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Author Simeon Petkov; Xavier Carrillo; Petia Radeva; Carlo Gatta edit   pdf
doi  openurl
  Title (up) Diaphragm border detection in coronary X-ray angiographies: New method and applications Type Journal Article
  Year 2014 Publication Computerized Medical Imaging and Graphics Abbreviated Journal CMIG  
  Volume 38 Issue 4 Pages 296-305  
  Keywords  
  Abstract 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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  Notes MILAB; LAMP; 600.079 Approved no  
  Call Number Admin @ si @ PCR2014 Serial 2468  
Permanent link to this record
 
 
Author Tao Wu; Kai Wang; Chuanming Tang; Jianlin Zhang edit  url
openurl 
  Title (up) Diffusion-based network for unsupervised landmark detection Type Journal Article
  Year 2024 Publication Knowledge-Based Systems Abbreviated Journal  
  Volume 292 Issue Pages 111627  
  Keywords  
  Abstract Landmark detection is a fundamental task aiming at identifying specific landmarks that serve as representations of distinct object features within an image. However, the present landmark detection algorithms often adopt complex architectures and are trained in a supervised manner using large datasets to achieve satisfactory performance. When faced with limited data, these algorithms tend to experience a notable decline in accuracy. To address these drawbacks, we propose a novel diffusion-based network (DBN) for unsupervised landmark detection, which leverages the generation ability of the diffusion models to detect the landmark locations. In particular, we introduce a dual-branch encoder (DualE) for extracting visual features and predicting landmarks. Additionally, we lighten the decoder structure for faster inference, referred to as LightD. By this means, we avoid relying on extensive data comparison and the necessity of designing complex architectures as in previous methods. Experiments on CelebA, AFLW, 300W and Deepfashion benchmarks have shown that DBN performs state-of-the-art compared to the existing methods. Furthermore, DBN shows robustness even when faced with limited data cases.  
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  Notes LAMP Approved no  
  Call Number Admin @ si @ WWT2024 Serial 4024  
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Author Sudeep Katakol; Basem Elbarashy; Luis Herranz; Joost Van de Weijer; Antonio Lopez edit   pdf
url  doi
openurl 
  Title (up) Distributed Learning and Inference with Compressed Images Type Journal Article
  Year 2021 Publication IEEE Transactions on Image Processing Abbreviated Journal TIP  
  Volume 30 Issue Pages 3069 - 3083  
  Keywords  
  Abstract Modern computer vision requires processing large amounts of data, both while training the model and/or during inference, once the model is deployed. Scenarios where images are captured and processed in physically separated locations are increasingly common (e.g. autonomous vehicles, cloud computing). In addition, many devices suffer from limited resources to store or transmit data (e.g. storage space, channel capacity). In these scenarios, lossy image compression plays a crucial role to effectively increase the number of images collected under such constraints. However, lossy compression entails some undesired degradation of the data that may harm the performance of the downstream analysis task at hand, since important semantic information may be lost in the process. Moreover, we may only have compressed images at training time but are able to use original images at inference time, or vice versa, and in such a case, the downstream model suffers from covariate shift. In this paper, we analyze this phenomenon, with a special focus on vision-based perception for autonomous driving as a paradigmatic scenario. We see that loss of semantic information and covariate shift do indeed exist, resulting in a drop in performance that depends on the compression rate. In order to address the problem, we propose dataset restoration, based on image restoration with generative adversarial networks (GANs). Our method is agnostic to both the particular image compression method and the downstream task; and has the advantage of not adding additional cost to the deployed models, which is particularly important in resource-limited devices. The presented experiments focus on semantic segmentation as a challenging use case, cover a broad range of compression rates and diverse datasets, and show how our method is able to significantly alleviate the negative effects of compression on the downstream visual task.  
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  Notes LAMP; ADAS; 600.120; 600.118 Approved no  
  Call Number Admin @ si @ KEH2021 Serial 3543  
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Author Francesco Ciompi; Oriol Pujol; Petia Radeva edit  doi
openurl 
  Title (up) ECOC-DRF: Discriminative random fields based on error correcting output codes Type Journal Article
  Year 2014 Publication Pattern Recognition Abbreviated Journal PR  
  Volume 47 Issue 6 Pages 2193-2204  
  Keywords Discriminative random fields; Error-correcting output codes; Multi-class classification; Graphical models  
  Abstract We present ECOC-DRF, a framework where potential functions for Discriminative Random Fields are formulated as an ensemble of classifiers. We introduce the label trick, a technique to express transitions in the pairwise potential as meta-classes. This allows to independently learn any possible transition between labels without assuming any pre-defined model. The Error Correcting Output Codes matrix is used as ensemble framework for the combination of margin classifiers. We apply ECOC-DRF to a large set of classification problems, covering synthetic, natural and medical images for binary and multi-class cases, outperforming state-of-the art in almost all the experiments.  
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  Notes LAMP; HuPBA; MILAB; 605.203; 600.046; 601.043; 600.079 Approved no  
  Call Number Admin @ si @ CPR2014b Serial 2470  
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Author Bogdan Raducanu; Fadi Dornaika edit   pdf
doi  openurl
  Title (up) Embedding new observations via sparse-coding for non-linear manifold learning Type Journal Article
  Year 2014 Publication Pattern Recognition Abbreviated Journal PR  
  Volume 47 Issue 1 Pages 480-492  
  Keywords  
  Abstract Non-linear dimensionality reduction techniques are affected by two critical aspects: (i) the design of the adjacency graphs, and (ii) the embedding of new test data-the out-of-sample problem. For the first aspect, the proposed solutions, in general, were heuristically driven. For the second aspect, the difficulty resides in finding an accurate mapping that transfers unseen data samples into an existing manifold. Past works addressing these two aspects were heavily parametric in the sense that the optimal performance is only achieved for a suitable parameter choice that should be known in advance. In this paper, we demonstrate that the sparse representation theory not only serves for automatic graph construction as shown in recent works, but also represents an accurate alternative for out-of-sample embedding. Considering for a case study the Laplacian Eigenmaps, we applied our method to the face recognition problem. To evaluate the effectiveness of the proposed out-of-sample embedding, experiments are conducted using the K-nearest neighbor (KNN) and Kernel Support Vector Machines (KSVM) classifiers on six public face datasets. The experimental results show that the proposed model is able to achieve high categorization effectiveness as well as high consistency with non-linear embeddings/manifolds obtained in batch modes.  
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  Notes LAMP; Approved no  
  Call Number Admin @ si @ RaD2013b Serial 2316  
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