |
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18 |
Issue |
1 |
Pages |
15-30 |
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Abstract |
Recent results on structured learning methods have shown the impact of structural information in a wide range of pattern recognition tasks. In the field of document image analysis, there is a long experience on structural methods for the analysis and information extraction of multiple types of documents. Yet, the lack of conveniently annotated and free access databases has not benefited the progress in some areas such as technical drawing understanding. In this paper, we present a floor plan database, named CVC-FP, that is annotated for the architectural objects and their structural relations. To construct this database, we have implemented a groundtruthing tool, the SGT tool, that allows to make specific this sort of information in a natural manner. This tool has been made for general purpose groundtruthing: It allows to define own object classes and properties, multiple labeling options are possible, grants the cooperative work, and provides user and version control. We finally have collected some of the recent work on floor plan interpretation and present a quantitative benchmark for this database. Both CVC-FP database and the SGT tool are freely released to the research community to ease comparisons between methods and boost reproducible research. |
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Springer Berlin Heidelberg |
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1433-2833 |
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DAG; ADAS; 600.061; 600.076; 600.077 |
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no |
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Call Number |
Admin @ si @ HRR2015 |
Serial |
2567 |
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Author |
Alicia Fornes; Anjan Dutta; Albert Gordo; Josep Llados |
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Title |
CVC-MUSCIMA: A Ground-Truth of Handwritten Music Score Images for Writer Identification and Staff Removal |
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Journal Article |
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Year |
2012 |
Publication |
International Journal on Document Analysis and Recognition |
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IJDAR |
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15 |
Issue |
3 |
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243-251 |
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Music scores; Handwritten documents; Writer identification; Staff removal; Performance evaluation; Graphics recognition; Ground truths |
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Abstract |
0,405JCR
The analysis of music scores has been an active research field in the last decades. However, there are no publicly available databases of handwritten music scores for the research community. In this paper we present the CVC-MUSCIMA database and ground-truth of handwritten music score images. The dataset consists of 1,000 music sheets written by 50 different musicians. It has been especially designed for writer identification and staff removal tasks. In addition to the description of the dataset, ground-truth, partitioning and evaluation metrics, we also provide some base-line results for easing the comparison between different approaches. |
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1433-2833 |
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DAG |
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Admin @ si @ FDG2012 |
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2129 |
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Marçal Rusiñol; Lluis Pere de las Heras; Joan Mas; Oriol Ramos Terrades; Dimosthenis Karatzas; Anjan Dutta; Gemma Sanchez; Josep Llados |
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Title |
CVC-UAB's participation in the Flowchart Recognition Task of CLEF-IP 2012 |
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Conference Article |
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2012 |
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Conference and Labs of the Evaluation Forum |
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Roma |
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CLEF |
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DAG |
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Admin @ si @ RHM2012 |
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2072 |
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Author |
Debora Gil; Aura Hernandez-Sabate; David Castells; Jordi Carrabina |
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Title |
CYBERH: Cyber-Physical Systems in Health for Personalized Assistance |
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Conference Article |
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Year |
2017 |
Publication |
International Symposium on Symbolic and Numeric Algorithms for Scientific Computing |
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Assistance systems for e-Health applications have some specific requirements that demand of new methods for data gathering, analysis and modeling able to deal with SmallData:
1) systems should dynamically collect data from, both, the environment and the user to issue personalized recommendations; 2) data analysis should be able to tackle a limited number of samples prone to include non-informative data and possibly evolving in time due to changes in patient condition; 3) algorithms should run in real time with possibly limited computational resources and fluctuant internet access.
Electronic medical devices (and CyberPhysical devices in general) can enhance the process of data gathering and analysis in several ways: (i) acquiring simultaneously multiple sensors data instead of single magnitudes (ii) filtering data; (iii) providing real-time implementations condition by isolating tasks in individual processors of multiprocessors Systems-on-chip (MPSoC) platforms and (iv) combining information through sensor fusion
techniques.
Our approach focus on both aspects of the complementary role of CyberPhysical devices and analysis of SmallData in the process of personalized models building for e-Health applications. In particular, we will address the design of Cyber-Physical Systems in Health for Personalized Assistance (CyberHealth) in two specific application cases: 1) A Smart Assisted Driving System (SADs) for dynamical assessment of the driving capabilities of Mild Cognitive Impaired (MCI) people; 2) An Intelligent Operating Room (iOR) for improving the yield of bronchoscopic interventions for in-vivo lung cancer diagnosis. |
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Timisoara; Rumania; September 2017 |
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SYNASC |
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IAM; 600.085; 600.096; 600.075; 600.145 |
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no |
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Admin @ si @ GHC2017 |
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3045 |
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Patricia Suarez; Angel Sappa; Boris X. Vintimilla; Riad I. Hammoud |
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Title |
Cycle Generative Adversarial Network: Towards A Low-Cost Vegetation Index Estimation |
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Conference Article |
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2021 |
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28th IEEE International Conference on Image Processing |
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19-22 |
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This paper presents a novel unsupervised approach to estimate the Normalized Difference Vegetation Index (NDVI). The NDVI is obtained as the ratio between information from the visible and near infrared spectral bands; in the current work, the NDVI is estimated just from an image of the visible spectrum through a Cyclic Generative Adversarial Network (CyclicGAN). This unsupervised architecture learns to estimate the NDVI index by means of an image translation between the red channel of a given RGB image and the NDVI unpaired index’s image. The translation is obtained by means of a ResNET architecture and a multiple loss function. Experimental results obtained with this unsupervised scheme show the validity of the implemented model. Additionally, comparisons with the state of the art approaches are provided showing improvements with the proposed approach. |
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Anchorage-Alaska; USA; September 2021 |
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MSIAU; 600.130; 600.122; 601.349 |
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Admin @ si @ SSV2021b |
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3579 |
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Author |
Jiaolong Xu; Sebastian Ramos; David Vazquez; Antonio Lopez |
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Title |
DA-DPM Pedestrian Detection |
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Conference Article |
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2013 |
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ICCV Workshop on Reconstruction meets Recognition |
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Domain Adaptation; Pedestrian Detection |
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no |
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Admin @ si @ XRV2013 |
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2569 |
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Aleksandr Setkov; Fabio Martinez Carillo; Michele Gouiffes; Christian Jacquemin; Maria Vanrell; Ramon Baldrich |
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DAcImPro: A Novel Database of Acquired Image Projections and Its Application to Object Recognition |
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Conference Article |
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2015 |
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Advances in Visual Computing. Proceedings of 11th International Symposium, ISVC 2015 Part II |
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9475 |
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463-473 |
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Projector-camera systems; Feature descriptors; Object recognition |
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Projector-camera systems are designed to improve the projection quality by comparing original images with their captured projections, which is usually complicated due to high photometric and geometric variations. Many research works address this problem using their own test data which makes it extremely difficult to compare different proposals. This paper has two main contributions. Firstly, we introduce a new database of acquired image projections (DAcImPro) that, covering photometric and geometric conditions and providing data for ground-truth computation, can serve to evaluate different algorithms in projector-camera systems. Secondly, a new object recognition scenario from acquired projections is presented, which could be of a great interest in such domains, as home video projections and public presentations. We show that the task is more challenging than the classical recognition problem and thus requires additional pre-processing, such as color compensation or projection area selection. |
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Springer International Publishing |
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0302-9743 |
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978-3-319-27862-9 |
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ISVC |
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Admin @ si @ SMG2015 |
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2736 |
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