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Simone Balocco; Carlo Gatta; Francesco Ciompi; A. Wahle; Petia Radeva; S. Carlier; G. Unal; E. Sanidas; F. Mauri; X. Carillo; T. Kovarnik; C. Wang; H. Chen; T. P. Exarchos; D. I. Fotiadis; F. Destrempes; G. Cloutier; Oriol Pujol; Marina Alberti; E. G. Mendizabal-Ruiz; M. Rivera; T. Aksoy; R. W. Downe; I. A. Kakadiaris |
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Title |
Standardized evaluation methodology and reference database for evaluating IVUS image segmentation |
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Journal Article |
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Year |
2014 |
Publication |
Computerized Medical Imaging and Graphics |
Abbreviated Journal |
CMIG |
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38 |
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2 |
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70-90 |
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Keywords |
IVUS (intravascular ultrasound); Evaluation framework; Algorithm comparison; Image segmentation |
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This paper describes an evaluation framework that allows a standardized and quantitative comparison of IVUS lumen and media segmentation algorithms. This framework has been introduced at the MICCAI 2011 Computing and Visualization for (Intra)Vascular Imaging (CVII) workshop, comparing the results of eight teams that participated.
We describe the available data-base comprising of multi-center, multi-vendor and multi-frequency IVUS datasets, their acquisition, the creation of the reference standard and the evaluation measures. The approaches address segmentation of the lumen, the media, or both borders; semi- or fully-automatic operation; and 2-D vs. 3-D methodology. Three performance measures for quantitative analysis have
been proposed. The results of the evaluation indicate that segmentation of the vessel lumen and media is possible with an accuracy that is comparable to manual annotation when semi-automatic methods are used, as well as encouraging results can be obtained also in case of fully-automatic segmentation. The analysis performed in this paper also highlights the challenges in IVUS segmentation that remains to be
solved. |
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MILAB; LAMP; HuPBA; 600.046; 600.063; 600.079 |
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Admin @ si @ BGC2013 |
Serial |
2314 |
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Author |
Simeon Petkov; Xavier Carrillo; Petia Radeva; Carlo Gatta |
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Title |
Diaphragm border detection in coronary X-ray angiographies: New method and applications |
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Journal Article |
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Year |
2014 |
Publication |
Computerized Medical Imaging and Graphics |
Abbreviated Journal |
CMIG |
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38 |
Issue |
4 |
Pages |
296-305 |
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X-ray angiography is widely used in cardiac disease diagnosis during or prior to intravascular interventions. The diaphragm motion and the heart beating induce gray-level changes, which are one of the main obstacles in quantitative analysis of myocardial perfusion. In this paper we focus on detecting the diaphragm border in both single images or whole X-ray angiography sequences. We show that the proposed method outperforms state of the art approaches. We extend a previous publicly available data set, adding new ground truth data. We also compose another set of more challenging images, thus having two separate data sets of increasing difficulty. Finally, we show three applications of our method: (1) a strategy to reduce false positives in vessel enhanced images; (2) a digital diaphragm removal algorithm; (3) an improvement in Myocardial Blush Grade semi-automatic estimation. |
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MILAB; LAMP; 600.079 |
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Admin @ si @ PCR2014 |
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2468 |
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Chengyi Zou; Shuai Wan; Tiannan Ji; Marc Gorriz Blanch; Marta Mrak; Luis Herranz |
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Title |
Chroma Intra Prediction with Lightweight Attention-Based Neural Networks |
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Journal Article |
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2023 |
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IEEE Transactions on Circuits and Systems for Video Technology |
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TCSVT |
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34 |
Issue |
1 |
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549 - 560 |
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Neural networks can be successfully used for cross-component prediction in video coding. In particular, attention-based architectures are suitable for chroma intra prediction using luma information because of their capability to model relations between difierent channels. However, the complexity of such methods is still very high and should be further reduced, especially for decoding. In this paper, a cost-effective attention-based neural network is designed for chroma intra prediction. Moreover, with the goal of further improving coding performance, a novel approach is introduced to utilize more boundary information effectively. In addition to improving prediction, a simplification methodology is also proposed to reduce inference complexity by simplifying convolutions. The proposed schemes are integrated into H.266/Versatile Video Coding (VVC) pipeline, and only one additional binary block-level syntax flag is introduced to indicate whether a given block makes use of the proposed method. Experimental results demonstrate that the proposed scheme achieves up to −0.46%/−2.29%/−2.17% BD-rate reduction on Y/Cb/Cr components, respectively, compared with H.266/VVC anchor. Reductions in the encoding and decoding complexity of up to 22% and 61%, respectively, are achieved by the proposed scheme with respect to the previous attention-based chroma intra prediction method while maintaining coding performance. |
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MACO; LAMP |
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Admin @ si @ ZWJ2023 |
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3875 |
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Author |
Pedro Martins; Paulo Carvalho; Carlo Gatta |
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Title |
On the completeness of feature-driven maximally stable extremal regions |
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2016 |
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Pattern Recognition Letters |
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PRL |
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74 |
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9-16 |
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Keywords |
Local features; Completeness; Maximally Stable Extremal Regions |
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By definition, local image features provide a compact representation of the image in which most of the image information is preserved. This capability offered by local features has been overlooked, despite being relevant in many application scenarios. In this paper, we analyze and discuss the performance of feature-driven Maximally Stable Extremal Regions (MSER) in terms of the coverage of informative image parts (completeness). This type of features results from an MSER extraction on saliency maps in which features related to objects boundaries or even symmetry axes are highlighted. These maps are intended to be suitable domains for MSER detection, allowing this detector to provide a better coverage of informative image parts. Our experimental results, which were based on a large-scale evaluation, show that feature-driven MSER have relatively high completeness values and provide more complete sets than a traditional MSER detection even when sets of similar cardinality are considered. |
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Elsevier B.V. |
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0167-8655 |
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LAMP;MILAB; |
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no |
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Admin @ si @ MCG2016 |
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2748 |
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Author |
Akshita Gupta; Sanath Narayan; Salman Khan; Fahad Shahbaz Khan; Ling Shao; Joost Van de Weijer |
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Title |
Generative Multi-Label Zero-Shot Learning |
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Journal Article |
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Year |
2023 |
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IEEE Transactions on Pattern Analysis and Machine Intelligence |
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TPAMI |
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Volume |
45 |
Issue |
12 |
Pages |
14611-14624 |
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Generalized zero-shot learning; Multi-label classification; Zero-shot object detection; Feature synthesis |
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Multi-label zero-shot learning strives to classify images into multiple unseen categories for which no data is available during training. The test samples can additionally contain seen categories in the generalized variant. Existing approaches rely on learning either shared or label-specific attention from the seen classes. Nevertheless, computing reliable attention maps for unseen classes during inference in a multi-label setting is still a challenge. In contrast, state-of-the-art single-label generative adversarial network (GAN) based approaches learn to directly synthesize the class-specific visual features from the corresponding class attribute embeddings. However, synthesizing multi-label features from GANs is still unexplored in the context of zero-shot setting. When multiple objects occur jointly in a single image, a critical question is how to effectively fuse multi-class information. In this work, we introduce different fusion approaches at the attribute-level, feature-level and cross-level (across attribute and feature-levels) for synthesizing multi-label features from their corresponding multi-label class embeddings. To the best of our knowledge, our work is the first to tackle the problem of multi-label feature synthesis in the (generalized) zero-shot setting. Our cross-level fusion-based generative approach outperforms the state-of-the-art on three zero-shot benchmarks: NUS-WIDE, Open Images and MS COCO. Furthermore, we show the generalization capabilities of our fusion approach in the zero-shot detection task on MS COCO, achieving favorable performance against existing methods. |
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December 2023 |
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LAMP; PID2021-128178OB-I00 |
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Admin @ si @ |
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3853 |
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