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Author |
Mario Hernandez; Joao Sanchez; Jordi Vitria |
![goto web page (via DOI) doi](img/doi.gif)
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Selected papers from Iberian Conference on Pattern Recognition and Image Analysis |
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2012 |
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Pattern Recognition |
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45 |
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9 |
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3047-3582 |
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0031-3203 |
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OR;MV |
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no |
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Admin @ si @ HSV2012 |
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2069 |
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Author |
X. Varona |
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Title ![sorted by Title field, descending order (down)](img/sort_desc.gif) |
Seguimiento visual robusto en entornos complejos, Tesis. |
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2001 |
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Admin @ si @ Var2001 |
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214 |
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Author |
Daniel Ponsa; Antonio Lopez |
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Seguimiento Visual de Contornos Computerizado |
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Miscellaneous |
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2009 |
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UAB Divulga, Revista de divulgacion cientifica |
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spreading;ADAS |
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no |
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ADAS @ adas @ PoL2009b |
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1270 |
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Author |
S. Casanovas |
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Seguiment de moviment articulat mitjançant flux òptic i metodes estocastics |
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2000 |
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CVC Technical Report #43 |
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CVC (UAB) |
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no |
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Admin @ si @ Cas2000 |
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344 |
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Author |
Jon Almazan; Albert Gordo; Alicia Fornes; Ernest Valveny |
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Title ![sorted by Title field, descending order (down)](img/sort_desc.gif) |
Segmentation-free Word Spotting with Exemplar SVMs |
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Journal Article |
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Year |
2014 |
Publication |
Pattern Recognition |
Abbreviated Journal |
PR |
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Volume |
47 |
Issue |
12 |
Pages |
3967–3978 |
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Keywords |
Word spotting; Segmentation-free; Unsupervised learning; Reranking; Query expansion; Compression |
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In this paper we propose an unsupervised segmentation-free method for word spotting in document images. Documents are represented with a grid of HOG descriptors, and a sliding-window approach is used to locate the document regions that are most similar to the query. We use the Exemplar SVM framework to produce a better representation of the query in an unsupervised way. Then, we use a more discriminative representation based on Fisher Vector to rerank the best regions retrieved, and the most promising ones are used to expand the Exemplar SVM training set and improve the query representation. Finally, the document descriptors are precomputed and compressed with Product Quantization. This offers two advantages: first, a large number of documents can be kept in RAM memory at the same time. Second, the sliding window becomes significantly faster since distances between quantized HOG descriptors can be precomputed. Our results significantly outperform other segmentation-free methods in the literature, both in accuracy and in speed and memory usage. |
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DAG; 600.045; 600.056; 600.061; 602.006; 600.077 |
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no |
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Admin @ si @ AGF2014b |
Serial |
2485 |
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Author |
Pau Torras; Mohamed Ali Souibgui; Sanket Biswas; Alicia Fornes |
![goto web page url](img/www.gif)
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Segmentation-Free Alignment of Arbitrary Symbol Transcripts to Images |
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Conference Article |
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Year |
2023 |
Publication |
Document Analysis and Recognition – ICDAR 2023 Workshops |
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Volume |
14193 |
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Pages |
83-93 |
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Keywords |
Historical Manuscripts; Symbol Alignment |
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Abstract |
Developing arbitrary symbol recognition systems is a challenging endeavour. Even using content-agnostic architectures such as few-shot models, performance can be substantially improved by providing a number of well-annotated examples into training. In some contexts, transcripts of the symbols are available without any position information associated to them, which enables using line-level recognition architectures. A way of providing this position information to detection-based architectures is finding systems that can align the input symbols with the transcription. In this paper we discuss some symbol alignment techniques that are suitable for low-data scenarios and provide an insight on their perceived strengths and weaknesses. In particular, we study the usage of Connectionist Temporal Classification models, Attention-Based Sequence to Sequence models and we compare them with the results obtained on a few-shot recognition system. |
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ICDAR |
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DAG |
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no |
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Admin @ si @ TSS2023 |
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3850 |
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Author |
Felipe Lumbreras |
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Title ![sorted by Title field, descending order (down)](img/sort_desc.gif) |
Segmentation, classification and modelization of textures by means of multiresolution decomposition techniques. |
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2001 |
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PhD Thesis, Universitat Autonoma de Barcelona-CVC |
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ADAS |
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no |
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ADAS @ adas @ Lum2001 |
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188 |
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Author |
Dimosthenis Karatzas; Marçal Rusiñol; Coen Antens; Miquel Ferrer |
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Title ![sorted by Title field, descending order (down)](img/sort_desc.gif) |
Segmentation Robust to the Vignette Effect for Machine Vision Systems |
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Conference Article |
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2008 |
Publication |
19th International Conference on Pattern Recognition |
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The vignette effect (radial fall-off) is commonly encountered in images obtained through certain image acquisition setups and can seriously hinder automatic analysis processes. In this paper we present a fast and efficient method for dealing with vignetting in the context of object segmentation in an existing industrial inspection setup. The vignette effect is modelled here as a circular, non-linear gradient. The method estimates the gradient parameters and employs them to perform segmentation. Segmentation results on a variety of images indicate that the presented method is able to successfully tackle the vignette effect. |
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Tampa, USA |
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ICPR |
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DAG |
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DAG @ dag @ KRA2008 |
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1065 |
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Author |
Carles Sanchez; Debora Gil; Antoni Rosell; Albert Andaluz; F. Javier Sanchez |
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Title ![sorted by Title field, descending order (down)](img/sort_desc.gif) |
Segmentation of Tracheal Rings in Videobronchoscopy combining Geometry and Appearance |
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Conference Article |
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2013 |
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Proceedings of the International Conference on Computer Vision Theory and Applications |
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1 |
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153--161 |
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Video-bronchoscopy, tracheal ring segmentation, trachea geometric and appearance model |
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Videobronchoscopy is a medical imaging technique that allows interactive navigation inside the respiratory pathways and minimal invasive interventions. Tracheal procedures are ordinary interventions that require measurement of the percentage of obstructed pathway for injury (stenosis) assessment. Visual assessment of stenosis in videobronchoscopic sequences requires high expertise of trachea anatomy and is prone to human error. Accurate detection of tracheal rings is the basis for automated estimation of the size of stenosed trachea. Processing of videobronchoscopic images acquired at the operating room is a challenging task due to the wide range of artifacts and acquisition conditions. We present a model of the geometric-appearance of tracheal rings for its detection in videobronchoscopic videos. Experiments on sequences acquired at the operating room, show a performance close to inter-observer variability |
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Barcelona; February 2013 |
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SciTePress |
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Portugal |
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Sebastiano Battiato and José Braz |
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978-989-8565-47-1 |
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800 |
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VISAPP |
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IAM;MV; 600.044; 600.047; 600.060; 605.203 |
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IAM @ iam @ SGR2013 |
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2123 |
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Author |
Mikkel Thogersen; Sergio Escalera; Jordi Gonzalez; Thomas B. Moeslund |
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Title ![sorted by Title field, descending order (down)](img/sort_desc.gif) |
Segmentation of RGB-D Indoor scenes by Stacking Random Forests and Conditional Random Fields |
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Journal Article |
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2016 |
Publication |
Pattern Recognition Letters |
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PRL |
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80 |
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208–215 |
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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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HuPBA; ISE;MILAB; 600.098; 600.119 |
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Admin @ si @ TEG2016 |
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2843 |
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Permanent link to this record |
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Author |
Felipe Lumbreras; Joan Serrat |
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Title ![sorted by Title field, descending order (down)](img/sort_desc.gif) |
Segmentation of petrographical images of marbles |
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1996 |
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Computers and Geosciences. 22(5):547–558 |
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ADAS |
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ADAS @ adas @ LuS1996b |
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82 |
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Author |
Felipe Lumbreras; Joan Serrat |
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Title ![sorted by Title field, descending order (down)](img/sort_desc.gif) |
Segmentation of petrographical image of marbles |
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1996 |
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CVC Technical Report #04 |
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CVC (UAB) |
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ADAS |
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ADAS @ adas @ LuS1996c |
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93 |
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Author |
Debora Gil; Carles Sanchez; Agnes Borras; Marta Diez-Ferrer; Antoni Rosell |
![download PDF file pdf](img/file_PDF.gif)
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Title ![sorted by Title field, descending order (down)](img/sort_desc.gif) |
Segmentation of Distal Airways using Structural Analysis |
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Journal Article |
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2019 |
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PloS one |
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Plos |
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14 |
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12 |
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Segmentation of airways in Computed Tomography (CT) scans is a must for accurate support of diagnosis and intervention of many pulmonary disorders. In particular, lung cancer diagnosis would benefit from segmentations reaching most distal airways. We present a method that combines descriptors of bronchi local appearance and graph global structural analysis to fine-tune thresholds on the descriptors adapted for each bronchial level. We have compared our method to the top performers of the EXACT09 challenge and to a commercial software for biopsy planning evaluated in an own-collected data-base of high resolution CT scans acquired under different breathing conditions. Results on EXACT09 data show that our method provides a high leakage reduction with minimum loss in airway detection. Results on our data-base show the reliability across varying breathing conditions and a competitive performance for biopsy planning compared to a commercial solution. |
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IAM; 600.139; 600.145 |
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no |
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Admin @ si @ GSB2019 |
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3357 |
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Permanent link to this record |
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Author |
Oscar Argudo; Marc Comino; Antonio Chica; Carlos Andujar; Felipe Lumbreras |
![goto web page url](img/www.gif)
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Title ![sorted by Title field, descending order (down)](img/sort_desc.gif) |
Segmentation of aerial images for plausible detail synthesis |
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Journal Article |
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2018 |
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Computers & Graphics |
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CG |
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71 |
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23-34 |
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Terrain editing; Detail synthesis; Vegetation synthesis; Terrain rendering; Image segmentation |
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The visual enrichment of digital terrain models with plausible synthetic detail requires the segmentation of aerial images into a suitable collection of categories. In this paper we present a complete pipeline for segmenting high-resolution aerial images into a user-defined set of categories distinguishing e.g. terrain, sand, snow, water, and different types of vegetation. This segmentation-for-synthesis problem implies that per-pixel categories must be established according to the algorithms chosen for rendering the synthetic detail. This precludes the definition of a universal set of labels and hinders the construction of large training sets. Since artists might choose to add new categories on the fly, the whole pipeline must be robust against unbalanced datasets, and fast on both training and inference. Under these constraints, we analyze the contribution of common per-pixel descriptors, and compare the performance of state-of-the-art supervised learning algorithms. We report the findings of two user studies. The first one was conducted to analyze human accuracy when manually labeling aerial images. The second user study compares detailed terrains built using different segmentation strategies, including official land cover maps. These studies demonstrate that our approach can be used to turn digital elevation models into fully-featured, detailed terrains with minimal authoring efforts. |
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0097-8493 |
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ADAS; 600.086; 600.118 |
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Admin @ si @ ACC2018 |
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3147 |
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Author |
Koen E.A. van de Sande; Jasper Uilings; Theo Gevers; Arnold Smeulders |
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Title ![sorted by Title field, descending order (down)](img/sort_desc.gif) |
Segmentation as Selective Search for Object Recognition |
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2011 |
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13th IEEE International Conference on Computer Vision |
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1879-1886 |
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For object recognition, the current state-of-the-art is based on exhaustive search. However, to enable the use of more expensive features and classifiers and thereby progress beyond the state-of-the-art, a selective search strategy is needed. Therefore, we adapt segmentation as a selective search by reconsidering segmentation: We propose to generate many approximate locations over few and precise object delineations because (1) an object whose location is never generated can not be recognised and (2) appearance and immediate nearby context are most effective for object recognition. Our method is class-independent and is shown to cover 96.7% of all objects in the Pascal VOC 2007 test set using only 1,536 locations per image. Our selective search enables the use of the more expensive bag-of-words method which we use to substantially improve the state-of-the-art by up to 8.5% for 8 out of 20 classes on the Pascal VOC 2010 detection challenge. |
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Barcelona |
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1550-5499 |
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978-1-4577-1101-5 |
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ICCV |
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ISE |
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Admin @ si @ SUG2011 |
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1780 |
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