| Records |
Author  |
X. Binefa; Xavier Roca; Jordi Vitria |
| Title |
A Contrast Approach to Depth from Focus. |
Type |
Miscellaneous |
| Year |
1997 |
Publication |
VII Simposium Nacional de Reconocimiento de Formas y Analisis de Imagenes. |
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Barcelona |
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OR;ISE;MV |
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no |
| Call Number |
BCNPCL @ bcnpcl @ BRV1997 |
Serial |
63 |
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| Author |
X. Binefa; Petia Radeva; J.A. Cortijo; J. Garcia |
| Title |
Contour detection and color influence in defocused environtments. |
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Miscellaneous |
| Year |
1998 |
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MILAB |
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no |
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BCNPCL @ bcnpcl @ BRC1998 |
Serial |
28 |
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| Author |
X. Binefa; Jordi Vitria; Xavier Roca |
| Title |
Deteccion de profundidad en imagenes monoculares mediante vision activa. |
Type |
Miscellaneous |
| Year |
1993 |
Publication |
Revista de Optica Pura y Aplicada, 26,3:636–648. |
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OR;ISE;MV |
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no |
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BCNPCL @ bcnpcl @ BVR1993 |
Serial |
144 |
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| Author |
X. Binefa; Jordi Vitria; Xavier Roca |
| Title |
Deteccion de profundidad en imagenes monoculares mediante vision activa. |
Type |
Miscellaneous |
| Year |
1992 |
Publication |
I Reunion Iberoamericana de Optica |
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Barcelona |
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OR;ISE;MV |
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BCNPCL @ bcnpcl @ BVR1992 |
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258 |
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| Author |
X. Binefa; Jordi Vitria; Maria Vanrell |
| Title |
Reconstruccion tridimensional de imagenes Microscopicas. |
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Miscellaneous |
| Year |
1992 |
Publication |
V Simposium Nacional de Reconocimiento de Formas y Analisis de Imagenes |
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OR;CIC;MV |
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no |
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BCNPCL @ bcnpcl @ BVV1992b |
Serial |
255 |
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| Author |
X. Binefa; Jordi Vitria; Juan J. Villanueva |
| Title |
Three dimensional inspection of integrated circuits: a depth from focus approach. |
Type |
Miscellaneous |
| Year |
1992 |
Publication |
SPIE/IS&T Symposium on Electronic Imaging (Conference on Machine Vision in Microelectronics Manufacturing) |
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OR;MV |
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no |
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BCNPCL @ bcnpcl @ BVV1992a |
Serial |
250 |
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| Author |
X. Binefa; Jordi Vitria |
| Title |
A contrast based focusing criterium. |
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Miscellaneous |
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1996 |
Publication |
IEEE International Conference on Pattern Recognition. Vol. A, pp. 805–809 |
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OR;MV |
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no |
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BCNPCL @ bcnpcl @ BiV1996 |
Serial |
79 |
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| Author |
X. Binefa; J.M. Sanchez; Petia Radeva; Jordi Vitria |
| Title |
Linking Visual Cues and Semantic Terms Under Specific Digital Video Domains. |
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Miscellaneous |
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2000 |
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Journal of Visual Languages and Computing, 11(3):253–271. |
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OR;MILAB;MV |
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no |
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BCNPCL @ bcnpcl @ BRS2000 |
Serial |
337 |
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| Author |
X. Binefa; F. Javier Sanchez; F.X. Perez; Xavier Roca; Jordi Vitria; Juan J. Villanueva |
| Title |
Using defocus in optical inspection of integrated circuits |
Type |
Conference Article |
| Year |
1993 |
Publication |
Institute of Physics Conferences Series |
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135 |
Issue |
10 |
Pages |
389-392 |
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Bristol |
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Institute of Physics |
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MV;OR;ISE |
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no |
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BCNPCL @ bcnpcl @ BSP1993; IAM @ iam @ BSP1993 |
Serial |
151 |
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| Author |
Wenwen Yu; Mingyu Liu; Mingrui Chen; Ning Lu; Yinlong We; Yuliang Liu; Dimosthenis Karatzas; Xiang Bai |
| Title |
ICDAR 2023 Competition on Reading the Seal Title |
Type |
Conference Article |
| Year |
2023 |
Publication |
17th International Conference on Document Analysis and Recognition |
Abbreviated Journal |
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| Volume |
14188 |
Issue |
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Pages |
522–535 |
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| Abstract |
Reading seal title text is a challenging task due to the variable shapes of seals, curved text, background noise, and overlapped text. However, this important element is commonly found in official and financial scenarios, and has not received the attention it deserves in the field of OCR technology. To promote research in this area, we organized ICDAR 2023 competition on reading the seal title (ReST), which included two tasks: seal title text detection (Task 1) and end-to-end seal title recognition (Task 2). We constructed a dataset of 10,000 real seal data, covering the most common classes of seals, and labeled all seal title texts with text polygons and text contents. The competition opened on 30th December, 2022 and closed on 20th March, 2023. The competition attracted 53 participants and received 135 submissions from academia and industry, including 28 participants and 72 submissions for Task 1, and 25 participants and 63 submissions for Task 2, which demonstrated significant interest in this challenging task. In this report, we present an overview of the competition, including the organization, challenges, and results. We describe the dataset and tasks, and summarize the submissions and evaluation results. The results show that significant progress has been made in the field of seal title text reading, and we hope that this competition will inspire further research and development in this important area of OCR technology. |
| Address |
San Jose; CA; USA; August 2023 |
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LNCS |
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ICDAR |
| Notes |
DAG |
Approved |
no |
| Call Number |
Admin @ si @ YLC2023 |
Serial |
3897 |
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| Author |
Wenwen Yu; Chengquan Zhang; Haoyu Cao; Wei Hua; Bohan Li; Huang Chen; Mingyu Liu; Mingrui Chen; Jianfeng Kuang; Mengjun Cheng; Yuning Du; Shikun Feng; Xiaoguang Hu; Pengyuan Lyu; Kun Yao; Yuechen Yu; Yuliang Liu; Wanxiang Che; Errui Ding; Cheng-Lin Liu; Jiebo Luo; Shuicheng Yan; Min Zhang; Dimosthenis Karatzas; Xing Sun; Jingdong Wang; Xiang Bai |
| Title |
ICDAR 2023 Competition on Structured Text Extraction from Visually-Rich Document Images |
Type |
Conference Article |
| Year |
2023 |
Publication |
17th International Conference on Document Analysis and Recognition |
Abbreviated Journal |
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| Volume |
14188 |
Issue |
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Pages |
536–552 |
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| Abstract |
Structured text extraction is one of the most valuable and challenging application directions in the field of Document AI. However, the scenarios of past benchmarks are limited, and the corresponding evaluation protocols usually focus on the submodules of the structured text extraction scheme. In order to eliminate these problems, we organized the ICDAR 2023 competition on Structured text extraction from Visually-Rich Document images (SVRD). We set up two tracks for SVRD including Track 1: HUST-CELL and Track 2: Baidu-FEST, where HUST-CELL aims to evaluate the end-to-end performance of Complex Entity Linking and Labeling, and Baidu-FEST focuses on evaluating the performance and generalization of Zero-shot/Few-shot Structured Text extraction from an end-to-end perspective. Compared to the current document benchmarks, our two tracks of competition benchmark enriches the scenarios greatly and contains more than 50 types of visually-rich document images (mainly from the actual enterprise applications). The competition opened on 30th December, 2022 and closed on 24th March, 2023. There are 35 participants and 91 valid submissions received for Track 1, and 15 participants and 26 valid submissions received for Track 2. In this report we will presents the motivation, competition datasets, task definition, evaluation protocol, and submission summaries. According to the performance of the submissions, we believe there is still a large gap on the expected information extraction performance for complex and zero-shot scenarios. It is hoped that this competition will attract many researchers in the field of CV and NLP, and bring some new thoughts to the field of Document AI. |
| Address |
San Jose; CA; USA; August 2023 |
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ICDAR |
| Notes |
DAG |
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no |
| Call Number |
Admin @ si @ YZC2023 |
Serial |
3896 |
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| Author |
Wenwen Fu; Zhihong An; Wendong Huang; Haoran Sun; Wenjuan Gong; Jordi Gonzalez |
| Title |
A Spatio-Temporal Spotting Network with Sliding Windows for Micro-Expression Detection |
Type |
Journal Article |
| Year |
2023 |
Publication |
Electronics |
Abbreviated Journal |
ELEC |
| Volume |
12 |
Issue |
18 |
Pages |
3947 |
| Keywords |
micro-expression spotting; sliding window; key frame extraction |
| Abstract |
Micro-expressions reveal underlying emotions and are widely applied in political psychology, lie detection, law enforcement and medical care. Micro-expression spotting aims to detect the temporal locations of facial expressions from video sequences and is a crucial task in micro-expression recognition. In this study, the problem of micro-expression spotting is formulated as micro-expression classification per frame. We propose an effective spotting model with sliding windows called the spatio-temporal spotting network. The method involves a sliding window detection mechanism, combines the spatial features from the local key frames and the global temporal features and performs micro-expression spotting. The experiments are conducted on the CAS(ME)2 database and the SAMM Long Videos database, and the results demonstrate that the proposed method outperforms the state-of-the-art method by 30.58% for the CAS(ME)2 and 23.98% for the SAMM Long Videos according to overall F-scores. |
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ISE |
Approved |
no |
| Call Number |
Admin @ si @ FAH2023 |
Serial |
3864 |
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| Author |
Wenlong Deng; Yongli Mou; Takahiro Kashiwa; Sergio Escalera; Kohei Nagai; Kotaro Nakayama; Yutaka Matsuo; Helmut Prendinger |
| Title |
Vision based Pixel-level Bridge Structural Damage Detection Using a Link ASPP Network |
Type |
Journal Article |
| Year |
2020 |
Publication |
Automation in Construction |
Abbreviated Journal |
AC |
| Volume |
110 |
Issue |
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Pages |
102973 |
| Keywords |
Semantic image segmentation; Deep learning |
| Abstract |
Structural Health Monitoring (SHM) has greatly benefited from computer vision. Recently, deep learning approaches are widely used to accurately estimate the state of deterioration of infrastructure. In this work, we focus on the problem of bridge surface structural damage detection, such as delamination and rebar exposure. It is well known that the quality of a deep learning model is highly dependent on the quality of the training dataset. Bridge damage detection, our application domain, has the following main challenges: (i) labeling the damages requires knowledgeable civil engineering professionals, which makes it difficult to collect a large annotated dataset; (ii) the damage area could be very small, whereas the background area is large, which creates an unbalanced training environment; (iii) due to the difficulty to exactly determine the extension of the damage, there is often a variation among different labelers who perform pixel-wise labeling. In this paper, we propose a novel model for bridge structural damage detection to address the first two challenges. This paper follows the idea of an atrous spatial pyramid pooling (ASPP) module that is designed as a novel network for bridge damage detection. Further, we introduce the weight balanced Intersection over Union (IoU) loss function to achieve accurate segmentation on a highly unbalanced small dataset. The experimental results show that (i) the IoU loss function improves the overall performance of damage detection, as compared to cross entropy loss or focal loss, and (ii) the proposed model has a better ability to detect a minority class than other light segmentation networks. |
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HuPBA; no proj |
Approved |
no |
| Call Number |
Admin @ si @ DMK2020 |
Serial |
3314 |
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| Author |
Wenjuan Gong; Zhang Yue; Wei Wang; Cheng Peng; Jordi Gonzalez |
| Title |
Meta-MMFNet: Meta-Learning Based Multi-Model Fusion Network for Micro-Expression Recognition |
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Journal Article |
| Year |
2022 |
Publication |
ACM Transactions on Multimedia Computing, Communications, and Applications |
Abbreviated Journal |
ACMTMC |
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Feature Fusion; Model Fusion; Meta-Learning; Micro-Expression Recognition |
| Abstract |
Despite its wide applications in criminal investigations and clinical communications with patients suffering from autism, automatic micro-expression recognition remains a challenging problem because of the lack of training data and imbalanced classes problems. In this study, we proposed a meta-learning based multi-model fusion network (Meta-MMFNet) to solve the existing problems. The proposed method is based on the metric-based meta-learning pipeline, which is specifically designed for few-shot learning and is suitable for model-level fusion. The frame difference and optical flow features were fused, deep features were extracted from the fused feature, and finally in the meta-learning-based framework, weighted sum model fusion method was applied for micro-expression classification. Meta-MMFNet achieved better results than state-of-the-art methods on four datasets. The code is available at https://github.com/wenjgong/meta-fusion-based-method. |
| Address |
May 2022 |
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ISE; 600.157 |
Approved |
no |
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Admin @ si @ GYW2022 |
Serial |
3692 |
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| Author |
Wenjuan Gong; Yue Zhang; Wei Wang; Peng Cheng; Jordi Gonzalez |
| Title |
Meta-MMFNet: Meta-learning-based Multi-model Fusion Network for Micro-expression Recognition |
Type |
Journal Article |
| Year |
2023 |
Publication |
ACM Transactions on Multimedia Computing, Communications, and Applications |
Abbreviated Journal |
TMCCA |
| Volume |
20 |
Issue |
2 |
Pages |
1–20 |
| Keywords |
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| Abstract |
Despite its wide applications in criminal investigations and clinical communications with patients suffering from autism, automatic micro-expression recognition remains a challenging problem because of the lack of training data and imbalanced classes problems. In this study, we proposed a meta-learning-based multi-model fusion network (Meta-MMFNet) to solve the existing problems. The proposed method is based on the metric-based meta-learning pipeline, which is specifically designed for few-shot learning and is suitable for model-level fusion. The frame difference and optical flow features were fused, deep features were extracted from the fused feature, and finally in the meta-learning-based framework, weighted sum model fusion method was applied for micro-expression classification. Meta-MMFNet achieved better results than state-of-the-art methods on four datasets. The code is available at https://github.com/wenjgong/meta-fusion-based-method. |
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ISE |
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no |
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Admin @ si @ GZW2023 |
Serial |
3862 |
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