IMPROVING CNN FEATURES FOR FACIAL EXPRESSION RECOGNITION

Abstract Facial expression recognition is one of the challenging tasks in computervision. In this paper, we analyzed and improved the performances bothhandcrafted features and deep features extracted by Convolutional NeuralNetwork (CNN). Eigenfaces, HOG, Dense-SIFT were used as handcrafted features.Additionally, we developed features based on the distances between faciallandmarks and SIFT descriptors around the centroids of the facial landmarks,leading to a better performance than Dense-SIFT. We achieved 68.34 % accuracywith a CNN model trained from scratch. By combining CNN features withhandcrafted features, we achieved 69.54 % test accuracy.Key Word: Neural network, facial expression recognition, handcrafted feature

TSCom-Net: Coarse-to-Fine 3D Textured Shape Completion Network

Reconstructing 3D human body shapes from 3D partial textured scans remains a fundamental task for many computer vision and graphics applications – e.g., body animation, and virtual dressing. We propose a new neural network architecture for 3D body shape and highresolution texture completion – TSCom-Net – that can reconstruct the full geometry from mid-level to high-level partial input scans. We decompose the overall reconstruction task into two stages – first, a joint implicit learning network (SCom-Net and TCom-Net) that takes a voxelized scan and its occupancy grid as input to reconstruct the full body shape and predict vertex textures. Second, a high-resolution texture completion network, that utilizes the predicted coarse vertex textures to inpaint the missing parts of the partial ‘texture atlas’. A Thorough experimental evaluation on 3DBodyTex.V2 dataset shows that our method achieves competitive results with respect to the state-of-the-art while generalizing to different types and levels of partial shapes. The proposed method has also ranked second in the track1 of SHApe Recovery from Partial textured 3D scans (SHARP [37 , 2]) 2022 1 challenge1

SHARP Challenge 2023: Solving CAD History and pArameters Recovery from Point clouds and 3D scans. Overview, Datasets, Metrics, and Baselines.

peer reviewedRecent breakthroughs in geometric Deep Learning (DL) and the availability of large Computer-Aided Design (CAD) datasets have advanced the research on learning CAD modeling processes and relating them to real objects. In this context, 3D reverse engineering of CAD models from 3D scans is considered to be one of the most sought-after goals for the CAD industry. However, recent efforts assume multiple simplifications limiting the applications in real-world settings. The SHARP Challenge 2023 aims at pushing the research a step closer to the real-world scenario of CAD reverse engineering from 3D scans through dedicated datasets and tracks. In this paper, we define the proposed SHARP 2023 tracks, describe the provided datasets, and propose a set of baseline methods along with suitable evaluation metrics to assess the performance of the track solutions. All proposed datasets along with useful routines and the evaluation metrics are publicly available

Karanlık Görüntü ve Videoların Küme Tabanlı Öğrenme Yöntemiyle İyileştrilmesi

Capturing images under extremely low-light conditions poses significant challenges for the standard camera pipeline. Images become too dark and too noisy, which makes traditional enhancement techniques almost impossible to apply. Recently, learning-based approaches have shown very promising results for this task since they have substantially more expressive capabilities to allow for improved quality. Motivated by these studies, in this thesis, we aim to leverage burst photography to boost the performance and obtain much sharper and more accurate RGB images from extremely dark raw images. The backbone of our proposed framework is a novel coarse-to-fine network architecture that generates high-quality outputs progressively. The coarse network predicts a low-resolution, denoised raw image, which is then fed to the fine network to recover fine-scale details and realistic textures. To further reduce the noise level and improve the color accuracy, we extend this network to a permutation invariant structure so that it takes a burst of low-light images as input and merges information from multiple images at the feature-level. Our experiments demonstrate that our approach leads to perceptually more pleasing results than the state-of-the-art methods by producing more detailed and considerably higher quality images.Aşırı düşük ışık koşullarında görüntü yakalamak, standart kamera hattı için önemli zorluklar yaratır. Görüntüler çok karanlık ve çok gürültülü olur, bu da geleneksel geliştirme tekniklerinin uygulanmasını neredeyse imkansız hale getirir. Son zamanlarda, öğrenme temelli yaklaşımlar bu problem için çok umut verici sonuçlar vermiştir, çünkü daha iyi kaliteyi sağlamak için bu yöntemlerin için ifade gücü yüksektir. Bu yöntemlerden motive olarak, bu tez çalışmasında, iyileştirme performansını artırmak ve aşırı karanlık ham görüntülerden daha keskin ve daha doğru RGB görüntüler elde etmek için seri çekimden yararlanmayı hedefliyoruz. Önerilen yapının bel kemiği, aşamalı olarak yüksek kaliteli çıktılar üreten yeni bir kabadan inceye ağ mimarisidir. Kaba ağ, daha sonra ince ölçekli ayrıntıları ve gerçekçi dokuları kurtarmak için ince ağa beslenen düşük çözünürlüklü bir ham görüntü öngörür. Gürültü seviyesini daha da azaltmak ve renk doğruluğunu artırmak için, bu ağı, giriş olarak seri çekilmiş düşük ışıklı görüntüler alan ve nitelik düzeyinde birden fazla görüntüden bilgi birleştirmesi yapan permütasyon değişmez bir yapıya genişletiyoruz. Deneylerimiz, yaklaşımımızın en son yöntemlerden daha detaylı ve çok daha yüksek kalitede görüntüler ürettiğini ve görsel olarak daha hoş sonuçlar verdiğini göstermektedir

Environmental Effects and Importance of the Risk Assessments for Mining Wastewater

Every scale of mining activities have huge impact to environment. Monitoring and assessing of the size of the effect and take measure is vital. In this present study risk assessment studies in mining areas and their effect to especially groundwater and ecosystem was investigated. Risk assessment steps were determined, explained detailed especially for huge amount of mining wastewater. Tailings are fine-grained waste material from the mining industry and main source of pollutant. Pollution risks of the groundwater, and after its reaching to human are crucial. Our study showed that management of mining wastewater is vital. Results show that Environmental impact assessment and monitoring studies must be carefully done before and closure time. Relevant policies must be ready and applicable. Factors of climate, geology and human health must be considered for long time period. International assumption, standards and health risk assessments should be done as mandatory

RESEARCH BURDEN OF INTERSTITIAL LUNG DISEASES IN TURKEY - RBILD

Introduction: The aim of our study is to investigate the etiological distribution of ILD in Turkey by stratifying the epidemiological characteristics of ILD cases, and the direct cost of initial diagnosis of the diag-nosed patients. Material-Method: The study was conducted as a multicenter, prospective, cross-sectional, clinical observation study. Patients over the age of 18 and who accepted to participate to the study were included and evaluated as considered to be ILD. The findings of diagnosis, examination and treatment carried out by the cent-ers in accordance with routine diagnostic procedures were recorded observationally. Results: In total,1070 patients were included in this study. 567 (53%) of the patients were male and 503 (47%) were female. The most frequently diagnosed disease was IPF (30.5%). Dyspnea (75.9%) was the highest incidence among the presenting symptoms. Physical examination found bibasilar inspiratory crackles in 56.2 % and radiological findings included reticular opacities and interlobular septal thickenings in 55.9 % of the cases. It was observed that clinical and radiological findings were used most frequently (74.9%) as a diagnostic tool. While the most common treatment approaches were the use of systemic steroids and antifibrotic drugs with a rate of 30.7% and 85.6%, respectively. The total median cost from the patient's admission to diagnosis was 540 Turkish Lira. Conclusion: We believe that our findings compared with data from other countries will be useful in showing the current situation of ILD in our country to discuss this problem and making plans for a solution

Suboptimal use of non-vitamin K antagonist oral anticoagulants: Results from the RAMSES study

WOS: 000384041400052PubMed ID: 27583892This study aimed to investigate the potential misuse of novel oral anticoagulants (NOACs) and the physicians' adherence to current European guideline recommendations in real-world using a large dataset from Real-life Multicenter Survey Evaluating Stroke Prevention Strategies in Turkey (RAMSES Study).RAMSES study is a prospective, multicenter, nationwide registry (ClinicalTrials.gov identifier NCT02344901). In this subgroup analysis of RAMSES study, patients who were on NOACs were classified as appropriately treated (AT), undertreated (UT), and overtreated (OT) according to the European Society of Cardiology (ESC) guidelines. The independent predictors of UT and OT were determined by multivariate logistic regression.Of the 2086 eligible patients, 1247 (59.8%) received adequate treatment. However, off-label use was detected in 839 (40.2%) patients; 634 (30.4%) patients received UT and 205 (9.8%) received OT. Independent predictors of UT included >65 years of age, creatinine clearance 50mL/min, urban living, existing dabigatran treatment, and HAS-BLED score of <3, whereas that of OT were creatinine clearance <50mL/min, ongoing rivaroxaban treatment, and HAS-BLED score of 3.The suboptimal use of NOACs is common because of physicians' poor compliance to the guideline recommendations in patients with nonvalvular atrial fibrillation (NVAF). Older patients who were on dabigatran treatment with good renal functions and low risk of bleeding were at risk of UT, whereas patients who were on rivaroxaban treatment with renal impairment and high risk of bleeding were at risk of OT. Therefore, a greater emphasis should be given to prescribe the recommended dose for the specified patients