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Henry O. Velesaca, Patricia L. Suarez, Dario Carpio, and Angel D. Sappa | ||||
| Title | Synthesized Image Datasets: Towards an Annotation-Free Instance Segmentation Strategy | Type | Conference Article | ||
| Year | 2021 | Publication | 16 International Symposium on Visual Computing. Octubre 4-6, 2021. Lecture Notes in Computer Science | Abbreviated Journal | |
| Volume | 13017 | Issue | Pages | 131-143 | |
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| Notes | Approved | no | |||
| Call Number | cidis @ cidis @ | Serial | 163 | ||
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Henry O. Velesaca, Steven Araujo, Patricia L. Suarez, Ángel Sanchez & Angel D. Sappa | ||||
| Title | Off-the-Shelf Based System for Urban Environment Video Analytics. | Type | Conference Article | ||
| Year | 2020 | Publication | The 27th International Conference on Systems, Signals and Image Processing (IWSSIP 2020) | Abbreviated Journal | |
| Volume | 2020-July | Issue | 9145121 | Pages | 459-464 |
| Keywords | Greenhouse gases, carbon footprint, object detection, object tracking, website framework, off-the-shelf video analytics. | ||||
| Abstract | This paper presents the design and implementation details of a system build-up by using off-the-shelf algorithms for urban video analytics. The system allows the connection to public video surveillance camera networks to obtain the necessary information to generate statistics from urban scenarios (e.g., amount of vehicles, type of cars, direction, numbers of persons, etc.). The obtained information could be used not only for traffic management but also to estimate the carbon footprint of urban scenarios. As a case study, a university campus is selected to evaluate the performance of the proposed system. The system is implemented in a modular way so that it is being used as a testbed to evaluate different algorithms. Implementation results are provided showing the validity and utility of the proposed approach. |
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| Language | English | Summary Language | Original Title | ||
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| ISSN | 21578672 | ISBN | 978-172817539-3 | Medium | |
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| Notes | Approved | no | |||
| Call Number | cidis @ cidis @ | Serial | 125 | ||
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Henry O. Velesaca; Raul A. Mira; Patricia L. Suarez; Christian X. Larrea; Angel D. Sappa. | ||||
| Title | Deep Learning based Corn Kernel Classification. | Type | Conference Article | ||
| Year | 2020 | Publication | The 1st International Workshop and Prize Challenge on Agriculture-Vision: Challenges & Opportunities for Computer Vision in Agriculture on the Conference Computer on Vision and Pattern Recongnition (CVPR 2020) | Abbreviated Journal | |
| Volume | 2020-June | Issue | 9150684 | Pages | 294-302 |
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| Abstract | This paper presents a full pipeline to classify sample sets of corn kernels. The proposed approach follows a segmentation-classification scheme. The image segmentation is performed through a well known deep learning based approach, the Mask R-CNN architecture, while the classification is performed by means of a novel-lightweight network specially designed for this task—good corn kernel, defective corn kernel and impurity categories are considered. As a second contribution, a carefully annotated multitouching corn kernel dataset has been generated. This dataset has been used for training the segmentation and the classification modules. Quantitative evaluations have been performed and comparisons with other approaches provided showing improvements with the proposed pipeline. |
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| Language | English | Summary Language | Original Title | ||
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| ISSN | 21607508 | ISBN | 978-172819360-1 | Medium | |
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| Notes | Approved | no | |||
| Call Number | cidis @ cidis @ | Serial | 124 | ||
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| Author |
Patricia L. Suarez | ||||
| Title | Procesamiento y representación de imágenes multiespectrales usando técnicas de aprendizaje profundo (Ph.D. Angel Sappa, Director & Ph.D. Boris Vintimilla, Codirector.). Ph.D. thesis. | Type | Book Chapter | ||
| Year | 2020 | Publication | Ediciones FIEC-ESPOL. | Abbreviated Journal | |
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| Corporate Author | Ph.D. Angel Sappa, Director & Ph.D. Boris Vintimilla, Codirector. | Thesis | |||
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| Language | Español | Summary Language | Original Title | ||
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| Notes | Approved | no | |||
| Call Number | cidis @ cidis @ | Serial | 144 | ||
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Patricia L. Suarez, Dario Carpio, Angel D. Sappa and Henry O. Velesaca | ||||
| Title | Transformer based Image Dehazing. | Type | Conference Article | ||
| Year | 2022 | Publication | 16TH International Conference On Signal Image Technology & Internet Based Systems SITIS 2022. | Abbreviated Journal | |
| Volume | Issue | Pages | 148-154 | ||
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| Notes | Approved | no | |||
| Call Number | cidis @ cidis @ | Serial | 195 | ||
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| Author |
Patricia L. Suarez, Dario Carpio, Angel Sappa | ||||
| Title | Boosting Guided Super-Resolution Performance with Synthesized Images | Type | Conference Article | ||
| Year | 2023 | Publication | 17th International Conference On Signal Image Technology & Internet Based Systems | Abbreviated Journal | |
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| Notes | Approved | no | |||
| Call Number | cidis @ cidis @ | Serial | 225 | ||
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| Author |
Patricia L. Suarez, Dario Carpio, Angel Sappa | ||||
| Title | Depth Map Estimation from a Single 2D Image | Type | Conference Article | ||
| Year | 2023 | Publication | 17th International Conference On Signal Image Technology & Internet Based Systems | Abbreviated Journal | |
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| Notes | Approved | no | |||
| Call Number | cidis @ cidis @ | Serial | 226 | ||
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Patricia L. Suarez; Angel D. Sappa; Boris X. Vintimilla | ||||
| Title | Image patch similarity through a meta-learning metric based approach | Type | Conference Article | ||
| Year | 2019 | Publication | 15th International Conference on Signal Image Technology & Internet based Systems (SITIS 2019); Sorrento, Italia | Abbreviated Journal | |
| Volume | Issue | Pages | 511-517 | ||
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| Abstract | Comparing images regions are one of the core methods used on computer vision for tasks like image classification, scene understanding, object detection and recognition. Hence, this paper proposes a novel approach to determine similarity of image regions (patches), in order to obtain the best representation of image patches. This problem has been studied by many researchers presenting different approaches, however, the ability to find the better criteria to measure the similarity on image regions are still a challenge. The present work tackles this problem using a few-shot metric based meta-learning framework able to compare image regions and determining a similarity measure to decide if there is similarity between the compared patches. Our model is training end-to-end from scratch. Experimental results have shown that the proposed approach effectively estimates the similarity of the patches and, comparing it with the state of the art approaches, shows better results. |
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| Notes | Approved | no | |||
| Call Number | gtsi @ user @ | Serial | 115 | ||
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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 | |
| Volume | 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 |
Patricia L. Suarez; Angel D. Sappa; Boris X. Vintimilla | ||||
| Title | Vegetation Index Estimation from Monospectral Images | Type | Conference Article | ||
| Year | 2018 | Publication | 15th International Conference, Image Analysis and Recognition (ICIAR 2018), Póvoa de Varzim, Portugal. Lecture Notes in Computer Science | Abbreviated Journal | |
| Volume | 10882 | Issue | Pages | 353-362 | |
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| Abstract | This paper proposes a novel approach to estimate Normalized Difference Vegetation Index (NDVI) from just the red channel of a RGB image. The NDVI index is defined as the ratio of the difference of the red and infrared radiances over their sum. In other words, information from the red channel of a RGB image and the corresponding infrared spectral band are required for its computation. In the current work the NDVI index is estimated just from the red channel by training a Conditional Generative Adversarial Network (CGAN). The architecture proposed for the generative network consists of a single level structure, which combines at the final layer results from convolutional operations together with the given red channel with Gaussian noise to enhance details, resulting in a sharp NDVI image. Then, the discriminative model estimates the probability that the NDVI generated index came from the training dataset, rather than the index automatically generated. Experimental results with a large set of real images are provided showing that a Conditional GAN single level model represents an acceptable approach to estimate NDVI index. |
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| Notes | Approved | no | |||
| Call Number | gtsi @ user @ | Serial | 82 | ||
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