Italy


In low light condition, the captured color (RGB) images are highly degraded by noise with severe texture loss. In this paper, we propose deep fusion of RGB and NIR paired images in low light condition using convolutional neural networks (CNNs). The proposed deep fusion network consists of three independent sub-networks: DenoisingNet, EnhancingNet, and FusionNet. We build a denoising sub-network to eliminate noise from noisy RGB images. After denoising, we perform an enhancing sub-network to increase the brightness of low light RGB images. Since NIR image contains fine details, we fuse it with the Y channel of RGB image through a fusion sub-network. Experimental results demonstrate that the proposed method successfully fuses RGB and NIR images, and generates high quality fusion results containing textures and colors.

ICPR 2020

Deep Fusion of RGB and NIR Paired Images Using Convolutional Neural Networks

deep learning for multimedia analysis

restoration and filtering

enhancement

image processing and analysis

ICPR 2020 is the premier international conference on Pattern Recognition, gathering leading researchers from around the world to present their latest achievements in Pattern Analysis and Recognition, applied Artificial Intelligence and Machine Learning. The topic of the year is " Putting Artificial intelligence to work on Patterns". 
For the first time, ICPR has employed a two-round review process similar to journal submissions, with new papers allowed to be submitted in either the first or second round and papers submitted in the first round and not accepted allowed to be revised and re-submitted for second round review. 

In this edition of the **ICPR 2020** Conference, there are 1407 Oral/Poster presentations organized in five Tracks: 
- Artificial Intelligence, Machine Learning for Pattern Analysis; 
- Biometrics, Human Analysis and Behavior Understanding; Computer Vision, Robotics and Intelligent Systems; 
- Document and Media Analysis; and 
- Image and Signal Processing. 

The **ICPR 2020** Main Conference program also includes 7 Keynotes from prominent world-renowned scientists, 8 Tutorials, 28 Demos, 8 Challenges, 2 Panels, as well as an Industry/Sponsor Exhibition. In addition, 41 Workshops on a range of important current and emerging research topics complete the ICPR 2020 program. 


poster


Historical watermark recognition is a highly practical, yet unsolved challenge for archivists and historians. With a large number of well-defined classes, cluttered and noisy samples, different types of representations, both subtle differences between classes and high intra-class variation, historical watermarks are also challenging for pattern recognition. In this paper, overcoming the difficulty of data collection, we present a large public dataset with more than 6k new photographs, allowing for the first time to tackle at scale the scenarios of practical interest for scholars: one-shot instance recognition and cross-domain one-shot instance recognition amongst more than 16k fine-grained classes. We demonstrate that this new dataset is large enough to train modern deep learning approaches, and show that standard methods can be improved considerably by using mid-level deep features. More precisely, we design both a matching score and a feature fine-tuning strategy based on filtering local matches using spatial consistency. This consistency-based approach provides
important performance boost compared to strong baselines. Our model achieves 55% top-1 accuracy on our very challenging 16,753-class one-shot cross-domain recognition task, each class described by a single drawing from the classic Briquet catalog. In addition to watermark classification, we show our approach provides promising results on fine-grained sketch-based image retrieval.

Large-Scale Historical Watermark Recognition: dataset and a new consistency-based approach

We tackle a novel problem of learning generators for cross-domain data under a specific scenario of low-shot learning. Basically, given a source domain with sufficient amount of training data, we aim to transfer the knowledge of its generative process to another target domain, which not only has few data samples but also contains the domain shift with respect to the source domain. This problem has great potential in practical use and is different from the well-known image translation task, as the target-domain data can be generated without requiring any source-domain ones and the large data consumption for learning target-domain generator can be alleviated. Built upon a cross-domain dataset where (1) each of the low shots in the target domain has its correspondence in the source and (2) these two domains share the similar content information but different appearance, two approaches are proposed: a Latent-Disentanglement-Orientated model (LaDo) and a Generative-Hierarchy-Oriented (GenHo) model. Our LaDo and GenHo approaches address the problem from different perspectives, where the former relies on learning the disentangled representation composed of domain-invariant content features and domain-specific appearance ones; while the later decomposes the generative process of a generator into two parts for synthesizing the content and appearance sequentially. We perform extensive experiments under various settings of cross-domain data and show the efficacy of our models for generating target-domain data with the abundant content variance as in the source domain, which lead to the favourable performance in comparison to several baselines.

Learning Low-Shot Generative Networks for Cross-Domain Data


Ultrasound is a non-invasive tool that is useful for medical diagnosis and treatment. To reduce long wait times and add convenience to patients, portable ultrasound scanning devices are becoming increasingly popular. These devices can be held in one hand, and are compatible with modern cell phones. However, the quality of ultrasound images captured from the portable scanners is relatively poor compared to standard ultrasound scanning systems in hospitals. To improve the quality of the ultrasound images obtained from portable ultrasound devices, we propose a new neural network architecture called Edge-guided Denoising Convolutional Neural Network (EDCNN), which can preserve significant edge information in ultrasound images when removing noise. We also study and compare the effectiveness of existing deep learning methods and classical filtering approaches in removing speckle noise in these images. Experimental results show that after applying the proposed EDCNN, various organs can be better recognized from ultrasound images. This approach is expected to lead to better accuracy in diagnostics in the future.

Edge-guided CNN for Denoising Images from Portable Ultrasound Devices


Autonomous vehicles and robots are typically equipped with Lidar and camera. Hence, calibrating the Lidar-camera system is of extreme importance for ego-motion estimation and scene understanding. In this paper, we propose a two-step approach (coarse + fine) for the external calibration between a camera and a multiple-line Lidar. First, a new closed-form solution is proposed to obtain the initial calibration parameters. We compare our solution with the state-of-the-art SVD-based algorithm, and show the benefits of both the efficiency and stability. With the initial calibration parameters, the ICP-based calibration framework is used to register the point clouds which extracted from the camera and Lidar coordinate frames, respectively. Our method has been applied to two Lidar-camera systems: an HDL-64E Lidar-camera system, and a VLP16 Lidar-camera system. Experimental results demonstrate that our method achieves promising performance and higher accuracy than two open-source methods.

A two-step approach to Lidar-Camera calibration

Accurate lane detection is critical for navigation in autonomous vehicles, particularly the active lane which demarcates the single road space that the vehicle is currently
traveling on. Recent state-of-the-art lane detection algorithms utilize convolutional neural networks (CNNs) to train deep learning models on popular benchmarks such as TuSimple and CULane. While each of these models works particularly well on train and test inputs obtained from the same dataset, the performance drops significantly on unseen datasets of different environments. In this paper, we present a real-time robust neural network output enhancement for active lane detection (RONELD) method to identify, track, and optimize active lanes from deep learning probability map outputs. We first adaptively extract lane points from the probability map outputs, followed by detecting curved and straight lanes before using weighted least squares linear regression on straight lanes to fix broken lane edges resulting from fragmentation of edge maps in real images. Lastly, we hypothesize true active lanes through tracking preceding frames. Experimental results demonstrate an up to two-fold increase in accuracy using RONELD on cross-dataset validation tests.

RONELD: Robust Neural Network Output Enhancment for Active Lane Detection


Scene text recognition is a challenging problem due to the wide variances in contents, styles, orientations, and image quality of text instances in natural scene images. To learn the intrinsic representation of scene texts, a novel multi-element attention (MEA) mechanism is proposed to exploit geometric structures from local to global levels in feature maps extracted from a scene text image. The MEA mechanism is a generalized form of self-attention technique. The elements in feature maps are taken as the nodes of an undirected graph, and three kinds of adjacency matrices are designed to aggregate information at local, neighborhood and global levels before calculating the attention weights. A multi-element attention network (MEAN) is implemented, which includes a CNN for feature extraction, an encoder with MEA mechanism and a decoder for predicting text codes. Orientational positional encoding is added to feature maps output by the CNN, and a feature vector sequence transformed from the feature maps is used as the input of the encoder. Experimental results show that MEAN has achieved state-of-the-art or competitive performance on seven public English scene text datasets (IIIT5k, SVT, IC03, IC13, IC15, SVTP, and CUTE). Further experiments have been conducted on a selected subset of the RCTW Chinese scene text dataset, demonstrating that MEAN can handle horizontal, vertical, and irregular scene text samples.

MEAN: Multi-Element Attention Network for Scene Text Recognition

Capsules are grouping of neurons that allow to represent sophisticated information of a visual entity such as pose and features. In the view of this property, Capsule Networks outperform CNNs in challenging tasks like object recognition in unseen viewpoints. In this paper, we present Quaternion Capsules (QCN) where pose information of capsules and their transformations are represented by quaternions. Quaternions are immune to the gimbal lock, have straightforward regularization of the rotation representation for capsules, and require a smaller number of parameters than matrices. The experimental results show that QCNs generalize better to novel viewpoints with fewer parameters, and also achieve on-par or better performances with the state-of-the-art Capsule architectures on well-known benchmarking datasets.

Quaternion Capsule Networks

Noisy data, crawled from the web or supplied by volunteers such as Mechanical Turkers or citizen scientists, is considered an alternative to professionally labeled data. There has been research focused on mitigating the effects of label noise. It is typically modeled as inaccuracy, where the correct label is replaced by an incorrect label from the same set. We consider an additional dimension of label noise: imprecision. For example, a non-breeding snow bunting is labeled as a bird. This label is correct, but not as precise as the task requires.
Standard softmax classifiers cannot learn from such a weak label because they consider all classes mutually exclusive, which non-breeding snow bunting and bird are not. We propose CHILLAX (Class Hierarchies for Imprecise Label Learning and Annotation eXtrapolation), a method based on hierarchical classification, to fully utilize labels of any precision.
Experiments on noisy variants of NABirds and ILSVRC2012 show that our method outperforms strong baselines by as much as 16.4 percentage points, and the current state of the art by up to 3.9 percentage points.

Making Every Label Count: Handling Semantic Imprecision By Integrating Domain Knowledge




We consider the problem of unsupervised domain adaptation for image classification. To learn target-domain-aware features from the unlabeled data, we create a self-supervised pretext task by augmenting the unlabeled data with a certain type of transformation (specifically, image rotation) and ask the learner to predict the properties of the transformation. However, the obtained feature representation may contain a large amount of irrelevant information with respect to the main task. To provide further guidance, we force the feature representation of the augmented data to be consistent with that of the original data. Intuitively, the consistency introduces additional constraints to representation learning, therefore, the learned representation is more likely to focus on the right information about the main task. Our experimental results validate the proposed method and demonstrate state-of-the-art performance on classical domain adaptation benchmarks.

Self-Supervised Domain Adaptation with Consistency Training


Background subtraction is a fundamental pre-processing task in computer vision. This task becomes challenging in real scenarios due to variations in the background for both static and moving camera sequences. Several deep learning methods for background subtraction have been proposed in the literature with competitive performances. However, these models show performance degradation when tested on unseen videos; and they require huge amount of data to avoid overfitting. Recently, graph-based algorithms have been successful approaching unsupervised and semi-supervised learning problems. Furthermore, the theory of graph signal processing and semi-supervised learning have been combined leading to new insights in the field of machine learning. In this paper, concepts of recovery of graph signals are introduced in the problem of background subtraction. We propose a new algorithm called Graph BackGround Subtraction (GraphBGS), which is composed of: instance segmentation, background initialization, graph construction, graph sampling, and a semi-supervised algorithm inspired from the theory of recovery of graph signals. Our algorithm has the advantage of requiring less labeled data than deep learning methods while having competitive results on both: static and moving camera videos. GraphBGS outperforms unsupervised and supervised methods in several challenging conditions on the publicly available Change Detection (CDNet2014), and UCSD background subtraction databases.

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