ChunYang, Xu Cheng Yin, Hong Yu, Dimosthenis Karatzas, & Yu Cao. (2017). ICDAR2017 Robust Reading Challenge on Text Extraction from Biomedical Literature Figures (DeTEXT). In 14th International Conference on Document Analysis and Recognition (pp. 1444–1447).
Abstract: Hundreds of millions of figures are available in the biomedical literature, representing important biomedical experimental evidence. Since text is a rich source of information in figures, automatically extracting such text may assist in the task of mining figure information and understanding biomedical documents. Unlike images in the open domain, biomedical figures present a variety of unique challenges. For example, biomedical figures typically have complex layouts, small font sizes, short text, specific text, complex symbols and irregular text arrangements. This paper presents the final results of the ICDAR 2017 Competition on Text Extraction from Biomedical Literature Figures (ICDAR2017 DeTEXT Competition), which aims at extracting (detecting and recognizing) text from biomedical literature figures. Similar to text extraction from scene images and web pictures, ICDAR2017 DeTEXT Competition includes three major tasks, i.e., text detection, cropped word recognition and end-to-end text recognition. Here, we describe in detail the data set, tasks, evaluation protocols and participants of this competition, and report the performance of the participating methods.
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Lluis Gomez, Marçal Rusiñol, Ali Furkan Biten, & Dimosthenis Karatzas. (2018). Subtitulació automàtica d'imatges. Estat de l'art i limitacions en el context arxivístic. In Jornades Imatge i Recerca.
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Lluis Gomez, Marçal Rusiñol, & Dimosthenis Karatzas. (2018). Cutting Sayre's Knot: Reading Scene Text without Segmentation. Application to Utility Meters. In 13th IAPR International Workshop on Document Analysis Systems (pp. 97–102).
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.
Keywords: Robust Reading; End-to-end Systems; CNN; Utility Meters
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Dimosthenis Karatzas, Lluis Gomez, Marçal Rusiñol, & Anguelos Nicolaou. (2018). The Robust Reading Competition Annotation and Evaluation Platform. In 13th IAPR International Workshop on Document Analysis Systems (pp. 61–66).
Abstract: The ICDAR Robust Reading Competition (RRC), initiated in 2003 and reestablished in 2011, has become the defacto evaluation standard for the international community. Concurrent with its second incarnation in 2011, a continuous
effort started to develop an online framework to facilitate the hosting and management of competitions. This short paper briefly outlines the Robust Reading Competition Annotation and Evaluation Platform, the backbone of the
Robust Reading Competition, comprising a collection of tools and processes that aim to simplify the management and annotation of data, and to provide online and offline performance evaluation and analysis services.
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David Aldavert, & Marçal Rusiñol. (2018). Manuscript text line detection and segmentation using second-order derivatives analysis. In 13th IAPR International Workshop on Document Analysis Systems (pp. 293–298).
Abstract: In this paper, we explore the use of second-order derivatives to detect text lines on handwritten document images. Taking advantage that the second derivative gives a minimum response when a dark linear element over a
bright background has the same orientation as the filter, we use this operator to create a map with the local orientation and strength of putative text lines in the document. Then, we detect line segments by selecting and merging the filter responses that have a similar orientation and scale. Finally, text lines are found by merging the segments that are within the same text region. The proposed segmentation algorithm, is learning-free while showing a performance similar to the state of the art methods in publicly available datasets.
Keywords: text line detection; text line segmentation; text region detection; second-order derivatives
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David Aldavert, & Marçal Rusiñol. (2018). Synthetically generated semantic codebook for Bag-of-Visual-Words based word spotting. In 13th IAPR International Workshop on Document Analysis Systems (pp. 223–228).
Abstract: Word-spotting methods based on the Bag-ofVisual-Words framework have demonstrated a good retrieval performance even when used in a completely unsupervised manner. Although unsupervised approaches are suitable for
large document collections due to the cost of acquiring labeled data, these methods also present some drawbacks. For instance, having to train a suitable “codebook” for a certain dataset has a high computational cost. Therefore, in
this paper we present a database agnostic codebook which is trained from synthetic data. The aim of the proposed approach is to generate a codebook where the only information required is the type of script used in the document. The use of synthetic data also allows to easily incorporate semantic
information in the codebook generation. So, the proposed method is able to determine which set of codewords have a semantic representation of the descriptor feature space. Experimental results show that the resulting codebook attains a state-of-the-art performance while having a more compact representation.
Keywords: Word Spotting; Bag of Visual Words; Synthetic Codebook; Semantic Information
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V. Poulain d'Andecy, Emmanuel Hartmann, & Marçal Rusiñol. (2018). Field Extraction by hybrid incremental and a-priori structural templates. In 13th IAPR International Workshop on Document Analysis Systems (pp. 251–256).
Abstract: In this paper, we present an incremental framework for extracting information fields from administrative documents. First, we demonstrate some limits of the existing state-of-the-art methods such as the delay of the system efficiency. This is a concern in industrial context when we have only few samples of each document class. Based on this analysis, we propose a hybrid system combining incremental learning by means of itf-df statistics and a-priori generic
models. We report in the experimental section our results obtained with a dataset of real invoices.
Keywords: Layout Analysis; information extraction; incremental learning
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Felipe Codevilla, Matthias Muller, Antonio Lopez, Vladlen Koltun, & Alexey Dosovitskiy. (2018). End-to-end Driving via Conditional Imitation Learning. In IEEE International Conference on Robotics and Automation (pp. 4693–4700).
Abstract: Deep networks trained on demonstrations of human driving have learned to follow roads and avoid obstacles. However, driving policies trained via imitation learning cannot be controlled at test time. A vehicle trained end-to-end to imitate an expert cannot be guided to take a specific turn at an upcoming intersection. This limits the utility of such systems. We propose to condition imitation learning on high-level command input. At test time, the learned driving policy functions as a chauffeur that handles sensorimotor coordination but continues to respond to navigational commands. We evaluate different architectures for conditional imitation learning in vision-based driving. We conduct experiments in realistic three-dimensional simulations of urban driving and on a 1/5 scale robotic truck that is trained to drive in a residential area. Both systems drive based on visual input yet remain responsive to high-level navigational commands. The supplementary video can be viewed at this https URL
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Stefan Schurischuster, Beatriz Remeseiro, Petia Radeva, & Martin Kampel. (2018). A Preliminary Study of Image Analysis for Parasite Detection on Honey Bees. In 15th International Conference on Image Analysis and Recognition (Vol. 10882, pp. 465–473). LNCS.
Abstract: Varroa destructor is a parasite harming bee colonies. As the worldwide bee population is in danger, beekeepers as well as researchers are looking for methods to monitor the health of bee hives. In this context, we present a preliminary study to detect parasites on bee videos by means of image analysis and machine learning techniques. For this purpose, each video frame is analyzed individually to extract bee image patches, which are then processed to compute image descriptors and finally classified into mite and no mite bees. The experimental results demonstrated the adequacy of the proposed method, which will be a perfect stepping stone for a further bee monitoring system.
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Md. Mostafa Kamal Sarker, Hatem A. Rashwan, Farhan Akram, Syeda Furruka Banu, Adel Saleh, Vivek Kumar Singh, et al. (2018). SLSDeep: Skin Lesion Segmentation Based on Dilated Residual and Pyramid Pooling Networks. In 21st International Conference on Medical Image Computing & Computer Assisted Intervention (Vol. 2, pp. 21–29).
Abstract: Skin lesion segmentation (SLS) in dermoscopic images is a crucial task for automated diagnosis of melanoma. In this paper, we present a robust deep learning SLS model, so-called SLSDeep, which is represented as an encoder-decoder network. The encoder network is constructed by dilated residual layers, in turn, a pyramid pooling network followed by three convolution layers is used for the decoder. Unlike the traditional methods employing a cross-entropy loss, we investigated a loss function by combining both Negative Log Likelihood (NLL) and End Point Error (EPE) to accurately segment the melanoma regions with sharp boundaries. The robustness of the proposed model was evaluated on two public databases: ISBI 2016 and 2017 for skin lesion analysis towards melanoma detection challenge. The proposed model outperforms the state-of-the-art methods in terms of segmentation accuracy. Moreover, it is capable to segment more than 100 images of size 384x384 per second on a recent GPU.
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Md. Mostafa Kamal Sarker, Mohammed Jabreel, Hatem A. Rashwan, Syeda Furruka Banu, Petia Radeva, & Domenec Puig. (2018). CuisineNet: Food Attributes Classification using Multi-scale Convolution Network. In 21st International Conference of the Catalan Association for Artificial Intelligence (pp. 365–372).
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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Shanxin Yuan, Guillermo Garcia-Hernando, Bjorn Stenger, Gyeongsik Moon, Ju Yong Chang, Kyoung Mu Lee, et al. (2018). Depth-Based 3D Hand Pose Estimation: From Current Achievements to Future Goals. In 31st IEEE Conference on Computer Vision and Pattern Recognition (pp. 2636–2645).
Abstract: In this paper, we strive to answer two questions: What is the current state of 3D hand pose estimation from depth images? And, what are the next challenges that need to be tackled? Following the successful Hands In the Million Challenge (HIM2017), we investigate the top 10 state-of-the-art methods on three tasks: single frame 3D pose estimation, 3D hand tracking, and hand pose estimation during object interaction. We analyze the performance of different CNN structures with regard to hand shape, joint visibility, view point and articulation distributions. Our findings include: (1) isolated 3D hand pose estimation achieves low mean errors (10 mm) in the view point range of [70, 120] degrees, but it is far from being solved for extreme view points; (2) 3D volumetric representations outperform 2D CNNs, better capturing the spatial structure of the depth data; (3) Discriminative methods still generalize poorly to unseen hand shapes; (4) While joint occlusions pose a challenge for most methods, explicit modeling of structure constraints can significantly narrow the gap between errors on visible and occluded joints.
Keywords: Three-dimensional displays; Task analysis; Pose estimation; Two dimensional displays; Joints; Training; Solid modeling
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Albert Clapes, Ozan Bilici, Dariia Temirova, Egils Avots, Gholamreza Anbarjafari, & Sergio Escalera. (2018). From apparent to real age: gender, age, ethnic, makeup, and expression bias analysis in real age estimation. In IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops (pp. 2373–2382).
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Mohammad A. Haque, Ruben B. Bautista, Kamal Nasrollahi, Sergio Escalera, Christian B. Laursen, Ramin Irani, et al. (2018). Deep Multimodal Pain Recognition: A Database and Comparision of Spatio-Temporal Visual Modalities, Faces and Gestures. In 13th IEEE Conference on Automatic Face and Gesture Recognition (pp. 250–257).
Abstract: Pain is a symptom of many disorders associated with actual or potential tissue damage in human body. Managing pain is not only a duty but also highly cost prone. The most primitive state of pain management is the assessment of pain. Traditionally it was accomplished by self-report or visual inspection by experts. However, automatic pain assessment systems from facial videos are also rapidly evolving due to the need of managing pain in a robust and cost effective way. Among different challenges of automatic pain assessment from facial video data two issues are increasingly prevalent: first, exploiting both spatial and temporal information of the face to assess pain level, and second, incorporating multiple visual modalities to capture complementary face information related to pain. Most works in the literature focus on merely exploiting spatial information on chromatic (RGB) video data on shallow learning scenarios. However, employing deep learning techniques for spatio-temporal analysis considering Depth (D) and Thermal (T) along with RGB has high potential in this area. In this paper, we present the first state-of-the-art publicly available database, 'Multimodal Intensity Pain (MIntPAIN)' database, for RGBDT pain level recognition in sequences. We provide a first baseline results including 5 pain levels recognition by analyzing independent visual modalities and their fusion with CNN and LSTM models. From the experimental evaluation we observe that fusion of modalities helps to enhance recognition performance of pain levels in comparison to isolated ones. In particular, the combination of RGB, D, and T in an early fusion fashion achieved the best recognition rate.
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Rain Eric Haamer, Kaustubh Kulkarni, Nasrin Imanpour, Mohammad Ahsanul Haque, Egils Avots, Michelle Breisch, et al. (2018). Changes in Facial Expression as Biometric: A Database and Benchmarks of Identification. In 8th International Workshop on Human Behavior Understanding.
Abstract: Facial dynamics can be considered as unique signatures for discrimination between people. These have started to become important topic since many devices have the possibility of unlocking using face recognition or verification. In this work, we evaluate the efficacy of the transition frames of video in emotion as compared to the peak emotion frames for identification. For experiments with transition frames we extract features from each frame of the video from a fine-tuned VGG-Face Convolutional Neural Network (CNN) and geometric features from facial landmark points. To model the temporal context of the transition frames we train a Long-Short Term Memory (LSTM) on the geometric and the CNN features. Furthermore, we employ two fusion strategies: first, an early fusion, in which the geometric and the CNN features are stacked and fed to the LSTM. Second, a late fusion, in which the prediction of the LSTMs, trained independently on the two features, are stacked and used with a Support Vector Machine (SVM). Experimental results show that the late fusion strategy gives the best results and the transition frames give better identification results as compared to the peak emotion frames.
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