Journal: Pattern Recognition

Abbreviation

Pattern recogn.

Publisher

Elsevier

Journal Volumes

ISSN

0031-3203
1873-5142

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Publications 1 - 10 of 18
  • Unsupervised ensemble learning for genome sequencing
    Item type: Journal Article
    Pagès-Zamora, Alba; Ochoa, Idoia; Ruiz Cavero, Gonzalo; et al. (2022)
    Pattern Recognition
    Unsupervised ensemble learning refers to methods devised for a particular task that combine data provided by decision learners taking into account their reliability, which is usually inferred from the data. Here, the variant calling step of the next generation sequencing technologies is formulated as an unsupervised ensemble classification problem. A variant calling algorithm based on the expectation-maximization algorithm is further proposed that estimates the maximum-a-posteriori decision among a number of classes larger than the number of different labels provided by the learners. Experimental results with real human DNA sequencing data show that the proposed algorithm is competitive compared to state-of-the-art variant callers as GATK, HTSLIB, and Platypus.
  • Cross-view panorama image synthesis with progressive attention GANs
    Item type: Journal Article
    Wu, Songsong; Tang, Hao; Jing, Xiao-Yuan; et al. (2022)
    Pattern Recognition
    Despite the significant progress of conditional image generation, it remains difficult to synthesize a ground-view panorama image from a top-view aerial image. Among the core challenges are the vast differences in image appearance and resolution between aerial images and panorama images, and the limited aside information available for top-to-ground viewpoint transformation. To address these challenges, we propose a new Progressive Attention Generative Adversarial Network (PAGAN) with two novel components: a multistage progressive generation framework and a cross-stage attention module. In the first stage, an aerial image is fed into a U-Net-like network to generate one local region of the panorama image and its corresponding segmentation map. Then, the synthetic panorama image region is extended and refined through the following generation stages with our proposed cross-stage attention module that passes semantic information forward stage-by-stage. In each of the successive generation stages, the synthetic panorama image and segmentation map are separately fed into an image discriminator and a segmentation discriminator to compute both later real and fake, as well as feature alignment score maps for discrimination. The model is trained with a novel orientation-aware data augmentation strategy based on the geometric relation between aerial and panorama images. Extensive experimental results on two cross-view datasets show that PAGAN generates high-quality panorama images with more convincing details than state-of-the-art methods.
  • Multi-hypothesis representation learning for transformer-based 3D human pose estimation
    Item type: Journal Article
    Li, Wenhao; Liu, Hong; Tang, Hao; et al. (2023)
    Pattern Recognition
    Despite significant progress, estimating 3D human poses from monocular videos remains a challenging task due to depth ambiguity and self-occlusion. Most existing works attempt to solve both issues by exploiting spatial and temporal relationships. However, those works ignore the fact that it is an inverse problem where multiple feasible solutions (i.e., hypotheses) exist. To relieve this limitation, we propose a Multi-Hypothesis Transformer that learns spatio-temporal representations of multiple plausible pose hypotheses. In order to effectively model multi-hypothesis dependencies and build strong relationships across hypothesis features, we introduce a one-to-many-to-one three-stage framework: (i) Generate multiple initial hypothesis representations; (ii) Model self-hypothesis communication, merge multiple hypotheses into a single converged representation and then partition it into several diverged hypotheses; (iii) Learn cross-hypothesis communication and aggregate the multi-hypothesis features to synthesize the final 3D pose. Through the above processes, the final representation is enhanced and the synthesized pose is much more accurate. Extensive experiments show that the proposed method achieves state-of-the-art results on two challenging datasets: Human3.6M and MPI-INF-3DHP. The code and models are available at https://github.com/Vegetebird/MHFormer.
  • Enhanced graph-based dimensionality reduction with repulsion Laplaceans
    Item type: Journal Article
    Kokiopoulou, Effrosyni; Saad, Yousef (2009)
    Pattern Recognition
  • Multivariate online kernel density estimation with Gaussian kernels
    Item type: Journal Article
    Kristan, Matej; Leonardis, Aleš; Skočaj, Danijel (2011)
    Pattern Recognition
  • Graph-based classification of multiple observation sets
    Item type: Journal Article
    Kokiopoulou, E.; Frossard, P. (2010)
    Pattern Recognition
  • Performance evaluation and optimization for content-based image retrieval
    Item type: Journal Article
    Vogel, Julia; Schiele, Bernt (2006)
    Pattern Recognition
  • GrEp: Graph-based epithelial cell classification refinement in histopathology H&E images
    Item type: Journal Article
    Frei, Ana Leni; Garcia-Baroja, Javier; Rau, Tilman; et al. (2026)
    Pattern Recognition
    The automatic cell segmentation and classification from whole slide images plays an important role in digital pathology, unlocking new opportunities for biomarker discovery. Despite extensive research, this task faces persistent challenges such as the differentiation of epithelial cells into normal and malignant. Many existing models lack reporting of epithelial subtyping, and when available, their performance is often suboptimal. This work benchmarks state-of-the-art methods to highlight this limitation and introduces GrEp, a geometric deep learning strategy that considers the broader epithelium tissue architecture to infer cell-level classification rather than relying exclusively on nuclei morphology. The proposed graph-based workflow significantly outperformed state-of-the-art nuclei classification models in colorectal cancer and generalized effectively to two unseen tissue types, endometrium and pancreas, proving the robustness of the geometry-based model. Given its speed and accuracy, we believe GrEp to be a valuable method to refine epithelial cell classification for downstream analyses in clinical and research settings.
  • Image warping for face recognition
    Item type: Journal Article
    Pishchulin, Leonid; Gass, Tobias; Dreuw, Philippe; et al. (2012)
    Pattern Recognition
  • DETEXTIVE optical character recognition with pattern matching on-the-fly
    Item type: Journal Article
    Caluori, Ursina; Simon, Klause (2015)
    Pattern Recognition
Publications 1 - 10 of 18