249 citations · 1.5k across the 70 of their papers we have counts for
18 papers · 1 filter
INTEL-TAU: A Color Constancy Dataset
Firas Laakom, Jenni Raitoharju, Alexandros Iosifidis +2
In this paper, we describe a new large dataset for illumination estimation. This dataset, called INTEL-TAU, contains 7022 images in total, which makes it the largest available high…
Automated Polysomnography Analysis for Detection of Non-Apneic and Non-Hypopneic Arousals using Feature Engineering and a Bidirectional LSTM Network
Ali Bahrami Rad, Morteza Zabihi, Zheng Zhao +3
Objective: The aim of this study is to develop an automated classification algorithm for polysomnography (PSG) recordings to detect non-apneic and non-hypopneic arousals. Our parti…
Neural Architecture Search by Estimation of Network Structure Distributions
Anton Muravev, Jenni Raitoharju, Moncef Gabbouj
The influence of deep learning is continuously expanding across different domains, and its new applications are ubiquitous. The question of neural network design thus increases in…
Reversible Privacy Preservation using Multi-level Encryption and Compressive Sensing
Mehmet Yamac, Mete Ahishali, Nikolaos Passalis +3
Security monitoring via ubiquitous cameras and their more extended in intelligent buildings stand to gain from advances in signal processing and machine learning. While these innov…
Bag of Color Features For Color Constancy
Firas Laakom, Nikolaos Passalis, Jenni Raitoharju +4
In this paper, we propose a novel color constancy approach, called Bag of Color Features (BoCF), building upon Bag-of-Features pooling. The proposed method substantially reduces th…
Color Constancy Convolutional Autoencoder
Firas Laakom, Jenni Raitoharju, Alexandros Iosifidis +2
In this paper, we study the importance of pre-training for the generalization capability in the color constancy problem. We propose two novel approaches based on convolutional auto…
Multilinear Compressive Learning
Dat Thanh Tran, Mehmet Yamac, Aysen Degerli +2
Compressive Learning is an emerging topic that combines signal acquisition via compressive sensing and machine learning to perform inference tasks directly on a small number of mea…
Speed-up and multi-view extensions to Subclass Discriminant Analysis
Kateryna Chumachenko, Jenni Raitoharju, Alexandros Iosifidis +1
In this paper, we propose a speed-up approach for subclass discriminant analysis and formulate a novel efficient multi-view solution to it. The speed-up approach is developed based…
3D Quantum Cuts for Automatic Segmentation of Porous Media in Tomography Images
Junaid Malik, Serkan Kiranyaz, Riyadh Al-Raoush +7
Binary segmentation of volumetric images of porous media is a crucial step towards gaining a deeper understanding of the factors governing biogeochemical processes at minute scales…
Multimodal Subspace Support Vector Data Description
Fahad Sohrab, Jenni Raitoharju, Alexandros Iosifidis +1
In this paper, we propose a novel method for projecting data from multiple modalities to a new subspace optimized for one-class classification. The proposed method iteratively tran…
Feature Engineering for Mid-Price Prediction with Deep Learning
Adamantios Ntakaris, Giorgio Mirone, Juho Kanniainen +2
Mid-price movement prediction based on limit order book (LOB) data is a challenging task due to the complexity and dynamics of the LOB. So far, there have been very limited attempt…
Colorectal cancer diagnosis from histology images: A comparative study
Junaid Malik, Serkan Kiranyaz, Suchitra Kunhoth +4
Computer-aided diagnosis (CAD) based on histopathological imaging has progressed rapidly in recent years with the rise of machine learning based methodologies. Traditional approach…
Data-driven Neural Architecture Learning For Financial Time-series Forecasting
Dat Thanh Tran, Juho Kanniainen, Moncef Gabbouj +1
Forecasting based on financial time-series is a challenging task since most real-world data exhibits nonstationary property and nonlinear dependencies. In addition, different data…
1D Convolutional Neural Network Models for Sleep Arousal Detection
Morteza Zabihi, Ali Bahrami Rad, Serkan Kiranyaz +2
Sleep arousals transition the depth of sleep to a more superficial stage. The occurrence of such events is often considered as a protective mechanism to alert the body of harmful s…
Real-time PCG Anomaly Detection by Adaptive 1D Convolutional Neural Networks
Serkan Kiranyaz, Morteza Zabihi, Ali Bahrami Rad +4
The heart sound signals (Phonocardiogram - PCG) enable the earliest monitoring to detect a potential cardiovascular pathology and have recently become a crucial tool as a diagnosti…
Operational Neural Networks
Serkan Kiranyaz, Turker Ince, Alexandros Iosifidis +1
Feed-forward, fully-connected Artificial Neural Networks (ANNs) or the so-called Multi-Layer Perceptrons (MLPs) are well-known universal approximators. However, their learning perf…
Deep Adaptive Input Normalization for Time Series Forecasting
Nikolaos Passalis, Anastasios Tefas, Juho Kanniainen +2
Deep Learning (DL) models can be used to tackle time series analysis tasks with great success. However, the performance of DL models can degenerate rapidly if the data are not appr…
Temporal Logistic Neural Bag-of-Features for Financial Time series Forecasting leveraging Limit Order Book Data
Nikolaos Passalis, Anastasios Tefas, Juho Kanniainen +2
Time series forecasting is a crucial component of many important applications, ranging from forecasting the stock markets to energy load prediction. The high-dimensionality, veloci…