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| Author | Patricia L. Suarez; Angel D. Sappa; Boris X. Vintimilla | ||||
| Title | Colorizing Infrared Images through a Triplet Condictional DCGAN Architecture | Type | Conference Article | ||
| Year | 2017 | Publication | 19th International Conference on Image Analysis and Processing. | Abbreviated Journal | |
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Issue | Pages | 287-297 | ||
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| Notes | Approved | no | |||
| Call Number | gtsi @ user @ | Serial | 66 | ||
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| Author | Patricia L. Suarez; Angel D. Sappa; Boris X. Vintimilla | ||||
| Title | Learning Image Vegetation Index through a Conditional Generative Adversarial Network | Type | Conference Article | ||
| Year | 2017 | Publication | 2nd IEEE Ecuador Tehcnnical Chapters Meeting (ETCM) | Abbreviated Journal | |
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| Call Number | gtsi @ user @ | Serial | 70 | ||
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| Author | Xavier Soria; Angel D. Sappa; Arash Akbarinia | ||||
| Title | Multispectral Single-Sensor RGB-NIR Imaging: New Challenges an Oppotunities | Type | Conference Article | ||
| Year | 2017 | Publication | The 7th International Conference on Image Processing Theory, Tools and Application | Abbreviated Journal | |
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Issue | Pages | 1-6 | ||
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| Call Number | gtsi @ user @ | Serial | 72 | ||
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| Author | Patricia L. Suarez; Angel D. Sappa; Boris X. Vintimilla; Riad I. Hammoud | ||||
| Title | Deep Learning based Single Image Dehazing | Type | Conference Article | ||
| Year | 2018 | Publication | 14th IEEE Workshop on Perception Beyond the Visible Spectrum – In conjunction with CVPR 2018. Salt Lake City, Utah. USA | Abbreviated Journal | |
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| Abstract | This paper proposes a novel approach to remove haze degradations in RGB images using a stacked conditional Generative Adversarial Network (GAN). It employs a triplet of GAN to remove the haze on each color channel independently. A multiple loss functions scheme, applied over a conditional probabilistic model, is proposed. The proposed GAN architecture learns to remove the haze, using as conditioned entrance, the images with haze from which the clear images will be obtained. Such formulation ensures a fast model training convergence and a homogeneous model generalization. Experiments showed that the proposed method generates high-quality clear images. |
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| Notes | Approved | no | |||
| Call Number | gtsi @ user @ | Serial | 83 | ||
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| Author | Patricia L. Suarez; Angel D. Sappa; Boris X. Vintimilla; Riad I. Hammoud | ||||
| Title | Near InfraRed Imagery Colorization | Type | Conference Article | ||
| Year | 2018 | Publication | 25 th IEEE International Conference on Image Processing, ICIP 2018 | Abbreviated Journal | |
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Issue | Pages | 2237-2241 | ||
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| Abstract | This paper proposes a stacked conditional Generative Adversarial Network-based method for Near InfraRed (NIR) imagery colorization. We propose a variant architecture of Generative Adversarial Network (GAN) that uses multiple loss functions over a conditional probabilistic generative model. We show that this new architecture/loss-function yields better generalization and representation of the generated colored IR images. The proposed approach is evaluated on a large test dataset and compared to recent state of the art methods using standard metrics.1 Index Terms—Convolutional Neural Networks (CNN), Generative Adversarial Network (GAN), Infrared Imagery colorization. |
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| Call Number | gtsi @ user @ | Serial | 81 | ||
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| Author | Xavier Soria; Angel D. Sappa | ||||
| Title | Improving Edge Detection in RGB Images by Adding NIR Channel. | Type | Conference Article | ||
| Year | 2018 | Publication | 14th IEEE International Conference on Signal Image Technology & Internet based Systems (SITIS 2018) | Abbreviated Journal | |
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Issue | Pages | 266-273 | ||
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| Notes | Approved | no | |||
| Call Number | gtsi @ user @ | Serial | 95 | ||
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| Author | Patricia L. Suarez; Angel D. Sappa; Boris X. Vintimilla | ||||
| Title | Cross-spectral image dehaze through a dense stacked conditional GAN based approach. | Type | Conference Article | ||
| Year | 2018 | Publication | 14th IEEE International Conference on Signal Image Technology & Internet based Systems (SITIS 2018) | Abbreviated Journal | |
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Issue | Pages | 358-364 | ||
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| Abstract | 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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| Notes | Approved | no | |||
| Call Number | gtsi @ user @ | Serial | 92 | ||
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| Author | Cristhian A. Aguilera; Angel D. Sappa; R. Toledo | ||||
| Title | LGHD: A feature descriptor for matching across non-linear intensity variations | Type | Conference Article | ||
| Year | 2015 | Publication | IEEE International Conference on, Quebec City, QC, 2015 | Abbreviated Journal | |
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Issue | Pages | 178 - 181 | ||
| Keywords | Feature descriptor, multi-modal, multispectral, NIR, LWIR | ||||
| Abstract | This paper presents a new feature descriptor suitable to the task of matching features points between images with nonlinear intensity variations. This includes image pairs with significant illuminations changes, multi-modal image pairs and multi-spectral image pairs. The proposed method describes the neighbourhood of feature points combining frequency and spatial information using multi-scale and multi-oriented Log- Gabor filters. Experimental results show the validity of the proposed approach and also the improvements with respect to the state of the art. | ||||
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| Publisher | IEEE | Place of Publication | Quebec City, QC, Canada | Editor | |
| Language | English | Summary Language | English | Original Title | |
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| Area | Expedition | Conference | 2015 IEEE International Conference on Image Processing (ICIP) | ||
| Notes | Approved | no | |||
| Call Number | cidis @ cidis @ | Serial | 40 | ||
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| Author | M. Oliveira; L. Seabra Lopes; G. Hyun Lim; S. Hamidreza Kasaei; Angel D. Sappa; A. Tomé | ||||
| Title | Concurrent Learning of Visual Codebooks and Object Categories in Open- ended Domains | Type | Conference Article | ||
| Year | 2015 | Publication | Intelligent Robots and Systems (IROS), 2015 IEEE/RSJ International Conference on, Hamburg, Germany, 2015 | Abbreviated Journal | |
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Issue | Pages | 2488 - 2495 | ||
| Keywords | Birds, Training, Legged locomotion, Visualization, Histograms, Object recognition, Gaussian mixture model | ||||
| Abstract | In open-ended domains, robots must continuously learn new object categories. When the training sets are created offline, it is not possible to ensure their representativeness with respect to the object categories and features the system will find when operating online. In the Bag of Words model, visual codebooks are usually constructed from training sets created offline. This might lead to non-discriminative visual words and, as a consequence, to poor recognition performance. This paper proposes a visual object recognition system which concurrently learns in an incremental and online fashion both the visual object category representations as well as the codebook words used to encode them. The codebook is defined using Gaussian Mixture Models which are updated using new object views. The approach contains similarities with the human visual object recognition system: evidence suggests that the development of recognition capabilities occurs on multiple levels and is sustained over large periods of time. Results show that the proposed system with concurrent learning of object categories and codebooks is capable of learning more categories, requiring less examples, and with similar accuracies, when compared to the classical Bag of Words approach using codebooks constructed offline. | ||||
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| Publisher | IEEE | Place of Publication | Hamburg, Germany | Editor | |
| Language | English | Summary Language | English | Original Title | |
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| Area | Expedition | Conference | 2015 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS) | ||
| Notes | Approved | no | |||
| Call Number | cidis @ cidis @ | Serial | 41 | ||
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| Author | Dennis G. Romero; A. Frizera; Angel D. Sappa; Boris X. Vintimilla; T.F. Bastos | ||||
| Title | A predictive model for human activity recognition by observing actions and context | Type | Conference Article | ||
| Year | 2015 | Publication | ACIVS 2015 (Advanced Concepts for Intelligent Vision Systems), International Conference on, Catania, Italy, 2015 | Abbreviated Journal | |
| Volume |
Issue | Pages | 323 - 333 | ||
| Keywords | Edge width, Image blu,r Defocus map, Edge model | ||||
| 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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| Notes | Approved | no | |||
| Call Number | cidis @ cidis @ | Serial | 43 | ||
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