Francisco Cruz, & Oriol Ramos Terrades. (2012). Document segmentation using relative location features. In 21st International Conference on Pattern Recognition (pp. 1562–1565).
Abstract: In this paper we evaluate the use of Relative Location Features (RLF) on a historical document segmentation task, and compare the quality of the results obtained on structured and unstructured documents using RLF and not using them. We prove that using these features improve the final segmentation on documents with a strong structure, while their application on unstructured documents does not show significant improvement. Although this paper is not focused on segmenting unstructured documents, results obtained on a benchmark dataset are equal or even overcome previous results of similar works.
|
Francisco Cruz, & Oriol Ramos Terrades. (2014). EM-Based Layout Analysis Method for Structured Documents. In 22nd International Conference on Pattern Recognition (pp. 315–320).
Abstract: In this paper we present a method to perform layout analysis in structured documents. We proposed an EM-based algorithm to fit a set of Gaussian mixtures to the different regions according to the logical distribution along the page. After the convergence, we estimate the final shape of the regions according
to the parameters computed for each component of the mixture. We evaluated our method in the task of record detection in a collection of historical structured documents and performed a comparison with other previous works in this task.
|
Francisco Cruz, & Oriol Ramos Terrades. (2018). A probabilistic framework for handwritten text line segmentation.
Abstract: We successfully combine Expectation-Maximization algorithm and variational
approaches for parameter learning and computing inference on Markov random fields. This is a general method that can be applied to many computer
vision tasks. In this paper, we apply it to handwritten text line segmentation.
We conduct several experiments that demonstrate that our method deal with
common issues of this task, such as complex document layout or non-latin
scripts. The obtained results prove that our method achieve state-of-theart performance on different benchmark datasets without any particular fine
tuning step.
Keywords: Document Analysis; Text Line Segmentation; EM algorithm; Probabilistic Graphical Models; Parameter Learning
|
Francisco Javier Orozco. (2007). Face Detection and Tracking for Facial Expression Analysis.
|
Francisco Javier Orozco. (2010). Human Emotion Evaluation on Facial Image Sequences (Jordi Gonzalez, & Xavier Roca, Eds.). Ph.D. thesis, Ediciones Graficas Rey, .
Abstract: Psychological evidence has emphasized the importance of affective behaviour understanding due to its high impact in nowadays interaction humans and computers. All
type of affective and behavioural patterns such as gestures, emotions and mental
states are highly displayed through the face, head and body. Therefore, this thesis is
focused to analyse affective behaviours on head and face. To this end, head and facial
movements are encoded by using appearance based tracking methods. Specifically,
a wise combination of deformable models captures rigid and non-rigid movements of
different kinematics; 3D head pose, eyebrows, mouth, eyelids and irises are taken into
account as basis for extracting features from databases of video sequences. This approach combines the strengths of adaptive appearance models, optimization methods
and backtracking techniques.
For about thirty years, computer sciences have addressed the investigation on
human emotions to the automatic recognition of six prototypic emotions suggested
by Darwin and systematized by Paul Ekman in the seventies. The Facial Action
Coding System (FACS) which uses discrete movements of the face (called Action
units or AUs) to code the six facial emotions named anger, disgust, fear, happy-Joy,
sadness and surprise. However, human emotions are much complex patterns that
have not received the same attention from computer scientists.
Simon Baron-Cohen proposed a new taxonomy of emotions and mental states
without a system coding of the facial actions. These 426 affective behaviours are
more challenging for the understanding of human emotions. Beyond of classically
classifying the six basic facial expressions, more subtle gestures, facial actions and
spontaneous emotions are considered here. By assessing confidence on the recognition
results, exploring spatial and temporal relationships of the features, some methods are
combined and enhanced for developing new taxonomy of expressions and emotions.
The objective of this dissertation is to develop a computer vision system, including both facial feature extraction, expression recognition and emotion understanding
by building a bottom-up reasoning process. Building a detailed taxonomy of human
affective behaviours is an interesting challenge for head-face-based image analysis
methods. In this paper, we exploit the strengths of Canonical Correlation Analysis
(CCA) to enhance an on-line head-face tracker. A relationship between head pose and
local facial movements is studied according to their cognitive interpretation on affective expressions and emotions. Active Shape Models are synthesized for AAMs based
on CCA-regression. Head pose and facial actions are fused into a maximally correlated space in order to assess expressiveness, confidence and classification in a CBR system. The CBR solutions are also correlated to the cognitive features, which allow
avoiding exhaustive search when recognizing new head-face features. Subsequently,
Support Vector Machines (SVMs) and Bayesian Networks are applied for learning the
spatial relationships of facial expressions. Similarly, the temporal evolution of facial
expressions, emotion and mental states are analysed based on Factorized Dynamic
Bayesian Networks (FaDBN).
As results, the bottom-up system recognizes six facial expressions, six basic emotions and six mental states, plus enhancing this categorization with confidence assessment at each level, intensity of expressions and a complete taxonomy
|
Francisco Javier Orozco, F.A. Garcia, J.L. Arcos, & Jordi Gonzalez. (2007). Spatio-Temporal Reasoning for Reliable Facial Expression Interpretation. In Proceedings of the 5th International Conference on Computer Vision Systems.
|
Francisco Javier Orozco, & Jordi Gonzalez. (2008). Confidence Assessment on Eyelid and Eyebrow Expression Recognition. In 2008 8th IEEE International Conference on Automatic Face and Gesture Recognition (FG 2008).
|
Francisco Javier Orozco, Jordi Gonzalez, Ignasi Rius, & Xavier Roca. (2007). Hierarchical Eyelid and Face Tracking. In 3rd Iberian Conference on Pattern Recognition and Image Analysis (IbPRIA 2007), J. Marti et al. (Eds.) LNCS 4477:499–506.
|
Francisco Javier Orozco, Ognjen Rudovic, Jordi Gonzalez, & Maja Pantic. (2013). Hierarchical On-line Appearance-Based Tracking for 3D Head Pose, Eyebrows, Lips, Eyelids and Irises. IMAVIS - Image and Vision Computing, 31(4), 322–340.
Abstract: In this paper, we propose an On-line Appearance-Based Tracker (OABT) for simultaneous tracking of 3D head pose, lips, eyebrows, eyelids and irises in monocular video sequences. In contrast to previously proposed tracking approaches, which deal with face and gaze tracking separately, our OABT can also be used for eyelid and iris tracking, as well as 3D head pose, lips and eyebrows facial actions tracking. Furthermore, our approach applies an on-line learning of changes in the appearance of the tracked target. Hence, the prior training of appearance models, which usually requires a large amount of labeled facial images, is avoided. Moreover, the proposed method is built upon a hierarchical combination of three OABTs, which are optimized using a Levenberg–Marquardt Algorithm (LMA) enhanced with line-search procedures. This, in turn, makes the proposed method robust to changes in lighting conditions, occlusions and translucent textures, as evidenced by our experiments. Finally, the proposed method achieves head and facial actions tracking in real-time.
Keywords: On-line appearance models; Levenberg–Marquardt algorithm; Line-search optimization; 3D face tracking; Facial action tracking; Eyelid tracking; Iris tracking
|
Francisco Javier Orozco, Pau Baiget, Jordi Gonzalez, & Xavier Roca. (2006). Eyelids and Face Tracking in Real-Time.
|
Francisco Javier Orozco, Xavier Roca, & Jordi Gonzalez. (2008). Real-Time Gaze Tracking with Appearance-Based Models. MVAP - Machine Vision Applications, 20(6), 353–364.
Abstract: Psychological evidence has emphasized the importance of eye gaze analysis in human computer interaction and emotion interpretation. To this end, current image analysis algorithms take into consideration eye-lid and iris motion detection using colour information and edge detectors. However, eye movement is fast and and hence difficult to use to obtain a precise and robust tracking. Instead, our
method proposed to describe eyelid and iris movements as continuous variables using appearance-based tracking. This approach combines the strengths of adaptive appearance models, optimization methods and backtracking techniques.Thus,
in the proposed method textures are learned on-line from near frontal images and illumination changes, occlusions and fast movements are managed. The method achieves real-time performance by combining two appearance-based trackers to a
backtracking algorithm for eyelid estimation and another for iris estimation. These contributions represent a significant advance towards a reliable gaze motion description for HCI and expression analysis, where the strength of complementary
methodologies are combined to avoid using high quality images, colour information, texture training, camera settings and other time-consuming processes.
Keywords: Keywords Eyelid and iris tracking, Appearance models, Blinking, Iris saccade, Real-time gaze tracking
|
Francisco Jose Perales, Juan J. Villanueva, & Y. Luo. (1991). Matching Criteria..
|
Francisco Jose Perales, Juan J. Villanueva, & Y. Luo. (1991). An automatic two-camera human motion perception system based on biomechanical model matching..
|
Francisco Jose Perales, Y. Luo, & Juan J. Villanueva. (1991). Un metodo Automatico de Rotoscopia Sin Marcas para el Estudio del Movimiento Humano Basado en un modelo Biomecanico..
|
Franck Davoine, & Fadi Dornaika. (2005). Head and facial animation tracking using appearance-adaptive models and particle filters. In V. Pavlovic and T.S. Huang (editors), Real–Time Vision for Human–Computer Interaction.
|