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Author | Katerine Diaz; Aura Hernandez-Sabate; Antonio Lopez | ||||
Title | A reduced feature set for driver head pose estimation | Type | Journal Article | ||
Year | 2016 | Publication | Applied Soft Computing | Abbreviated Journal | ASOC |
Volume | 45 | Issue | Pages | 98-107 | |
Keywords | Head pose estimation; driving performance evaluation; subspace based methods; linear regression | ||||
Abstract | Evaluation of driving performance is of utmost importance in order to reduce road accident rate. Since driving ability includes visual-spatial and operational attention, among others, head pose estimation of the driver is a crucial indicator of driving performance. This paper proposes a new automatic method for coarse and fine head's yaw angle estimation of the driver. We rely on a set of geometric features computed from just three representative facial keypoints, namely the center of the eyes and the nose tip. With these geometric features, our method combines two manifold embedding methods and a linear regression one. In addition, the method has a confidence mechanism to decide if the classification of a sample is not reliable. The approach has been tested using the CMU-PIE dataset and our own driver dataset. Despite the very few facial keypoints required, the results are comparable to the state-of-the-art techniques. The low computational cost of the method and its robustness makes feasible to integrate it in massive consume devices as a real time application. | ||||
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Notes | ADAS; 600.085; 600.076; | Approved | no | ||
Call Number | Admin @ si @ DHL2016 | Serial | 2760 | ||
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Author | Miguel Oliveira; Victor Santos; Angel Sappa; P. Dias; A. Moreira | ||||
Title | Incremental texture mapping for autonomous driving | Type | Journal Article | ||
Year | 2016 | Publication | Robotics and Autonomous Systems | Abbreviated Journal | RAS |
Volume | 84 | Issue | Pages | 113-128 | |
Keywords | Scene reconstruction; Autonomous driving; Texture mapping | ||||
Abstract | Autonomous vehicles have a large number of on-board sensors, not only for providing coverage all around the vehicle, but also to ensure multi-modality in the observation of the scene. Because of this, it is not trivial to come up with a single, unique representation that feeds from the data given by all these sensors. We propose an algorithm which is capable of mapping texture collected from vision based sensors onto a geometric description of the scenario constructed from data provided by 3D sensors. The algorithm uses a constrained Delaunay triangulation to produce a mesh which is updated using a specially devised sequence of operations. These enforce a partial configuration of the mesh that avoids bad quality textures and ensures that there are no gaps in the texture. Results show that this algorithm is capable of producing fine quality textures. | ||||
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Notes | ADAS; 600.086 | Approved | no | ||
Call Number | Admin @ si @ OSS2016b | Serial | 2912 | ||
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Author | Marc Oliu; Ciprian Corneanu; Laszlo A. Jeni; Jeffrey F. Cohn; Takeo Kanade; Sergio Escalera | ||||
Title | Continuous Supervised Descent Method for Facial Landmark Localisation | Type | Conference Article | ||
Year | 2016 | Publication | 13th Asian Conference on Computer Vision | Abbreviated Journal | |
Volume | 10112 | Issue | Pages | 121-135 | |
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Abstract | Recent methods for facial landmark location perform well on close-to-frontal faces but have problems in generalising to large head rotations. In order to address this issue we propose a second order linear regression method that is both compact and robust against strong rotations. We provide a closed form solution, making the method fast to train. We test the method’s performance on two challenging datasets. The first has been intensely used by the community. The second has been specially generated from a well known 3D face dataset. It is considerably more challenging, including a high diversity of rotations and more samples than any other existing public dataset. The proposed method is compared against state-of-the-art approaches, including RCPR, CGPRT, LBF, CFSS, and GSDM. Results upon both datasets show that the proposed method offers state-of-the-art performance on near frontal view data, improves state-of-the-art methods on more challenging head rotation problems and keeps a compact model size. | ||||
Address | Taipei; Taiwan; November 2016 | ||||
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Series Editor | Series Title | Abbreviated Series Title | LNCS | ||
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Area | Expedition | Conference | ACCV | ||
Notes | HuPBA;MILAB; | Approved | no | ||
Call Number | Admin @ si @ OCJ2016 | Serial | 2838 | ||
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Author | Joana Maria Pujadas-Mora; Alicia Fornes; Josep Llados; Anna Cabre | ||||
Title | Bridging the gap between historical demography and computing: tools for computer-assisted transcription and the analysis of demographic sources | Type | Book Chapter | ||
Year | 2016 | Publication | The future of historical demography. Upside down and inside out | Abbreviated Journal | |
Volume | Issue | Pages | 127-131 | ||
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Publisher | Acco Publishers | Place of Publication | Editor | K.Matthijs; S.Hin; H.Matsuo; J.Kok | |
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ISSN | ISBN | 978-94-6292-722-3 | Medium | ||
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Notes | DAG; 600.097 | Approved | no | ||
Call Number | Admin @ si @ PFL2016 | Serial | 2907 | ||
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Author | Miguel Angel Bautista; Antonio Hernandez; Sergio Escalera; Laura Igual; Oriol Pujol; Josep Moya; Veronica Violant; Maria Teresa Anguera | ||||
Title | A Gesture Recognition System for Detecting Behavioral Patterns of ADHD | Type | Journal Article | ||
Year | 2016 | Publication | IEEE Transactions on System, Man and Cybernetics, Part B | Abbreviated Journal | TSMCB |
Volume | 46 | Issue | 1 | Pages | 136-147 |
Keywords | Gesture Recognition; ADHD; Gaussian Mixture Models; Convex Hulls; Dynamic Time Warping; Multi-modal RGB-Depth data | ||||
Abstract | We present an application of gesture recognition using an extension of Dynamic Time Warping (DTW) to recognize behavioural patterns of Attention Deficit Hyperactivity Disorder (ADHD). We propose an extension of DTW using one-class classifiers in order to be able to encode the variability of a gesture category, and thus, perform an alignment between a gesture sample and a gesture class. We model the set of gesture samples of a certain gesture category using either GMMs or an approximation of Convex Hulls. Thus, we add a theoretical contribution to classical warping path in DTW by including local modeling of intra-class gesture variability. This methodology is applied in a clinical context, detecting a group of ADHD behavioural patterns defined by experts in psychology/psychiatry, to provide support to clinicians in the diagnose procedure. The proposed methodology is tested on a novel multi-modal dataset (RGB plus Depth) of ADHD children recordings with behavioural patterns. We obtain satisfying results when compared to standard state-of-the-art approaches in the DTW context. | ||||
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Notes | HuPBA; MILAB; | Approved | no | ||
Call Number | Admin @ si @ BHE2016 | Serial | 2566 | ||
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Author | Daniel Hernandez; Alejandro Chacon; Antonio Espinosa; David Vazquez; Juan Carlos Moure; Antonio Lopez | ||||
Title | Embedded real-time stereo estimation via Semi-Global Matching on the GPU | Type | Conference Article | ||
Year | 2016 | Publication | 16th International Conference on Computational Science | Abbreviated Journal | |
Volume | 80 | Issue | Pages | 143-153 | |
Keywords | Autonomous Driving; Stereo; CUDA; 3d reconstruction | ||||
Abstract | Dense, robust and real-time computation of depth information from stereo-camera systems is a computationally demanding requirement for robotics, advanced driver assistance systems (ADAS) and autonomous vehicles. Semi-Global Matching (SGM) is a widely used algorithm that propagates consistency constraints along several paths across the image. This work presents a real-time system producing reliable disparity estimation results on the new embedded energy-efficient GPU devices. Our design runs on a Tegra X1 at 41 frames per second for an image size of 640x480, 128 disparity levels, and using 4 path directions for the SGM method. | ||||
Address | San Diego; CA; USA; June 2016 | ||||
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Area | Expedition | Conference | ICCS | ||
Notes | ADAS; 600.085; 600.082; 600.076 | Approved | no | ||
Call Number | ADAS @ adas @ HCE2016a | Serial | 2740 | ||
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Author | Maedeh Aghaei; Mariella Dimiccoli; Petia Radeva | ||||
Title | Multi-face tracking by extended bag-of-tracklets in egocentric photo-streams | Type | Journal Article | ||
Year | 2016 | Publication | Computer Vision and Image Understanding | Abbreviated Journal | CVIU |
Volume | 149 | Issue | Pages | 146-156 | |
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Abstract | Wearable cameras offer a hands-free way to record egocentric images of daily experiences, where social events are of special interest. The first step towards detection of social events is to track the appearance of multiple persons involved in them. In this paper, we propose a novel method to find correspondences of multiple faces in low temporal resolution egocentric videos acquired through a wearable camera. This kind of photo-stream imposes additional challenges to the multi-tracking problem with respect to conventional videos. Due to the free motion of the camera and to its low temporal resolution, abrupt changes in the field of view, in illumination condition and in the target location are highly frequent. To overcome such difficulties, we propose a multi-face tracking method that generates a set of tracklets through finding correspondences along the whole sequence for each detected face and takes advantage of the tracklets redundancy to deal with unreliable ones. Similar tracklets are grouped into the so called extended bag-of-tracklets (eBoT), which is aimed to correspond to a specific person. Finally, a prototype tracklet is extracted for each eBoT, where the occurred occlusions are estimated by relying on a new measure of confidence. We validated our approach over an extensive dataset of egocentric photo-streams and compared it to state of the art methods, demonstrating its effectiveness and robustness. | ||||
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Notes | MILAB; | Approved | no | ||
Call Number | Admin @ si @ ADR2016b | Serial | 2742 | ||
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Author | Anders Hast; Alicia Fornes | ||||
Title | A Segmentation-free Handwritten Word Spotting Approach by Relaxed Feature Matching | Type | Conference Article | ||
Year | 2016 | Publication | 12th IAPR Workshop on Document Analysis Systems | Abbreviated Journal | |
Volume | Issue | Pages | 150-155 | ||
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Abstract | The automatic recognition of historical handwritten documents is still considered challenging task. For this reason, word spotting emerges as a good alternative for making the information contained in these documents available to the user. Word spotting is defined as the task of retrieving all instances of the query word in a document collection, becoming a useful tool for information retrieval. In this paper we propose a segmentation-free word spotting approach able to deal with large document collections. Our method is inspired on feature matching algorithms that have been applied to image matching and retrieval. Since handwritten words have different shape, there is no exact transformation to be obtained. However, the sufficient degree of relaxation is achieved by using a Fourier based descriptor and an alternative approach to RANSAC called PUMA. The proposed approach is evaluated on historical marriage records, achieving promising results. | ||||
Address | Santorini; Greece; April 2016 | ||||
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Area | Expedition | Conference | DAS | ||
Notes | DAG; 602.006; 600.061; 600.077; 600.097 | Approved | no | ||
Call Number | HaF2016 | Serial | 2753 | ||
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Author | Jiaolong Xu; Sebastian Ramos; David Vazquez; Antonio Lopez | ||||
Title | Hierarchical Adaptive Structural SVM for Domain Adaptation | Type | Journal Article | ||
Year | 2016 | Publication | International Journal of Computer Vision | Abbreviated Journal | IJCV |
Volume | 119 | Issue | 2 | Pages | 159-178 |
Keywords | Domain Adaptation; Pedestrian Detection | ||||
Abstract | A key topic in classification is the accuracy loss produced when the data distribution in the training (source) domain differs from that in the testing (target) domain. This is being recognized as a very relevant problem for many
computer vision tasks such as image classification, object detection, and object category recognition. In this paper, we present a novel domain adaptation method that leverages multiple target domains (or sub-domains) in a hierarchical adaptation tree. The core idea is to exploit the commonalities and differences of the jointly considered target domains. Given the relevance of structural SVM (SSVM) classifiers, we apply our idea to the adaptive SSVM (A-SSVM), which only requires the target domain samples together with the existing source-domain classifier for performing the desired adaptation. Altogether, we term our proposal as hierarchical A-SSVM (HA-SSVM). As proof of concept we use HA-SSVM for pedestrian detection, object category recognition and face recognition. In the former we apply HA-SSVM to the deformable partbased model (DPM) while in the rest HA-SSVM is applied to multi-category classifiers. We will show how HA-SSVM is effective in increasing the detection/recognition accuracy with respect to adaptation strategies that ignore the structure of the target data. Since, the sub-domains of the target data are not always known a priori, we shown how HA-SSVM can incorporate sub-domain discovery for object category recognition. |
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Publisher | Springer US | Place of Publication | Editor | ||
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ISSN | 0920-5691 | ISBN | Medium | ||
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Notes | ADAS; 600.085; 600.082; 600.076 | Approved | no | ||
Call Number | Admin @ si @ XRV2016 | Serial | 2669 | ||
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Author | Maria Elena Meza-de-Luna; Juan Ramon Terven Salinas; Bogdan Raducanu; Joaquin Salas | ||||
Title | Assessing the Influence of Mirroring on the Perception of Professional Competence using Wearable Technology | Type | Journal Article | ||
Year | 2016 | Publication | IEEE Transactions on Affective Computing | Abbreviated Journal | TAC |
Volume | 9 | Issue | 2 | Pages | 161-175 |
Keywords | Mirroring; Nodding; Competence; Perception; Wearable Technology | ||||
Abstract | Nonverbal communication is an intrinsic part in daily face-to-face meetings. A frequently observed behavior during social interactions is mirroring, in which one person tends to mimic the attitude of the counterpart. This paper shows that a computer vision system could be used to predict the perception of competence in dyadic interactions through the automatic detection of mirroring
events. To prove our hypothesis, we developed: (1) A social assistant for mirroring detection, using a wearable device which includes a video camera and (2) an automatic classifier for the perception of competence, using the number of nodding gestures and mirroring events as predictors. For our study, we used a mixed-method approach in an experimental design where 48 participants acting as customers interacted with a confederated psychologist. We found that the number of nods or mirroring events has a significant influence on the perception of competence. Our results suggest that: (1) Customer mirroring is a better predictor than psychologist mirroring; (2) the number of psychologist’s nods is a better predictor than the number of customer’s nods; (3) except for the psychologist mirroring, the computer vision algorithm we used worked about equally well whether it was acquiring images from wearable smartglasses or fixed cameras. |
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Notes | LAMP; 600.072; | Approved | no | ||
Call Number | Admin @ si @ MTR2016 | Serial | 2826 | ||
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Author | Santiago Segui; Michal Drozdzal; Guillem Pascual; Petia Radeva; Carolina Malagelada; Fernando Azpiroz; Jordi Vitria | ||||
Title | Generic Feature Learning for Wireless Capsule Endoscopy Analysis | Type | Journal Article | ||
Year | 2016 | Publication | Computers in Biology and Medicine | Abbreviated Journal | CBM |
Volume | 79 | Issue | Pages | 163-172 | |
Keywords | Wireless capsule endoscopy; Deep learning; Feature learning; Motility analysis | ||||
Abstract | The interpretation and analysis of wireless capsule endoscopy (WCE) recordings is a complex task which requires sophisticated computer aided decision (CAD) systems to help physicians with video screening and, finally, with the diagnosis. Most CAD systems used in capsule endoscopy share a common system design, but use very different image and video representations. As a result, each time a new clinical application of WCE appears, a new CAD system has to be designed from the scratch. This makes the design of new CAD systems very time consuming. Therefore, in this paper we introduce a system for small intestine motility characterization, based on Deep Convolutional Neural Networks, which circumvents the laborious step of designing specific features for individual motility events. Experimental results show the superiority of the learned features over alternative classifiers constructed using state-of-the-art handcrafted features. In particular, it reaches a mean classification accuracy of 96% for six intestinal motility events, outperforming the other classifiers by a large margin (a 14% relative performance increase). | ||||
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Notes | OR; MILAB;MV; | Approved | no | ||
Call Number | Admin @ si @ SDP2016 | Serial | 2836 | ||
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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 | ||
Year | 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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Area | Expedition | Conference | STACOM | ||
Notes | IAM; | Approved | no | ||
Call Number | Admin @ si @ GGM2016 | Serial | 2884 | ||
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Author | Lluis Gomez; Dimosthenis Karatzas | ||||
Title | A fine-grained approach to scene text script identification | Type | Conference Article | ||
Year | 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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Area | Expedition | Conference | DAS | ||
Notes | DAG; 601.197; 600.084 | Approved | no | ||
Call Number | Admin @ si @ GoK2016b | Serial | 2863 | ||
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Author | Mikkel Thogersen; Sergio Escalera; Jordi Gonzalez; Thomas B. Moeslund | ||||
Title | Segmentation of RGB-D Indoor scenes by Stacking Random Forests and Conditional Random Fields | Type | Journal Article | ||
Year | 2016 | Publication | Pattern Recognition Letters | Abbreviated Journal | PRL |
Volume | 80 | Issue | Pages | 208–215 | |
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Abstract | This paper proposes a technique for RGB-D scene segmentation using Multi-class
Multi-scale Stacked Sequential Learning (MMSSL) paradigm. Following recent trends in state-of-the-art, a base classifier uses an initial SLIC segmentation to obtain superpixels which provide a diminution of data while retaining object boundaries. A series of color and depth features are extracted from the superpixels, and are used in a Conditional Random Field (CRF) to predict superpixel labels. Furthermore, a Random Forest (RF) classifier using random offset features is also used as an input to the CRF, acting as an initial prediction. As a stacked classifier, another Random Forest is used acting on a spatial multi-scale decomposition of the CRF confidence map to correct the erroneous labels assigned by the previous classifier. The model is tested on the popular NYU-v2 dataset. The approach shows that simple multi-modal features with the power of the MMSSL paradigm can achieve better performance than state of the art results on the same dataset. |
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Notes | HuPBA; ISE;MILAB; 600.098; 600.119 | Approved | no | ||
Call Number | Admin @ si @ TEG2016 | Serial | 2843 | ||
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Author | Cristina Palmero; Albert Clapes; Chris Bahnsen; Andreas Møgelmose; Thomas B. Moeslund; Sergio Escalera | ||||
Title | Multi-modal RGB-Depth-Thermal Human Body Segmentation | Type | Journal Article | ||
Year | 2016 | Publication | International Journal of Computer Vision | Abbreviated Journal | IJCV |
Volume | 118 | Issue | 2 | Pages | 217-239 |
Keywords | Human body segmentation; RGB ; Depth Thermal | ||||
Abstract | This work addresses the problem of human body segmentation from multi-modal visual cues as a first stage of automatic human behavior analysis. We propose a novel RGB–depth–thermal dataset along with a multi-modal segmentation baseline. The several modalities are registered using a calibration device and a registration algorithm. Our baseline extracts regions of interest using background subtraction, defines a partitioning of the foreground regions into cells, computes a set of image features on those cells using different state-of-the-art feature extractions, and models the distribution of the descriptors per cell using probabilistic models. A supervised learning algorithm then fuses the output likelihoods over cells in a stacked feature vector representation. The baseline, using Gaussian mixture models for the probabilistic modeling and Random Forest for the stacked learning, is superior to other state-of-the-art methods, obtaining an overlap above 75 % on the novel dataset when compared to the manually annotated ground-truth of human segmentations. | ||||
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Publisher | Springer US | Place of Publication | Editor | ||
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Notes | HuPBA;MILAB; | Approved | no | ||
Call Number | Admin @ si @ PCB2016 | Serial | 2767 | ||
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