Conference
Intelligent Human Computer Interaction
About: Intelligent Human Computer Interaction is an academic conference. The conference publishes majorly in the area(s): Computer science & Deep learning. Over the lifetime, 296 publications have been published by the conference receiving 1725 citations.
Topics: Computer science, Deep learning, Support vector machine, Augmented reality, Interface (computing)
Papers
1 Jan 2015
TL;DR: This paper practically demonstrates how Internet of Things (IoT) integration with data access networks, Geographic Information Systems (GIS), combinatorial optimization, and electronic engineering can contribute to improve cities’ management systems.
Abstract: Cities around the world are on the run to become smarter. Some of these have seen an opportunity on deploying dedicated municipal access networks to support all types of city management and maintenance services requiring a data connection. This paper practically demonstrates how Internet of Things (IoT) integration with data access networks, Geographic Information Systems (GIS), combinatorial optimization, and electronic engineering can contribute to improve cities’ management systems. We present a waste collection solution based on providing intelligence to trashcans, by using an IoT prototype embedded with sensors, which can read, collect, and transmit trash volume data over the Internet. This data put into a spatio-temporal context and processed by graph theory optimization algorithms can be used to dynamically and efficiently manage waste collection strategies. Experiments are carried out to investigate the benefits of such a system, in comparison to a traditional sectorial waste collection approaches, also including economic factors. A realistic scenario is set up by using Open Data from the city of Copenhagen, highlighting the opportunities created by this type of initiatives for third parties to contribute and develop Smart city solutions.
186 citations
1 Dec 2012
TL;DR: In this paper, a facial expression classification algorithm is proposed which uses Haar classifier for face detection purpose, Local Binary Patterns(LBP) histogram of different block sizes of a face image as feature vectors and classifies various facial expressions using Principal Component Analysis (PCA).
Abstract: Facial expression analysis is one of the popular fields of research in human computer interaction (HCI). It has several applications in next generation user interfaces, human emotion analysis, behavior and cognitive modeling. In this paper, a facial expression classification algorithm is proposed which uses Haar classifier for face detection purpose, Local Binary Patterns(LBP) histogram of different block sizes of a face image as feature vectors and classifies various facial expressions using Principal Component Analysis (PCA). The algorithm is implemented in real time for expression classification since the computational complexity of the algorithm is small. A customizable approach is proposed for facial expression analysis, since the various expressions and intensity of expressions vary from person to person. The system uses grayscale frontal face images of a person to classify six basic emotions namely happiness, sadness, disgust, fear, surprise and anger.
125 citations
1 Dec 2012
TL;DR: A study of three EEG signals feature extraction techniques used in researches of emotional states recognition: statistical characteristics, features based on PSD (Power Spectral Density) and featuresbased on HOC (High Order Crossings).
Abstract: We present in this paper a study of three EEG signals feature extraction techniques. These techniques have been widely employed in researches of emotional states recognition: statistical characteristics, features based on PSD (Power Spectral Density) and features based on HOC (High Order Crossings). The validation was performed via classification of emotional states of calm and stress using the K-NN based classifier in off-line mode using EEG signals from available DEAP database. The best results achieved were 70.1%, using the PSD based technique, and 69.59% using the HOC based technique.
89 citations
1 Dec 2012
TL;DR: The experimental results provide evidence of the effectiveness of the proposed approach with 93% recognition rate and the method consists of three phases: segmentation, Feature Extraction and Recognition.
Abstract: Sign Language is the most natural and expressive way for the hearing impaired. This paper presents a method for automatic recognition of two handed signs of Indian Sign Language (ISL). The method consists of three phases: Segmentation, Feature Extraction and Recognition. The segmentation is done through Otsu's algorithm. In the feature extraction phase, shape descriptors, HOG descriptors (Histogram of Oriented Gradient) and SIFT (Scale Invariant Feature Transform) feature have been fused to compute a feature vector. In the recognition phase, a multi-class Support Vector Machine (MSVM) is used for training and classifying signs of ISL. The experimental results provide evidence of the effectiveness of the proposed approach with 93% recognition rate.
56 citations
12 Dec 2019
TL;DR: A modified state-of-the-art deep learning to tackle the problem of Bengali handwritten character recognition using Resnet-50 deep convolutional neural network model and shows the effectiveness of ResNet-50 for classification of Bangla HCR.
Abstract: Bengali is the sixth most popular spoken language in the world. Computerized detection of handwritten Bengali (Bangla Lekha) character is very difficult due to the diversity and veracity of characters. In this paper, we have proposed a modified state-of-the-art deep learning to tackle the problem of Bengali handwritten character recognition. This method used the lesser number of iterations to train than other comparable methods. The transfer learning on Resnet-50 deep convolutional neural network model is used on pretrained ImageNet dataset. One cycle policy is modified with varying the input image sizes to ensure faster training. Proposed method executed on BanglaLekha-Isolated dataset for evaluation that consists of 84 classes (50 Basic, 10 Numerals and 24 Compound Characters). We have achieved 97.12% accuracy in just 47 epochs. Proposed method gives very good results in terms of epoch and accuracy compare to other recent methods by considering number of classes. Without ensembling, proposed solution achieves state-of-the-art result and shows the effectiveness of ResNet-50 for classification of Bangla HCR.
34 citations
Performance Metrics
| Year | Papers |
|---|---|
| 2021 | 1 |
| 2020 | 89 |
| 2019 | 26 |
| 2018 | 28 |
| 2017 | 16 |
| 2016 | 22 |