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| Author | Angel Sappa; P. Carvajal; Cristhian A. Aguilera-Carrasco; Miguel Oliveira; Dennis Romero; Boris X. Vintimilla | ||||
| Title | Wavelet based visible and infrared image fusion: a comparative study | Type | Journal Article | ||
| Year | 2016 | Publication | Sensors | Abbreviated Journal | SENS |
| Volume | 16 | Issue | 6 | Pages  |
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 Long Wave InfraRed (LWIR). | ||||
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| Notes | ADAS; 600.086; 600.076 | Approved | no | ||
| Call Number | Admin @ si @SCA2016 | Serial | 2807 | ||
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| Author | Cristhian A. Aguilera-Carrasco; C. Aguilera; Angel Sappa | ||||
| Title | Melamine Faced Panels Defect Classification beyond the Visible Spectrum | Type | Journal Article | ||
| Year | 2018 | Publication | Sensors | Abbreviated Journal | SENS |
| Volume | 18 | Issue | 11 | Pages |
1-10 |
| Keywords | industrial application; infrared; machine learning | ||||
| Abstract | In this work, we explore the use of images from different spectral bands to classify defects in melamine faced panels, which could appear through the production process. Through experimental evaluation, we evaluate the use of images from the visible (VS), near-infrared (NIR), and long wavelength infrared (LWIR), to classify the defects using a feature descriptor learning approach together with a support vector machine classifier. Two descriptors were evaluated, Extended Local Binary Patterns (E-LBP) and SURF using a Bag of Words (BoW) representation. The evaluation was carried on with an image set obtained during this work, which contained five different defect categories that currently occurs in the industry. Results show that using images from beyond the visual spectrum helps to improve classification performance in contrast with a single visible spectrum solution. | ||||
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| Notes | MSIAU; 600.122 | Approved | no | ||
| Call Number | Admin @ si @ AAS2018 | Serial | 3191 | ||
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| Author | Mark Philip Philipsen; Jacob Velling Dueholm; Anders Jorgensen; Sergio Escalera; Thomas B. Moeslund | ||||
| Title | Organ Segmentation in Poultry Viscera Using RGB-D | Type | Journal Article | ||
| Year | 2018 | Publication | Sensors | Abbreviated Journal | SENS |
| Volume | 18 | Issue | 1 | Pages |
117 |
| Keywords | semantic segmentation; RGB-D; random forest; conditional random field; 2D; 3D; CNN | ||||
| Abstract | We present a pattern recognition framework for semantic segmentation of visual structures, that is, multi-class labelling at pixel level, and apply it to the task of segmenting organs in the eviscerated viscera from slaughtered poultry in RGB-D images. This is a step towards replacing the current strenuous manual inspection at poultry processing plants. Features are extracted from feature maps such as activation maps from a convolutional neural network (CNN). A random forest classifier assigns class probabilities, which are further refined by utilizing context in a conditional random field. The presented method is compatible with both 2D and 3D features, which allows us to explore the value of adding 3D and CNN-derived features. The dataset consists of 604 RGB-D images showing 151 unique sets of eviscerated viscera from four different perspectives. A mean Jaccard index of 78.11% is achieved across the four classes of organs by using features derived from 2D, 3D and a CNN, compared to 74.28% using only basic 2D image features. | ||||
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| Notes | HUPBA; no proj | Approved | no | ||
| Call Number | Admin @ si @ PVJ2018 | Serial | 3072 | ||
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| Author | Gabriel Villalonga; Joost Van de Weijer; Antonio Lopez | ||||
| Title | Recognizing new classes with synthetic data in the loop: application to traffic sign recognition | Type | Journal Article | ||
| Year | 2020 | Publication | Sensors | Abbreviated Journal | SENS |
| Volume | 20 | Issue | 3 | Pages |
583 |
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| Abstract | On-board vision systems may need to increase the number of classes that can be recognized in a relatively short period. For instance, a traffic sign recognition system may suddenly be required to recognize new signs. Since collecting and annotating samples of such new classes may need more time than we wish, especially for uncommon signs, we propose a method to generate these samples by combining synthetic images and Generative Adversarial Network (GAN) technology. In particular, the GAN is trained on synthetic and real-world samples from known classes to perform synthetic-to-real domain adaptation, but applied to synthetic samples of the new classes. Using the Tsinghua dataset with a synthetic counterpart, SYNTHIA-TS, we have run an extensive set of experiments. The results show that the proposed method is indeed effective, provided that we use a proper Convolutional Neural Network (CNN) to perform the traffic sign recognition (classification) task as well as a proper GAN to transform the synthetic images. Here, a ResNet101-based classifier and domain adaptation based on CycleGAN performed extremely well for a ratio∼ 1/4 for new/known classes; even for more challenging ratios such as∼ 4/1, the results are also very positive. | ||||
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| Notes | LAMP; ADAS; 600.118; 600.120 | Approved | no | ||
| Call Number | Admin @ si @ VWL2020 | Serial | 3405 | ||
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| Author | Alejandro Gonzalez Alzate; Zhijie Fang; Yainuvis Socarras; Joan Serrat; David Vazquez; Jiaolong Xu; Antonio Lopez | ||||
| Title | Pedestrian Detection at Day/Night Time with Visible and FIR Cameras: A Comparison | Type | Journal Article | ||
| Year | 2016 | Publication | Sensors | Abbreviated Journal | SENS |
| Volume | 16 | Issue | 6 | Pages |
820 |
| Keywords | Pedestrian Detection; FIR | ||||
| Abstract | Despite all the significant advances in pedestrian detection brought by computer vision for driving assistance, it is still a challenging problem. One reason is the extremely varying lighting conditions under which such a detector should operate, namely day and night time. Recent research has shown that the combination of visible and non-visible imaging modalities may increase detection accuracy, where the infrared spectrum plays a critical role. The goal of this paper is to assess the accuracy gain of different pedestrian models (holistic, part-based, patch-based) when training with images in the far infrared spectrum. Specifically, we want to compare detection accuracy on test images recorded at day and nighttime if trained (and tested) using (a) plain color images, (b) just infrared images and (c) both of them. In order to obtain results for the last item we propose an early fusion approach to combine features from both modalities. We base the evaluation on a new dataset we have built for this purpose as well as on the publicly available KAIST multispectral dataset. | ||||
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| ISSN | 1424-8220 | ISBN | Medium | ||
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| Notes | ADAS; 600.085; 600.076; 600.082; 601.281 | Approved | no | ||
| Call Number | ADAS @ adas @ GFS2016 | Serial | 2754 | ||
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| Author | Cristhian A. Aguilera-Carrasco; Angel Sappa; Cristhian Aguilera; Ricardo Toledo | ||||
| Title | Cross-Spectral Local Descriptors via Quadruplet Network | Type | Journal Article | ||
| Year | 2017 | Publication | Sensors | Abbreviated Journal | SENS |
| Volume | 17 | Issue | 4 | Pages |
873 |
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| Abstract | This paper presents a novel CNN-based architecture, referred to as Q-Net, to learn local feature descriptors that are useful for matching image patches from two different spectral bands. Given correctly matched and non-matching cross-spectral image pairs, a quadruplet network is trained to map input image patches to a common Euclidean space, regardless of the input spectral band. Our approach is inspired by the recent success of triplet networks in the visible spectrum, but adapted for cross-spectral scenarios, where, for each matching pair, there are always two possible non-matching patches: one for each spectrum. Experimental evaluations on a public cross-spectral VIS-NIR dataset shows that the proposed approach improves the state-of-the-art. Moreover, the proposed technique can also be used in mono-spectral settings, obtaining a similar performance to triplet network descriptors, but requiring less training data. | ||||
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| Notes | ADAS; 600.086; 600.118 | Approved | no | ||
| Call Number | Admin @ si @ ASA2017 | Serial | 2914 | ||
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| Author | Aura Hernandez-Sabate; Jose Elias Yauri; Pau Folch; Daniel Alvarez; Debora Gil | ||||
| Title | EEG Dataset Collection for Mental Workload Predictions in Flight-Deck Environment | Type | Journal Article | ||
| Year | 2024 | Publication | Sensors | Abbreviated Journal | SENS |
| Volume | 24 | Issue | 4 | Pages |
1174 |
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| Abstract | High mental workload reduces human performance and the ability to correctly carry out complex tasks. In particular, aircraft pilots enduring high mental workloads are at high risk of failure, even with catastrophic outcomes. Despite progress, there is still a lack of knowledge about the interrelationship between mental workload and brain functionality, and there is still limited data on flight-deck scenarios. Although recent emerging deep-learning (DL) methods using physiological data have presented new ways to find new physiological markers to detect and assess cognitive states, they demand large amounts of properly annotated datasets to achieve good performance. We present a new dataset of electroencephalogram (EEG) recordings specifically collected for the recognition of different levels of mental workload. The data were recorded from three experiments, where participants were induced to different levels of workload through tasks of increasing cognition demand. The first involved playing the N-back test, which combines memory recall with arithmetical skills. The second was playing Heat-the-Chair, a serious game specifically designed to emphasize and monitor subjects under controlled concurrent tasks. The third was flying in an Airbus320 simulator and solving several critical situations. The design of the dataset has been validated on three different levels: (1) correlation of the theoretical difficulty of each scenario to the self-perceived difficulty and performance of subjects; (2) significant difference in EEG temporal patterns across the theoretical difficulties and (3) usefulness for the training and evaluation of AI models. | ||||
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| Notes | IAM | Approved | no | ||
| Call Number | Admin @ si @ HYF2024 | Serial | 4019 | ||
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| Author | Sergio Escalera; Xavier Baro; Jordi Vitria; Petia Radeva; Bogdan Raducanu | ||||
| Title | Social Network Extraction and Analysis Based on Multimodal Dyadic Interaction | Type | Journal Article | ||
| Year | 2012 | Publication | Sensors | Abbreviated Journal | SENS |
| Volume | 12 | Issue | 2 | Pages |
1702-1719 |
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| Abstract | IF=1.77 (2010)
Social interactions are a very important component in peopleís lives. Social network analysis has become a common technique used to model and quantify the properties of social interactions. In this paper, we propose an integrated framework to explore the characteristics of a social network extracted from multimodal dyadic interactions. For our study, we used a set of videos belonging to New York Timesí Blogging Heads opinion blog. The Social Network is represented as an oriented graph, whose directed links are determined by the Influence Model. The linksí weights are a measure of the ìinfluenceî a person has over the other. The states of the Influence Model encode automatically extracted audio/visual features from our videos using state-of-the art algorithms. Our results are reported in terms of accuracy of audio/visual data fusion for speaker segmentation and centrality measures used to characterize the extracted social network. |
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| Publisher | Molecular Diversity Preservation International | Place of Publication | Editor | ||
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| Notes | MILAB; OR;HuPBA;MV | Approved | no | ||
| Call Number | Admin @ si @ EBV2012 | Serial | 1885 | ||
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| Author | O. Fors; J. Nuñez; Xavier Otazu; A. Prades; Robert D. Cardinal | ||||
| Title | Improving the Ability of Image Sensors to Detect Faint Stars and Moving Objects Using Image Deconvolution Techniques | Type | Journal Article | ||
| Year | 2010 | Publication | Sensors | Abbreviated Journal | SENS |
| Volume | 10 | Issue | 3 | Pages |
1743–1752 |
| Keywords | image processing; image deconvolution; faint stars; space debris; wavelet transform | ||||
| Abstract | Abstract: In this paper we show how the techniques of image deconvolution can increase the ability of image sensors as, for example, CCD imagers, to detect faint stars or faint orbital objects (small satellites and space debris). In the case of faint stars, we show that this benefit is equivalent to double the quantum efficiency of the used image sensor or to increase the effective telescope aperture by more than 30% without decreasing the astrometric precision or introducing artificial bias. In the case of orbital objects, the deconvolution technique can double the signal-to-noise ratio of the image, which helps to discover and control dangerous objects as space debris or lost satellites. The benefits obtained using CCD detectors can be extrapolated to any kind of image sensors. | ||||
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| Notes | CIC | Approved | no | ||
| Call Number | CAT @ cat @ FNO2010 | Serial | 1285 | ||
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| Author | Wenjuan Gong; Xuena Zhang; Jordi Gonzalez; Andrews Sobral; Thierry Bouwmans; Changhe Tu; El-hadi Zahzah | ||||
| Title | Human Pose Estimation from Monocular Images: A Comprehensive Survey | Type | Journal Article | ||
| Year | 2016 | Publication | Sensors | Abbreviated Journal | SENS |
| Volume | 16 | Issue | 12 | Pages |
1966 |
| Keywords | human pose estimation; human bodymodels; generativemethods; discriminativemethods; top-down methods; bottom-up methods | ||||
| Abstract | Human pose estimation refers to the estimation of the location of body parts and how they are connected in an image. Human pose estimation from monocular images has wide applications (e.g., image indexing). Several surveys on human pose estimation can be found in the literature, but they focus on a certain category; for example, model-based approaches or human motion analysis, etc. As far as we know, an overall review of this problem domain has yet to be provided. Furthermore, recent advancements based on deep learning have brought novel algorithms for this problem. In this paper, a comprehensive survey of human pose estimation from monocular images is carried out including milestone works and recent advancements. Based on one standard pipeline for the solution of computer vision problems, this survey splits the problem into several modules: feature extraction and description, human body models, and modeling
methods. Problem modeling methods are approached based on two means of categorization in this survey. One way to categorize includes top-down and bottom-up methods, and another way includes generative and discriminative methods. Considering the fact that one direct application of human pose estimation is to provide initialization for automatic video surveillance, there are additional sections for motion-related methods in all modules: motion features, motion models, and motion-based methods. Finally, the paper also collects 26 publicly available data sets for validation and provides error measurement methods that are frequently used. |
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| Notes | ISE; 600.098; 600.119 | Approved | no | ||
| Call Number | Admin @ si @ GZG2016 | Serial | 2933 | ||
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| Author | Xavier Soria; Angel Sappa; Riad I. Hammoud | ||||
| Title | Wide-Band Color Imagery Restoration for RGB-NIR Single Sensor Images | Type | Journal Article | ||
| Year | 2018 | Publication | Sensors | Abbreviated Journal | SENS |
| Volume | 18 | Issue | 7 | Pages |
2059 |
| Keywords | RGB-NIR sensor; multispectral imaging; deep learning; CNNs | ||||
| Abstract | Multi-spectral RGB-NIR sensors have become ubiquitous in recent years. These sensors allow the visible and near-infrared spectral bands of a given scene to be captured at the same time. With such cameras, the acquired imagery has a compromised RGB color representation due to near-infrared bands (700–1100 nm) cross-talking with the visible bands (400–700 nm).
This paper proposes two deep learning-based architectures to recover the full RGB color images, thus removing the NIR information from the visible bands. The proposed approaches directly restore the high-resolution RGB image by means of convolutional neural networks. They are evaluated with several outdoor images; both architectures reach a similar performance when evaluated in different scenarios and using different similarity metrics. Both of them improve the state of the art approaches. |
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| Notes | ADAS; MSIAU; 600.086; 600.130; 600.122; 600.118 | Approved | no | ||
| Call Number | Admin @ si @ SSH2018 | Serial | 3145 | ||
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| Author | Zhijie Fang; David Vazquez; Antonio Lopez | ||||
| Title | On-Board Detection of Pedestrian Intentions | Type | Journal Article | ||
| Year | 2017 | Publication | Sensors | Abbreviated Journal | SENS |
| Volume | 17 | Issue | 10 | Pages |
2193 |
| Keywords | pedestrian intention; ADAS; self-driving | ||||
| Abstract | Avoiding vehicle-to-pedestrian crashes is a critical requirement for nowadays advanced driver assistant systems (ADAS) and future self-driving vehicles. Accordingly, detecting pedestrians from raw sensor data has a history of more than 15 years of research, with vision playing a central role.
During the last years, deep learning has boosted the accuracy of image-based pedestrian detectors. However, detection is just the first step towards answering the core question, namely is the vehicle going to crash with a pedestrian provided preventive actions are not taken? Therefore, knowing as soon as possible if a detected pedestrian has the intention of crossing the road ahead of the vehicle is essential for performing safe and comfortable maneuvers that prevent a crash. However, compared to pedestrian detection, there is relatively little literature on detecting pedestrian intentions. This paper aims to contribute along this line by presenting a new vision-based approach which analyzes the pose of a pedestrian along several frames to determine if he or she is going to enter the road or not. We present experiments showing 750 ms of anticipation for pedestrians crossing the road, which at a typical urban driving speed of 50 km/h can provide 15 additional meters (compared to a pure pedestrian detector) for vehicle automatic reactions or to warn the driver. Moreover, in contrast with state-of-the-art methods, our approach is monocular, neither requiring stereo nor optical flow information. |
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| Notes | ADAS; 600.085; 600.076; 601.223; 600.116; 600.118 | Approved | no | ||
| Call Number | Admin @ si @ FVL2017 | Serial | 2983 | ||
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| Author | Rafael E. Rivadeneira; Angel Sappa; Boris X. Vintimilla; Riad I. Hammoud | ||||
| Title | A Novel Domain Transfer-Based Approach for Unsupervised Thermal Image Super-Resolution | Type | Journal Article | ||
| Year | 2022 | Publication | Sensors | Abbreviated Journal | SENS |
| Volume | 22 | Issue | 6 | Pages |
2254 |
| Keywords | Thermal image super-resolution; unsupervised super-resolution; thermal images; attention module; semiregistered thermal images | ||||
| Abstract | This paper presents a transfer domain strategy to tackle the limitations of low-resolution thermal sensors and generate higher-resolution images of reasonable quality. The proposed technique employs a CycleGAN architecture and uses a ResNet as an encoder in the generator along with an attention module and a novel loss function. The network is trained on a multi-resolution thermal image dataset acquired with three different thermal sensors. Results report better performance benchmarking results on the 2nd CVPR-PBVS-2021 thermal image super-resolution challenge than state-of-the-art methods. The code of this work is available online. | ||||
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| Notes | MSIAU; | Approved | no | ||
| Call Number | Admin @ si @ RSV2022b | Serial | 3688 | ||
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| Author | Jose Luis Gomez; Gabriel Villalonga; Antonio Lopez | ||||
| Title | Co-Training for Deep Object Detection: Comparing Single-Modal and Multi-Modal Approaches | Type | Journal Article | ||
| Year | 2021 | Publication | Sensors | Abbreviated Journal | SENS |
| Volume | 21 | Issue | 9 | Pages |
3185 |
| Keywords | co-training; multi-modality; vision-based object detection; ADAS; self-driving | ||||
| Abstract | Top-performing computer vision models are powered by convolutional neural networks (CNNs). Training an accurate CNN highly depends on both the raw sensor data and their associated ground truth (GT). Collecting such GT is usually done through human labeling, which is time-consuming and does not scale as we wish. This data-labeling bottleneck may be intensified due to domain shifts among image sensors, which could force per-sensor data labeling. In this paper, we focus on the use of co-training, a semi-supervised learning (SSL) method, for obtaining self-labeled object bounding boxes (BBs), i.e., the GT to train deep object detectors. In particular, we assess the goodness of multi-modal co-training by relying on two different views of an image, namely, appearance (RGB) and estimated depth (D). Moreover, we compare appearance-based single-modal co-training with multi-modal. Our results suggest that in a standard SSL setting (no domain shift, a few human-labeled data) and under virtual-to-real domain shift (many virtual-world labeled data, no human-labeled data) multi-modal co-training outperforms single-modal. In the latter case, by performing GAN-based domain translation both co-training modalities are on par, at least when using an off-the-shelf depth estimation model not specifically trained on the translated images. | ||||
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| Notes | ADAS; 600.118 | Approved | no | ||
| Call Number | Admin @ si @ GVL2021 | Serial | 3562 | ||
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| Author | Saad Minhas; Zeba Khanam; Shoaib Ehsan; Klaus McDonald Maier; Aura Hernandez-Sabate | ||||
| Title | Weather Classification by Utilizing Synthetic Data | Type | Journal Article | ||
| Year | 2022 | Publication | Sensors | Abbreviated Journal | SENS |
| Volume | 22 | Issue | 9 | Pages |
3193 |
| Keywords | Weather classification; synthetic data; dataset; autonomous car; computer vision; advanced driver assistance systems; deep learning; intelligent transportation systems | ||||
| Abstract | Weather prediction from real-world images can be termed a complex task when targeting classification using neural networks. Moreover, the number of images throughout the available datasets can contain a huge amount of variance when comparing locations with the weather those images are representing. In this article, the capabilities of a custom built driver simulator are explored specifically to simulate a wide range of weather conditions. Moreover, the performance of a new synthetic dataset generated by the above simulator is also assessed. The results indicate that the use of synthetic datasets in conjunction with real-world datasets can increase the training efficiency of the CNNs by as much as 74%. The article paves a way forward to tackle the persistent problem of bias in vision-based datasets. | ||||
| Address | 21 April 2022 | ||||
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| Publisher | MDPI | Place of Publication | Editor | ||
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| Notes | IAM; 600.139; 600.159; 600.166; 600.145; | Approved | no | ||
| Call Number | Admin @ si @ MKE2022 | Serial | 3761 | ||
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