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Author Patricia Suarez; Henry Velesaca; Dario Carpio; Angel Sappa edit  url
openurl 
  Title Corn kernel classification from few training samples Type Journal
  Year (down) 2023 Publication Artificial Intelligence in Agriculture Abbreviated Journal  
  Volume 9 Issue Pages 89-99  
  Keywords  
  Abstract This article presents an efficient approach to classify a set of corn kernels in contact, which may contain good, or defective kernels along with impurities. The proposed approach consists of two stages, the first one is a next-generation segmentation network, trained by using a set of synthesized images that is applied to divide the given image into a set of individual instances. An ad-hoc lightweight CNN architecture is then proposed to classify each instance into one of three categories (ie good, defective, and impurities). The segmentation network is trained using a strategy that avoids the time-consuming and human-error-prone task of manual data annotation. Regarding the classification stage, the proposed ad-hoc network is designed with only a few sets of layers to result in a lightweight architecture capable of being used in integrated solutions. Experimental results and comparisons with previous approaches showing both the improvement in accuracy and the reduction in time are provided. Finally, the segmentation and classification approach proposed can be easily adapted for use with other cereal types.  
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  Notes MSIAU Approved no  
  Call Number Admin @ si @ SVC2023 Serial 3892  
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Author Pau Cano; Alvaro Caravaca; Debora Gil; Eva Musulen edit   pdf
url  openurl
  Title Diagnosis of Helicobacter pylori using AutoEncoders for the Detection of Anomalous Staining Patterns in Immunohistochemistry Images Type Miscellaneous
  Year (down) 2023 Publication Arxiv Abbreviated Journal  
  Volume Issue Pages 107241  
  Keywords  
  Abstract This work addresses the detection of Helicobacter pylori a bacterium classified since 1994 as class 1 carcinogen to humans. By its highest specificity and sensitivity, the preferred diagnosis technique is the analysis of histological images with immunohistochemical staining, a process in which certain stained antibodies bind to antigens of the biological element of interest. This analysis is a time demanding task, which is currently done by an expert pathologist that visually inspects the digitized samples.
We propose to use autoencoders to learn latent patterns of healthy tissue and detect H. pylori as an anomaly in image staining. Unlike existing classification approaches, an autoencoder is able to learn patterns in an unsupervised manner (without the need of image annotations) with high performance. In particular, our model has an overall 91% of accuracy with 86\% sensitivity, 96% specificity and 0.97 AUC in the detection of H. pylori.		  
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  Area Expedition Conference  
  Notes IAM Approved no  
  Call Number Admin @ si @ CCG2023 Serial 3855  
Permanent link to this record
 
 
Author Pau Cano; Debora Gil; Eva Musulen edit  openurl
  Title Towards automatic detection of helicobacter pylori in histological samples of gastric tissue Type Conference Article
  Year (down) 2023 Publication IEEE International Symposium on Biomedical Imaging Abbreviated Journal  
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  Abstract  
  Address Cartagena de Indias; Colombia; April 2023  
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  Series Editor Series Title Abbreviated Series Title  
  Series Volume Series Issue Edition  
  ISSN ISBN Medium  
  Area Expedition Conference ISBI  
  Notes IAM Approved no  
  Call Number Admin @ si @ CGM2023 Serial 3953  
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Author Pau Torras; Mohamed Ali Souibgui; Sanket Biswas; Alicia Fornes edit  url
openurl 
  Title Segmentation-Free Alignment of Arbitrary Symbol Transcripts to Images Type Conference Article
  Year (down) 2023 Publication Document Analysis and Recognition – ICDAR 2023 Workshops Abbreviated Journal  
  Volume 14193 Issue Pages 83-93  
  Keywords Historical Manuscripts; Symbol Alignment  
  Abstract Developing arbitrary symbol recognition systems is a challenging endeavour. Even using content-agnostic architectures such as few-shot models, performance can be substantially improved by providing a number of well-annotated examples into training. In some contexts, transcripts of the symbols are available without any position information associated to them, which enables using line-level recognition architectures. A way of providing this position information to detection-based architectures is finding systems that can align the input symbols with the transcription. In this paper we discuss some symbol alignment techniques that are suitable for low-data scenarios and provide an insight on their perceived strengths and weaknesses. In particular, we study the usage of Connectionist Temporal Classification models, Attention-Based Sequence to Sequence models and we compare them with the results obtained on a few-shot recognition system.  
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  Language Summary Language Original Title  
  Series Editor Series Title Abbreviated Series Title LNCS  
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  ISSN ISBN Medium  
  Area Expedition Conference ICDAR  
  Notes DAG Approved no  
  Call Number Admin @ si @ TSS2023 Serial 3850  
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Author Qingshan Chen; Zhenzhen Quan; Yifan Hu; Yujun Li; Zhi Liu; Mikhail Mozerov edit  url
openurl 
  Title MSIF: multi-spectrum image fusion method for cross-modality person re-identification Type Journal Article
  Year (down) 2023 Publication International Journal of Machine Learning and Cybernetics Abbreviated Journal IJMLC  
  Volume Issue Pages  
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  Abstract Sketch-RGB cross-modality person re-identification (ReID) is a challenging task that aims to match a sketch portrait drawn by a professional artist with a full-body photo taken by surveillance equipment to deal with situations where the monitoring equipment is damaged at the accident scene. However, sketch portraits only provide highly abstract frontal body contour information and lack other important features such as color, pose, behavior, etc. The difference in saliency between the two modalities brings new challenges to cross-modality person ReID. To overcome this problem, this paper proposes a novel dual-stream model for cross-modality person ReID, which is able to mine modality-invariant features to reduce the discrepancy between sketch and camera images end-to-end. More specifically, we propose a multi-spectrum image fusion (MSIF) method, which aims to exploit the image appearance changes brought by multiple spectrums and guide the network to mine modality-invariant commonalities during training. It only processes the spectrum of the input images without adding additional calculations and model complexity, which can be easily integrated into other models. Moreover, we introduce a joint structure via a generalized mean pooling (GMP) layer and a self-attention (SA) mechanism to balance background and texture information and obtain the regional features with a large amount of information in the image. To further shrink the intra-class distance, a weighted regularized triplet (WRT) loss is developed without introducing additional hyperparameters. The model was first evaluated on the PKU Sketch ReID dataset, and extensive experimental results show that the Rank-1/mAP accuracy of our method is 87.00%/91.12%, reaching the current state-of-the-art performance. To further validate the effectiveness of our approach in handling cross-modality person ReID, we conducted experiments on two commonly used IR-RGB datasets (SYSU-MM01 and RegDB). The obtained results show that our method achieves competitive performance. These results confirm the ability of our method to effectively process images from different modalities.  
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  Notes LAMP Approved no  
  Call Number Admin @ si @ CQH2023 Serial 3885  
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Author Qingshan Chen; Zhenzhen Quan; Yujun Li; Chao Zhai; Mikhail Mozerov edit  url
doi  openurl
  Title An Unsupervised Domain Adaption Approach for Cross-Modality RGB-Infrared Person Re-Identification Type Journal Article
  Year (down) 2023 Publication IEEE Sensors Journal Abbreviated Journal IEEE-SENS  
  Volume 23 Issue 24 Pages  
  Keywords Q. Chen, Z. Quan, Y. Li, C. Zhai and M. G. Mozerov  
  Abstract Dual-camera systems commonly employed in surveillance serve as the foundation for RGB-infrared (IR) cross-modality person re-identification (ReID). However, significant modality differences give rise to inferior performance compared to single-modality scenarios. Furthermore, most existing studies in this area rely on supervised training with meticulously labeled datasets. Labeling RGB-IR image pairs is more complex than labeling conventional image data, and deploying pretrained models on unlabeled datasets can lead to catastrophic performance degradation. In contrast to previous solutions that focus solely on cross-modality or domain adaptation issues, this article presents an end-to-end unsupervised domain adaptation (UDA) framework for the cross-modality person ReID, which can simultaneously address both of these challenges. This model employs source domain classes, target domain clusters, and unclustered instance samples for the training, maximizing the comprehensive use of the dataset. Moreover, it addresses the problem of mismatched clustering labels between the two modalities in the target domain by incorporating a label matching module that reassigns reliable clusters with labels, ensuring correspondence between different modality labels. We construct the loss function by incorporating distinctiveness loss and multiplicity loss, both of which are determined by the similarity of neighboring features in the predicted feature space and the difference between distant features. This approach enables efficient feature clustering and cluster class assignment to occur concurrently. Eight UDA cross-modality person ReID experiments are conducted on three real datasets and six synthetic datasets. The experimental results unequivocally demonstrate that the proposed model outperforms the existing state-of-the-art algorithms to a significant degree. Notably, in RegDB → RegDB_light, the Rank-1 accuracy exhibits a remarkable improvement of 8.24%.  
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  Notes LAMP Approved no  
  Call Number Admin @ si @ CQL2023 Serial 3884  
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Author Rafael E. Rivadeneira; Angel Sappa; Boris X. Vintimilla; Chenyang Wang; Junjun Jiang; Xianming Liu; Zhiwei Zhong; Dai Bin; Li Ruodi; Li Shengye edit  url
doi  openurl
  Title Thermal Image Super-Resolution Challenge Results-PBVS 2023 Type Conference Article
  Year (down) 2023 Publication Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops Abbreviated Journal  
  Volume Issue Pages 470-478  
  Keywords  
  Abstract This paper presents the results of two tracks from the fourth Thermal Image Super-Resolution (TISR) challenge, held at the Perception Beyond the Visible Spectrum (PBVS) 2023 workshop. Track-1 uses the same thermal image dataset as previous challenges, with 951 training images and 50 validation images at each resolution. In this track, two evaluations were conducted: the first consists of generating a SR image from a HR thermal noisy image downsampled by four, and the second consists of generating a SR image from a mid-resolution image and compare it with its semi-registered HR image (acquired with another camera). The results of Track-1 outperformed those from last year’s challenge. On the other hand, Track-2 uses a new acquired dataset consisting of 160 registered visible and thermal images of the same scenario for training and 30 validation images. This year, more than 150 teams participated in the challenge tracks, demonstrating the community’s ongoing interest in this topic.  
  Address Vancouver; Canada; June 2023  
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  Area Expedition Conference CVPRW  
  Notes MSIAU Approved no  
  Call Number Admin @ si @ RSV2023 Serial 3914  
Permanent link to this record
 
 
Author Razieh Rastgoo; Kourosh Kiani; Sergio Escalera edit  url
openurl 
  Title A deep co-attentive hand-based video question answering framework using multi-view skeleton Type Journal Article
  Year (down) 2023 Publication Multimedia Tools and Applications Abbreviated Journal MTAP  
  Volume 82 Issue Pages 1401–1429  
  Keywords  
  Abstract In this paper, we present a novel hand –based Video Question Answering framework, entitled Multi-View Video Question Answering (MV-VQA), employing the Single Shot Detector (SSD), Convolutional Neural Network (CNN), Long Short-Term Memory (LSTM), Bidirectional Encoder Representations from Transformers (BERT), and Co-Attention mechanism with RGB videos as the inputs. Our model includes three main blocks: vision, language, and attention. In the vision block, we employ a novel representation to obtain some efficient multiview features from the hand object using the combination of five 3DCNNs and one LSTM network. To obtain the question embedding, we use the BERT model in language block. Finally, we employ a co-attention mechanism on vision and language features to recognize the final answer. For the first time, we propose such a hand-based Video-QA framework including the multi-view hand skeleton features combined with the question embedding and co-attention mechanism. Our framework is capable of processing the arbitrary numbers of questions in the dataset annotations. There are different application domains for this framework. Here, as an application domain, we applied our framework to dynamic hand gesture recognition for the first time. Since the main object in dynamic hand gesture recognition is the human hand, we performed a step-by-step analysis of the hand detection and multi-view hand skeleton impact on the model performance. Evaluation results on five datasets, including two datasets in VideoQA, two datasets in dynamic hand gesture, and one dataset in hand action recognition show that MV-VQA outperforms state-of-the-art alternatives.  
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  Notes HUPBA Approved no  
  Call Number Admin @ si @ RKE2023b Serial 3881  
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Author Razieh Rastgoo; Kourosh Kiani; Sergio Escalera edit  url
openurl 
  Title ZS-GR: zero-shot gesture recognition from RGB-D videos Type Journal Article
  Year (down) 2023 Publication Multimedia Tools and Applications Abbreviated Journal MTAP  
  Volume 82 Issue Pages 43781-43796  
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  Abstract Gesture Recognition (GR) is a challenging research area in computer vision. To tackle the annotation bottleneck in GR, we formulate the problem of Zero-Shot Gesture Recognition (ZS-GR) and propose a two-stream model from two input modalities: RGB and Depth videos. To benefit from the vision Transformer capabilities, we use two vision Transformer models, for human detection and visual features representation. We configure a transformer encoder-decoder architecture, as a fast and accurate human detection model, to overcome the challenges of the current human detection models. Considering the human keypoints, the detected human body is segmented into nine parts. A spatio-temporal representation from human body is obtained using a vision Transformer and a LSTM network. A semantic space maps the visual features to the lingual embedding of the class labels via a Bidirectional Encoder Representations from Transformers (BERT) model. We evaluated the proposed model on five datasets, Montalbano II, MSR Daily Activity 3D, CAD-60, NTU-60, and isoGD obtaining state-of-the-art results compared to state-of-the-art ZS-GR models as well as the Zero-Shot Action Recognition (ZS-AR).  
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  Notes HUPBA Approved no  
  Call Number Admin @ si @ RKE2023a Serial 3879  
Permanent link to this record
 
 
Author Reuben Dorent; Aaron Kujawa; Marina Ivory; Spyridon Bakas; Nikola Rieke; Samuel Joutard; Ben Glocker; Jorge Cardoso; Marc Modat; Kayhan Batmanghelich; Arseniy Belkov; Maria Baldeon Calisto; Jae Won Choi; Benoit M. Dawant; Hexin Dong; Sergio Escalera; Yubo Fan; Lasse Hansen; Mattias P. Heinrich; Smriti Joshi; Victoriya Kashtanova; Hyeon Gyu Kim; Satoshi Kondo; Christian N. Kruse; Susana K. Lai-Yuen; Hao Li; Han Liu; Buntheng Ly; Ipek Oguz; Hyungseob Shin; Boris Shirokikh; Zixian Su; Guotai Wang; Jianghao Wu; Yanwu Xu; Kai Yao; Li Zhang; Sebastien Ourselin, edit   pdf
url  doi
openurl 
  Title CrossMoDA 2021 challenge: Benchmark of Cross-Modality Domain Adaptation techniques for Vestibular Schwannoma and Cochlea Segmentation Type Journal Article
  Year (down) 2023 Publication Medical Image Analysis Abbreviated Journal MIA  
  Volume 83 Issue Pages 102628  
  Keywords Domain Adaptation; Segmen tation; Vestibular Schwnannoma  
  Abstract Domain Adaptation (DA) has recently raised strong interests in the medical imaging community. While a large variety of DA techniques has been proposed for image segmentation, most of these techniques have been validated either on private datasets or on small publicly available datasets. Moreover, these datasets mostly addressed single-class problems. To tackle these limitations, the Cross-Modality Domain Adaptation (crossMoDA) challenge was organised in conjunction with the 24th International Conference on Medical Image Computing and Computer Assisted Intervention (MICCAI 2021). CrossMoDA is the first large and multi-class benchmark for unsupervised cross-modality DA. The challenge's goal is to segment two key brain structures involved in the follow-up and treatment planning of vestibular schwannoma (VS): the VS and the cochleas. Currently, the diagnosis and surveillance in patients with VS are performed using contrast-enhanced T1 (ceT1) MRI. However, there is growing interest in using non-contrast sequences such as high-resolution T2 (hrT2) MRI. Therefore, we created an unsupervised cross-modality segmentation benchmark. The training set provides annotated ceT1 (N=105) and unpaired non-annotated hrT2 (N=105). The aim was to automatically perform unilateral VS and bilateral cochlea segmentation on hrT2 as provided in the testing set (N=137). A total of 16 teams submitted their algorithm for the evaluation phase. The level of performance reached by the top-performing teams is strikingly high (best median Dice – VS:88.4%; Cochleas:85.7%) and close to full supervision (median Dice – VS:92.5%; Cochleas:87.7%). All top-performing methods made use of an image-to-image translation approach to transform the source-domain images into pseudo-target-domain images. A segmentation network was then trained using these generated images and the manual annotations provided for the source image.  
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  Area Expedition Conference  
  Notes HUPBA Approved no  
  Call Number Admin @ si @ DKI2023 Serial 3706  
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