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IWSSIP 2020

Biomedical Signal Processing and Analysis III

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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.

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The 27th International Conference on Systems, Signals and Image Processing.

Water Holding Capacity (WHC) is one of the most important attributes related to the quality of pork meat. Normally, WHC is estimated by measuring the juice released after the compression of meat samples. However, all the evaluation process is still manually handled by experts, making the samples subject to many factors, such as variations in illumination during the acquisition process. In this sense, there is a great need to automate this process and digital image processing techniques can be very useful for this. In this paper, we approach the problem of image enhancement, which is an important and necessary stage to better identify the different regions of a WHC image. We developed an image enhancement approach that corrects both illumination and color variations present in the images. We compared our approach with other approaches found in literature in terms of visual aspect and through Measure of Enhancement (EME). Results indicated that our technique presents a superior performance in the problem of correcting illumination problems in WHC images.

Image Enhancement Applied to Water Holding Capacity Images

The study of shape objects, in special, its description and recognition is a field of intense research in computer vision. This paper presents a novel approach for shape descriptor based on two methods: the randomized neural network (RNN), which has a single hidden neuron layer and a training algorithm much faster than the traditional \emph{backpropagation} approach, and the Fourier transform, a traditional signal analysis tool that enables us to decompose a signal into its frequencies. By combining these methods, we are able to compute a robust shape signature, which is capable of recognizing very different types of images, from occluded shapes to large sets of images under affine transformations. We compared the proposed approach with several shape analysis methods. The results indicate that our proposed approach can be successfully applied in different shape analysis problems.

Shape Classification Using Randomized Neural Network Descriptors Extracted from the Fourier Spectrum

Transmission Electron Microscopy (TEM) is one of the most important tools for virus detection and identification. It enables researchers to study the characteristics of a virus in order to classify it into groups and families. From morphological characteristics of viruses in TEM images, this paper proposes a computer vision approach for virus classification. For this purpose, we compared ten texture descriptors methods and fused them in order to obtain a higher discriminative signature. We compared this signature to other works that addressed the same virus classification problem. The result achieved by our proposed fusion approach (87.27\%) surpassed the highest accuracies of previous papers, thus proving to be very effective for discriminating virus in TEM images. Thus, we believe that our paper brings a relevant contribution to this important medical problem.

Virus Classification by using a Fusion of Texture Analysis Methods

The human brain is a complex system and many approaches are currently being used to study its disorders. Functional Connectivity is a well-known approach which characterizes the relationship between two or more regions of the brain.
This relationship is inferred from measuring and correlating activity from the brain. On the other hand, Effective Connectivity quantifies activities instead of correlating them. For Functional Connectivity, Phase Locked Value (PLV) and other measures are used. For Effective Connectivity, Transfer Entropy is the main measure used. The problem is: Functional Connectivity describes the flow of information in the complex network of the brain in terms of correlation, and allows time delays computations for
better correlations – but gives no information regarding causality; Transfer Entropy is very accurate regarding causality, but has assessed interactions at only one time delay. This paper will address these issues by analysing a data set of absence seizures
on both connectivity analyses. PLV (Phase Locking Value) and Transfer Entropy (TE) algorithms will be performed on EEG data from 11 subjects diagnosed with Absence Seizures. Afterwards, both networks will be described in terms of each connectivity
type. The main contribution of this paper is that the network specification of both Functional and Effective Connectivity are complementary when characterizing absence seizures from EEG data.

Functional and Effective Connectivity Characterization of Absence Seizures

Due to the positive relationship between the presence of students in classes and their performance, student attendance assessment is considered essential within the classroom environment, even as a tiring and time-consuming task. We proposed a solution for student attendance control using face recognition with deep one-shot learning and evaluated our approach in different conditions and image capturing devices to confirm that such a pipeline may work in a real-world setting. For better results regarding the high number of false negatives that often occur in uncontrolled environments, we also proposed a face detection stage using HOG and a CNN with Max-Margin Object Detection based features. We achieved accuracy and F1 scores of 97% and 98.4% with an iPhone 7 camera, 91.9% and 94.8% with a Moto G camera, and 51.2% and 61.1% with a WebCam respectively. These experiments reinforce the effectiveness and availability of this approach to the student attendance assessment problem since the recognition pipeline can be either made available for embedded processing with limited computational resources (smartphones), or offered as "Software as a Service" tool.

Automatic Attendance Management System based on Deep One-Shot Learning

Face recognition has been one of the most relevant and explored Biometrics fields. In real-world applications, face recognition methods usually must deal with scenarios where not all probe individuals were seen during training phase (open-set scenarios). Therefore, open-set face recognition is a subject of increasing interest as it deals with identifying individuals in a space where not all faces are known in advance. This is useful in several applications, such as access authentication, on which only a few individuals that have been previously enrolled in a gallery are allowed. The present work introduces a novel approach towards open-set face recognition focusing on small galleries and in enrollment detection, not identity retrieval. A Siamese Network architecture is proposed to learn a model to detect if a face probe is enrolled in the gallery based on a verification-like approach. Promising results were achieved for small galleries, outperforming state-of-the-art methods like HFCN and HPLS, on experiments carried out on Pubfig83, FRGCv1 and LFW datasets. In addition, a new evaluation protocol is introduced for experiments in small galleries.

Open-set Face Recognition for Small Galleries Using Siamese Networks

Automatic speaker recognition is drastically
degraded in presence of noise. This paper focuses on the application of the cubic law and histogram mapping for the textindependent speaker recognition task. Our aim of this study is the application of these two methods in the feature extraction stage of the Power-Normalized Cepstral Coefficients (PNCC) and the conventional Mel Frequency Cepstral Coefficients (MFCC) techniques. Recognition results show that the cubic law combined with the histogram mapping improve the recognition rates.

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Biomedical Signal Processing and Analysis III

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