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Convolutional Neural Networks (CNNs) are widely used due to their high performance in many tasks related to computer vision. In particular, image classification is one of the fields where CNNs are employed with success. However, images can be heavily affected by several inconveniences such as noise or illumination. Therefore, image enhancement algorithms have been developed to improve the quality of the images. In this work, the impact that brightness and image contrast enhancement techniques have on the performance achieved by CNNs in classification tasks is analyzed. More specifically, several well known CNNs architectures such as Alexnet or Googlenet, and image contrast enhancement techniques such as Gamma Correction or Logarithm Transformation are studied. Different experiments have been carried out, and the obtained qualitative and quantitative results are reported.

ICPR 2020

The effect of image enhancement algorithms\\on convolutional neural networks

image enhancement techniques

brightness

convolutional neural networks

image classification

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

The achievement of neural networks in a variety of applications is accompanied by a dramatic increase in computational costs and memory requirements. In this paper, we propose an efficient method to simultaneously identify the critical neurons and prune the model during training without involving any pre-training or fine-tuning procedures. Unlike existing methods, which accomplish this task in a greedy fash- ion, we propose a majority voting technique to compare the activation values among neurons and assign a voting score to quantitatively evaluate their importance. This mechanism helps to effectively reduce model complexity by eliminating the less influential neurons and aims to determine a subset of the whole model that can represent the reference model with much fewer parameters within the training process. Experimental results show that majority voting efficiently compresses the network with no drop in model accuracy, pruning more than 79% of the original model parameters on CIFAR10 and more than 91% of the original parameters on MNIST. Moreover, we show that with our proposed method, sparse models can be further pruned into even smaller models by removing more than 60% of the parameters, whilst preserving the reference model accuracy.

Neuron-based Network Pruning Based on Majority Voting

We propose a machine learning based approach for automatic regularization and polygonization of building segmentation masks. Taking an image as input, we first predict building segmentation maps exploiting generic fully convolutional network (FCN). A generative adversarial network (GAN) is then involved to perform a regularization of building boundaries to make them more realistic, i.e., having more rectilinear outlines which construct right angles if required. This is achieved through the interplay between the discriminator which gives a probability of input image being true and generator that learns from discriminator's response to create more realistic images. Finally, we train the backbone convolutional neural network (CNN) which is adapted to predict sparse outcomes corresponding to building corners out of regularized building segmentation results. Experiments on three building segmentation datasets demonstrate that the proposed method is not only capable of obtaining accurate results, but also of producing visually pleasing building outlines parameterized as polygons.

Machine-learned Regularization and Polygonization of Building Segmentation Masks


Homography estimation from a pair of natural images is a problem of paramount importance for computer vision. Specialized deep convolutional neural networks have been proposed to accomplish this task. In this work, a method to enhance the result of this kind of homography estimators is proposed. Our approach generates a set of tentative color transformations for the image pair. Then the color transformed image pairs are evaluated by a regressor that estimates the quality of the homography that would be obtained by supplying the transformed image pairs to the homography estimator. Then the image pair that is predicted to yield the best result is provided to the homography estimator. Experimental results are shown, which demonstrate that our approach performs better than the direct application of the homography estimator to the original image pair, both in qualitative and quantitative terms.

Adaptive estimation of optimal color transformations for deep convolutional network based homography estimation


Manipulation and re-use of images in scientific publications is a growing issue, not only for biomedical publishers but also for the research community in general. In this work, we introduce BINDER – Bio-Image Near-Duplicate Examples Repository, a novel dataset to help researchers develop, train, and test models to detect same-source biomedical images. We further use the dataset to demonstrate how novel adaptations of existing image retrieval and metric learning models can be applied to achieve high-accuracy inference results, creating a baseline for future work. In aggregate, we thus present a supervised protocol for near-duplicate image identification and retrieval without any “real-world” training example.

On Identification and Retrieval of Near-Duplicate Biological Images: a New Dataset and Protocol


With the development of the Internet and the globalization of economy, transforming text images to understandable information automatically has become an intense demand.
To the best of our knowledge, all schemes for handling this problem are separating it into two parts, Mono-Lingual Text Image Recognition (MLTIR) and machine translation (MT), illustrated in the Figure.
Nevertheless, this kind of apart system is potentially error prone.
Inspired by the above observations, we propose a novel end-to-end method for Cross Lingual Text Image Recognition.

Cross-Lingual Text Image Recognition via Multi-Task Sequence to Sequence Learning


Gaining control over the dynamics of multi-task learning should help to unlock the potential of the deep network to a great extent. In the existing multi-task learning (MTL) approaches of a deep network, all the parameters of its feature encoding part are subjected to adjustments corresponding to each of the underlying sub-tasks. On the other hand, different functional areas of the human brain are responsible for distinct functions such as the Broca's area of the cerebrum is responsible for speech formation whereas its Wernicke's area is related to the language development etc. Inspired by this fact, in the present study, we used here a Feature Representation Block (FRB) of connection weights spanned over a few successive layers of a deep multi-task learning architecture and stratify the same into distinct subsets for their adjustments exclusively corresponding to different sub-tasks. Additionally, we have introduced a novel regularization component for controlled training of this Feature Representation Block. The purpose of the development of this learning framework is efficient end-to-end recognition of scene texts.

Stratified Multi-Task Learning for Robust Spotting of Scene Texts


We propose a method for Offline Handwritten Mathematical Expression recognition. The method is a fast and accurate thanks to its architecture, which include both a Convolutional Neural Network and a Recurrent Neural Network. The CNN extracts features from the image to recognize and its output is provided to the RNN which produces the mathematical expression encoded in the LaTeX language.
To process both sequential and non-sequential mathematical expressions we also included a deconvolutional module which, in a recursive way, segments the image for additional analysis trough a recursive process. The results obtained show a very high accuracy obtained on a large handwritten data set of 9100 samples of handwritten expressions.

Recursive Recognition of Offline Handwritten Mathematical Expressions

In the computational prediction of chemical compound properties, molecular descriptors and fingerprints encoded to low dimensional vectors are used. The selection of proper molecular descriptors and fingerprints is both important and challenging as the performance of such models is highly dependent on descriptors. To overcome this challenge, natural language processing models that utilize simplified molecular input lineentry system as input were studied, and several transformer variant models achieved superior results when compared with conventional methods. In this study, we explored the structural differences of the transformer-variant model and proposed a new self-attention based model. The representation learning performance of the self-attention module was evaluated in a multi-task learning environment using imbalanced chemical datasets. The experiment results showed that our model achieved competitive outcomes on several benchmark datasets. The source code of our experiment is available at https://github.com/arwhirang/samtl and the dataset is available from the same URL.

Predicting Chemical Properties using Self-Attention Multi-task Learning based on SMILES Representation


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.

Appearance Features and Graph Representation


Content-based classification of manuscripts is an important task that is generally performed in archives and libraries
by experts with a wealth of knowledge on the manuscript’s
contents. Unfortunately, many manuscript collections are so vast
that it is not feasible to rely solely on experts to perform this
task. Current approaches for textual-content-based manuscript
classification generally require the handwritten images to be
first transcribed into text – but achieving sufficiently accurate
transcripts are generally unfeasible for large sets of historical
manuscripts. We propose a new approach to perform automatically this classification task which does not rely on any explicit
image transcripts. It is based on “probabilistic indexing”, a
relatively novel technology which allows to effectively represent
the intrinsic word-level uncertainty generally exhibited by handwritten text images. We assess the performance of this approach
on a large collection of complex manuscripts from the Spanish
Archivo General de Indias, with promising results. To the best
of our knowledge, this is the first published work proposing,
developing and assessing a successful approach for content-based
classification of untranscribed manuscript images.

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The effect of image enhancement algorithms\\on convolutional neural networks

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