Italy

The improvement of the spatial resolution of natural images is an important task for many practical applications of current image processing. The endeavor of applying single image super-resolution, which only uses one low-resolution input image, is frequently hampered by the presence of noise or artifacts in the input image, so denoising and super-resolution algorithms are usually applied to obtain a noiseless high-resolution image. In this work, several traditional and deep learning methods for denoising and super-resolution are hybridized to ascertain which combinations of them yield the best possible quality. The experimental design includes the introduction of Gaussian noise, Poisson noise, salt-and-pepper noise, and uniform noise into the low-resolution inputs. It is found that denoising must be carried out before super-resolution for the best results. Moreover, the obtained results indicate that deep learning techniques clearly outperform the traditional ones.

ICPR 2020

Performance of Deep Learning and Traditional Techniques in Single Image Super-Resolution of Noisy Images

image denoising

super-resolution

deep learning

We are pleased to announce the Thirty-Eighth AAAI Conference on Artificial Intelligence (AAAI-24), which will be held in Vancouver, British Columbia at the Vancouver Convention Centre – West Building from 20-27 February, 2024.

The purpose of the AAAI conference series is to promote research in Artificial Intelligence (AI) and foster scientific exchange between researchers, practitioners, scientists, students, and engineers across the entirety of AI and its affiliated disciplines. AAAI-24 will feature technical paper presentations, special tracks, invited speakers, workshops, tutorials, poster sessions, senior member presentations, competitions, and exhibit programs, and a range of other activities to be announced.

We expect for AAAI-24 to be an in-person conference – one author of all accepted papers will be expected to present work in person unless there are exceptional circumstances that prevent this.<br><br><br><br>

In order to access the AAAI-24 event page you need to register [here](https://aaai.org/aaai-conference/registration/)

AAAI 2024

The purpose of the AAAI conference series is to promote research in Artificial Intelligence (AI) and foster scientific exchange between researchers, practitioners, scientists, students, and engineers across the entirety of AI and its affiliated disciplines. 

technical paper

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. 


The classification performance of Convolutional Neural Networks (CNNs) can be hampered by several factors. Sensor noise is one of these nuisances. In this work, a study of the effect of noise on these networks is presented. The methodological framework includes two realistic models of noise for present day CMOS vision sensors. The models allow to include separately Poisson, Gaussian, salt \& pepper, speckle and uniform noise as sources of defects in image acquisition sensors. Then, synthetic noise may be added to images by using that methodology in order to simulate common sources of image distortion. Additionally, the impact of the brightness in conjunction with each selected kind of noise is also addressed. This way, the proposed methodology incorporates a brightness scale factor to emulate images with low illumination conditions. Based on these models, experiments are carried out for a selection of state of the art CNNs. The results of the study demonstrate that Poisson noise has a small impact on the performance of CNNs, while speckle and salt \& pepper noise together the global illumination level can substantially degrade the classification accuracy. Also, Gaussian and uniform noise have a moderate effect on the CNNs.

The effect of noise and brightness on convolutional deep neural networks

Noise which can appear in images affects the classification performance of Convolutional Neural Networks (CNNs). In particular, the impact of linear motion blur, which is one of the possible noises, in the classification performance of CNNs is assessed in this work. A realistic vision sensor model has been proposed to produce a linear motion blur effect in input images. This methodology allows analyzing how the performance of several considered state of the art CNNs is affected. Experiments that have been carried out indicate that the accuracy is heavily degraded by a high length of the displacement, while the angle of displacement deteriorates the performance to a lesser extent.

The impact of linear motion blur on the object recognition efficiency of deep convolutional neural networks

IML - International Workshop on Industrial Machine Learning

A network to solve the segmentation of ultra-high resolution images

UHRSNet: A Semantic Segmentation Network Specifically for Ultra-High-Resolution Images

AutoML and interpretability: Powering the Machine Learning Revolution in Healthcare in the Era of COVID-19 and Beyond

First-person-view (FPV) cameras are finding wide use in daily life to record activities and sports. In this presentation, I'm going to present a succinct and robust 3D convolutional neural network (CNN) architecture accompanied with an ensemble-learning network for activity recognition with FPV videos. The proposed 3D CNN is trained on low-resolution (32 x 32) sparse optical flows using FPV video datasets consisting of daily activities. According to the experimental results, our network achieves an average accuracy of 90%.

Activity Recognition Using First-Person-View Cameras Based on Sparse Optical Flows

In recent years, Convolutional Neural Networks (CNNs) have enabled significant advancements to the state-of-the-art in computer vision. For classification tasks, CNNs have widely employed probabilistic output and have shown the significance of providing additional confidence for predictions. However, such probabilistic methodologies are not widely applicable for addressing regression problems using CNNs, as regression involves learning unconstrained continuous and, in many cases, multi-variate target variables. We propose a PRObabilistic Parametric rEgression Loss (PROPEL) that facilitates CNNs to learn parameters of probability distributions for addressing probabilistic regression problems. PROPEL is fully differentiable and, hence, can be easily incorporated for end-to-end training of existing CNN regression architectures using existing optimization algorithms. The proposed method is flexible as it enables learning complex unconstrained probabilities while being generalizable to higher dimensional multi-variate regression problems. We utilize a PROPEL-based CNN to address the problem of learning hand and head orientation from uncalibrated color images. Our experimental validation and comparison with existing CNN regression loss functions show that PROPEL improves the accuracy of a CNN by enabling probabilistic regression, while significantly reducing required model parameters by $10 \times$, resulting in improved generalization as compared to the existing state-of-the-art.

PROPEL: Probabilistic Parametric Regression Loss for Convolutional Neural Networks

Typically, loss functions, regularization mechanisms and other important aspects of training parametric models are chosen heuristically from a limited set of options. In this paper, we take the first step towards automating this process, with the view of producing models which train faster and more robustly. Concretely, we present a meta-learning method for learning parametric loss functions that can generalize across different tasks and model architectures. We develop a pipeline for meta-training such loss functions, targeted at maximizing the performance of the model trained under them. The loss landscape produced by our learned losses significantly improves upon the original task-specific losses in both supervised and reinforcement learning tasks. Furthermore, we show that our meta-learning framework is flexible enough to incorporate additional information at meta-train time. This information shapes the learned loss function such that the environment does not need to provide this information during meta-test time.

Meta-Learning via Learned Loss

This report summarises the Pollen Grain Classification Challenge 2020, and the related findings. It serves as an introduction to the technical reports that were submitted to the competition section at the 25th International Conference on Pattern Recognition (ICPR 2020), related to the Pollen Grain Classification Challenge.

Pollen Grain Classification Challenge 2020 - Challenge Report

Traditionally, it is a time-consuming work for experts to accomplish pollen grains classification. With the popularity of deep Convolutional Neural Network(CNN) in computer vision, many automatic
pollen grains classification methods based on CNN have been proposed
in recent years. However, The CNN they used often focus on the most
proniment area in the center of pollen grains and neglect the less discriminative local features in the surrounding of pollen grains. In order to
alleviate this situation, we propose two data augmentation operations.
Our experiment results on Pollen13K achieve a weighted F1 score of
97.26% and an accuracy of 97.29%.

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