Arjan Gijsenij, Theo Gevers, & Joost Van de Weijer. (2012). Improving Color Constancy by Photometric Edge Weighting. TPAMI - IEEE Transaction on Pattern Analysis and Machine Intelligence, 34(5), 918–929.
Abstract: : Edge-based color constancy methods make use of image derivatives to estimate the illuminant. However, different edge types exist in real-world images such as material, shadow and highlight edges. These different edge types may have a distinctive influence on the performance of the illuminant estimation. Therefore, in this paper, an extensive analysis is provided of different edge types on the performance of edge-based color constancy methods. First, an edge-based taxonomy is presented classifying edge types based on their photometric properties (e.g. material, shadow-geometry and highlights). Then, a performance evaluation of edge-based color constancy is provided using these different edge types. From this performance evaluation it is derived that specular and shadow edge types are more valuable than material edges for the estimation of the illuminant. To this end, the (iterative) weighted Grey-Edge algorithm is proposed in which these edge types are more emphasized for the estimation of the illuminant. Images that are recorded under controlled circumstances demonstrate that the proposed iterative weighted Grey-Edge algorithm based on highlights reduces the median angular error with approximately $25\%$. In an uncontrolled environment, improvements in angular error up to $11\%$ are obtained with respect to regular edge-based color constancy.
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Miquel Ferrer, Dimosthenis Karatzas, Ernest Valveny, I. Bardaji, & Horst Bunke. (2011). A Generic Framework for Median Graph Computation based on a Recursive Embedding Approach. CVIU - Computer Vision and Image Understanding, 115(7), 919–928.
Abstract: The median graph has been shown to be a good choice to obtain a represen- tative of a set of graphs. However, its computation is a complex problem. Recently, graph embedding into vector spaces has been proposed to obtain approximations of the median graph. The problem with such an approach is how to go from a point in the vector space back to a graph in the graph space. The main contribution of this paper is the generalization of this previ- ous method, proposing a generic recursive procedure that permits to recover the graph corresponding to a point in the vector space, introducing only the amount of approximation inherent to the use of graph matching algorithms. In order to evaluate the proposed method, we compare it with the set me- dian and with the other state-of-the-art embedding-based methods for the median graph computation. The experiments are carried out using four dif- ferent databases (one semi-artificial and three containing real-world data). Results show that with the proposed approach we can obtain better medi- ans, in terms of the sum of distances to the training graphs, than with the previous existing methods.
Keywords: Median Graph, Graph Embedding, Graph Matching, Structural Pattern Recognition
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Shifeng Zhang, Xiaobo Wang, Ajian Liu, Chenxu Zhao, Jun Wan, Sergio Escalera, et al. (2019). A Dataset and Benchmark for Large-scale Multi-modal Face Anti-spoofing. In 32nd IEEE Conference on Computer Vision and Pattern Recognition (pp. 919–928).
Abstract: Face anti-spoofing is essential to prevent face recognition systems from a security breach. Much of the progresses have been made by the availability of face anti-spoofing benchmark datasets in recent years. However, existing face anti-spoofing benchmarks have limited number of subjects (≤170) and modalities (≤2), which hinder the further development of the academic community. To facilitate face anti-spoofing research, we introduce a large-scale multi-modal dataset, namely CASIA-SURF, which is the largest publicly available dataset for face anti-spoofing in terms of both subjects and visual modalities. Specifically, it consists of 1,000 subjects with 21,000 videos and each sample has 3 modalities (i.e., RGB, Depth and IR). We also provide a measurement set, evaluation protocol and training/validation/testing subsets, developing a new benchmark for face anti-spoofing. Moreover, we present a new multi-modal fusion method as baseline, which performs feature re-weighting to select the more informative channel features while suppressing the less useful ones for each modal. Extensive experiments have been conducted on the proposed dataset to verify its significance and generalization capability. The dataset is available at https://sites.google.com/qq.com/chalearnfacespoofingattackdete/.
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Hugo Jair Escalante, Victor Ponce, Sergio Escalera, Xavier Baro, Alicia Morales-Reyes, & Jose Martinez-Carranza. (2017). Evolving weighting schemes for the Bag of Visual Words. Neural Computing and Applications - Neural Computing and Applications, 28(5), 925–939.
Abstract: The Bag of Visual Words (BoVW) is an established representation in computer vision. Taking inspiration from text mining, this representation has proved
to be very effective in many domains. However, in most cases, standard term-weighting schemes are adopted (e.g.,term-frequency or TF-IDF). It remains open the question of whether alternative weighting schemes could boost the
performance of methods based on BoVW. More importantly, it is unknown whether it is possible to automatically learn and determine effective weighting schemes from
scratch. This paper brings some light into both of these unknowns. On the one hand, we report an evaluation of the most common weighting schemes used in text mining, but rarely used in computer vision tasks. Besides, we propose an evolutionary algorithm capable of automatically learning weighting schemes for computer vision problems. We report empirical results of an extensive study in several computer vision problems. Results show the usefulness of the proposed method.
Keywords: Bag of Visual Words; Bag of features; Genetic programming; Term-weighting schemes; Computer vision
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Fadi Dornaika, & Bogdan Raducanu. (2009). Three-Dimensional Face Pose Detection and Tracking Using Monocular Videos: Tool and Application. TSMCB - IEEE Transactions on Systems, Man and Cybernetics part B, 39(4), 935–944.
Abstract: Recently, we have proposed a real-time tracker that simultaneously tracks the 3-D head pose and facial actions in monocular video sequences that can be provided by low quality cameras. This paper has two main contributions. First, we propose an automatic 3-D face pose initialization scheme for the real-time tracker by adopting a 2-D face detector and an eigenface system. Second, we use the proposed methods-the initialization and tracking-for enhancing the human-machine interaction functionality of an AIBO robot. More precisely, we show how the orientation of the robot's camera (or any active vision system) can be controlled through the estimation of the user's head pose. Applications based on head-pose imitation such as telepresence, virtual reality, and video games can directly exploit the proposed techniques. Experiments on real videos confirm the robustness and usefulness of the proposed methods.
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Carles Sanchez, Jorge Bernal, F. Javier Sanchez, Antoni Rosell, Marta Diez-Ferrer, & Debora Gil. (2015). Towards On-line Quantification of Tracheal Stenosis from Videobronchoscopy. IJCAR - International Journal of Computer Assisted Radiology and Surgery, 10(6), 935–945.
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Carles Sanchez, Jorge Bernal, F. Javier Sanchez, Marta Diez-Ferrer, Antoni Rosell, & Debora Gil. (2015). Towards On-line Quantification of Tracheal Stenosis from Videobronchoscopy. In 6th International Conference on Information Processing in Computer-Assisted Interventions IPCAI2015 (Vol. 10, pp. 935–945).
Abstract: PURPOSE:
Lack of objective measurement of tracheal obstruction degree has a negative impact on the chosen treatment prone to lead to unnecessary repeated explorations and other scanners. Accurate computation of tracheal stenosis in videobronchoscopy would constitute a breakthrough for this noninvasive technique and a reduction in operation cost for the public health service.
METHODS:
Stenosis calculation is based on the comparison of the region delimited by the lumen in an obstructed frame and the region delimited by the first visible ring in a healthy frame. We propose a parametric strategy for the extraction of lumen and tracheal ring regions based on models of their geometry and appearance that guide a deformable model. To ensure a systematic applicability, we present a statistical framework to choose optimal parametric values and a strategy to choose the frames that minimize the impact of scope optical distortion.
RESULTS:
Our method has been tested in 40 cases covering different stenosed tracheas. Experiments report a non- clinically relevant [Formula: see text] of discrepancy in the calculated stenotic area and a computational time allowing online implementation in the operating room.
CONCLUSIONS:
Our methodology allows reliable measurements of airway narrowing in the operating room. To fully assess its clinical impact, a prospective clinical trial should be done.
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Jose Antonio Rodriguez, Gemma Sanchez, & Josep Llados. (2007). A Pen-based Interface for Real-time Document Edition. In 9th International Conference on Document Analysis and Recognition. (Vol. 2, 939–944).
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Mario Rojas, David Masip, & Jordi Vitria. (2011). Predicting Dominance Judgements Automatically: A Machine Learning Approach. In IEEE International Workshop on Social Behavior Analysis (pp. 939–944).
Abstract: The amount of multimodal devices that surround us is growing everyday. In this context, human interaction and communication have become a focus of attention and a hot topic of research. A crucial element in human relations is the evaluation of individuals with respect to facial traits, what is called a first impression. Studies based on appearance have suggested that personality can be expressed by appearance and the observer may use such information to form judgments. In the context of rapid facial evaluation, certain personality traits seem to have a more pronounced effect on the relations and perceptions inside groups. The perception of dominance has been shown to be an active part of social roles at different stages of life, and even play a part in mate selection. The aim of this paper is to study to what extent this information is learnable from the point of view of computer science. Specifically we intend to determine if judgments of dominance can be learned by machine learning techniques. We implement two different descriptors in order to assess this. The first is the histogram of oriented gradients (HOG), and the second is a probabilistic appearance descriptor based on the frequencies of grouped binary tests. State of the art classification rules validate the performance of both descriptors, with respect to the prediction task. Experimental results show that machine learning techniques can predict judgments of dominance rather accurately (accuracies up to 90%) and that the HOG descriptor may characterize appropriately the information necessary for such task.
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Miguel Reyes, Jose Ramirez Moreno, Juan R Revilla, Petia Radeva, & Sergio Escalera. (2011). ADiBAS: Sistema Multisensor de Adquisicion Automatica de Datos Corporales Objetivos, Robustos y Fiables para el Analisis de la Postura y el Movimiento. In 6th Congreso Iberoamericano de Tecnologia de Apoyo a la Discapacidad (pp. 939–944).
Abstract: El análisis de la postura y del rango de movimiento son fundamentales para conocer la optimización del gesto y mejorar, de este modo, el rendimiento y la detección de posibles lesiones. Esta cuantificación es especialmente interesante en deportistas o en pacientes que presentan alguna lesión neurológica o del sistema musculo-esquelético, ya que permite conocer el proceso evolutivo de estos pacientes, evaluar la eficacia de la terapia aplicada y proponer, en caso necesario, una modificación del protocolo de tratamiento.
En este trabajo presentamos un sistema automático que permite, mediante una tecnología no invasiva, la captación automática de marcadores LED situados sobre el paciente y su posterior análisis con el fin de mostrar al especialista datos objetivos que permitan un mejor soporte diagnóstico. También se describe un
sistema analítico de la postura corporal sin marcadores, donde su ejecución durante secuencias dinámicas aporta un alto grado de naturalidad al paciente a la hora de realizar los ejercicios funcionales.
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Mikhail Mozerov, Fei Yang, & Joost Van de Weijer. (2019). Sparse Data Interpolation Using the Geodesic Distance Affinity Space. SPL - IEEE Signal Processing Letters, 26(6), 943–947.
Abstract: In this letter, we adapt the geodesic distance-based recursive filter to the sparse data interpolation problem. The proposed technique is general and can be easily applied to any kind of sparse data. We demonstrate its superiority over other interpolation techniques in three experiments for qualitative and quantitative evaluation. In addition, we compare our method with the popular interpolation algorithm presented in the paper on EpicFlow optical flow, which is intuitively motivated by a similar geodesic distance principle. The comparison shows that our algorithm is more accurate and considerably faster than the EpicFlow interpolation technique.
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Alejandro Cartas, Juan Marin, Petia Radeva, & Mariella Dimiccoli. (2018). Batch-based activity recognition from egocentric photo-streams revisited. PAA - Pattern Analysis and Applications, 21(4), 953–965.
Abstract: Wearable cameras can gather large amounts of image data that provide rich visual information about the daily activities of the wearer. Motivated by the large number of health applications that could be enabled by the automatic recognition of daily activities, such as lifestyle characterization for habit improvement, context-aware personal assistance and tele-rehabilitation services, we propose a system to classify 21 daily activities from photo-streams acquired by a wearable photo-camera. Our approach combines the advantages of a late fusion ensemble strategy relying on convolutional neural networks at image level with the ability of recurrent neural networks to account for the temporal evolution of high-level features in photo-streams without relying on event boundaries. The proposed batch-based approach achieved an overall accuracy of 89.85%, outperforming state-of-the-art end-to-end methodologies. These results were achieved on a dataset consists of 44,902 egocentric pictures from three persons captured during 26 days in average.
Keywords: Egocentric vision; Lifelogging; Activity recognition; Deep learning; Recurrent neural networks
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Fadi Dornaika, Jose Manuel Alvarez, Angel Sappa, & Antonio Lopez. (2011). A New Framework for Stereo Sensor Pose through Road Segmentation and Registration. TITS - IEEE Transactions on Intelligent Transportation Systems, 12(4), 954–966.
Abstract: This paper proposes a new framework for real-time estimation of the onboard stereo head's position and orientation relative to the road surface, which is required for any advanced driver-assistance application. This framework can be used with all road types: highways, urban, etc. Unlike existing works that rely on feature extraction in either the image domain or 3-D space, we propose a framework that directly estimates the unknown parameters from the stream of stereo pairs' brightness. The proposed approach consists of two stages that are invoked for every stereo frame. The first stage segments the road region in one monocular view. The second stage estimates the camera pose using a featureless registration between the segmented monocular road region and the other view in the stereo pair. This paper has two main contributions. The first contribution combines a road segmentation algorithm with a registration technique to estimate the online stereo camera pose. The second contribution solves the registration using a featureless method, which is carried out using two different optimization techniques: 1) the differential evolution algorithm and 2) the Levenberg-Marquardt (LM) algorithm. We provide experiments and evaluations of performance. The results presented show the validity of our proposed framework.
Keywords: road detection
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M. Altillawi, S. Li, S.M. Prakhya, Z. Liu, & Joan Serrat. (2024). Implicit Learning of Scene Geometry From Poses for Global Localization. ROBOTAUTOMLET - IEEE Robotics and Automation Letters, 9(2), 955–962.
Abstract: Global visual localization estimates the absolute pose of a camera using a single image, in a previously mapped area. Obtaining the pose from a single image enables many robotics and augmented/virtual reality applications. Inspired by latest advances in deep learning, many existing approaches directly learn and regress 6 DoF pose from an input image. However, these methods do not fully utilize the underlying scene geometry for pose regression. The challenge in monocular relocalization is the minimal availability of supervised training data, which is just the corresponding 6 DoF poses of the images. In this letter, we propose to utilize these minimal available labels (i.e., poses) to learn the underlying 3D geometry of the scene and use the geometry to estimate the 6 DoF camera pose. We present a learning method that uses these pose labels and rigid alignment to learn two 3D geometric representations ( X, Y, Z coordinates ) of the scene, one in camera coordinate frame and the other in global coordinate frame. Given a single image, it estimates these two 3D scene representations, which are then aligned to estimate a pose that matches the pose label. This formulation allows for the active inclusion of additional learning constraints to minimize 3D alignment errors between the two 3D scene representations, and 2D re-projection errors between the 3D global scene representation and 2D image pixels, resulting in improved localization accuracy. During inference, our model estimates the 3D scene geometry in camera and global frames and aligns them rigidly to obtain pose in real-time. We evaluate our work on three common visual localization datasets, conduct ablation studies, and show that our method exceeds state-of-the-art regression methods' pose accuracy on all datasets.
Keywords: Localization; Localization and mapping; Deep learning for visual perception; Visual learning
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Fadi Dornaika, & Bogdan Raducanu. (2008). Constructing Panoramic Views Through Facial Gaze Tracking. In IEEE International Conference on Multimedia and Expo, (969–972).
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