Showing papers in "Pattern Recognition in 2018"

TL;DR: A broad survey of the recent advances in convolutional neural networks can be found in this article, where the authors discuss the improvements of CNN on different aspects, namely, layer design, activation function, loss function, regularization, optimization and fast computation.

TL;DR: The background of deep visual tracking is introduced, including the fundamental concepts of visual tracking and related deep learning algorithms, and the existing deep-learning-based trackers are categorize into three classes according to network structure, network function and network training.

TL;DR: This paper proposes a simple yet powerful remedy, called Adaptive Batch Normalization (AdaBN) to increase the generalization ability of a DNN, and demonstrates that the method is complementary with other existing methods and may further improve model performance.

TL;DR: The survey provides an overview on deep learning and the popular architectures used for cancer detection and diagnosis and presents four popular deep learning architectures, including convolutional neural networks, fully Convolutional networks, auto-encoders, and deep belief networks in the survey.

TL;DR: This article revisit Multiple Instance Neural Networks (MINNs) that the neural networks aim at solving the MIL problems and proposes a new type of MINN to learn bag representations, which is different from the existing MINNs that focus on estimating instance label.

TL;DR: This task challenges state-of-the-art methods from a variety of research fields to applications including fraud detection, intrusion detection, medical diagnoses and data cleaning.

TL;DR: This work presents a novel background subtraction from video sequences algorithm that uses a deep Convolutional Neural Network (CNN) to perform the segmentation, and it outperforms the existing algorithms with respect to the average ranking over different evaluation metrics announced in CDnet 2014.

TL;DR: An approach to multi-view subspace clustering that learns a joint subspace representation by constructing affinity matrix shared among all views is presented, relying on the importance of both low-rank and sparsity constraints in the construction of the affinity matrix.

TL;DR: A deep learning-based approach for temporal 3D pose recognition problems based on a combination of a Convolutional Neural Network (CNN) and a Long Short-Term Memory (LSTM) recurrent network and a data augmentation method that has also been validated experimentally is proposed.

TL;DR: It is proved that selecting useful deep descriptors contributes well to fine-grained image recognition, and a novel Mask-CNN model without the fully connected layers is proposed, which has a small feature dimensionality and efficient inference speed by comparing with other fine- grained approaches.

TL;DR: This paper first introduces fully convolutional auto-encoders for image feature learning and then proposes a unified clustering framework to learn image representations and cluster centers jointly based on a fully Convolutional Auto-encoder and soft k-means scores.

TL;DR: IDNet is the first system that exploits a deep learning approach as universal feature extractors for gait recognition, and that combines classification results from subsequent walking cycles into a multi-stage decision making framework.

TL;DR: A survey about recent methods that localize a visual acquisition system according to a known environment by categorizing VBL methods into two distinct families: indirect and direct localization systems.

TL;DR: An overview of current LDW system is provided, describing in particular pre-processing, lane models, lane de Ntection techniques and departure warning system.

TL;DR: A novel objective function is proposed to jointly optimize similarity metric learning, local positive mining and robust deep feature embedding for person re-id by proposing a novel sampling to mine suitable positives within a local range to improve the deep embedding in the context of large intra-class variations.

TL;DR: Commentary on the proposed revision of the IASP definition of pain of 1979 summarizes, why this proposal is useful for guiding assessment of pain, but not its definition.

TL;DR: A human-related multi-stream CNN (HR-MSCNN) architecture that encodes appearance, motion, and the captured tubes of the human- related regions is introduced that achieves state-of-the-art results on these four datasets.

TL;DR: Quantization-based Hashing (QBH) is a generic framework which incorporates the advantages of quantization error reduction methods into conventional property preserving hashing methods and can be applied to both unsupervised and supervised hashing methods.

TL;DR: This paper develops a novel medical image fusion, denoising, and enhancement method based on low-rank sparse component decomposition and dictionary learning that consistently outperforms existing state-of-the-art methods in terms of both visual and quantitative evaluations.

TL;DR: A novel distance metric optimization driven learning approach that integrates these traditional steps via a deep convolutional neural network, which learns feature representations and the decision function in an end-to-end way, and can be optimized simultaneously by backward propagation.

TL;DR: Inspired by the recent breakthrough via deep recurrent convolutional neural networks (CNNs) on classifying mental load, improved CNNs methods for this task are proposed, which contain less parameters than state-of-the-art ones, making it be more competitive in further practical application.

TL;DR: The basic ideas, theories, pros and cons of the approaches, group them into categories, and extensively review each category in depth by discussing the principles, application issues, and advantages/disadvantages are studied.

TL;DR: A novel deep multiplicative integration gating function is proposed, which answers the question of what-and-where to match for effective person re-id and is designed to be end-to-end trainable to characterize local pairwise feature interactions in a spatially aligned manner.

TL;DR: A fingerprint and finger-vein based cancelable multi-biometric system, which provides template protection and revocability and security is strengthened, thanks to the enhanced partial discrete Fourier transform based non-invertible transformation.

TL;DR: This article provides a bird's eye view of data irregularities, beginning with a taxonomy and characterization of various distribution-based and feature-based irregularities, and discusses the notable and recent approaches that have been taken to make the existing stand-alone as well as ensemble classifiers robust against such irregularities.

TL;DR: This article introduces a regularized ensemble framework of deep learning to address the imbalanced, multi-class learning problems in medical diagnosis and demonstrates the superior performance of the method compared to several state-of-the-art algorithms.

TL;DR: This work treats the small-dim targets as a special sparse noise component of the complex background noise and adopt Mixture of Gaussians (MoG) with Markov random field (MRF) with MRF to model the small target detection problem.

TL;DR: An integrated framework consisting of bottom-up and top-down attention mechanisms that enable attention to be computed at the level of salient objects and/or regions is proposed.

TL;DR: A novel regression-based Convolutional Neural Network (CNN) pipeline is presented for polyp detection during Colonoscopy and has great potential to be used to assist endoscopists in tracking polyps during colonoscopy.

TL;DR: The International Association for the Study of Pain (IASP) definition of pain has been widely accepted as a pragmatic characterisation of human experience as mentioned in this paper, but it fails to sufficiently integrate phenomenological aspects of pain.