Publications

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Refereed publications with international audience only (patents, French publications and technical reports are here)

2011

Vincent Vidal, Christian Wolf, Florent Dupont Combinatorial Mesh Optimization, To appear in The Visual Computer,
- Abstract
- PDF
A new mesh optimization framework for 3D triangular surface meshes is presented, which formulates the task as an energy minimization problem in the same spirit as in Hoppe et al. [1]. The desired mesh properties are controlled through a global energy function including data attached terms measuring the fidelity to the original mesh, shape potentials favoring high quality triangles and connectivity as well as budget terms controlling the sampling density. The optimization algorithm modifies mesh connectivity as well as the vertex positions. Solutions for the vertex repositioning step are obtained by a discrete graph cut algorithm examining global combinations of local candidates. Results on various 3D meshes compare favorably to recent state-of-the-art algorithms. Applications consist in optimizing triangular meshes and in simplifying meshes, while maintaining high mesh quality. Targeted areas are the improvement of the accuracy of numerical simulations, the convergence of numerical schemes, improvements of mesh rendering (normal field smoothness) or improvements of the geometric prediction in mesh compression techniques.
Moez Baccouche, Frank Mamalet Christian Wolf, Christophe Garcia and Atilla Baskurt, Sequential Deep Learning for Human Action Recognition, to appear in the Proceedings of the Inernational Workshop on Human Behavior Understanding: Inducing Behavioral Change, 2011.
Vincent Vidal, Christian Wolf, Florent Dupont Robust feature line extraction on CAD triangular meshes, in the Proceedings of the International Conference on Computer Graphics Theory and Applications, 2011 (Oral presentation).

2010

Christian Wolf Document Ink bleed-through removal with two hidden Markov random fields and a single observation field. To appear in IEEE Transactions on Pattern Analysis and Machine Intelligence (PAMI), 32(3):431-447, 2010.
- Abstract
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We present a new method for blind document bleed through removal based on separate Markov Random Field (MRF) regularization for the recto and for the verso side, where separate priors are derived from the full graph. The segmentation algorithm is based on Bayesian Maximum a Posteriori (MAP) estimation. The advantages of this separate approach are the adaptation of the prior to the contents creation process (e.g. superimposing two hand written pages), and the improvement of the estimation of the recto pixels through an estimation of the verso pixels covered by recto pixels; Moreover, the formulation as a binary labeling problem with two hidden labels per pixels naturally leads to an efficient optimization method based on the minimum cut/maximum flow in a graph. The proposed method is evaluated on scanned document images from the 18th century, showing an improvement of character recognition results compared to other restoration methods.
Christian Wolf and Gérald Gavin Inference and parameter estimation on hierarchical belief networks for image segmentation. In Neurocomputing 73(4-6):563-569, 2010.
- Abstract
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We introduce a new causal hierarchical belief network for image segmentation. Contrary to classical tree structured (or pyramidal) models, the factor graph of the network contains cycles. Each level of the hierarchical structure features the same number of sites as the base level and each site on a given level has several neighbors on the parent level. Compared to tree structured models, the (spatial) random process on the base level of the model is stationary which avoids known drawbacks, namely visual artifacts in the segmented image. We propose different parameterizations of the conditional probability distributions governing the transitions between the image levels. A parametric distribution depending on a single parameter allows the design of a fast inference algorithm on graph cuts, whereas for arbitrary distributions, we propose inference with loopy belief propagation. The method is evaluated on scanned documents, showing an improvement of character recognition results compared to other methods.
Christian Wolf and Jean-Michel Jolion Integrating a discrete motion model into GMM based background subtraction, in the Proceedings of the IEEE International Conference on Pattern Recognition, 2010. (Oral presentation).
GMM based algorithms have become the de facto standard for background subtraction in video sequences, mainly because of their ability to track multiple background distributions, which allows them to handle complex scenes including moving trees, flags moving in the wind etc. However, it is not always easy to determine which distributions of the mixture belong to the background and which distributions belong to the foreground, which disturbs the results of the labeling process for each pixel. In this work we tackle this problem by taking the labeling decision together for all pixels of several consecutive frames minimizing a global energy function taking into account spatial and temporal relationships. A discrete approximative optical-flow like motion model is integrated into the energy function and solved with Ishikawa's convex graph cuts algorithm.
Anh-Phong Ta, Christian Wolf, Guillaume Lavoué, Atilla Baskurt and Jean-Michel Jolion Pairwise features for human action recognition, to appear in the Proceedings of the IEEE International Conference on Pattern Recognition, 2010.
- Abstract
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Existing action recognition approaches mainly rely on the discriminative power of individual local descriptors extracted from spatio-temporal interest points (STIP), while the geometric relationships among the local features are ignored. This paper presents new features, called pairwise features (PWF), which encode both the appearance and the spatio-temporal relations of the local features for action recognition. First STIPs are extracted, then PWFs are constructed by grouping pairs of STIPs which are both close in space and close in time. We propose a combination of two codebooks for video representation. Experiments on two standard human action datasets: the KTH dataset and the Weizmann dataset show that the proposed approach outperforms most existing methods.
Anh-Phong Ta, Christian Wolf, Guillaume Lavoué and Atilla Baskurt Recognizing and localizing individual activities through graph matching, in the Proceedings of the International Conference on Advanced Video and Signal-Based Surveillance, 2010 (IEEE). ,oral presentation, 22.5% acceptance rate; Best Paper for track 'recognition', 5% acceptance rate.
- Abstract
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In this paper we tackle the problem of detecting individual human actions in video sequences. While the most successful methods are based on local features, which proved that they can deal with changes in background, scale and illumination, most existing methods have two main shortcomings: first, they are mainly based on the individual power of spatio-temporal interest points (STIP), and therefore ignore the spatio-temporal relationships between them. Second, these methods mainly focus on direct classification techniques to classify the human activities, as opposed to detection and localization. In order to overcome these limitations, we propose a new approach, which is based on a graph matching algorithm for activity recognition. In contrast to most previous methods which classify entire video sequences, we design a video matching method from two sets of ST-points for human activity recognition. First, points are extracted, and a hyper graphs are constructed from them, i.e. graphs with edges involving more than 2 nodes (3 in our case). The activity recognition problem is then transformed into a problem of finding instances of model graphs in the scene graph. By matching local features instead of classifying entire sequences, our method is able to detect multiple different activities which occur simultaneously in a video sequence. Experiments on two standard datasets demonstrate that our method is comparable to the existing techniques on classification, and that it can, additionally, detect and localize activities.
Pierre-Yves Laffont, Jong-Yun Jun, Christian Wolf, Yu-Wing Tai, Khalid Idrissi, George Drettakis, Sung-Eui Yoon, Interactive Content-Aware Zooming, to appear in the Proceedings of Grapĥics Interface, 2010.
- Abstract
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We propose a novel, interactive content-aware zooming operator that allows effective and efficient visualization of high resolution images on small screens, which may have different aspect ratios compared to the input images. Our approach applies an image retargeting method in order to fit an entire image into the limited screen space. This can provide global, but approximate views for lower zoom levels. However, as we zoom more closely into the image, we continuously unroll the distortion to provide local, but more detailed and accurate views for higher zoom levels. In addition, we propose to use an adaptive view-dependent mesh to achieve high retargeting quality, while maintaining interactive performance. We demonstrate the effectiveness of the proposed operator by comparing it against the traditional zooming approach, and a method stemming from a direct combination of existing works.
Moez Baccouche, Frank Mamalet Christian Wolf, Christophe Garcia, Atilla Baskurt Action Classifcation in Soccer Videos with Long Short-Term Memory Recurrent Neural Networks to appear in the Proceedings of the International Conference on Artificial Neural Networks, 2010.
- Abstract
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In this paper, we propose a novel approach for action classification in soccer videos using a recurrent neural network scheme. Thereby, we extract from each video action at each timestep a set of features which describe both the visual content (by the mean of a BoW approach) and the dominant motion (with a key point based approach). A Long Short-Term Memory-based Recurrent Neural Network is then trained to classify each video sequence considering the temporal evolution of the features for each timestep. Experimental results on the MICC-Soccer-Actions-4 database show that the proposed approach outperforms classification methods of related works (with a classification rate of 77 %), and that the combination of the two features (BoW and dominant motion) leads to a classification rate of 92 %.

2009

Ranked 5th of 43 in the ICDAR 2009 document image binarisation contest!
Anh-Phong Ta, Christian Wolf, Guillaume Lavoué, Atilla Baskurt 3D Object detection and viewpoint selection in sketch images using local patch-based Zernike moments, in the Proceedings of the IEEE Workshop on Content Based Multimedia Indexing, pp. 189-194, 2009.
- Abstract
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In this paper we present a new approach to detect and recognize 3D models in 2D storyboards which have been drawn during the production process of animated cartoons. Our method is robust to occlusion, scale and rotation. The lack of texture and color makes it difficult to extract local features of the target object from the sketched storyboard. Therefore the existing approaches using local descriptors like interest points can fail in such images. We propose a new framework which combines patch-based Zernike descriptors with a method enforcing spatial constraints for exactly detecting 3D models represented as a set of 2D views in the storyboards. Experimental results show that the proposed method can deal with partial object occlusion and is suitable for poorly textured objects.
Marc Mouret, Christine Solnon, Christian Wolf Classification of images based on Hidden Markov Models, in the Proceedings of the IEEE Workshop on Content Based Multimedia Indexing, pp. 169-174, 2009.
- Abstract
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We propose to use hidden Markov models (HMMs) to classify images. Images are modeled by extracting symbols corresponding to 3x3 binary neighborhoods of interest points, and by ordering these symbols by decreasing saliency order, thus obtaining strings of symbols. HMMs are learned from sets of strings modeling classes of images. The method has been tested on the SIMPLIcity database and shows an improvement over competing approaches based on interest points. We also evaluate these approaches for classifying thumbnail images, i.e., low resolution images.
Vincent Vidal, Christian Wolf, Florent Dupont, Guillaume Lavoué Global triangular mesh regularization using conditional Markov random fields. Poster (refereed but not published, acceptance rate ~35%) at Symposium on Geometry Processing, 2009
- Abstract
We present a global mesh optimization framework based on a Conditional Markov Random Fied (CMRF or CRF) model suited for 3D triangular meshes of arbitrary topology. The remeshing task is formulated as a Bayesian estimation problem including data attached terms measuring the fidelity to the original mesh as well as a prior favoring high quality triangles. Since the best solution for vertex relocation is strongly related to the mesh connectivity, our approach iteratively modifies the mesh structure (connectivity plus vertex addition/removal) as well as the vertex positions, which are moved according to a well-defined energy function resulting from the CMRF model. Good solutions for the proposed model are obtained by a discrete graph cut algorithm examining global combinations of local candidates. Results on various 3D meshes compare favorably to recent state-of-the-art algorithms regarding the trade-off between triangle shape improvement and surface fidelity. Applications of this work mainly consist in regularizing meshes for numerical simulations and for improving mesh rendering.
Christian Wolf Families of Markov models for document image segmentation, to appear in the Proceedings of the IEEE Machine Learning for Signal Processing Workshop, 2009
- Abstract
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In this paper we compare several directed and undirected graphical models for different image segmentation problems in the domain of document image processing and analysis. We show that adapting the structure of the model to specific sitations at hand, for instance character restoration, recto/verso separation and segmenting high resolution character images, can significantly improve segmentation performance. We propose inference algorithms for the different models and we test them on different data sets.

2008

Christian Wolf, Improving recto document side restoration with an estimation of the verso side from a single scanned page In the Proceedings of the IEEE International Conference on Pattern Recognition, pp. 1-4, 2008. .
- Abstract
- PDF
We present a new method for blind document bleed through removal based on separately restoring the recto and the verso side. The segmentation algorithm is based on separate Markov random fields (MRF) which results in a better adaptation of the prior to the content creation process (e.g. superimposing two pages), and the improvement of the estimation of the verso pixels through an estimation of the verso pixels covered by recto pixels. The labels of the initial recto and verso clusters are recognized without using any color or gray value information. The proposed method is evaluated empirically as well as through OCR improvement.
Guillaume Lavoué and Christian Wolf , Markov Random Fields for Improving 3D Mesh Analysis and Segmentation, In the Proceedings of the Eurographics 2008 Workshop on 3D Object Retrieval.
- Abstract
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Abstract Mesh analysis and clustering have became important issues in order to improve the efficiency of common processing operations like compression, watermarking or simplification. In this context we present a new method for clustering / labeling a 3D mesh given any field of scalar values associated with its vertices (curvature, density, roughness etc.). Our algorithm is based on Markov Random Fields, graphical probabilistic models. This Bayesian framework allows (1) to integrate both the attributes and the geometry in the clustering, and (2) to obtain an optimal global solution using only local interactions, du to the Markov property of the random field. We have defined new observation and prior models for 3D meshes, adapted from image processing which achieve very good results in terms of spatial coherency of the labeling. All model parameters are estimated, resulting in a fully automatic process (the only required parameter is the number of clusters) which works in reasonable time (several seconds).

2007

Christian Wolf and Jean-Michel Jolion Quality, quantity and generality in the evaluation of object detection algorithms Proceedings of the Image Eval Conference, July 12th, 2007, Amsterdam, NL. 8 pages.
- Abstract
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Evaluation of object detection algorithms is a non-trivial task: a detection result is usually evaluated by comparing the bounding box of the detected object with the bounding box of the ground truth object. The commonly used precision and recall measures are computed from the overlap area of these two rectangles. However, these measures have several drawbacks: they don't give intuitive information about the proportion of the correctly detected objects and the number of false alarms, and they cannot be accumulated across multiple images without creating ambiguity in their interpretation. Furthermore, quantitative and qualitative evaluation is often mixed resulting in ambiguous measures.

In this paper we propose an approach to evaluation which tackles these problems. The performance of a detection algorithm is illustrated intuitively by performance graphs which present object level precision and recall depending on constraints on detection quality. In order to compare different detection algorithms, a representative single performance value is computed from the graphs. The evaluation method can be applied to different types of object detection algorithms. It has been tested on different text detection algorithms, among which are the participants of the Image Eval text detection competition.

2006

Christian Wolf and Jean-Michel Jolion. Object count/Area Graphs for the Evaluation of Object Detection and Segmentation Algorithms, In International Journal on Document Analysis and Recognition , 8(4):280-296, 2006.
Evaluation of object detection algorithms is a non-trivial task: a detection result is usually evaluated by comparing the bounding box of the detected object with the bounding box of the ground truth object. The commonly used precision and recall measures are computed from the overlap area of these two rectangles. However, these measures have several drawbacks: they don't give intuitive information about the proportion of the correctly detected objects and the number of false alarms, and they cannot be accumulated across multiple images without creating ambiguity in their interpretation. Furthermore, quantitative and qualitative evaluation is often mixed resulting in ambiguous measures.
In this paper we propose a new approach which tackles these problems. The performance of a detection algorithm is illustrated intuitively by performance graphs which present object level precision and recall depending on constraints on detection quality. In order to compare different detection algorithms, a representative single performance value is computed from the graphs. The influence of the test database on the detection performance is illustrated by performance/generality graphs. The evaluation method can be applied to different types of object detection algorithms. It has been tested on different text detection algorithms, among which are the participants of the ICDAR 2003 text detection competition.
```
@Article{WolfIJDAR2006,
  Author         = {C. Wolf and J.-M. Jolion},
  Title          = {Object count/Area Graphs for the Evaluation of Object Detection and Segmentation Algorithms},
  Journal        = {International Journal on Document Analysis and Recognition},
  year           = {2006},
  volume     = {8},
  number     = {4},
  pages      = {280-296}
}
		
```

2005

S.M. Lucas, A. Panaretos, L. Sosa, A. Tang, S. Wong, R. Young, K. Ashida, H. Nagai, M. Okamoto, H. Yamamoto, H. Miyao, J. Zhu, W. Ou, C. Wolf, J.-M. Jolion, L. Todoran, M. Worring, et X. Lin. ICDAR 2003 Robust Reading Competitions: Entries, Results and Future Directions International Journal on Document Analysis and Recognition (IJDAR), 7(2-3):105-122, 2005 (Special Issue on Camera-based Text and Document Recognition)
- Abstract
This paper describes the robust reading competitions for ICDAR 2003. With the rapid growth in research over the last few years on recognizing text in natural scenes, there is an urgent need to establish some common benchmark datasets, and gain a clear understanding of the current state of the art. We use the term robust reading to refer to text images that are beyond the capabilities of current commercial OCR packages. We chose to break down the robust reading problem into three sub-problems, and run competitions for each stage, and also a competition for the best overall system. The sub-problems we chose were text locating, character recognition and word recognition. By breaking down the problem in this way, we hoped to gain a better understanding of the state of the art in each of the sub-problems. Furthermore, our methodology involved storing detailed results of applying each algorithm to each image in the data sets, allowing researchers to study in depth the strengths and weaknesses of each algorithm. The text locating contest was the only one to have any entries. We give a brief description of each entry, and present the results of this contest, showing cases where the leading entries succeed and fail. We also describe an algorithm for combining the outputs of the individual text locaters, and show how the combination scheme improves on any of the individual systems.

2004

Graham W. Taylor and Christian Wolf Reinforcement Learning for Parameter Control of Text Detection in Images and Video Sequences Proceedings of the IEEE International Conference on Information & Communication Technologies , 2004. 6 pages.
- Abstract
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A framework for parameterization in computer vision algorithms is evaluated by optimizing ten parameters of the text detection for semantic indexing algorithm preposed by Wolf et al. The Fuzzy ARTMAP neural network is used for generalization, offering much faster learning than in a previous tabular implementation. Difficulties in using a continuous action space are overcome by employing the DIRECT method for global optimization without derivatives. The chosen parameters are evaluated using metrics of recall and precision, and are shown to be superior to the parameters previously recommended.

2003

Christian Wolf and Jean-Michel Jolion. Extraction and Recognition of Artificial Text in Multimedia Documents. Pattern Analysis and Applications, 6(4):309-326, 2003.
The systems currently available for content based image and video retrieval work without semantic knowledge, i.e. they use image processing methods to extract low level features of the data. The similarity obtained by these approaches does not always correspond to the similarity a human user would expect. A way to include more semantic knowledge into the indexing process is to use the text included in the images and video sequences. It is rich in information but easy to use, e.g. by key word based queries. In this paper we present an algorithm to localize artificial text in images and videos using a measure of accumulated gradients and morphological processing. The quality of the localized text is improved by robust multiple frame integration. A new technique for the binarization of the text boxes based on a criterion maximizing local contrast is proposed. Finally, detection and OCR results for a commercial OCR are presented, justifying the choice of the binarization technique
```
@Article{WolfPAA03,
  Author         = {C. Wolf and J.-M. Jolion},
  Title          = {Extraction and {R}ecognition of {A}rtificial {T}ext in {M}ultimedia {D}ocuments},
  Journal        = {Pattern {A}nalysis and {A}pplications},
  year           = {2003},
  volume     = {6},
  number     = {4},
  pages      = {309-326}
}
		
```

2002

Christian Wolf , Jean-Michel Jolion and Francoise Chassaing. Text Localization, Enhancement and Binarization in Multimedia Documents Proceedings of the International Conference on Pattern Recognition (ICPR), volume 4, pages 1037-1040, IEEE Computer Society. August 11th-15th, 2002, Quebec City, Canada. 4 pages.
The systems currently available for content based image and video retrieval work without semantic knowledge, i.e. they use image processing methods to extract low level features of the data. The similarity obtained by these ap-proaches does not always correspond to the similarity a human user would expect. A way to include more semantic knowledge into the indexing process is to use the text included in the images and video sequences. It is rich in information but easy to use, e.g. by key word based queries. In this paper we present an algorithm to localize artificial text in images and videos using a measure of accumulated gradients and morphological post processing to detect the text. The quality of the localized text is improved by robust multiple frame integration. A new technique for the bina-rization of the text boxes is proposed. Finally, detection and OCR results for a commercial OCR are presented.
```
@InProceedings{WolfICPR2002V,
  Author         = {C. Wolf and J.-M. Jolion and F. Chassaing},
  Title          = {Text {L}ocalization, {E}nhancement and {B}inarization in {M}ultimedia {D}ocuments},
  BookTitle      = {Proceedings of the {I}nternational {C}onference on {P}attern {R}ecognition},
  Volume         = {2},
  Pages          = {1037-1040},
  year           = 2002,
}		
		
```
Christian Wolf and David Doermann Binarization of Low Quality Text using a Markov Random Field Model. Proceedings of the International Conference on Pattern Recognition (ICPR), volume 2, pages 160-163, IEEE Computer Society. August 11th-15th, 2002, Quebec City, Canada. 4 pages.
- Abstract
- PDF
- Powerpoint
- BibTeX
Binarization techniques have been developed in the document analysis community for over 30 years and many algorithms have been used successfully. On the other hand, document analysis tasks are more and more frequently being applied to multimedia documents such as video sequences. Due to low resolution and lossy compression, the binarization of text included in the frames is a non trivial task. Existing techniques work without a model of the spatial relationships in the image, which makes them less powerful. We introduce a new technique based on a Markov Random Field (MRF) model of the document. The model parameters (clique potentials) are learned from training data and the binary image is estimated in a Bayesian framework. The performance is evaluated using commercial OCR software.
```
		
@InProceedings{WolfICPR2002M,
  Author         = {C. Wolf and D. Doermann},
  Title          = {Binarization of {L}ow {Q}uality {T}ext using a {M}arkov {R}andom {F}ield {M}odel},
  BookTitle      = {Proceedings of the {I}nternational {C}onference on {P}attern {R}ecognition},
  Volume         = {3},
  Pages          = {160-163},
  year           = 2002,
}
		
```
Christian Wolf, David Doermann and Mika Rautiainen. Video Indexing and Retrieval at UMD, Proceedings of the Text Retrieval Conference (TREC), November 19th-22th, 2002, Gaithersburg, USA. 10 pages.
- Abstract
Our team from the University of Maryland and INSA de Lyon participated in the feature extraction evaluation with overlay text features and in the search evaluation with a query retrieval and browsing system. For search we developed a weighted query mechanism by integrating 1) text (OCR and speech recognition) content using full text and n-grams through the MG system, 2) color correlogram indexing of image and video shots reported last year in TREC, and 3) ranked versions of the extracted binary features. A command line version of the interface allows users to formulate simple queries, store them and use weighted combinations of the simple queries to generate compound queries.
One novel component of our interactive approach is the ability for the users to formulate dynamic queries previously developed for database applications at Maryland. The interactive interface treats each video clip as visual object in a multi-dimensional space, and each "feature" of that clip is mapped to one dimension. The user can visualize any two dimensions by placing any two features on the horizontal and vertical axis with additional dimensions visualized by adding attributes to each object.

2000

Christian Wolf , Jean-Michel Jolion , Walter Kropatsch , and Horst Bischof . Content based Image Retrieval using Interest Points and Texture Features, Proceedings of the International Conference on Pattern Recognition (ICPR), volume 4, pages 234-237. IEEE Computer Society. September 3rd, 2000, Barcelona, Spain. 4 pages.
- Abstract
- Postscript
- Powerpoint
- BibTeX
- Demo
Interest point detectors are used in computer vision to detect image points with special properties, which can be geometric (corners) or non-geometric (contrast etc.). Gabor functions and Gabor filters are regarded as excellent tools for feature extraction and texture segmentation. This article presents methods how to combine these methods for content based image retrieval and to generate a textural description of images. Special emphasis is devoted to distance measure texture descriptions. Experimental results of a query system are given.
This work was supported in part by the Austrian Science Foundation (FWF) under grant S-7002-MAT.
```
@InProceedings{WolfICPR2000,
  Author         = {C. Wolf and J.M. Jolion and W. Kropatsch and H. Bischof},
  Title          = {Content {B}ased {I}mage {R}etrieval using {I}nterest {P}oints and {T}exture {F}eatures},
  BookTitle      = {Proceedings of the {I}nternational {C}onference on {P}attern {R}ecognition},
  Volume         = {4},
  Pages          = {234-237},
  year           = 2000,
}		
		
```