다중혈관 관상동맥 환자에서 y-문합을 이용하여 양쪽 내흉동맥만을 사용한 우회술의 조기 성적

“Unpaired Image-to-Image Translation using Cycle-Consistent Adversarial Networks”の学習報告

The PASCAL Visual Object Classes Challenge

A morphable model for the synthesis of 3D faces

This survey aims at providing multimedia researchers with a state-of-the-art overview of fusion strategies, which are used for combining multiple modalities in order to accomplish various multimedia analysis tasks. The existing literature on multimodal fusion research is presented through several classifications based on the fusion methodology and the level of fusion (feature, decision, and hybrid). The fusion methods are described from the perspective of the basic concept, advantages, weaknesses, and their usage in various analysis tasks as reported in the literature. Moreover, several distinctive issues that influence a multimodal fusion process such as, the use of correlation and independence, confidence level, contextual information, synchronization between different modalities, and the optimal modality selection are also highlighted. Finally, we present the open issues for further research in the area of multimodal fusion.

/pdf/multimodal-fusion-for-multimedia-analysis-a-survey-5f9wxprbby.pdf

Multimodal fusion for multimedia analysis: a survey

In this study, facial expression recognition is defined as a pair matching problem. Our objectives to formulate this talk in this way are to be able to decide whether the facial expressions of the unlabeled images of two people are the same or different and to benefit from the proposed pair matching methods that have been studied for many years in the face recognition field. The Extended Cohn-Kanade (CK+) dataset which is commonly used for classification of facial expression is chosen to obtain match and mismatch pairs. To provide a baseline approach for the proposed pair matching formulation, in our paper, feature extraction by using local binary pattern is applied and match and mismatch facial expressions are classified by using support vector machines. 99.28% matching accuracy was achieved.

Facial expression pair matching

Automatic face identification in multimedia archives such as broadcast news videos is useful for indexing or retrieving documents based on important persons that appear in the video. In this paper, we propose a system which automatically detects a list of important targets such as anchor speakers or active politicians in broadcast news videos. This involves several steps including detecting faces in various conditions, associating faces to tracks and identifying whether a face track contains certain faces defined in a watch list. We evaluated this system on a database, which contains about 36 hours of broadcast news videos. Experiments show that our system achieves a very high precision with a reasonable recall rate.

/pdf/identifying-important-people-in-broadcast-news-videos-3bxfht6qcw.pdf

Identifying Important People in Broadcast News Videos

The COVID-19 pandemic affects every area of daily life globally. To avoid the spread of coronavirus and retrieve the daily normal worldwide, health organizations advise social distancing, wearing face mask, and avoiding touching face. Based on these recommended protective measures, we developed a deep learning-based computer vision system to help prevent the transmission of COVID-19. Specifically, the developed system performs face mask detection, face-hand interaction detection, and measures social distance. For these purposes, we collected and annotated images that represent face mask usage and face-hand interaction in the real world. We introduce two different face datasets, namely, Interactive Systems Labs Unconstrained Face Mask Dataset (ISL-UFMD) and Interactive Systems Labs Unconstrained Face Hand Interaction Dataset (ISL-UFHD). We trained the proposed models on our own datasets and evaluated them on both our datasets and already existing datasets in the literature without performing any adaptation on these target datasets. Besides, we proposed a distance measurement module to track social distance between people. Experimental results indicate that ISL-UFMD and ISL-UFHD represent the real-world's diversity well. The proposed system achieved very high performance and generalization capacity in a real-world scenario for unseen data from outside the training data to detect face mask usage, face-hand interaction detection, and measuring social distance. The ISL-UFMD and ISL-UFHD datasets will be available at this https URL.

A Computer Vision System to Help Prevent the Transmission of COVID-19

In this paper, we present a computer vision-based mobile plant classification application. The objectives to develop such a mobile application is to utilize smartphones and benefit from the photographs taken by their camera in order to perform environmental monitoring. Moreover, enabling individuals to use such a technnology is expected to increase individuals' awareness to their environment. The developed application identifies the plants by processing a photograph of its leaf. The main processing steps of the system are: determining whether there is a leaf in the photo, detecting whether there is a blur, leaf segmentation, feature extraction, and classification. Each building block of the system is tested separately and promising results have been obtained in the experiments.

A mobile plant identification application for environmental monitoring

Facial appearance variations due to occlusion has been one of the main challenges for face recognition systems. To facilitate further research in this area, it is necessary and important to have occluded face datasets collected from real-world, as synthetically generated occluded faces cannot represent the nature of the problem. In this paper, we present the Real World Occluded Faces (ROF) dataset, that contains faces with both upper face occlusion, due to sunglasses, and lower face occlusion, due to masks. We propose two evaluation protocols for this dataset. Benchmark experiments on the dataset have shown that no matter how powerful the deep face representation models are, their performance degrades significantly when they are tested on real-world occluded faces. It is observed that the performance drop is far less when the models are tested on synthetically generated occluded faces. The ROF dataset and the associated evaluation protocols are publicly available at the following link this https URL.

Hazim Kemal Ekenel

Papers

Facial expression pair matching

Identifying Important People in Broadcast News Videos

A Computer Vision System to Help Prevent the Transmission of COVID-19

A mobile plant identification application for environmental monitoring

On Recognizing Occluded Faces in the Wild.