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Author | German Ros | ||||
Title | Visual Scene Understanding for Autonomous Vehicles: Understanding Where and What | Type | Book Whole | ||
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2016 | Publication | PhD Thesis, Universitat Autonoma de Barcelona-CVC | Abbreviated Journal | |
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Abstract | Making Ground Autonomous Vehicles (GAVs) a reality as a service for the society is one of the major scientific and technological challenges of this century. The potential benefits of autonomous vehicles include reducing accidents, improving traffic congestion and better usage of road infrastructures, among others. These vehicles must operate in our cities, towns and highways, dealing with many different types of situations while respecting traffic rules and protecting human lives. GAVs are expected to deal with all types of scenarios and situations, coping with an uncertain and chaotic world.
Therefore, in order to fulfill these demanding requirements GAVs need to be endowed with the capability of understanding their surrounding at many different levels, by means of affordable sensors and artificial intelligence. This capacity to understand the surroundings and the current situation that the vehicle is involved in is called scene understanding. In this work we investigate novel techniques to bring scene understanding to autonomous vehicles by combining the use of cameras as the main source of information—due to their versatility and affordability—and algorithms based on computer vision and machine learning. We investigate different degrees of understanding of the scene, starting from basic geometric knowledge about where is the vehicle within the scene. A robust and efficient estimation of the vehicle location and pose with respect to a map is one of the most fundamental steps towards autonomous driving. We study this problem from the point of view of robustness and computational efficiency, proposing key insights to improve current solutions. Then we advance to higher levels of abstraction to discover what is in the scene, by recognizing and parsing all the elements present on a driving scene, such as roads, sidewalks, pedestrians, etc. We investigate this problem known as semantic segmentation, proposing new approaches to improve recognition accuracy and computational efficiency. We cover these points by focusing on key aspects such as: (i) how to leverage computation moving semantics to an offline process, (ii) how to train compact architectures based on deconvolutional networks to achieve their maximum potential, (iii) how to use virtual worlds in combination with domain adaptation to produce accurate models in a cost-effective fashion, and (iv) how to use transfer learning techniques to prepare models to new situations. We finally extend the previous level of knowledge enabling systems to reasoning about what has change in a scene with respect to a previous visit, which in return allows for efficient and cost-effective map updating. |
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Corporate Author | Thesis | Ph.D. thesis | |||
Publisher | Ediciones Graficas Rey | Place of Publication | Editor | Angel Sappa;Julio Guerrero;Antonio Lopez | |
Language | Summary Language | Original Title | |||
Series Editor | Series Title | Abbreviated Series Title | |||
Series Volume | Series Issue | Edition | |||
ISSN | ISBN | 978-84-945373-1-8 | Medium | ||
Area | Expedition | Conference | |||
Notes | ADAS | Approved | no | ||
Call Number | Admin @ si @ Ros2016 | Serial | 2860 | ||
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Author | Francisco Cruz | ||||
Title | Probabilistic Graphical Models for Document Analysis | Type | Book Whole | ||
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2016 | Publication | PhD Thesis, Universitat Autonoma de Barcelona-CVC | Abbreviated Journal | |
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Abstract | Latest advances in digitization techniques have fostered the interest in creating digital copies of collections of documents. Digitized documents permit an easy maintenance, loss-less storage, and efficient ways for transmission and to perform information retrieval processes. This situation has opened a new market niche to develop systems able to automatically extract and analyze information contained in these collections, specially in the ambit of the business activity.
Due to the great variety of types of documents this is not a trivial task. For instance, the automatic extraction of numerical data from invoices differs substantially from a task of text recognition in historical documents. However, in order to extract the information of interest, is always necessary to identify the area of the document where it is located. In the area of Document Analysis we refer to this process as layout analysis, which aims at identifying and categorizing the different entities that compose the document, such as text regions, pictures, text lines, or tables, among others. To perform this task it is usually necessary to incorporate a prior knowledge about the task into the analysis process, which can be modeled by defining a set of contextual relations between the different entities of the document. The use of context has proven to be useful to reinforce the recognition process and improve the results on many computer vision tasks. It presents two fundamental questions: What kind of contextual information is appropriate for a given task, and how to incorporate this information into the models. In this thesis we study several ways to incorporate contextual information to the task of document layout analysis, and to the particular case of handwritten text line segmentation. We focus on the study of Probabilistic Graphical Models and other mechanisms for this purpose, and propose several solutions to these problems. First, we present a method for layout analysis based on Conditional Random Fields. With this model we encode local contextual relations between variables, such as pair-wise constraints. Besides, we encode a set of structural relations between different classes of regions at feature level. Second, we present a method based on 2D-Probabilistic Context-free Grammars to encode structural and hierarchical relations. We perform a comparative study between Probabilistic Graphical Models and this syntactic approach. Third, we propose a method for structured documents based on Bayesian Networks to represent the document structure, and an algorithm based in the Expectation-Maximization to find the best configuration of the page. We perform a thorough evaluation of the proposed methods on two particular collections of documents: a historical collection composed of ancient structured documents, and a collection of contemporary documents. In addition, we present a general method for the task of handwritten text line segmentation. We define a probabilistic framework where we combine the EM algorithm with variational approaches for computing inference and parameter learning on a Markov Random Field. We evaluate our method on several collections of documents, including a general dataset of annotated administrative documents. Results demonstrate the applicability of our method to real problems, and the contribution of the use of contextual information to this kind of problems. |
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Corporate Author | Thesis | Ph.D. thesis | |||
Publisher | Ediciones Graficas Rey | Place of Publication | Editor | Oriol Ramos Terrades | |
Language | Summary Language | Original Title | |||
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Series Volume | Series Issue | Edition | |||
ISSN | ISBN | 978-84-945373-2-5 | Medium | ||
Area | Expedition | Conference | |||
Notes | DAG | Approved | no | ||
Call Number | Admin @ si @ Cru2016 | Serial | 2861 | ||
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Author | Lluis Gomez; Dimosthenis Karatzas | ||||
Title | A fast hierarchical method for multi‐script and arbitrary oriented scene text extraction | Type | Journal Article | ||
Year ![]() |
2016 | Publication | International Journal on Document Analysis and Recognition | Abbreviated Journal | IJDAR |
Volume | 19 | Issue | 4 | Pages | 335-349 |
Keywords | scene text; segmentation; detection; hierarchical grouping; perceptual organisation | ||||
Abstract | Typography and layout lead to the hierarchical organisation of text in words, text lines, paragraphs. This inherent structure is a key property of text in any script and language, which has nonetheless been minimally leveraged by existing text detection methods. This paper addresses the problem of text
segmentation in natural scenes from a hierarchical perspective. Contrary to existing methods, we make explicit use of text structure, aiming directly to the detection of region groupings corresponding to text within a hierarchy produced by an agglomerative similarity clustering process over individual regions. We propose an optimal way to construct such an hierarchy introducing a feature space designed to produce text group hypotheses with high recall and a novel stopping rule combining a discriminative classifier and a probabilistic measure of group meaningfulness based in perceptual organization. Results obtained over four standard datasets, covering text in variable orientations and different languages, demonstrate that our algorithm, while being trained in a single mixed dataset, outperforms state of the art methods in unconstrained scenarios. |
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Notes | DAG; 600.056; 601.197 | Approved | no | ||
Call Number | Admin @ si @ GoK2016a | Serial | 2862 | ||
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Author | Lluis Gomez; Dimosthenis Karatzas | ||||
Title | A fine-grained approach to scene text script identification | Type | Conference Article | ||
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2016 | Publication | 12th IAPR Workshop on Document Analysis Systems | Abbreviated Journal | |
Volume | Issue | Pages | 192-197 | ||
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Abstract | This paper focuses on the problem of script identification in unconstrained scenarios. Script identification is an important prerequisite to recognition, and an indispensable condition for automatic text understanding systems designed for multi-language environments. Although widely studied for document images and handwritten documents, it remains an almost unexplored territory for scene text images. We detail a novel method for script identification in natural images that combines convolutional features and the Naive-Bayes Nearest Neighbor classifier. The proposed framework efficiently exploits the discriminative power of small stroke-parts, in a fine-grained classification framework. In addition, we propose a new public benchmark dataset for the evaluation of joint text detection and script identification in natural scenes. Experiments done in this new dataset demonstrate that the proposed method yields state of the art results, while it generalizes well to different datasets and variable number of scripts. The evidence provided shows that multi-lingual scene text recognition in the wild is a viable proposition. Source code of the proposed method is made available online. | ||||
Address | Santorini; Grecia; April 2016 | ||||
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ISSN | ISBN | Medium | |||
Area | Expedition | Conference | DAS | ||
Notes | DAG; 601.197; 600.084 | Approved | no | ||
Call Number | Admin @ si @ GoK2016b | Serial | 2863 | ||
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Author | Aura Hernandez-Sabate; Lluis Albarracin; Daniel Calvo; Nuria Gorgorio | ||||
Title | EyeMath: Identifying Mathematics Problem Solving Processes in a RTS Video Game | Type | Conference Article | ||
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2016 | Publication | 5th International Conference Games and Learning Alliance | Abbreviated Journal | |
Volume | 10056 | Issue | Pages | 50-59 | |
Keywords | Simulation environment; Automated Driving; Driver-Vehicle interaction | ||||
Abstract | Photorealistic virtual environments are crucial for developing and testing automated driving systems in a safe way during trials. As commercially available simulators are expensive and bulky, this paper presents a low-cost, extendable, and easy-to-use (LEE) virtual environment with the aim to highlight its utility for level 3 driving automation. In particular, an experiment is performed using the presented simulator to explore the influence of different variables regarding control transfer of the car after the system was driving autonomously in a highway scenario. The results show that the speed of the car at the time when the system needs to transfer the control to the human driver is critical. | ||||
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Language | Summary Language | Original Title | |||
Series Editor | Series Title | Abbreviated Series Title | LNCS | ||
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ISSN | ISBN | Medium | |||
Area | Expedition | Conference | GALA | ||
Notes | ADAS;IAM; | Approved | no | ||
Call Number | HAC2016 | Serial | 2864 | ||
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Author | Saad Minhas; Aura Hernandez-Sabate; Shoaib Ehsan; Katerine Diaz; Ales Leonardis; Antonio Lopez; Klaus McDonald Maier | ||||
Title | LEE: A photorealistic Virtual Environment for Assessing Driver-Vehicle Interactions in Self-Driving Mode | Type | Conference Article | ||
Year ![]() |
2016 | Publication | 14th European Conference on Computer Vision Workshops | Abbreviated Journal | |
Volume | 9915 | Issue | Pages | 894-900 | |
Keywords | Simulation environment; Automated Driving; Driver-Vehicle interaction | ||||
Abstract | Photorealistic virtual environments are crucial for developing and testing automated driving systems in a safe way during trials. As commercially available simulators are expensive and bulky, this paper presents a low-cost, extendable, and easy-to-use (LEE) virtual environment with the aim to highlight its utility for level 3 driving automation. In particular, an experiment is performed using the presented simulator to explore the influence of different variables regarding control transfer of the car after the system was driving autonomously in a highway scenario. The results show that the speed of the car at the time when the system needs to transfer the control to the human driver is critical. | ||||
Address | Amsterdam; The Netherlands; October 2016 | ||||
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Language | Summary Language | Original Title | |||
Series Editor | Series Title | Abbreviated Series Title | LNCS | ||
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Area | Expedition | Conference | ECCVW | ||
Notes | ADAS;IAM; 600.085; 600.076 | Approved | no | ||
Call Number | MHE2016 | Serial | 2865 | ||
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Author | Arash Akbarinia; C. Alejandro Parraga | ||||
Title | Biologically plausible boundary detection | Type | Conference Article | ||
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2016 | Publication | 27th British Machine Vision Conference | Abbreviated Journal | |
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Abstract | Edges are key components of any visual scene to the extent that we can recognise objects merely by their silhouettes. The human visual system captures edge information through neurons in the visual cortex that are sensitive to both intensity discontinuities and particular orientations. The “classical approach” assumes that these cells are only responsive to the stimulus present within their receptive fields, however, recent studies demonstrate that surrounding regions and inter-areal feedback connections influence their responses significantly. In this work we propose a biologically-inspired edge detection model in which orientation selective neurons are represented through the first derivative of a Gaussian function resembling double-opponent cells in the primary visual cortex (V1). In our model we account for four kinds of surround, i.e. full, far, iso- and orthogonal-orientation, whose contributions are contrast-dependant. The output signal from V1 is pooled in its perpendicular direction by larger V2 neurons employing a contrast-variant centre-surround kernel. We further introduce a feedback connection from higher-level visual areas to the lower ones. The results of our model on two benchmark datasets show a big improvement compared to the current non-learning and biologically-inspired state-of-the-art algorithms while being competitive to the learning-based methods. | ||||
Address | York; UK; September 2016 | ||||
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Language | Summary Language | Original Title | |||
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Area | Expedition | Conference | BMVC | ||
Notes | NEUROBIT; 600.068; 600.072 | Approved | no | ||
Call Number | Admin @ si @ AkP2016a | Serial | 2867 | ||
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Author | Azadeh S. Mozafari; David Vazquez; Mansour Jamzad; Antonio Lopez | ||||
Title | Node-Adapt, Path-Adapt and Tree-Adapt:Model-Transfer Domain Adaptation for Random Forest | Type | Miscellaneous | ||
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2016 | Publication | Arxiv | Abbreviated Journal | |
Volume | Issue | Pages | |||
Keywords | Domain Adaptation; Pedestrian detection; Random Forest | ||||
Abstract | Random Forest (RF) is a successful paradigm for learning classifiers due to its ability to learn from large feature spaces and seamlessly integrate multi-class classification, as well as the achieved accuracy and processing efficiency. However, as many other classifiers, RF requires domain adaptation (DA) provided that there is a mismatch between the training (source) and testing (target) domains which provokes classification degradation. Consequently, different RF-DA methods have been proposed, which not only require target-domain samples but revisiting the source-domain ones, too. As novelty, we propose three inherently different methods (Node-Adapt, Path-Adapt and Tree-Adapt) that only require the learned source-domain RF and a relatively few target-domain samples for DA, i.e. source-domain samples do not need to be available. To assess the performance of our proposals we focus on image-based object detection, using the pedestrian detection problem as challenging proof-of-concept. Moreover, we use the RF with expert nodes because it is a competitive patch-based pedestrian model. We test our Node-, Path- and Tree-Adapt methods in standard benchmarks, showing that DA is largely achieved. | ||||
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Notes | ADAS | Approved | no | ||
Call Number | ADAS @ adas @ MVJ2016 | Serial | 2868 | ||
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Author | Youssef El Rhabi; Simon Loic; Brun Luc; Josep Llados; Felipe Lumbreras | ||||
Title | Information Theoretic Rotationwise Robust Binary Descriptor Learning | Type | Conference Article | ||
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2016 | Publication | Joint IAPR International Workshops on Statistical Techniques in Pattern Recognition (SPR) and Structural and Syntactic Pattern Recognition (SSPR) | Abbreviated Journal | |
Volume | Issue | Pages | 368-378 | ||
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Abstract | In this paper, we propose a new data-driven approach for binary descriptor selection. In order to draw a clear analysis of common designs, we present a general information-theoretic selection paradigm. It encompasses several standard binary descriptor construction schemes, including a recent state-of-the-art one named BOLD. We pursue the same endeavor to increase the stability of the produced descriptors with respect to rotations. To achieve this goal, we have designed a novel offline selection criterion which is better adapted to the online matching procedure. The effectiveness of our approach is demonstrated on two standard datasets, where our descriptor is compared to BOLD and to several classical descriptors. In particular, it emerges that our approach can reproduce equivalent if not better performance as BOLD while relying on twice shorter descriptors. Such an improvement can be influential for real-time applications. | ||||
Address | Mérida; Mexico; November 2016 | ||||
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Area | Expedition | Conference | S+SSPR | ||
Notes | DAG; ADAS; 600.097; 600.086 | Approved | no | ||
Call Number | Admin @ si @ RLL2016 | Serial | 2871 | ||
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Author | Anjan Dutta; Umapada Pal; Josep Llados | ||||
Title | Compact Correlated Features for Writer Independent Signature Verification | Type | Conference Article | ||
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2016 | Publication | 23rd International Conference on Pattern Recognition | Abbreviated Journal | |
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Abstract | This paper considers the offline signature verification problem which is considered to be an important research line in the field of pattern recognition. In this work we propose hybrid features that consider the local features and their global statistics in the signature image. This has been done by creating a vocabulary of histogram of oriented gradients (HOGs). We impose weights on these local features based on the height information of water reservoirs obtained from the signature. Spatial information between local features are thought to play a vital role in considering the geometry of the signatures which distinguishes the originals from the forged ones. Nevertheless, learning a condensed set of higher order neighbouring features based on visual words, e.g., doublets and triplets, continues to be a challenging problem as possible combinations of visual words grow exponentially. To avoid this explosion of size, we create a code of local pairwise features which are represented as joint descriptors. Local features are paired based on the edges of a graph representation built upon the Delaunay triangulation. We reveal the advantage of combining both type of visual codebooks (order one and pairwise) for signature verification task. This is validated through an encouraging result on two benchmark datasets viz. CEDAR and GPDS300. | ||||
Address | Cancun; Mexico; December 2016 | ||||
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Language | Summary Language | Original Title | |||
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ISSN | ISBN | Medium | |||
Area | Expedition | Conference | ICPR | ||
Notes | DAG; 600.097 | Approved | no | ||
Call Number | Admin @ si @ DPL2016 | Serial | 2875 | ||
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Author | Sounak Dey; Anguelos Nicolaou; Josep Llados; Umapada Pal | ||||
Title | Local Binary Pattern for Word Spotting in Handwritten Historical Document | Type | Conference Article | ||
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2016 | Publication | Joint IAPR International Workshops on Statistical Techniques in Pattern Recognition (SPR) and Structural and Syntactic Pattern Recognition (SSPR) | Abbreviated Journal | |
Volume | Issue | Pages | 574-583 | ||
Keywords | Local binary patterns; Spatial sampling; Learning-free; Word spotting; Handwritten; Historical document analysis; Large-scale data | ||||
Abstract | Digital libraries store images which can be highly degraded and to index this kind of images we resort to word spotting as our information retrieval system. Information retrieval for handwritten document images is more challenging due to the difficulties in complex layout analysis, large variations of writing styles, and degradation or low quality of historical manuscripts. This paper presents a simple innovative learning-free method for word spotting from large scale historical documents combining Local Binary Pattern (LBP) and spatial sampling. This method offers three advantages: firstly, it operates in completely learning free paradigm which is very different from unsupervised learning methods, secondly, the computational time is significantly low because of the LBP features, which are very fast to compute, and thirdly, the method can be used in scenarios where annotations are not available. Finally, we compare the results of our proposed retrieval method with other methods in the literature and we obtain the best results in the learning free paradigm. | ||||
Address | Merida; Mexico; December 2016 | ||||
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Publisher | Place of Publication | Editor | |||
Language | Summary Language | Original Title | |||
Series Editor | Series Title | Abbreviated Series Title | LNCS | ||
Series Volume | Series Issue | Edition | |||
ISSN | ISBN | Medium | |||
Area | Expedition | Conference | S+SSPR | ||
Notes | DAG; 600.097; 602.006; 603.053 | Approved | no | ||
Call Number | Admin @ si @ DNL2016 | Serial | 2876 | ||
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Author | Juan Ignacio Toledo; Sebastian Sudholt; Alicia Fornes; Jordi Cucurull; A. Fink; Josep Llados | ||||
Title | Handwritten Word Image Categorization with Convolutional Neural Networks and Spatial Pyramid Pooling | Type | Conference Article | ||
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2016 | Publication | Joint IAPR International Workshops on Statistical Techniques in Pattern Recognition (SPR) and Structural and Syntactic Pattern Recognition (SSPR) | Abbreviated Journal | |
Volume | 10029 | Issue | Pages | 543-552 | |
Keywords | Document image analysis; Word image categorization; Convolutional neural networks; Named entity detection | ||||
Abstract | The extraction of relevant information from historical document collections is one of the key steps in order to make these documents available for access and searches. The usual approach combines transcription and grammars in order to extract semantically meaningful entities. In this paper, we describe a new method to obtain word categories directly from non-preprocessed handwritten word images. The method can be used to directly extract information, being an alternative to the transcription. Thus it can be used as a first step in any kind of syntactical analysis. The approach is based on Convolutional Neural Networks with a Spatial Pyramid Pooling layer to deal with the different shapes of the input images. We performed the experiments on a historical marriage record dataset, obtaining promising results. | ||||
Address | Merida; Mexico; December 2016 | ||||
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Publisher | Springer International Publishing | Place of Publication | Editor | ||
Language | Summary Language | Original Title | |||
Series Editor | Series Title | Abbreviated Series Title | LNCS | ||
Series Volume | Series Issue | Edition | |||
ISSN | ISBN | 978-3-319-49054-0 | Medium | ||
Area | Expedition | Conference | S+SSPR | ||
Notes | DAG; 600.097; 602.006 | Approved | no | ||
Call Number | Admin @ si @ TSF2016 | Serial | 2877 | ||
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Author | Antoni Gurgui; Debora Gil; Enric Marti; Vicente Grau | ||||
Title | Left-Ventricle Basal Region Constrained Parametric Mapping to Unitary Domain | Type | Conference Article | ||
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2016 | Publication | 7th International Workshop on Statistical Atlases & Computational Modelling of the Heart | Abbreviated Journal | |
Volume | 10124 | Issue | Pages | 163-171 | |
Keywords | Laplacian; Constrained maps; Parameterization; Basal ring | ||||
Abstract | Due to its complex geometry, the basal ring is often omitted when putting different heart geometries into correspondence. In this paper, we present the first results on a new mapping of the left ventricle basal rings onto a normalized coordinate system using a fold-over free approach to the solution to the Laplacian. To guarantee correspondences between different basal rings, we imposed some internal constrained positions at anatomical landmarks in the normalized coordinate system. To prevent internal fold-overs, constraints are handled by cutting the volume into regions defined by anatomical features and mapping each piece of the volume separately. Initial results presented in this paper indicate that our method is able to handle internal constrains without introducing fold-overs and thus guarantees one-to-one mappings between different basal ring geometries. | ||||
Address | Athens; October 2016 | ||||
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Series Editor | Series Title | Abbreviated Series Title | LNCS | ||
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ISSN | ISBN | Medium | |||
Area | Expedition | Conference | STACOM | ||
Notes | IAM; | Approved | no | ||
Call Number | Admin @ si @ GGM2016 | Serial | 2884 | ||
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Author | Carles Sanchez; Debora Gil; Jorge Bernal; F. Javier Sanchez; Marta Diez-Ferrer; Antoni Rosell | ||||
Title | Navigation Path Retrieval from Videobronchoscopy using Bronchial Branches | Type | Conference Article | ||
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2016 | Publication | 19th International Conference on Medical Image Computing and Computer Assisted Intervention Workshops | Abbreviated Journal | |
Volume | 9401 | Issue | Pages | 62-70 | |
Keywords | Bronchoscopy navigation; Lumen center; Brochial branches; Navigation path; Videobronchoscopy | ||||
Abstract | Bronchoscopy biopsy can be used to diagnose lung cancer without risking complications of other interventions like transthoracic needle aspiration. During bronchoscopy, the clinician has to navigate through the bronchial tree to the target lesion. A main drawback is the difficulty to check whether the exploration is following the correct path. The usual guidance using fluoroscopy implies repeated radiation of the clinician, while alternative systems (like electromagnetic navigation) require specific equipment that increases intervention costs. We propose to compute the navigated path using anatomical landmarks extracted from the sole analysis of videobronchoscopy images. Such landmarks allow matching the current exploration to the path previously planned on a CT to indicate clinician whether the planning is being correctly followed or not. We present a feasibility study of our landmark based CT-video matching using bronchoscopic videos simulated on a virtual bronchoscopy interactive interface. | ||||
Address | Quebec; Canada; September 2016 | ||||
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Series Editor | Series Title | Abbreviated Series Title | LNCS | ||
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ISSN | ISBN | Medium | |||
Area | Expedition | Conference | MICCAIW | ||
Notes | IAM; MV; 600.060; 600.075 | Approved | no | ||
Call Number | Admin @ si @ SGB2016 | Serial | 2885 | ||
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Author | Juan A. Carvajal Ayala; Dennis Romero; Angel Sappa | ||||
Title | Fine-tuning based deep convolutional networks for lepidopterous genus recognition | Type | Conference Article | ||
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2016 | Publication | 21st Ibero American Congress on Pattern Recognition | Abbreviated Journal | |
Volume | Issue | Pages | 467-475 | ||
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Abstract | This paper describes an image classification approach oriented to identify specimens of lepidopterous insects at Ecuadorian ecological reserves. This work seeks to contribute to studies in the area of biology about genus of butterflies and also to facilitate the registration of unrecognized specimens. The proposed approach is based on the fine-tuning of three widely used pre-trained Convolutional Neural Networks (CNNs). This strategy is intended to overcome the reduced number of labeled images. Experimental results with a dataset labeled by expert biologists is presented, reaching a recognition accuracy above 92%. | ||||
Address | Lima; Perú; November 2016 | ||||
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Publisher | Place of Publication | Editor | |||
Language | Summary Language | Original Title | |||
Series Editor | Series Title | Abbreviated Series Title | LNCS | ||
Series Volume | Series Issue | Edition | |||
ISSN | ISBN | Medium | |||
Area | Expedition | Conference | CIARP | ||
Notes | ADAS; 600.086 | Approved | no | ||
Call Number | Admin @ si @ CRS2016 | Serial | 2913 | ||
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