Because digital images may contain a variety of data, they are regarded as an important source for information sharing. Also, images are widely used as evidence in a variety of real-life cases. The rapid rise in popularity of digital photographs is due to the improvement of technologies. Several software programs have been developed in recent years to modify digital images, such as Photoshop and Corel Photo, however these programs are now being used extensively for forgery. Because of technological advancements, it is difficult for people to recognize faked images with their naked eyes Therefore, in this study, the features used in forgery detection problems are combined to ensure accurate labeling of even forgery images that are difficult to detect. Stronger feature is obtained by combining Speeded-Up Robust Features (SURF) and Maximally Stable Extremal Regions (MSER). Considering the experimental results; it has been observed that the use of the proposed method, which is obtained as a result of combining the two methods in copy-move forgery detection problems, is more successful than using the SURF and MSER features separately.
Emotion recognition from facial expression is a current research topic that can be applied in the many fields of computer vision, such as human-computer interaction, emotional computing, etc. In this study, an application for emotion recognition through deep learning was made using KDEF and PICS datasets. A new model was established using the convolutional neural network (CNN), an artificial intelligence approach that involves artificial neural networks, which is one of the deep learning techniques for attribute inference. Large datasets are needed for the high performance of deep learning. There are 4900 images in the KDEF dataset and 322 images in the PICS dataset. For this purpose, primarily due to the small number of images in the PICS dataset, image iteration was applied with the data augmentation method, and the PICS dataset was increased to 4830 images. Then, the new model developed by conducting separate training on these two different datasets was tested. Seven different classes of emotion (afraid, angry, disgusted, happy, neutral, sad, surprised) were covered in each dataset in the study conducted with a new model developed based on VGGNet which is one of the CNN models. With the developed model, a high success rate was achieved by obtaining 97.44% accuracy values in the validation set of the KDEF and 98.24% accuracy values in the validation set of the PICS dataset.
Among the many applications in the field of computer vision, face recognition systems; is a subject that has been studied extensively and has been working for a long time. In general, the success of facial recognition systems, which consist of feature extraction and classifier steps, depends not only on the classifier but also on the features used. In a face recognition system, the feature selection is to obtain distinctive features for recognition of different facial images of interest. For this purpose, SIFT, SURF and SIFT + SURF features, which are unchanging features to scaling and affine transformations, are used in this study. In addition, to be able to compare with these local features, the HOG feature which is a global feature, also has been added to the study. Classification was performed using support vector machine. Experimental results show that local features are more successful than the global feature HOG.
Günümüzde mikrodenetleyiciler çok fonksiyonlu ve düşük maliyetli olmalarından dolayı birçok uygulamada tercih edilmektedir. Bu çalışmada Erzincan Üniversitesi bünyesindeki ağ cihazlarının konfigürasyonu ve yedeklenmesi işlemlerinde klasik masaüstü ya da dizüstü bilgisayar ihtiyacı ve yetkili personel bulunma zorunluluğu yerine; zamandan, maliyetten ve personelden kazanç sağlayan ve Arduino kullanılarak tasarlanan "Erserial" isimli bir sistem geliştirilmiştir. Geliştirilen sistem ayrıca kablolama işlerinde planlamanın kolaylaştırılmasını sağlamakla birlikte saha personelinin de işe dahil edilmesine olanak tanıyarak, genel olarak çalışma şartlarının daha iyi, işlemlerin daha hızlı ve kolay yapılabilmesini sağlamıştır.
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