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Full-Text Articles in Engineering
Lung Segmentation In Chest Radiographs Using Fully Convolutional Networks, Rahul Hooda, Ajay Mittal, Sanjeev Sofat
Lung Segmentation In Chest Radiographs Using Fully Convolutional Networks, Rahul Hooda, Ajay Mittal, Sanjeev Sofat
Turkish Journal of Electrical Engineering and Computer Sciences
Automated segmentation of medical images that aims at extracting anatomical boundaries is a fundamental step in any computer-aided diagnosis (CAD) system. Chest radiographic CAD systems, which are used to detect pulmonary diseases, first segment the lung field to precisely define the region-of-interest from which radiographic patterns are sought. In this paper, a deep learning-based method for segmenting lung fields from chest radiographs has been proposed. Several modifications in the fully convolutional network, which is used for segmenting natural images to date, have been attempted and evaluated to finally evolve a network fine-tuned for segmenting lung fields. The testing accuracy and …
Enhancing Face Pose Normalization With Deep Learning, Anil Çeli̇k, Nafi̇z Arica
Enhancing Face Pose Normalization With Deep Learning, Anil Çeli̇k, Nafi̇z Arica
Turkish Journal of Electrical Engineering and Computer Sciences
In this study, we propose a hybrid method for face pose normalization, which combines the 3-D model-based method with stacked denoising autoencoder (SDAE) deep network. Instead of applying a mirroring operation for the invisible face parts of the posed image, SDAE learns how to fill in those regions by a large set of training samples. In the performance evaluation, we compare the proposed method to four different pose normalization methods and investigate their effects on facial emotion recognition and verification problems in addition to visual quality tests. Methods evaluated in the experiments include 2-D alignment, 3-D model-based method, pure SDAE-based …
Elimination Of Useless Images From Raw Camera-Trap Data, Ulaş Tekeli̇, Yalin Baştanlar
Elimination Of Useless Images From Raw Camera-Trap Data, Ulaş Tekeli̇, Yalin Baştanlar
Turkish Journal of Electrical Engineering and Computer Sciences
Camera-traps are motion triggered cameras that are used to observe animals in nature. The number of images collected from camera-traps has increased significantly with the widening use of camera-traps thanks to advances in digital technology. A great workload is required for wild-life researchers to group and label these images. We propose a system to decrease the amount of time spent by the researchers by eliminating useless images from raw camera-trap data. These images are too bright, too dark, blurred, or they contain no animals. To eliminate bright, dark, and blurred images we employ techniques based on image histograms and fast …
Improving Word Embeddings Projection For Turkish Hypernym Extraction, Savaş Yildirim
Improving Word Embeddings Projection For Turkish Hypernym Extraction, Savaş Yildirim
Turkish Journal of Electrical Engineering and Computer Sciences
Corpus-driven approaches can automatically explore is-a relations between the word pairs from corpus. This problem is also called hypernym extraction. Formerly, lexico-syntactic patterns have been used to solve hypernym relations. The language-specific syntactic rules have been manually crafted to build the patterns. On the other hand, recent studies have applied distributional approaches to word semantics. They extracted the semantic relations relying on the idea that similar words share similar contexts. Former distributional approaches have applied one-hot bag-of-word (BOW) encoding. The dimensionality problem of BOW has been solved by various neural network approaches, which represent words in very short and dense …