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Author |
Patricia L. Suarez; Angel D. Sappa; Boris X. Vintimilla |
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Title |
Adaptive Harris Corners Detector Evaluated with Cross-Spectral Images |
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Conference Article |
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2018 |
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International Conference on Information Technology & Systems (ICITS 2018). ICITS 2018. Advances in Intelligent Systems and Computing |
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721 |
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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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gtsi @ user @ |
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84 |
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Author |
Patricia L. Suarez; Angel D. Sappa; Boris X. Vintimilla |
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Title |
Cross-spectral image dehaze through a dense stacked conditional GAN based approach. |
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Conference Article |
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2018 |
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14th IEEE International Conference on Signal Image Technology & Internet based Systems (SITIS 2018) |
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358-364 |
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This paper proposes a novel approach to remove haze from RGB images using a near infrared images based on a dense stacked conditional Generative Adversarial Network (CGAN). The architecture of the deep network implemented receives, besides the images with haze, its corresponding image in the near infrared spectrum, which serve to accelerate the learning process of the details of the characteristics of the images. The model uses a triplet layer that allows the independence learning of each channel of the visible spectrum image to remove the haze on each color channel separately. A multiple loss function scheme is proposed, which ensures balanced learning between the colors and the structure of the images. Experimental results have shown that the proposed method effectively removes the haze from the images. Additionally, the proposed approach is compared with a state of the art approach showing better results. |
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gtsi @ user @ |
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92 |
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Author |
Dennis G. Romero; A. F. Neto; T. F. Bastos; Boris X. Vintimilla |
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Title |
RWE patterns extraction for on-line human action recognition through window-based analysis of invariant moments |
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Conference Article |
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2012 |
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5th Workshop in applied Robotics and Automation (RoboControl) |
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Human action recognition, Relative Wavelet Energy, Window-based temporal analysis. |
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This paper presents a method for on-line human action recognition on video sequences. An analysis based on Mahalanobis distance is performed to identify the “idle” state, which defines the beginning and end of the person movement, for posterior patterns extraction based on Relative Wavelet Energy from sequences of invariant moments. |
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English |
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English |
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Call Number |
cidis @ cidis @ |
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23 |
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Author |
Dennis G. Romero; A. F. Neto; T. F. Bastos; Boris X. Vintimilla |
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Title |
An approach to automatic assistance in physiotherapy based on on-line movement identification. |
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Conference Article |
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2012 |
Publication |
VI Andean Region International Conference – ANDESCON 2012 |
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patient rehabilitation, patient treatment, statistical analysis |
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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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IEEE |
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Andean Region International Conference (ANDESCON), 2012 VI |
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cidis @ cidis @ |
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24 |
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