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ICPR 2020 MAIN CONFERENCE

RRPR - Third Workshop on Reproducible Research in Pattern Recognition

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. 


technical paper

Noisy image processing is a fundamental task of computer vision. The first
example is the detection of faint edges in noisy images, a challenging
problem studied in the last decades. A recent study introduced a fast method
to detect faint edges in the highest accuracy among all the existing
approaches. Their complexity is nearly linear in the image's pixels and
their runtime is seconds for a noisy image. Their approach utilizes a
multi-scale binary partitioning of the image. By utilizing the multi-scale
U-net architecture, we show in this paper that their method can be
dramatically improved in both aspects of run time and accuracy. By training
the network on a dataset of binary images, we developed an approach for
faint edge detection that works in linear complexity. Our runtime of a
noisy image is milliseconds on a GPU. Even though our method is orders of
magnitude faster, we still achieve higher accuracy of detection under many
challenging scenarios. In addition, we show that our approach to performing
multi-scale preprocessing of noisy images using U-net improves the ability
to perform other vision tasks under the presence of noise. We prove it on
the problems of noisy objects classification and classical image denoising.
We show that multi-scale denoising can be carried out by a novel edge
preservation loss. As our experiments show, we achieve high-quality results
in the three aspects of faint edge detection, noisy image classification, and
natural image denoising.

Multi-scale Processing of Noisy Images using Edge Preservation Losses

This work addresses the pose estimation task on low-resolution images captured using thermal sensors which can operate in a no-light environment. Low-resolution thermal sensors have been widely adopted in various applications for cost control and privacy protection purposes. In this paper, targeting the challenging scenario of ultra-low resolution thermal imaging (32 x 32 pixels), we aim to estimate human poses for the purpose of monitoring health conditions and indoor events. To overcome the challenges in ultra-low resolution thermal imaging such as blurred boundaries and data scarcity, we propose a new Image to Image (I2I) translation architecture which can translate the original blurred thermal image into a visible light image with sharper boundaries. Then the generated visible light image can
be fed into the off-the-shelf pose estimator which was well-trained in the visible domain. Experimental results suggest that the proposed framework outperforms other state-of-the-art methods in the I2I based pose estimation task for our thermal image dataset. Furthermore, we also demonstrated the merits of the proposed method on the publicly available FLIR dataset by measuring the quality of translated images.

Boundary guided image translation for pose estimation from ultra-low resolution thermal sensor


This paper studies the problem of Person Re-Identification (ReID)
for large-scale applications. Recent research efforts have been devoted
to building complicated part models, which introduce considerably
high computational cost and memory consumption, inhibiting its practicability
in large-scale applications. This paper aims to develop a novel learning
strategy to find efficient feature embeddings while maintaining the
balance of accuracy and model complexity. More specifically, we find
by enhancing the classical triplet loss together with cross-entropy
loss, our method can explore the hard examples and build a discriminant
feature embedding yet compact enough for large-scale applications.
Our method is carried out progressively using Bayesian optimization,
and we call it the Progressive Learning Algorithm (PLA). Extensive
experiments on three large-scale datasets show that our PLA is comparable
or better than the state-of-the-arts. Especially, on the challenging
Market-1501 dataset, we achieve Rank-1=94.7%/mAP=89.4% while saving
at least 30\% parameters than strong part models.

New York University, New York University Abu Dhabi




This paper describes the analysis of defocus information when a finger touching a virtual keyboard by using DCT (Discrete Cosine Transform) coefficient without detecting 3D position (especially exact depth value) of thr finger. We use 2 cameras and a half mirror to get the images of the finger with same optical axis. The focal length of the two cameras are slightly different. We can know the finger touching the virtual keyboard when the finger is in the middle of the two focal length from the defocus information, because the position of the virtual keyboard is in the middle of the two focal position where the defocus information of the two cameras are same. Previous work of a virtual keyboard is realized by detecting the feature point of the finger at first. After that the finger position and the depth are checked if they are same to the position and the depth of the keyboard. A problem of the previous virtual keyboard is 3D point of the finger (feature point or edge, small region) should be detected. This is very time consuming. We overcome this problem by comparing the defocus information of the finger in the key area of virtual keyboard on the 2 images captured by the two cameras. In this paper we describe the optical system of the virtual keyboard and the resolution of the depth of the finger. Experimental result make it clear that middle range of the DCT coefficients is effective for detecting the finger. In our system, the finger touch is detected by comparing the DCT coefficients of the 2 images. The minimum distance of the detectable finger is depended on the diameter and the focal length of the lens, the resolution of the image sensor. We formulate this theoretical minimum distance and verify it by experiment. Besides finger, our system can also be generalized to detect other object touching the keyboard or not.

Image Defocus Analysis for Finger Detection on A Virtual Keyboard

WAAMI - Workshop on Analysis of Aerial Motion Imagery

Change and Anomaly Detection in Images, Signals and datastreams

Multiple Parametric Models Fitting

More than 20 years of High-Dynamic-Range imaging

Synchronization: a general framework for mosaicking, 3D reconstruction, matching and segmentation problems

In this work we compare the performance of a location-specific and a location-agnostic machine learning metamodel for crop nitrogen response rate prediction. We conduct a case study for grass-only pasture in several locations in New Zealand. We generate a large dataset of APSIM simulation outputs and train machine learning models based on that data. Initially, we examine how the models perform at the location where the location-specific model was trained. We then perform the Mann–Whitney U test to see if the difference in the predictions of the two models (i.e. location-specific and location-agnostic) is significant. We expand this procedure to other locations to investigate the generalization capability of the models. We find that there is no statistically significant difference in the predictions of the two models. This is both interesting and useful because the location-agnostic model generalizes better than the location-specific model which means that it can be applied to virgin sites with similar confidence to experienced sites.

RRPR - Third Workshop on Reproducible Research in Pattern Recognition

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Multi-scale Processing of Noisy Images using Edge Preservation Losses

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