Description: Statistical approaches to early vision processes need a huge amount of computing power. These algorithms can usually be implemented on parallel computing structures. With the Cellular Neural Networks [9] (CNN), a new image processing tool is coming into consideration. Its VLSI implementation takes place on a single analog chip containing several thousands (recently about 10,000 to ...

In this work we propose a method for computing a minimum size training set consistent subset for the Nearest Neighbor rule (also said CNN problem) via SAT encodings. We introduce the SAT–CNN algorithm, which exploits a suitable encoding of the CNN problem in a sequence of SAT problems in order to exactly solve it, provided that enough computational resources are available. Comparison of SAT–CNN...

Description: Statistical approaches to early vision processes need a huge amount of computing power. These algorithms can usually be implemented on parallel computing structures. With the Cellular Neural Networks [9] (CNN), a new image processing tool is coming into consideration. Its VLSI implementation takes place on a single analog chip containing several thousands (recently about 10,000 to ...

Deep convolutional neural networks (CNN) have shown their promise as a universal representation for recognition. However, global CNN activations lack geometric invariance, which limits their robustness for classification and matching of highly variable scenes. To improve the invariance of CNN activations without degrading their discriminative power, this paper presents a simple but effective sc...

This work presents a method for classification and segmentation of brain tumors based on deep learning analysis contrast T1 (T1c) MR images. To achieve this goal, three different networks are investigated i.e., U-Net, VGG16-Segnet, DeepLabv3+ models. In addition, the integration 3D narrow-band information MRI volumes is imported to input Convolutional Neural Network (CNN) describe more accurate...

Cilt kanseri kötü huylu tümörlerin kontrolsüz çoğalması ile başlar. Dünya çapında sık karşılaşılan bir kanser türüdür. Uzman hekimler tarafından çıplak gözle incelemesi ve teşhis konulması güçtür. Bu yüzden bilgisayar destekli sistemleri hekimlere tanı koymada yardımcı olabilir. sistemler günümüzde yapay zekanın türü olan derin sinir ağlarını yaygın olarak kullanır. Pek çok ağı içeren çalışmada...

Smoking during pregnancy exposes the fetus to nicotine, resulting in nicotine-stimulated neurotransmitter release. Recent evidence suggests that the hippocampus develops differently in males and females with delayed maturation in males. We show that chronic nicotine exposure during the first postnatal week has sex-specific long-term effects. Neonatal rat pups were chronically treated with nicot...

The automated building detection in aerial images is a fundamental problem encountered in aerial and satellite images analysis. Recently, thanks to the advances in feature descriptions, Region-based CNN model (R-CNN) for object detection is receiving an increasing attention. Despite the excellent performance in object detection, it is problematic to directly leverage the features of R-CNN model...

Alzheimer's disease indicates one of the highest difficult to heal diseases, and it is acutely affecting elderly normal lives their households. Early, effective, accurate detection represents an important blueprint for minimizing progression risk. The modalities brain imaging can assist in identifying abnormalities associated with disease. This research presents a developed deep learning scheme...

The purpose of the study is to analyse and compare most common machine learning deep techniques used for computer vision 2D object classification tasks. Firstly, we will present theoretical background Bag Visual words model Deep Convolutional Neural Networks (DCNN). Secondly, implement a Words model, VGG16 CNN Architecture. Thirdly, our custom novice DCNN in which test aforementioned implementa...