SciTePress - Affiliation Profile

Research.Publish.Connect.

*Please fill out at least one Field. *Value must be an number!

Title:
ISBN:
Year:
Acronym:
Subject:

Advanced Search Proceedings Search

If you're looking for an exact phrase use quotation marks on text fields.

*Please fill out at least one Field.

Title:
Author:
Affiliation:
Subject:

Advanced Search Papers Search

If you're looking for an exact phrase use quotation marks on text fields.

*Please fill out at least one Field.

Name:
Affiliation:
Country:
Conference:
Subject:

Advanced Search Authors Search

If you're looking for an exact phrase use quotation marks on text fields.

*Please fill out at least one Field.

Name:
Country:
Subject:

Advanced Search Affiliations Search

If you're looking for an exact phrase use quotation marks on text fields.

Proceedings

Proceedings Search *Please fill out at least one Field. *Value must be an number!

Title:
ISBN:
Year:
Acronym:
Subject:

Advanced Search Proceedings Search

If you're looking for an exact phrase use quotation marks on text fields.

Papers

Papers Search *Please fill out at least one Field.

Title:
Author:
Affiliation:
Subject:

Advanced Search Papers Search

If you're looking for an exact phrase use quotation marks on text fields.

Authors

Authors Search *Please fill out at least one Field.

Name:
Affiliation:
Country:
Conference:
Subject:

Advanced Search Authors Search

If you're looking for an exact phrase use quotation marks on text fields.

Advanced Search

Affiliation

Select a Subject
Refine Ontology Subject

ESPOL Polytechnic University, Ecuador

Context (please use complete words only)

Field

Sort by

11 Papers

Application-Guided Image Fusion: A Path to Improve Results in High-Level Vision Tasks

Topics: Segmentation and Grouping; Multimodal and Multi-Sensor Models of Image Formation

Authors: Gisel Bastidas-Guacho ^{1

;

2} , Patricio Moreno-Vallejo ^{1

;

2} , Boris Vintimilla ¹ and Angel Sappa ^{1

;

3}

Affiliations: ¹ ESPOL Polytechnic University, Campus Gustavo Galindo, Km. 30.5 Vía Perimetral, Guayaquil, Ecuador , ² ESPOCH Polytechnic University, South Pan-American Highway km 1 1/2, Riobamba, Ecuador , ³ Computer Vision Center, Campus UAB, 08193 Bellaterra, Barcelona, Spain

Digital Object Identifier: 10.5220/0013307300003912

Proceedings of VISAPP 2025, ISBN: 978-989-758-728-3

Quick Abstract

This paper proposes an enhanced application-driven image fusion framework to improve final application results. This framework is based on a deep learning architecture that generates fused images to better align with the requirements of applications such as semantic segmentation and object detection. The color-based and edge-weighted correlation loss functions are introduced to ensure consistency in the YCbCr space and emphasize structural integrity in high-gradient regions, respectively. Together, these loss components allow the fused image to retain more features from the source images by producing an application-ready fused image. Experiments conducted on two public datasets demonstrate a significant improvement in mIoU achieved by the proposed approach compared to state-of-the-art methods.

Banana Ripeness Level Classification Using a Simple CNN Model Trained with Real and Synthetic Datasets

Topics: Color and Texture Analyses; Features Extraction; Deep Learning for Visual Understanding ; Transfer Learning

Authors: Luis Chuquimarca ^{1

;

2} , Boris Vintimilla ¹ and Sergio Velastin ^{3

;

4}

Affiliations: ¹ ESPOL Polytechnic University, ESPOL, CIDIS, Guayaquil, Ecuador , ² UPSE Santa Elena Peninsula State University, UPSE, FACSISTEL, La Libertad, Ecuador , ³ Queen Mary University of London, London, U.K. , ⁴ University Carlos III, Madrid, Spain

Digital Object Identifier: 10.5220/0011654600003417

Proceedings of VISAPP 2023/VISIGRAPP, ISBN: 978-989-758-634-7

Quick Abstract

The level of ripeness is essential in determining the quality of bananas. To correctly estimate banana maturity, the metrics of international marketing standards need to be considered. However, the process of assessing the maturity of bananas at an industrial level is still carried out using manual methods. The use of CNN models is an attractive tool to solve the problem, but there is a limitation regarding the availability of sufficient data to train these models reliably. On the other hand, in the state-of-the-art, existing CNN models and the available data have reported that the accuracy results are acceptable in identifying banana maturity. For this reason, this work presents the generation of a robust dataset that combines real and synthetic data for different levels of banana ripeness. In addition, it proposes a simple CNN architecture, which is trained with synthetic data and using the transfer learning technique, the model is improved to classify real data, managing to determine the level of maturity of the banana. The proposed CNN model is evaluated with several architectures, then hyper-parameter configurations are varied, and optimizers are used. The results show that the proposed CNN model reaches a high accuracy of 0.917 and a fast execution time.

Fruit Defect Detection Using CNN Models with Real and Virtual Data

Topics: Color and Texture Analyses; Features Extraction; Deep Learning for Visual Understanding

Authors: Renzo Pacheco ¹ , Paula González ¹ , Luis Chuquimarca ^{1

;

2} , Boris Vintimilla ¹ and Sergio Velastin ^{3

;

4}

Digital Object Identifier: 10.5220/0011679800003417

Proceedings of VISAPP 2023/VISIGRAPP, ISBN: 978-989-758-634-7

Quick Abstract

The present study seeks to evaluate different CNN models in order to compare their performance in recognizing a range of defects in apples and mangoes to ensure the quality of the production of these foods. Using the CNN models, InceptionV3, MobileNetV2, VGG16 and DenseNet121, which were trained with a dataset of real and synthetic images of apples and mangoes covering fruit in acceptable quality condition and with defects: rot, bruises, scabs and black spots. Training was performed with variations on the hyper-parameters and the metric is accuracy. The MobileNetV2 model achieved the highest accuracy in training and testing, obtaining 97.50% for apples and 92.50% for mangoes, making it the most suitable model for defect detection in these fruits. The InceptionV3 and DenseNet121 models presented accuracy values above 90%, while the VGG16 model obtained the poorest performance by not exceeding 80% accuracy for any of the fruits. The trained models, especially MobileNetV2, are capable of recognizing a range of defects in the fruits under study with a high degree of accuracy and are suitable for use in the development of automation applications for the quality assessment process of apples and mangoes.

Towards a Robust Solution for the Supermarket Shelf Audit Problem

Topics: Object Detection and Localization; Vision for Robotics; Assistive Computer Vision; Simultaneous Localization and Mapping; Transfer Learning

Authors: Emmanuel Morán , Boris Vintimilla and Miguel Realpe

Affiliation: ESPOL Polytechnic University, Escuela Superior Politecnica del Litoral, ESPOL, CIDIS, Campus Gustavo Galindo Km. 30.5 Vía Perimetral, P.O. Box 09-01-5863, Guayaquil, Ecuador

Digital Object Identifier: 10.5220/0011747000003417

Proceedings of VISAPP 2023/VISIGRAPP, ISBN: 978-989-758-634-7

Quick Abstract

Retail supermarket is an industrial sector with repetitive tasks performed using visual analysis by the store’s operators. Tasks such as checking the status of the shelves can contain multiple sequential sub-tasks, each of which needs to be performed correctly. In recent years, there has been some intents to create a solution for the tasks mentioned without been complete solution for retails. In this article, a first realistic approach is proposed to solve the supermarket shelf audit problem. For this, a workflow is presented to deliver compliance level with respect to the expected store’s planogram.

Human Pose Estimation through a Novel Multi-view Scheme

Topics: Image-Based Modeling and 3D Reconstruction; Stereo Vision and Structure from Motion; Camera Networks and Vision; Event and Human Activity Recognition; Simultaneous Localization and Mapping

Authors: Jorge L. Charco ^{1

;

2} , Angel D. Sappa ^{3

;

1} and Boris X. Vintimilla ¹

Affiliations: ¹ ESPOL Polytechnic University, Escuela Superior Politécnica del Litoral, ESPOL, Campus Gustavo Galindo Km. 30.5 Vía Perimetral, P.O. Box 09-01-5863, Guayaquil, Ecuador , ² Universidad de Guayaquil, Delta and Kennedy Av., P.B. EC090514, Guayaquil, Ecuador , ³ Computer Vision Center, Edifici O, Campus UAB, 08193 Bellaterra, Barcelona, Spain

Digital Object Identifier: 10.5220/0010899900003124

Proceedings of VISAPP 2022/VISIGRAPP, ISBN: 978-989-758-555-5

Quick 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.

Multi-Image Super-Resolution for Thermal Images

Topic: Image Enhancement and Restoration

Authors: Rafael E. Rivadeneira ¹ , Angel D. Sappa ^{2

;

1} and Boris X. Vintimilla ¹

Affiliations: ¹ Escuela Superior Politécnica del Litoral, ESPOL, Facultad de Ingeniería en Electricidad y Computación, CIDIS, Campus Gustavo Galindo Km. 30.5 Vía Perimetral, P.O. Box 09-01-5863, Guayaquil, Ecuador , ² Computer Vision Center, Edifici O, Campus UAB, 08193 Bellaterra, Barcelona, Spain

Digital Object Identifier: 10.5220/0010899500003124

Proceedings of VISAPP 2022/VISIGRAPP, ISBN: 978-989-758-555-5

Quick Abstract

This paper proposes a novel CNN architecture for the multi-thermal image super-resolution problem. In the proposed scheme, the multi-images are synthetically generated by downsampling and slightly shifting the given image; noise is also added to each of these synthesized images. The proposed architecture uses two attention blocks paths to extract high-frequency details taking advantage of the large information extracted from multiple images of the same scene. Experimental results are provided, showing the proposed scheme has overcome the state-of-the-art approaches.

Thermal Image Super-resolution: A Novel Architecture and Dataset

Topics: Image Formation, Acquisition Devices and Sensors; Image Enhancement and Restoration

Authors: Rafael E. Rivadeneira ¹ , Angel D. Sappa ^{2

;

1} and Boris X. Vintimilla ¹

Affiliations: ¹ Escuela Superior Politécnica del Litoral, ESPOL, Facultad de Ingeniería en Electricidad y Computación, CIDIS, Campus Gustavo Galindo, Km. 30.5 Vía Perimetral, P.O. Box 09-01-5863, Guayaquil, Ecuador , ² Computer Vision Center, Edifici O, Campus UAB, 08193 Bellaterra, Barcelona, Spain

Digital Object Identifier: 10.5220/0009173601110119

Proceedings of VISAPP 2020/VISIGRAPP, ISBN: 978-989-758-402-2

Quick Abstract

This paper proposes a novel CycleGAN architecture for thermal image super-resolution, together with a large dataset consisting of thermal images at different resolutions. The dataset has been acquired using three thermal cameras at different resolutions, which acquire images from the same scenario at the same time. The thermal cameras are mounted in a rig trying to minimize the baseline distance to make easier the registration problem. The proposed architecture is based on ResNet6 as a Generator and PatchGAN as a Discriminator. The novelty on the proposed unsupervised super-resolution training (CycleGAN) is possible due to the existence of aforementioned thermal images—images of the same scenario with different resolutions. The proposed approach is evaluated in the dataset and compared with classical bicubic interpolation. The dataset and the network are available.

Transfer Learning from Synthetic Data in the Camera Pose Estimation Problem

Topics: Stereo Vision and Structure from Motion; Device Calibration, Characterization and Modeling; Camera Networks and Vision

Authors: Jorge L. Charco ^{1

;

2} , Angel D. Sappa ^{3

;

1} , Boris X. Vintimilla ¹ and Henry O. Velesaca ¹

Digital Object Identifier: 10.5220/0009167604980505

Proceedings of VISAPP 2020/VISIGRAPP, ISBN: 978-989-758-402-2

Quick Abstract

This paper presents a novel Siamese network architecture, as a variant of Resnet-50, to estimate the relative camera pose on multi-view environments. In order to improve the performance of the proposed model a transfer learning strategy, based on synthetic images obtained from a virtual-world, is considered. The transfer learning consists of first training the network using pairs of images from the virtual-world scenario considering different conditions (i.e., weather, illumination, objects, buildings, etc.); then, the learned weight of the network are transferred to the real case, where images from real-world scenarios are considered. Experimental results and comparisons with the state of the art show both, improvements on the relative pose estimation accuracy using the proposed model, as well as further improvements when the transfer learning strategy (synthetic-world data transfer learning real-world data) is considered to tackle the limitation on the training due to the reduced number of pairs of real-images on most of the public data sets.

Cross-spectral Stereo Correspondence using Dense Flow Fields

Topics: Stereo Vision and Structure from Motion; Optical Flow and Motion Analyses; Vision for Robotics

Authors: Naveen Onkarappa ¹ , Cristhian A. Aguilera-Carrasco ¹ , Boris X. Vintimilla ² and Angel D. Sappa ³

Affiliations: ¹ Universitat Autònoma de Barcelona, Spain , ² Escuela Superior Politécnica del Litoral (ESPOL), Ecuador , ³ Universitat Autònoma de Barcelona and Escuela Superior Politécnica del Litoral (ESPOL), Spain

Digital Object Identifier: 10.5220/0004736006130617

Proceedings of VISAPP 2014/VISIGRAPP, ISBN: 978-989-758-009-3

Quick Abstract

This manuscript addresses the cross-spectral stereo correspondence problem. It proposes the usage of a dense flow field based representation instead of the original cross-spectral images, which have a low correlation. In this way, working in the flow field space, classical cost functions can be used as similarity measures. Preliminary experimental results on urban environments have been obtained showing the validity of the proposed approach.

Related Ontology Subjects/Areas/Topics (8)

Applications	Optical Flow and Motion Analyses	Software Engineering
Computer Vision, Visualization and Computer Graphics	Pattern Recognition	Stereo Vision and Structure from Motion
Motion, Tracking and Stereo Vision	Robotics

Performance Evaluation of Feature Point Descriptors in the Infrared Domain

Topic: Features Extraction

Authors: Pablo Ricaurte ¹ , Carmen Chilán ¹ , Cristhian A. Aguilera-Carrasco ² , Boris X. Vintimilla ¹ and Angel D. Sappa ³

Affiliations: ¹ Escuela Superior Politénica del Litoral (ESPOL), Ecuador , ² Universitat Autònoma de Barcelona, Spain , ³ Escuela Superior Politénica del Litoral (ESPOL) and Universitat Autònoma de Barcelona, Ecuador

Digital Object Identifier: 10.5220/0004725305450550

Proceedings of VISAPP 2014/VISIGRAPP, ISBN: 978-989-758-003-1

Quick 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.

Related Ontology Subjects/Areas/Topics (3)

Computer Vision, Visualization and Computer Graphics

Features Extraction

Image and Video Analysis