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| Author | Juan A. Carvajal; Dennis G. Romero; Angel D. Sappa | ||||
| Title | Fine-tuning based deep covolutional networks for lepidopterous genus recognition | Type | Conference Article | ||
| Year | 2016 | Publication | XXI IberoAmerican Congress on Pattern Recognition | Abbreviated Journal | |
| Volume | Issue | Pages | 1-9 | ||
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Abstract  |
This paper describes an image classi cation approach ori- ented to identify specimens of lepidopterous insects recognized at Ecuado- rian ecological reserves. This work seeks to contribute to studies in the area of biology about genus of butter ies and also to facilitate the reg- istration of unrecognized specimens. The proposed approach is based on the ne-tuning of three widely used pre-trained Convolutional Neural Networks (CNNs). This strategy is intended to overcome the reduced number of labeled images. Experimental results with a dataset labeled by expert biologists, is presented|a recognition accuracy above 92% is reached. 1 Introductio | ||||
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| Notes | Approved | no | |||
| Call Number | cidis @ cidis @ | Serial | 53 | ||
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| Author | Low S., Inkawhich N., Nina O., Sappa A. and Blasch E. | ||||
| Title | Multi-modal Aerial View Object Classification Challenge Results-PBVS 2022. | Type | Conference Article | ||
| Year | 2022 | Publication | Conference on Computer Vision and Pattern Recognition Workshops, (CVPRW 2022), junio 19-24. | Abbreviated Journal | CONFERENCE |
| Volume | 2022-June | Issue | Pages | 417-425 | |
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| Abstract |
This paper details the results and main findings of the second iteration of the Multi-modal Aerial View Object Classification (MAVOC) challenge. This year’s MAVOC challenge is the second iteration. The primary goal of both MAVOC challenges is to inspire research into methods for building recognition models that utilize both synthetic aperture radar (SAR) and electro-optical (EO) input modalities. Teams are encouraged/challenged to develop multi-modal approaches that incorporate complementary information from both domains. While the 2021 challenge showed a proof of concept that both modalities could be used together, the 2022 challenge focuses on the detailed multi-modal models. Using the same UNIfied COincident Optical and Radar for recognitioN (UNICORN) dataset and competition format that was used in 2021. Specifically, the challenge focuses on two techniques, (1) SAR classification and (2) SAR + EO classification. The bulk of this document is dedicated to discussing the top performing methods and describing their performance on our blind test set. Notably, all of the top ten teams outperform our baseline. For SAR classification, the top team showed a 129% improvement over our baseline and an 8% average improvement from the 2021 winner. The top team for SAR + EO classification shows a 165% improvement with a 32% average improvement over 2021. |
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| Notes | Approved | no | |||
| Call Number | cidis @ cidis @ | Serial | 177 | ||
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| Author | Angel D. Sappa; Juan A. Carvajal; Cristhian A. Aguilera; Miguel Oliveira; Dennis G. Romero; Boris X. Vintimilla | ||||
| Title | Wavelet-Based Visible and Infrared Image Fusion: A Comparative Study | Type | Journal Article | ||
| Year | 2016 | Publication | Sensors Journal | Abbreviated Journal | |
| Volume | Vol. 16 | Issue | Pages | pp. 1-15 | |
| Keywords | image fusion; fusion evaluation metrics; visible and infrared imaging; discrete wavelet transform | ||||
| Abstract |
This paper evaluates different wavelet-based cross-spectral image fusion strategies adopted to merge visible and infrared images. The objective is to find the best setup independently of the evaluation metric used to measure the performance. Quantitative performance results are obtained with state of the art approaches together with adaptations proposed in the current work. The options evaluated in the current work result from the combination of different setups in the wavelet image decomposition stage together with different fusion strategies for the final merging stage that generates the resulting representation. Most of the approaches evaluate results according to the application for which they are intended for. Sometimes a human observer is selected to judge the quality of the obtained results. In the current work, quantitative values are considered in order to find correlations between setups and performance of obtained results; these correlations can be used to define a criteria for selecting the best fusion strategy for a given pair of cross-spectral images. The whole procedure is evaluated with a large set of correctly registered visible and infrared image pairs, including both Near InfraRed (NIR) and LongWave InfraRed (LWIR). | ||||
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| Language | English | Summary Language | English | Original Title | |
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| Notes | Approved | no | |||
| Call Number | cidis @ cidis @ | Serial | 47 | ||
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| Author | Nayeth I. Solorzano Alcivar, Robert Loor, Stalyn Gonzabay Yagual, & Boris X. Vintimilla | ||||
| Title | Statistical Representations of a Dashboard to Monitor Educational Videogames in Natural Language | Type | Conference Article | ||
| Year | 2020 | Publication | ETLTC – ACM Chapter: International Conference on Educational Technology, Language and Technical Communication; Fukushima, Japan, 27-31 Enero 2020 | Abbreviated Journal | |
| Volume | 77 | Issue | Pages | ||
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| Abstract |
This paper explains how Natural Language (NL) processing by computers, through smart programs as a way of Machine Learning (ML), can represent large sets of quantitative data as written statements. The study recognized the need to improve the implemented web platform using a dashboard in which we collected a set of extensive data to measure assessment factors of using children´s educational games. In this case, applying NL is a strategy to give assessments, build, and display more precise written statements to enhance the understanding of children´s gaming behavior. We propose the development of a new tool to assess the use of written explanations rather than a statistical representation of feedback information for the comprehension of parents and teachers with a lack of primary level knowledge in statistics. Applying fuzzy logic theory, we present verbatim explanations of children´s behavior playing educational videogames as NL interpretation instead of statistical representations. An educational series of digital game applications for mobile devices, identified as MIDI (Spanish acronym of “Interactive Didactic Multimedia for Children”) linked to a dashboard in the cloud, is evaluated using the dashboard metrics. MIDI games tested in local primary schools helps to evaluate the results of using the proposed tool. The guiding results allow analyzing the degrees of playability and usability factors obtained from the data produced when children play a MIDI game. The results obtained are presented in a comprehensive guiding evaluation report applying NL for parents and teachers. These guiding evaluations are useful to enhance children's learning understanding related to the school curricula applied to ludic digital games. |
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| Notes | Approved | no | |||
| Call Number | cidis @ cidis @ | Serial | 131 | ||
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| Author | P. Ricaurte; C. Chilán; C. A. Aguilera-Carrasco; B. X. Vintimilla; Angel D. Sappa | ||||
| Title | Performance Evaluation of Feature Point Descriptors in the Infrared Domain | Type | Conference Article | ||
| Year | 2014 | Publication | Computer Vision Theory and Applications (VISAPP), 2014 International Conference on, Lisbon, Portugal, 2013 | Abbreviated Journal | |
| Volume | 1 | Issue | Pages | 545 -550 | |
| Keywords | Infrared Imaging, Feature Point Descriptors | ||||
| Abstract |
This paper presents a comparative evaluation of classical feature point descriptors when they are used in the long-wave infrared spectral band. Robustness to changes in rotation, scaling, blur, and additive noise are evaluated using a state of the art framework. Statistical results using an outdoor image data set are presented together with a discussion about the differences with respect to the results obtained when images from the visible spectrum are considered. | ||||
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| Publisher | IEEE | Place of Publication | Editor | ||
| Language | English | Summary Language | English | Original Title | |
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| Area | Expedition | Conference | 2014 International Conference on Computer Vision Theory and Applications (VISAPP) | ||
| Notes | Approved | no | |||
| Call Number | cidis @ cidis @ | Serial | 26 | ||
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| Author | Charco, J.L., Sappa, A.D., Vintimilla, B.X., Velesaca, H.O. | ||||
| Title | Camera pose estimation in multi-view environments:from virtual scenarios to the real world | Type | Journal Article | ||
| Year | 2021 | Publication | In Image and Vision Computing Journal. (Article number 104182) | Abbreviated Journal | |
| Volume | Vol. 110 | Issue | Pages | ||
| Keywords | Relative camera pose estimation, Domain adaptation, Siamese architecture, Synthetic data, Multi-view environments | ||||
| Abstract |
This paper presents a domain adaptation strategy to efficiently train network architectures for estimating the relative camera pose in multi-view scenarios. The network architectures are fed by a pair of simultaneously acquired images, hence in order to improve the accuracy of the solutions, and due to the lack of large datasets with pairs of overlapped images, a domain adaptation strategy is proposed. The domain adaptation strategy consists on transferring the knowledge learned from synthetic images to real-world scenarios. For this, the networks are firstly trained using pairs of synthetic images, which are captured at the same time by a pair of cameras in a virtual environment; and then, the learned weights of the networks are transferred to the real-world case, where the networks are retrained with a few real images. Different virtual 3D scenarios are generated to evaluate the relationship between the accuracy on the result and the similarity between virtual and real scenarios—similarity on both geometry of the objects contained in the scene as well as relative pose between camera and objects in the scene. Experimental results and comparisons are provided showing that the accuracy of all the evaluated networks for estimating the camera pose improves when the proposed domain adaptation strategy is used, highlighting the importance on the similarity between virtual-real scenarios. |
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| Language | English | Summary Language | English | Original Title | |
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| Notes | Approved | no | |||
| Call Number | cidis @ cidis @ | Serial | 147 | ||
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| Author | 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 | Dennis G. Romero; A. F. Neto; T. F. Bastos; Boris X. Vintimilla | ||||
| Title | RWE patterns extraction for on-line human action recognition through window-based analysis of invariant moments | Type | Conference Article | ||
| Year | 2012 | Publication | 5th Workshop in applied Robotics and Automation (RoboControl) | Abbreviated Journal | |
| Volume | Issue | Pages | |||
| Keywords | 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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| Language | English | Summary Language | English | Original Title | |
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| Notes | Approved | no | |||
| Call Number | cidis @ cidis @ | Serial | 23 | ||
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| Author | Jorge L. Charco, Angel D. Sappa, Boris X. Vintimilla | ||||
| Title | Human Pose Estimation through A Novel Multi-View Scheme | Type | Conference Article | ||
| Year | 2022 | Publication | Proceedings of the International Joint Conference on Computer Vision, Imaging and Computer Graphics Theory and Applications VISIGRAPP 2022 | Abbreviated Journal | |
| Volume | 5 | Issue | Pages | 855-862 | |
| Keywords | Multi-View Scheme, Human Pose Estimation, Relative Camera Pose, Monocular Approach | ||||
| Abstract |
This paper presents a multi-view scheme to tackle the challenging problem of the self-occlusion in human pose estimation problem. The proposed approach first obtains the human body joints of a set of images, which are captured from different views at the same time. Then, it enhances the obtained joints by using a multi-view scheme. Basically, the joints from a given view are used to enhance poorly estimated joints from another view, especially intended to tackle the self occlusions cases. A network architecture initially proposed for the monocular case is adapted to be used in the proposed multi-view scheme. Experimental results and comparisons with the state-of-the-art approaches on Human3.6m dataset are presented showing improvements in the accuracy of body joints estimations. |
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| Notes | Approved | yes | |||
| Call Number | cidis @ cidis @ | Serial | 169 | ||
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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 | |
| Volume | Issue | Pages | 178 - 181 | ||
| Keywords | Feature descriptor, multi-modal, multispectral, NIR, LWIR | ||||
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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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