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Sianna Puente, Cindy Madrid, Miguel Realpe, & Boris X. Vintimilla. (2017). An Empirical Comparison of DCNN libraries to implement the Vision Module of a Danger Management System. In 2017 International Conference on Deep Learning Technologies (ICDLT 2017) (Vol. Part F128535, pp. 60–65).
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Dennis G. Romero, A. F. Neto, T. F. Bastos, & Boris X. Vintimilla. (2012). An approach to automatic assistance in physiotherapy based on on-line movement identification. In VI Andean Region International Conference – ANDESCON 2012. Andean Region International Conference (ANDESCON), 2012 VI: IEEE.
Abstract: This paper describes a method for on-line movement identification, oriented to patient’s movement evaluation during physiotherapy. An analysis based on Mahalanobis distance between temporal windows is performed to identify the “idle/motion” state, which defines the beginning and end of the patient’s movement, for posterior patterns extraction based on Relative Wavelet Energy from sequences of invariant moments.
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Patricia L. Suarez, Angel D. Sappa, & Boris X. Vintimilla. (2018). Adaptive Harris Corners Detector Evaluated with Cross-Spectral Images. In International Conference on Information Technology & Systems (ICITS 2018). ICITS 2018. Advances in Intelligent Systems and Computing (Vol. 721).
Abstract: This paper proposes a novel approach to use cross-spectral
images to achieve a better performance with the proposed Adaptive Harris
corner detector comparing its obtained results with those achieved
with images of the visible spectra. The images of urban, field, old-building
and country category were used for the experiments, given the variety of
the textures present in these images, with which the complexity of the
proposal is much more challenging for its verification. It is a new scope,
which means improving the detection of characteristic points using crossspectral
images (NIR, G, B) and applying pruning techniques, the combination
of channels for this fusion is the one that generates the largest
variance based on the intensity of the merged pixels, therefore, it is that
which maximizes the entropy in the resulting Cross-spectral images.
Harris is one of the most widely used corner detection algorithm, so
any improvement in its efficiency is an important contribution in the
field of computer vision. The experiments conclude that the inclusion of
a (NIR) channel in the image as a result of the combination of the spectra,
greatly improves the corner detection due to better entropy of the
resulting image after the fusion, Therefore the fusion process applied to
the images improves the results obtained in subsequent processes such as
identification of objects or patterns, classification and/or segmentation.
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Julien Poujol, Cristhian A. Aguilera, Etienne Danos, Boris X. Vintimilla, Ricardo Toledo, & Angel D. Sappa. (2015). A visible-Thermal Fusion based Monocular Visual Odometry. In Iberian Robotics Conference (ROBOT 2015), International Conference on, Lisbon, Portugal, 2015 (Vol. 417, pp. 517–528).
Abstract: The manuscript evaluates the performance of a monocular visual odometry approach when images from different spectra are considered, both independently and fused. The objective behind this evaluation is to analyze if classical approaches can be improved when the given images, which are from different spectra, are fused and represented in new domains. The images in these new domains should have some of the following properties: i) more robust to noisy data; ii) less sensitive to changes (e.g., lighting); iii) more rich in descriptive information, among other. In particular in the current work two different image fusion strategies are considered. Firstly, images from the visible and thermal spectrum are fused using a Discrete Wavelet Transform (DWT) approach. Secondly, a monochrome threshold strategy is considered. The obtained representations are evaluated under a visual odometry framework, highlighting their advantages and disadvantages, using different urban and semi-urban scenarios. Comparisons with both monocular-visible spectrum and monocular-infrared spectrum, are also provided showing the validity of the proposed approach.
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Dennis G. Romero, A. Frizera, Angel D. Sappa, Boris X. Vintimilla, & T.F. Bastos. (2015). A predictive model for human activity recognition by observing actions and context. In ACIVS 2015 (Advanced Concepts for Intelligent Vision Systems), International Conference on, Catania, Italy, 2015 (pp. 323–333).
Abstract: This paper presents a novel model to estimate human activities – a human activity is defined by a set of human actions. The proposed approach is based on the usage of Recurrent Neural Networks (RNN) and Bayesian inference through the continuous monitoring of human actions and its surrounding environment. In the current work human activities are inferred considering not only visual analysis but also additional resources; external sources of information, such as context information, are incorporated to contribute to the activity estimation. The novelty of the proposed approach lies in the way the information is encoded, so that it can be later associated according to a predefined semantic structure. Hence, a pattern representing a given activity can be defined by a set of actions, plus contextual information or other kind of information that could be relevant to describe the activity. Experimental results with real data are provided showing the validity of the proposed approach.
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Miguel Realpe, Boris X. Vintimilla, & Ljubo Vlacic. (2016). A Fault Tolerant Perception system for autonomous vehicles. In 35th Chinese Control Conference (CCC2016), International Conference on, Chengdu (pp. 1–6).
Abstract: Driverless vehicles are currently being tested on public roads in order to examine their ability to perform in a safe and reliable way in real world situations. However, the long-term reliable operation of a vehicle’s diverse sensors and the effects of potential sensor faults in the vehicle system have not been tested yet. This paper is proposing a sensor fusion architecture that minimizes the influence of a sensor fault. Experimental results are presented simulating faults by introducing displacements in the sensor information from the KITTI dataset.
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Angely Oyola, Dennis G. Romero, & Boris X. Vintimilla. (2017). A Dijkstra-based algorithm for selecting the Shortest-Safe Evacuation Routes in dynamic environments (SSER). In The 30th International Conference on Industrial, Engineering, Other Applications of Applied Intelligent Systems (IEA/AIE 2017) (pp. 131–135).
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