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David Geronimo; Antonio Lopez; Angel Sappa; Thorsten Graf |
![download PDF file pdf](img/file_PDF.gif)
![goto web page (via DOI) doi](img/doi.gif)
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Title |
Survey on Pedestrian Detection for Advanced Driver Assistance Systems |
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Journal Article |
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2010 |
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IEEE Transaction on Pattern Analysis and Machine Intelligence |
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TPAMI |
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32 |
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7 |
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1239–1258 |
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ADAS, pedestrian detection, on-board vision, survey |
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Abstract ![sorted by Abstract field, ascending order (up)](img/sort_asc.gif) |
Advanced driver assistance systems (ADASs), and particularly pedestrian protection systems (PPSs), have become an active research area aimed at improving traffic safety. The major challenge of PPSs is the development of reliable on-board pedestrian detection systems. Due to the varying appearance of pedestrians (e.g., different clothes, changing size, aspect ratio, and dynamic shape) and the unstructured environment, it is very difficult to cope with the demanded robustness of this kind of system. Two problems arising in this research area are the lack of public benchmarks and the difficulty in reproducing many of the proposed methods, which makes it difficult to compare the approaches. As a result, surveying the literature by enumerating the proposals one-after-another is not the most useful way to provide a comparative point of view. Accordingly, we present a more convenient strategy to survey the different approaches. We divide the problem of detecting pedestrians from images into different processing steps, each with attached responsibilities. Then, the different proposed methods are analyzed and classified with respect to each processing stage, favoring a comparative viewpoint. Finally, discussion of the important topics is presented, putting special emphasis on the future needs and challenges. |
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0162-8828 |
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ADAS |
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ADAS @ adas @ GLS2010 |
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1340 |
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Author |
Ivan Huerta; Michael Holte; Thomas B. Moeslund; Jordi Gonzalez |
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Title |
Chromatic shadow detection and tracking for moving foreground segmentation |
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Journal Article |
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2015 |
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Image and Vision Computing |
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IMAVIS |
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41 |
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42-53 |
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Detecting moving objects; Chromatic shadow detection; Temporal local gradient; Spatial and Temporal brightness and angle distortions; Shadow tracking |
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Abstract ![sorted by Abstract field, ascending order (up)](img/sort_asc.gif) |
Advanced segmentation techniques in the surveillance domain deal with shadows to avoid distortions when detecting moving objects. Most approaches for shadow detection are still typically restricted to penumbra shadows and cannot cope well with umbra shadows. Consequently, umbra shadow regions are usually detected as part of moving objects, thus aecting the performance of the nal detection. In this paper we address the detection of both penumbra and umbra shadow regions. First, a novel bottom-up approach is presented based on gradient and colour models, which successfully discriminates between chromatic moving cast shadow regions and those regions detected as moving objects. In essence, those regions corresponding to potential shadows are detected based on edge partitioning and colour statistics. Subsequently (i) temporal similarities between textures and (ii) spatial similarities between chrominance angle and brightness distortions are analysed for each potential shadow region for detecting the umbra shadow regions. Our second contribution renes even further the segmentation results: a tracking-based top-down approach increases the performance of our bottom-up chromatic shadow detection algorithm by properly correcting non-detected shadows.
To do so, a combination of motion lters in a data association framework exploits the temporal consistency between objects and shadows to increase
the shadow detection rate. Experimental results exceed current state-of-the-
art in shadow accuracy for multiple well-known surveillance image databases which contain dierent shadowed materials and illumination conditions. |
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ISE; 600.078; 600.063 |
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Admin @ si @ HHM2015 |
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2703 |
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Carles Fernandez; Jordi Gonzalez; Xavier Roca |
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Title |
Automatic Learning of Background Semantics in Generic Surveilled Scenes |
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Conference Article |
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2010 |
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11th European Conference on Computer Vision |
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6313 |
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II |
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678–692 |
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Advanced surveillance systems for behavior recognition in outdoor traffic scenes depend strongly on the particular configuration of the scenario. Scene-independent trajectory analysis techniques statistically infer semantics in locations where motion occurs, and such inferences are typically limited to abnormality. Thus, it is interesting to design contributions that automatically categorize more specific semantic regions. State-of-the-art approaches for unsupervised scene labeling exploit trajectory data to segment areas like sources, sinks, or waiting zones. Our method, in addition, incorporates scene-independent knowledge to assign more meaningful labels like crosswalks, sidewalks, or parking spaces. First, a spatiotemporal scene model is obtained from trajectory analysis. Subsequently, a so-called GI-MRF inference process reinforces spatial coherence, and incorporates taxonomy-guided smoothness constraints. Our method achieves automatic and effective labeling of conceptual regions in urban scenarios, and is robust to tracking errors. Experimental validation on 5 surveillance databases has been conducted to assess the generality and accuracy of the segmentations. The resulting scene models are used for model-based behavior analysis. |
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Crete (Greece) |
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Springer Berlin Heidelberg |
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0302-9743 |
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978-3-642-15551-2 |
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ECCV |
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ISE |
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ISE @ ise @ FGR2010 |
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1439 |
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Sergio Escalera; Jordi Gonzalez; Xavier Baro; Fernando Alonso; Martha Mackay |
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Title |
Care Respite: a remote monitoring eHealth system for improving ambient assisted living |
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Conference Article |
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2016 |
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Human Motion Analysis for Healthcare Applications |
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Advances in technology that capture human motion have been quite remarkable during the last five years. New sensors have been developed, such as the Microsoft Kinect, Asus Xtion Pro live, PrimeSense Carmine and Leap Motion. Their main advantages are their non-intrusive nature, low cost and widely available support for developers offered by large corporations or Open Communities. Although they were originally developed for computer games, they have inspired numerous healthcare related ideas and projects in areas such as Medical Disorder Diagnosis, Assisted Living, Rehabilitation and Surgery.
In Assisted Living, human motion analysis allows continuous monitoring of elderly and vulnerable people and their activities to potentially detect life-threatening events such as falls. Human motion analysis in rehabilitation provides the opportunity for motivating patients through gamification, evaluating prescribed programmes of exercises and assessing patients’ progress. In operating theatres, surgeons may use a gesture-based interface to access medical information or control a tele-surgery system. Human motion analysis may also be used to diagnose a range of mental and physical diseases and conditions.
This event will discuss recent advances in human motion sensing and provide an application to healthcare for networking and exploring potential synergies and collaborations. |
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Savoy Place; London; uk; May 2016 |
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HMAHA |
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HuPBA; ISE; |
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Admin @ si @ EGB2016 |
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2852 |
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Author |
Kunal Biswas; Palaiahnakote Shivakumara; Umapada Pal; Tong Lu; Michel Blumenstein; Josep Llados |
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Title |
Classification of aesthetic natural scene images using statistical and semantic features |
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Journal Article |
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2023 |
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Multimedia Tools and Applications |
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MTAP |
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82 |
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9 |
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13507-13532 |
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Aesthetic image analysis is essential for improving the performance of multimedia image retrieval systems, especially from a repository of social media and multimedia content stored on mobile devices. This paper presents a novel method for classifying aesthetic natural scene images by studying the naturalness of image content using statistical features, and reading text in the images using semantic features. Unlike existing methods that focus only on image quality with human information, the proposed approach focuses on image features as well as text-based semantic features without human intervention to reduce the gap between subjectivity and objectivity in the classification. The aesthetic classes considered in this work are (i) Very Pleasant, (ii) Pleasant, (iii) Normal and (iv) Unpleasant. The naturalness is represented by features of focus, defocus, perceived brightness, perceived contrast, blurriness and noisiness, while semantics are represented by text recognition, description of the images and labels of images, profile pictures, and banner images. Furthermore, a deep learning model is proposed in a novel way to fuse statistical and semantic features for the classification of aesthetic natural scene images. Experiments on our own dataset and the standard datasets demonstrate that the proposed approach achieves 92.74%, 88.67% and 83.22% average classification rates on our own dataset, AVA dataset and CUHKPQ dataset, respectively. Furthermore, a comparative study of the proposed model with the existing methods shows that the proposed method is effective for the classification of aesthetic social media images. |
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DAG |
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Admin @ si @ BSP2023 |
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3873 |
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Author |
Bogdan Raducanu; Fadi Dornaika |
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Title |
Natural Facial Expression Recognition Using Dynamic and Static Schemes |
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Conference Article |
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2009 |
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5th International Symposium on Visual Computing |
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5875 |
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730–739 |
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Affective computing is at the core of a new paradigm in HCI and AI represented by human-centered computing. Within this paradigm, it is expected that machines will be enabled with perceiving capabilities, making them aware about users’ affective state. The current paper addresses the problem of facial expression recognition from monocular videos sequences. We propose a dynamic facial expression recognition scheme, which is proven to be very efficient. Furthermore, it is conveniently compared with several static-based systems adopting different magnitude of facial expression. We provide evaluations of performance using Linear Discriminant Analysis (LDA), Non parametric Discriminant Analysis (NDA), and Support Vector Machines (SVM). We also provide performance evaluations using arbitrary test video sequences. |
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Las Vegas, USA |
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Springer Berlin Heidelberg |
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0302-9743 |
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978-3-642-10330-8 |
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ISVC |
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OR;MV |
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no |
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BCNPCL @ bcnpcl @ RaD2009 |
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1257 |
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Author |
Eirikur Agustsson; Radu Timofte; Sergio Escalera; Xavier Baro; Isabelle Guyon; Rasmus Rothe |
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Title |
Apparent and real age estimation in still images with deep residual regressors on APPA-REAL database |
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Conference Article |
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2017 |
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12th IEEE International Conference on Automatic Face and Gesture Recognition |
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After decades of research, the real (biological) age estimation from a single face image reached maturity thanks to the availability of large public face databases and impressive accuracies achieved by recently proposed methods.
The estimation of “apparent age” is a related task concerning the age perceived by human observers. Significant advances have been also made in this new research direction with the recent Looking At People challenges. In this paper we make several contributions to age estimation research. (i) We introduce APPA-REAL, a large face image database with both real and apparent age annotations. (ii) We study the relationship between real and apparent age. (iii) We develop a residual age regression method to further improve the performance. (iv) We show that real age estimation can be successfully tackled as an apparent age estimation followed by an apparent to real age residual regression. (v) We graphically reveal the facial regions on which the CNN focuses in order to perform apparent and real age estimation tasks. |
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Washington;USA; May 2017 |
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HUPBA; no menciona |
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Admin @ si @ ATE2017 |
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3013 |
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A.S. Coquel; Jean-Pascal Jacob; M. Primet; A. Demarez; Mariella Dimiccoli; T. Julou; L. Moisan; A. Lindner; H. Berry |
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Title |
Localization of protein aggregation in Escherichia coli is governed by diffusion and nucleoid macromolecular crowding effect |
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Journal Article |
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2013 |
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Plos Computational Biology |
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PCB |
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9 |
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4 |
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Aggregates of misfolded proteins are a hallmark of many age-related diseases. Recently, they have been linked to aging of Escherichia coli (E. coli) where protein aggregates accumulate at the old pole region of the aging bacterium. Because of the potential of E. coli as a model organism, elucidating aging and protein aggregation in this bacterium may pave the way to significant advances in our global understanding of aging. A first obstacle along this path is to decipher the mechanisms by which protein aggregates are targeted to specific intercellular locations. Here, using an integrated approach based on individual-based modeling, time-lapse fluorescence microscopy and automated image analysis, we show that the movement of aging-related protein aggregates in E. coli is purely diffusive (Brownian). Using single-particle tracking of protein aggregates in live E. coli cells, we estimated the average size and diffusion constant of the aggregates. Our results provide evidence that the aggregates passively diffuse within the cell, with diffusion constants that depend on their size in agreement with the Stokes-Einstein law. However, the aggregate displacements along the cell long axis are confined to a region that roughly corresponds to the nucleoid-free space in the cell pole, thus confirming the importance of increased macromolecular crowding in the nucleoids. We thus used 3D individual-based modeling to show that these three ingredients (diffusion, aggregation and diffusion hindrance in the nucleoids) are sufficient and necessary to reproduce the available experimental data on aggregate localization in the cells. Taken together, our results strongly support the hypothesis that the localization of aging-related protein aggregates in the poles of E. coli results from the coupling of passive diffusion-aggregation with spatially non-homogeneous macromolecular crowding. They further support the importance of “soft” intracellular structuring (based on macromolecular crowding) in diffusion-based protein localization in E. coli. |
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: Stanislav Shvartsman, Princeton University, United States of America |
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Admin @ si @CJP2013 |
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2786 |
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Author |
Pierluigi Casale; Oriol Pujol; Petia Radeva |
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Title |
Classyfing Agitation in Sedated ICU Patients |
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Conference Article |
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2010 |
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Medical Image Computing in Catalunya: Graduate Student Workshop |
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19–20 |
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Agitation is a serious problem in sedated intensive care unit (ICU) patients. In this work, standard machine learning techniques working on wearable accelerometer data have been used to classifying agitation levels achieving very good classification performances. |
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Girona |
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MICCAT |
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MILAB;HUPBA |
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BCNPCL @ bcnpcl @ COR2010 |
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1467 |
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Dimosthenis Karatzas; V. Poulain d'Andecy; Marçal Rusiñol |
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Human-Document Interaction – a new frontier for document image analysis |
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Conference Article |
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2016 |
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12th IAPR Workshop on Document Analysis Systems |
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369-374 |
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All indications show that paper documents will not cede in favour of their digital counterparts, but will instead be used increasingly in conjunction with digital information. An open challenge is how to seamlessly link the physical with the digital – how to continue taking advantage of the important affordances of paper, without missing out on digital functionality. This paper
presents the authors’ experience with developing systems for Human-Document Interaction based on augmented document interfaces and examines new challenges and opportunities arising for the document image analysis field in this area. The system presented combines state of the art camera-based document
image analysis techniques with a range of complementary tech-nologies to offer fluid Human-Document Interaction. Both fixed and nomadic setups are discussed that have gone through user testing in real-life environments, and use cases are presented that span the spectrum from business to educational application |
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Santorini; Greece; April 2016 |
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DAG; 600.084; 600.077 |
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KPR2016 |
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2756 |
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Author |
Mikhail Mozerov; Joost Van de Weijer |
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Title |
Improved Recursive Geodesic Distance Computation for Edge Preserving Filter |
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Journal Article |
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2017 |
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IEEE Transactions on Image Processing |
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TIP |
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26 |
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8 |
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3696 - 3706 |
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Geodesic distance filter; color image filtering; image enhancement |
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All known recursive filters based on the geodesic distance affinity are realized by two 1D recursions applied in two orthogonal directions of the image plane. The 2D extension of the filter is not valid and has theoretically drawbacks, which lead to known artifacts. In this paper, a maximum influence propagation method is proposed to approximate the 2D extension for the
geodesic distance-based recursive filter. The method allows to partially overcome the drawbacks of the 1D recursion approach. We show that our improved recursion better approximates the true geodesic distance filter, and the application of this improved filter for image denoising outperforms the existing recursive implementation of the geodesic distance. As an application,
we consider a geodesic distance-based filter for image denoising.
Experimental evaluation of our denoising method demonstrates comparable and for several test images better results, than stateof-the-art approaches, while our algorithm is considerably fasterwith computational complexity O(8P). |
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LAMP; ISE; 600.120; 600.098; 600.119 |
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Admin @ si @ Moz2017 |
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2921 |
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Author |
Thierry Brouard; Jordi Gonzalez; Caifeng Shan; Massimo Piccardi; Larry S. Davis |
![download PDF file pdf](img/file_PDF.gif)
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Title |
Special issue on background modeling for foreground detection in real-world dynamic scenes |
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Journal Article |
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2014 |
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Machine Vision and Applications |
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MVAP |
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25 |
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5 |
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1101-1103 |
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Although background modeling and foreground detection are not mandatory steps for computer vision applications, they may prove useful as they separate the primal objects usually called “foreground” from the remaining part of the scene called “background”, and permits different algorithmic treatment in the video processing field such as video surveillance, optical motion capture, multimedia applications, teleconferencing and human–computer interfaces. Conventional background modeling methods exploit the temporal variation of each pixel to model the background, and the foreground detection is made using change detection. The last decade witnessed very significant publications on background modeling but recently new applications in which background is not static, such as recordings taken from mobile devices or Internet videos, need new developments to detect robustly moving objects in challenging environments. Thus, effective methods for robustness to deal both with dynamic backgrounds, i |
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Springer Berlin Heidelberg |
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0932-8092 |
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ISE; 600.078 |
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BGS2014a |
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2411 |
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Author |
Lei Kang; Pau Riba; Yaxing Wang; Marçal Rusiñol; Alicia Fornes; Mauricio Villegas |
![download PDF file pdf](img/file_PDF.gif)
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Title |
GANwriting: Content-Conditioned Generation of Styled Handwritten Word Images |
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Conference Article |
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2020 |
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16th European Conference on Computer Vision |
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Although current image generation methods have reached impressive quality levels, they are still unable to produce plausible yet diverse images of handwritten words. On the contrary, when writing by hand, a great variability is observed across different writers, and even when analyzing words scribbled by the same individual, involuntary variations are conspicuous. In this work, we take a step closer to producing realistic and varied artificially rendered handwritten words. We propose a novel method that is able to produce credible handwritten word images by conditioning the generative process with both calligraphic style features and textual content. Our generator is guided by three complementary learning objectives: to produce realistic images, to imitate a certain handwriting style and to convey a specific textual content. Our model is unconstrained to any predefined vocabulary, being able to render whatever input word. Given a sample writer, it is also able to mimic its calligraphic features in a few-shot setup. We significantly advance over prior art and demonstrate with qualitative, quantitative and human-based evaluations the realistic aspect of our synthetically produced images. |
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Virtual; August 2020 |
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ECCV |
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DAG; 600.140; 600.121; 600.129 |
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Admin @ si @ KPW2020 |
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3426 |
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Author |
Jaume Gibert; Ernest Valveny |
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Title |
Graph Embedding based on Nodes Attributes Representatives and a Graph of Words Representation. |
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Conference Article |
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2010 |
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13th International worshop on structural and syntactic pattern recognition and 8th international worshop on statistical pattern recognition |
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6218 |
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223–232 |
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Although graph embedding has recently been used to extend statistical pattern recognition techniques to the graph domain, some existing embeddings are usually computationally expensive as they rely on classical graph-based operations. In this paper we present a new way to embed graphs into vector spaces by first encapsulating the information stored in the original graph under another graph representation by clustering the attributes of the graphs to be processed. This new representation makes the association of graphs to vectors an easy step by just arranging both node attributes and the adjacency matrix in the form of vectors. To test our method, we use two different databases of graphs whose nodes attributes are of different nature. A comparison with a reference method permits to show that this new embedding is better in terms of classification rates, while being much more faster. |
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Springer Berlin Heidelberg |
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In E.R. Hancock, R.C. Wilson, T. Windeatt, I. Ulusoy and F. Escolano, |
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0302-9743 |
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978-3-642-14979-5 |
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S+SSPR |
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DAG |
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DAG @ dag @ GiV2010 |
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1416 |
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Author |
Michael Teutsch; Angel Sappa; Riad I. Hammoud |
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Title |
Cross-Spectral Image Processing |
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Book Chapter |
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2022 |
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Computer Vision in the Infrared Spectrum. Synthesis Lectures on Computer Vision |
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23-34 |
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Although this book is on IR computer vision and its main focus lies on IR image and video processing and analysis, a special attention is dedicated to cross-spectral image processing due to the increasing number of publications and applications in this domain. In these cross-spectral frameworks, IR information is used together with information from other spectral bands to tackle some specific problems by developing more robust solutions. Tasks considered for cross-spectral processing are for instance dehazing, segmentation, vegetation index estimation, or face recognition. This increasing number of applications is motivated by cross- and multi-spectral camera setups available already on the market like for example smartphones, remote sensing multispectral cameras, or multi-spectral cameras for automotive systems or drones. In this chapter, different cross-spectral image processing techniques will be reviewed together with possible applications. Initially, image registration approaches for the cross-spectral case are reviewed: the registration stage is the first image processing task, which is needed to align images acquired by different sensors within the same reference coordinate system. Then, recent cross-spectral image colorization approaches, which are intended to colorize infrared images for different applications are presented. Finally, the cross-spectral image enhancement problem is tackled by including guided super resolution techniques, image dehazing approaches, cross-spectral filtering and edge detection. Figure 3.1 illustrates cross-spectral image processing stages as well as their possible connections. Table 3.1 presents some of the available public cross-spectral datasets generally used as reference data to evaluate cross-spectral image registration, colorization, enhancement, or exploitation results. |
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Springer |
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SLCV |
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978-3-031-00698-2 |
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MSIAU; MACO |
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Admin @ si @ TSH2022b |
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3805 |
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