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Marc Serra. (2015). Modeling, estimation and evaluation of intrinsic images considering color information (Robert Benavente, & Olivier Penacchio, Eds.). Ph.D. thesis, Ediciones Graficas Rey, .
Abstract: Image values are the result of a combination of visual information coming from multiple sources. Recovering information from the multiple factors thatproduced an image seems a hard and ill-posed problem. However, it is important to observe that humans develop the ability to interpret images and recognize and isolate specific physical properties of the scene.
Images describing a single physical characteristic of an scene are called intrinsic images. These images would benefit most computer vision tasks which are often affected by the multiple complex effects that are usually found in natural images (e.g. cast shadows, specularities, interreflections...).
In this thesis we analyze the problem of intrinsic image estimation from different perspectives, including the theoretical formulation of the problem, the visual cues that can be used to estimate the intrinsic components and the evaluation mechanisms of the problem.
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Mikhail Mozerov, & Joost Van de Weijer. (2015). Global Color Sparseness and a Local Statistics Prior for Fast Bilateral Filtering. TIP - IEEE Transactions on Image Processing, 24(12), 5842–5853.
Abstract: The property of smoothing while preserving edges makes the bilateral filter a very popular image processing tool. However, its non-linear nature results in a computationally costly operation. Various works propose fast approximations to the bilateral filter. However, the majority does not generalize to vector input as is the case with color images. We propose a fast approximation to the bilateral filter for color images. The filter is based on two ideas. First, the number of colors, which occur in a single natural image, is limited. We exploit this color sparseness to rewrite the initial non-linear bilateral filter as a number of linear filter operations. Second, we impose a statistical prior to the image values that are locally present within the filter window. We show that this statistical prior leads to a closed-form solution of the bilateral filter. Finally, we combine both ideas into a single fast and accurate bilateral filter for color images. Experimental results show that our bilateral filter based on the local prior yields an extremely fast bilateral filter approximation, but with limited accuracy, which has potential application in real-time video filtering. Our bilateral filter, which combines color sparseness and local statistics, yields a fast and accurate bilateral filter approximation and obtains the state-of-the-art results.
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Dimosthenis Karatzas, Lluis Gomez, Anguelos Nicolaou, Suman Ghosh, Andrew Bagdanov, Masakazu Iwamura, et al. (2015). ICDAR 2015 Competition on Robust Reading. In 13th International Conference on Document Analysis and Recognition ICDAR2015 (pp. 1156–1160).
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Lluis Gomez, & Dimosthenis Karatzas. (2015). Object Proposals for Text Extraction in the Wild. In 13th International Conference on Document Analysis and Recognition ICDAR2015 (pp. 206–210).
Abstract: Object Proposals is a recent computer vision technique receiving increasing interest from the research community. Its main objective is to generate a relatively small set of bounding box proposals that are most likely to contain objects of interest. The use of Object Proposals techniques in the scene text understanding field is innovative. Motivated by the success of powerful while expensive techniques to recognize words in a holistic way, Object Proposals techniques emerge as an alternative to the traditional text detectors. In this paper we study to what extent the existing generic Object Proposals methods may be useful for scene text understanding. Also, we propose a new Object Proposals algorithm that is specifically designed for text and compare it with other generic methods in the state of the art. Experiments show that our proposal is superior in its ability of producing good quality word proposals in an efficient way. The source code of our method is made publicly available
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Anguelos Nicolaou, Andrew Bagdanov, Marcus Liwicki, & Dimosthenis Karatzas. (2015). Sparse Radial Sampling LBP for Writer Identification. In 13th International Conference on Document Analysis and Recognition ICDAR2015 (pp. 716–720).
Abstract: In this paper we present the use of Sparse Radial Sampling Local Binary Patterns, a variant of Local Binary Patterns (LBP) for text-as-texture classification. By adapting and extending the standard LBP operator to the particularities of text we get a generic text-as-texture classification scheme and apply it to writer identification. In experiments on CVL and ICDAR 2013 datasets, the proposed feature-set demonstrates State-Of-the-Art (SOA) performance. Among the SOA, the proposed method is the only one that is based on dense extraction of a single local feature descriptor. This makes it fast and applicable at the earliest stages in a DIA pipeline without the need for segmentation, binarization, or extraction of multiple features.
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Suman Ghosh, Lluis Gomez, Dimosthenis Karatzas, & Ernest Valveny. (2015). Efficient indexing for Query By String text retrieval. In 6th IAPR International Workshop on Camera Based Document Analysis and Recognition CBDAR2015 (pp. 1236–1240).
Abstract: This paper deals with Query By String word spotting in scene images. A hierarchical text segmentation algorithm based on text specific selective search is used to find text regions. These regions are indexed per character n-grams present in the text region. An attribute representation based on Pyramidal Histogram of Characters (PHOC) is used to compare text regions with the query text. For generation of the index a similar attribute space based Pyramidal Histogram of character n-grams is used. These attribute models are learned using linear SVMs over the Fisher Vector [1] representation of the images along with the PHOC labels of the corresponding strings.
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J.Kuhn, A.Nussbaumer, J.Pirker, Dimosthenis Karatzas, A. Pagani, O.Conlan, et al. (2015). Advancing Physics Learning Through Traversing a Multi-Modal Experimentation Space. In Workshop Proceedings on the 11th International Conference on Intelligent Environments (Vol. 19, pp. 373–380).
Abstract: Translating conceptual knowledge into real world experiences presents a significant educational challenge. This position paper presents an approach that supports learners in moving seamlessly between conceptual learning and their application in the real world by bringing physical and virtual experiments into everyday settings. Learners are empowered in conducting these situated experiments in a variety of physical settings by leveraging state of the art mobile, augmented reality, and virtual reality technology. A blend of mobile-based multi-sensory physical experiments, augmented reality and enabling virtual environments can allow learners to bridge their conceptual learning with tangible experiences in a completely novel manner. This approach focuses on the learner by applying self-regulated personalised learning techniques, underpinned by innovative pedagogical approaches and adaptation techniques, to ensure that the needs and preferences of each learner are catered for individually.
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Maedeh Aghaei, Mariella Dimiccoli, & Petia Radeva. (2015). Towards social interaction detection in egocentric photo-streams. In Proceedings of SPIE, 8th International Conference on Machine Vision , ICMV 2015 (Vol. 9875).
Abstract: Detecting social interaction in videos relying solely on visual cues is a valuable task that is receiving increasing attention in recent years. In this work, we address this problem in the challenging domain of egocentric photo-streams captured by a low temporal resolution wearable camera (2fpm). The major difficulties to be handled in this context are the sparsity of observations as well as unpredictability of camera motion and attention orientation due to the fact that the camera is worn as part of clothing. Our method consists of four steps: multi-faces localization and tracking, 3D localization, pose estimation and analysis of f-formations. By estimating pair-to-pair interaction probabilities over the sequence, our method states the presence or absence of interaction with the camera wearer and specifies which people are more involved in the interaction. We tested our method over a dataset of 18.000 images and we show its reliability on our considered purpose. © (2015) COPYRIGHT Society of Photo-Optical Instrumentation Engineers (SPIE). Downloading of the abstract is permitted for personal use only.
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Ivan Huerta, Michael Holte, Thomas B. Moeslund, & Jordi Gonzalez. (2015). Chromatic shadow detection and tracking for moving foreground segmentation. IMAVIS - Image and Vision Computing, 41, 42–53.
Abstract: 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.
Keywords: Detecting moving objects; Chromatic shadow detection; Temporal local gradient; Spatial and Temporal brightness and angle distortions; Shadow tracking
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Sergio Escalera, Junior Fabian, Pablo Pardo, Xavier Baro, Jordi Gonzalez, Hugo Jair Escalante, et al. (2015). ChaLearn Looking at People 2015: Apparent Age and Cultural Event Recognition Datasets and Results. In 16th IEEE International Conference on Computer Vision Workshops (pp. 243–251).
Abstract: Following previous series on Looking at People (LAP) competitions [14, 13, 11, 12, 2], in 2015 ChaLearn ran two new competitions within the field of Looking at People: (1) age estimation, and (2) cultural event recognition, both in
still images. We developed a crowd-sourcing application to collect and label data about the apparent age of people (as opposed to the real age). In terms of cultural event recognition, one hundred categories had to be recognized. These
tasks involved scene understanding and human body analysis. This paper summarizes both challenges and data, as well as the results achieved by the participants of the competition.
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Josep M. Gonfaus, Marco Pedersoli, Jordi Gonzalez, Andrea Vedaldi, & Xavier Roca. (2015). Factorized appearances for object detection. CVIU - Computer Vision and Image Understanding, 138, 92–101.
Abstract: Deformable object models capture variations in an object’s appearance that can be represented as image deformations. Other effects such as out-of-plane rotations, three-dimensional articulations, and self-occlusions are often captured by considering mixture of deformable models, one per object aspect. A more scalable approach is representing instead the variations at the level of the object parts, applying the concept of a mixture locally. Combining a few part variations can in fact cheaply generate a large number of global appearances.
A limited version of this idea was proposed by Yang and Ramanan [1], for human pose dectection. In this paper we apply it to the task of generic object category detection and extend it in several ways. First, we propose a model for the relationship between part appearances more general than the tree of Yang and Ramanan [1], which is more suitable for generic categories. Second, we treat part locations as well as their appearance as latent variables so that training does not need part annotations but only the object bounding boxes. Third, we modify the weakly-supervised learning of Felzenszwalb et al. and Girshick et al. [2], [3] to handle a significantly more complex latent structure.
Our model is evaluated on standard object detection benchmarks and is found to improve over existing approaches, yielding state-of-the-art results for several object categories.
Keywords: Object recognition; Deformable part models; Learning and sharing parts; Discovering discriminative parts
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Alejandro Gonzalez Alzate. (2015). Multi-modal Pedestrian Detection (David Vazquez, Antonio Lopez, &, Ed.). Ph.D. thesis, Ediciones Graficas Rey, .
Abstract: Pedestrian detection continues to be an extremely challenging problem in real scenarios, in which situations like illumination changes, noisy images, unexpected objects, uncontrolled scenarios and variant appearance of objects occur constantly. All these problems force the development of more robust detectors for relevant applications like vision-based autonomous vehicles, intelligent surveillance, and pedestrian tracking for behavior analysis. Most reliable vision-based pedestrian detectors base their decision on features extracted using a single sensor capturing complementary features, e.g., appearance, and texture. These features usually are extracted from the current frame, ignoring temporal information, or including it in a post process step e.g., tracking or temporal coherence. Taking into account these issues we formulate the following question: can we generate more robust pedestrian detectors by introducing new information sources in the feature extraction step?
In order to answer this question we develop different approaches for introducing new information sources to well-known pedestrian detectors. We start by the inclusion of temporal information following the Stacked Sequential Learning (SSL) paradigm which suggests that information extracted from the neighboring samples in a sequence can improve the accuracy of a base classifier.
We then focus on the inclusion of complementary information from different sensors like 3D point clouds (LIDAR – depth), far infrared images (FIR), or disparity maps (stereo pair cameras). For this end we develop a multi-modal framework in which information from different sensors is used for increasing detection accuracy (by increasing information redundancy). Finally we propose a multi-view pedestrian detector, this multi-view approach splits the detection problem in n sub-problems.
Each sub-problem will detect objects in a given specific view reducing in that way the variability problem faced when a single detectors is used for the whole problem. We show that these approaches obtain competitive results with other state-of-the-art methods but instead of design new features, we reuse existing ones boosting their performance.
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Adriana Romero. (2015). Assisting the training of deep neural networks with applications to computer vision (Carlo Gatta, & Petia Radeva, Eds.). Ph.D. thesis, Ediciones Graficas Rey, .
Abstract: Deep learning has recently been enjoying an increasing popularity due to its success in solving challenging tasks. In particular, deep learning has proven to be effective in a large variety of computer vision tasks, such as image classification, object recognition and image parsing. Contrary to previous research, which required engineered feature representations, designed by experts, in order to succeed, deep learning attempts to learn representation hierarchies automatically from data. More recently, the trend has been to go deeper with representation hierarchies.
Learning (very) deep representation hierarchies is a challenging task, which
involves the optimization of highly non-convex functions. Therefore, the search
for algorithms to ease the learning of (very) deep representation hierarchies from data is extensive and ongoing.
In this thesis, we tackle the challenging problem of easing the learning of (very) deep representation hierarchies. We present a hyper-parameter free, off-the-shelf, simple and fast unsupervised algorithm to discover hidden structure from the input data by enforcing a very strong form of sparsity. We study the applicability and potential of the algorithm to learn representations of varying depth in a handful of applications and domains, highlighting the ability of the algorithm to provide discriminative feature representations that are able to achieve top performance.
Yet, while emphasizing the great value of unsupervised learning methods when
labeled data is scarce, the recent industrial success of deep learning has revolved around supervised learning. Supervised learning is currently the focus of many recent research advances, which have shown to excel at many computer vision tasks. Top performing systems often involve very large and deep models, which are not well suited for applications with time or memory limitations. More in line with the current trends, we engage in making top performing models more efficient, by designing very deep and thin models. Since training such very deep models still appears to be a challenging task, we introduce a novel algorithm that guides the training of very thin and deep models by hinting their intermediate representations.
Very deep and thin models trained by the proposed algorithm end up extracting feature representations that are comparable or even better performing
than the ones extracted by large state-of-the-art models, while compellingly
reducing the time and memory consumption of the model.
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Sergio Vera. (2015). Anatomic Registration based on Medial Axis Parametrizations (Debora Gil, & Miguel Angel Gonzalez Ballester, Eds.). Ph.D. thesis, Ediciones Graficas Rey, .
Abstract: Image registration has been for many years the gold standard method to bring two images into correspondence. It has been used extensively in the eld of medical imaging in order to put images of dierent patients into a common overlapping spatial position. However, medical image registration is a slow, iterative optimization process, where many variables and prone to fall into the pit traps local minima.
A coordinate system parameterizing the interior of organs is a powerful tool for a systematic localization of injured tissue. If the same coordinate values are assigned to specic anatomical sites, parameterizations ensure integration of data across different medical image modalities. Harmonic mappings have been used to produce parametric meshes over the surface of anatomical shapes, given their ability to set values at specic locations through boundary conditions. However, most of the existing implementations in medical imaging restrict to either anatomical surfaces, or the depth coordinate with boundary conditions is given at discrete sites of limited geometric diversity.
The medial surface of the shape can be used to provide a continuous basis for the denition of a depth coordinate. However, given that dierent methods for generation of medial surfaces generate dierent manifolds, not all of them are equally suited to be the basis of radial coordinate for a parameterization. It would be desirable that the medial surface will be smooth, and robust to surface shape noise, with low number of spurious branches or surfaces.
In this thesis we present methods for computation of smooth medial manifolds and apply them to the generation of for anatomical volumetric parameterization that extends current harmonic parameterizations to the interior anatomy using information provided by the volume medial surface. This reference system sets a solid base for creating anatomical models of the anatomical shapes, and allows comparing several patients in a common framework of reference.
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Joan M. Nuñez. (2015). Vascular Pattern Characterization in Colonoscopy Images (Fernando Vilariño, Ed.). Ph.D. thesis, Ediciones Graficas Rey, .
Abstract: Colorectal cancer is the third most common cancer worldwide and the second most common malignant tumor in Europe. Screening tests have shown to be very eective in increasing the survival rates since they allow an early detection of polyps. Among the dierent screening techniques, colonoscopy is considered the gold standard although clinical studies mention several problems that have an impact in the quality of the procedure. The navigation through the rectum and colon track can be challenging for the physicians which can increase polyp miss rates. The thorough visualization of the colon track must be ensured so that
the chances of missing lesions are minimized. The visual analysis of colonoscopy images can provide important information to the physicians and support their navigation during the procedure.
Blood vessels and their branching patterns can provide descriptive power to potentially develop biometric markers. Anatomical markers based on blood vessel patterns could be used to identify a particular scene in colonoscopy videos and to support endoscope navigation by generating a sequence of ordered scenes through the dierent colon sections. By verifying the presence of vascular content in the endoluminal scene it is also possible to certify a proper
inspection of the colon mucosa and to improve polyp localization. Considering the potential uses of blood vessel description, this contribution studies the characterization of the vascular content and the analysis of the descriptive power of its branching patterns.
Blood vessel characterization in colonoscopy images is shown to be a challenging task. The endoluminal scene is conformed by several elements whose similar characteristics hinder the development of particular models for each of them. To overcome such diculties we propose the use of the blood vessel branching characteristics as key features for pattern description. We present a model to characterize junctions in binary patterns. The implementation
of the junction model allows us to develop a junction localization method. We
created two data sets including manually labeled vessel information as well as manual ground truths of two types of keypoint landmarks: junctions and endpoints. The proposed method outperforms the available algorithms in the literature in experiments in both, our newly created colon vessel data set, and in DRIVE retinal fundus image data set. In the latter case, we created a manual ground truth of junction coordinates. Since we want to explore the descriptive potential of junctions and vessels, we propose a graph-based approach to
create anatomical markers. In the context of polyp localization, we present a new method to inhibit the in uence of blood vessels in the extraction valley-prole information. The results show that our methodology decreases vessel in
uence, increases polyp information and leads to an improvement in state-of-the-art polyp localization performance. We also propose a polyp-specic segmentation method that outperforms other general and specic approaches.
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