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.

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Multimodal fusion for multimedia analysis: a survey

This paper investigates the main reason for the obtained low performance when the face recognition algorithms are tested on partially occluded face images. It has been observed that in the case of upper face occlusion, missing discriminative information due to occlusion only accounts for a very small part of the performance drop. The main factor is found to be the registration errors due to erroneous facial feature localization. It has been shown that by solving the misalignment problem, very high correct recognition rates can be achieved with a generic local appearance-based face recognition algorithm. In the case of a lower face occlusion, only a slight decrease in the performance is observed, when a local appearance-based face representation approach is used. This indicates the importance of local processing when dealing with partial face occlusion. Moreover, improved alignment increases the correct recognition rate also in the experiments against the lower face occlusion, which shows that face registration plays a key role on face recognition performance.

/pdf/why-is-facial-occlusion-a-challenging-problem-2pg4pamxzt.pdf

Why Is Facial Occlusion a Challenging Problem

In prior work, we proposed using an extended Kalman filter to directly update position estimates in a speaker localization system based on time delays of arrival. We found that such a scheme provided superior tracking quality as compared with the conventional closed-form approximation methods. In this work, we enhance our audio localizer with video information. We propose an algorithm to incorporate detected face positions in different camera views into the Kalman filter without doing any explicit triangulation. This approach yields a robust source localizer that functions reliably both for segments wherein the speaker is silent, which would be detrimental for an audio only tracker, and wherein many faces appear, which would confuse a video only tracker. We tested our algorithm on a data set consisting of seminars held by actual speakers. Our experiments revealed that the audio-video localizer functioned better than a localizer based solely on audio or solely on video features.

/pdf/kalman-filters-for-audio-video-source-localization-s8x8kwar4r.pdf

Kalman filters for audio-video source localization

We focused this work on handling variation in facial appearance caused by 3D head pose. A pose normalization approach based on fitting active appearance models (AAM) on a given face image was investigated. Profile faces with different rotation angles in depth were warped into shape-free frontal view faces. Face recognition experiments were carried out on the pose normalized facial images with a local appearance-based approach. The experimental results showed a significant improvement in accuracy. The local appearance-based face recognition approach is found to be robust against errors introduced by face model fitting.

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Pose Normalization for Local Appearance-Based Face Recognition

In this paper, we address the problem of apparent age estimation. Different from estimating the real age of individuals, in which each face image has a single age label, in this problem, face images have multiple age labels, corresponding to the ages perceived by the annotators, when they look at these images. This provides an intriguing computer vision problem, since in generic image or object classification tasks, it is typical to have a single ground truth label per class. To account for multiple labels per image, instead of using average age of the annotated face image as the class label, we have grouped the face images that are within a specified age range. Using these age groups and their age-shifted groupings, we have trained an ensemble of deep learning models. Before feeding an input face image to a deep learning model, five facial landmark points are detected and used for 2-D alignment. We have employed and fine tuned convolutional neural networks (CNNs) that are based on VGG-16 [24] architecture and pretrained on the IMDB-WIKI dataset [22]. The outputs of these deep learning models are then combined to produce the final estimation. Proposed method achieves 0.3668 error in the final ChaLearn LAP 2016 challenge test set [5].

/pdf/apparent-age-estimation-using-ensemble-of-deep-learning-1e9ryof0fs.pdf

Apparent Age Estimation Using Ensemble of Deep Learning Models

In this paper we present the results of the Unconstrained Ear Recognition Challenge (UERC), a group benchmarking effort centered around the problem of person recognition from ear images captured in uncontrolled conditions. The goal of the challenge was to assess the performance of existing ear recognition techniques on a challenging large-scale dataset and identify open problems that need to be addressed in the future. Five groups from three continents participated in the challenge and contributed six ear recognition techniques for the evaluation, while multiple baselines were made available for the challenge by the UERC organizers. A comprehensive analysis was conducted with all participating approaches addressing essential research questions pertaining to the sensitivity of the technology to head rotation, flipping, gallery size, large-scale recognition and others. The top performer of the UERC was found to ensure robust performance on a smaller part of the dataset (with 180 subjects) regardless of image characteristics, but still exhibited a significant performance drop when the entire dataset comprising 3,704 subjects was used for testing.

Hazim Kemal Ekenel

Papers

Why Is Facial Occlusion a Challenging Problem

Kalman filters for audio-video source localization

Pose Normalization for Local Appearance-Based Face Recognition

Apparent Age Estimation Using Ensemble of Deep Learning Models

The unconstrained ear recognition challenge