Italy


Structural reconstruction of plant roots from MRI
is challenging, because of low resolution and low signal-to-noise
ratio of the 3D measurements which may lead to disconnectivities
and wrongly connected roots. We propose a two-stage approach
for this task. The first stage is based on semantic root vs.
soil segmentation and finds lowest-cost paths from any root
voxel to the shoot. The second stage takes the largest fully
connected component generated in the first stage and uses
3D skeletonization to extract a graph structure. We evaluate
our method on 22 MRI scans and compare to human expert
reconstructions.

ICPR 2020

Robust Skeletonization for Plant Root Structure Reconstruction from MRI

volumetric image processing

magnetic resonance imaging

plant root

structure extraction

stages 

COLING, the International Conference on Computational Linguistics, is one of the premier conferences for natural language processing and computational linguistics. Often grouped within the field of artificial intelligence, but actually pre-dating the development of artificial intelligence, advances in computational linguistics and natural language processing are now some of the major drivers behind the use of artificial intelligence for commercial and social applications – for example, on-line search, machine translation and with voice-assisted conversational devices.

First established in 1965, the biennial COLING conference is held in diverse parts of the globe and attracts participants from both top-ranked research centers and emerging countries. Today, the most important developments in our field are taking place not only in universities and academic research institutes, but also in industrial research departments and in technological startups. COLING conferences provide opportunities for all these communities to showcase their exciting developments.

COLING 2020

COLING, the International Conference on Computational Linguistics, is one of the premier conferences for natural language processing and computational linguistics. Often grouped within the field of artificial intelligence, but actually pre-dating the development of artificial intelligence, advances in computational linguistics and natural language processing are now some of the major drivers behind the use of artificial intelligence for commercial and social applications – for example, on-line search, machine translation and with voice-assisted conversational devices.

poster

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. 



Fractal Dimension (FD) is widely used for image
segmentation because of its successful approach toward
quantifying texture information. In this paper, we present a FD-based multi-focus image fusion method that utilizes FD to identify
focused regions, as the primary step for the multi-focus image
fusion process. The algorithm aims to extract the local FD features
of each multi-focus pair estimated using the differential box-counting method. A guided filter is employed to further specify the
spatial information and increase the robustness of the FD features
to noise. The outcome would be analyzed to achieve a focus map
that identifies sharp regions in each partially focused image.
Afterwards, the detected regions are combined into a single all-focused image. The experiments, along with the objective
assessments, demonstrate the competitive performance of the
proposed method compared to several state-of-the-art multi-focus
image fusion methods.

A Multi-Focus Image Fusion Method based on Fractal Dimension and Guided Filtering

The success of deep neural networks in the traditional keypoint detection task encourages researchers to solve new problems and collect more complex datasets. The size of the DeepFashion2 dataset poses a new challenge on the keypoint detection task, as it comprises 13 clothing categories that span a wide range of keypoints (294 in total). The direct prediction of all keypoints leads to huge memory consumption, slow training, and a slow inference time. This paper studies the keypoint grouping approach and how it affects the performance of the CenterNet architecture. We propose a simple and efficient automatic grouping technique with a powerful post-processing method and apply it to the DeepFashion2 fashion landmark task and the MS COCO pose estimation task. This reduces memory consumption and processing time during inference by up to 19% and 30% respectively, and during the training stage by 28% and 26% respectively, without compromising accuracy.

Efficient grouping for keypoint detection

In cataract surgery, the operation is performed with the help of a microscope. Since the microscope enables watching real-time surgery by up to two people only, a major part of surgical training is conducted using the recorded videos. To optimize the training procedure with the video content, the surgeons require an automatic relevance detection approach. In addition to relevance-based retrieval, these results can be further used for skill assessment and irregularity detection in cataract surgery videos. In this paper, a three-module framework is proposed to detect and classify the relevant phase segments in cataract videos. Taking advantage of an idle frame recognition network, the video is divided into idle and action segments. To boost the performance in relevance detection, the cornea where the relevant surgical actions are conducted is detected in all frames using Mask R-CNN. The spatiotemporally localized segments containing higher-resolution information about the pupil texture and actions, and complementary temporal information from the same phase are fed into the relevance detection module. This module consists of four parallel recurrent CNNs being responsible to detect four relevant phases that have been defined with medical experts. The results will then be integrated to classify the action phases as irrelevant or one of four relevant phases. Experimental results reveal that the proposed approach outperforms static CNNs and different configurations of feature-based and end-to-end recurrent networks.

Relevance Detection in Cataract Surgery Videos by Spatio-Temporal Action Localization


This presentation discusses the properties of the generalized conics that are used to create a unified framework for generating various types of the distance fields. The proposed representation provides a possibility to efficiently compute the proximity, arithmetic mean of the distances and a space tessellation with regard to the given set of polygonal objects, line segments and points. In addition, the weights can be introduced for objects, their parts and combinations. This fact leads to a hierarchical representation that can be efficiently obtained using the pixel-wise operations. The practical value of the proposed ideas is demonstrated on an example of applications like skeletonization, smoothing and optimal location finding.

Generalized Conics: properties and applications

Appliance identification using a histogram post-processing of 2D local binary patterns for smart grid applications

Pipeline for XAI based Automatic Audio Call Audit

Assessement of CNN-Based Methods for Poverty Estimation from Satellite images

Despite the rapid development of pedestrian detection, the problem of dense pedestrian detection is still unsolved, especially the upper limit of Recall caused by Non-Maximum-Suppression (NMS). For this reason, R2NMS is proposed to simultaneously detect full and visible body bounding boxes, by replacing the full-body BBoxes with less occluded visible body BBoxes in the NMS algorithm, achieving a higher recall. However, the P-RPN and P-RCNN modules proposed in R2NMS for simultaneous high quality full and visible body prediction require non-trivial positive/negative assigning strategies for anchor BBoxes. To simplify the prerequisites and improve the utility of R2NMS, we incorporate clustering analysis into the learning of visible body proposals from full-body proposals. Furthermore, to reduce the computation complexity caused by a large number of potentially visible body proposals, we introduce a novel occlusion pattern prediction branch on top of the R-CNN module (i.e. F-RCNN) to select the best-matched visible proposals for each full-body proposals and then feed them into another R-CNN module. Incorporated with R2NMS, our DualBox model can achieve competitive performance while only requires a few hyper-parameters. We validate the effectiveness of the proposed approach on the CrowdHuman and CityPersons datasets. Experimental results show that our approach achieves promising performance for detecting both non-occluded and occluded pedestrians, especially heavily occluded ones.

DualBox: Generating BBox Pair with Strong Correspondence via Occlusio­­n Pattern Clustering and Proposal Refinement

We introduce a real-time method that estimates the 3D human pose directly in the popular Bio Vision Hierarchy (BVH) format, given estimations of the 2D body joints originating from monocular color images. Our contributions include: (a) A novel and compact 2D pose representation. (b) A human body orientation classifier and an ensemble of orientation-tuned neural networks that regress the 3D human pose by also allowing for the decomposition of the body to an upper and lower kinematic hierarchy. This permits the recovery of the human pose even in the case of significant occlusions. (c) An efficient Inverse Kinematics solver that refines the neural-network-based solution providing 3D human pose estimations that are consistent with the limb sizes of a target person (if known). All the above yield a 33\% accuracy improvement on the Human 3.6 Million (H3.6M) dataset compared to the baseline method (MocapNET) while maintaining real-time performance (70 fps in CPU-only execution).

Occlusion-tolerant and personalized 3D human pose estimation in RGB images


Robotics applications in urban environments are subject to obstacles that exhibit specular reflections hampering autonomous navigation. On the other hand, these reflections are highly polarized and this extra information can successfully be used to segment the specular areas. In nature, polarized light is obtained by reflection or scattering. Deep Convolutional Neural Networks (DCNNs) have shown excellent segmentation results, but require a significant amount of data to achieve best performances. The lack of data is usually overcomed by using augmentation methods. However, unlike RGB images, polarization images are not only scalar (intensity) images and standard augmentation techniques cannot be applied straightforwardly. We propose to enhance deep learning models through a regularized augmentation procedure applied to polarimetric data in order to characterize scenes more effectively under challenging conditions. We subsequently observe an average of 18.1% improvement in IoU between non augmented and regularized training procedures on real world data.

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Robust Skeletonization for Plant Root Structure Reconstruction from MRI

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