The convolutional neural network (ConvNet or CNN) has proven to be very successful in many tasks such as those in computer vision. Recently there has been growing interest in visualizing the knowledge discriminatively learned by CNNs, by generating images based on CNN features. This paper is a contribution towards this theme of research on knowledge visualization via image generation. Specifica...

Algorithms of filtering, edge detection, and extraction of details and their implementation using cellular neural networks (CNN) are developed in this paper. The theory of CNN based on universal binary neurons (UBN) is also developed. A new learning algorithm for this type of neurons is carried out. Implementation of low-pass filtering algorithms using CNN is considered. Separate processing of ...

ABSTRACT: Recently, much more high-resolution satellite images became available. We can detect vehicles in such high-resolution images, and estimation of earnings by counting vehicles in commercial facilities is becoming popular. For this purpose, we need to detect and count vehicles in satellite images accurately. In applications for detecting vehicles, deep neural network has achieved state-o...

where u denotes the CNN input vector, x the state vector, y = f ( x ) the output vector, f (.) a nonlinear function, A the feedback template matrix, B the control template matrix, i the bias vector, and ∂ the contribution from the boundary. CNN cell capacitance and resistance are assumed to be normalised to unity. Existing simulators mostly aim to increase the computational efficiency. In fact,...

This paper proposes a principled information theoretic analysis of classification for deep neural network structures, e.g. convolutional neural networks (CNN). The output of convolutional filters is modeled as a random variable Y conditioned on the object class C and network filter bank F . The conditional entropy (CENT) H(Y |C,F ) is shown in theory and experiments to be a highly compact and c...

Breast cancer is one of the most common types of cancer in women. Screening mammography is a low‑dose X‑ray examination of breasts, which is conducted to detect breast cancer at early stages when the cancerous tumor is too small to be felt as a lump. Screening mammography is conducted for women with no symptoms of breast cancer, for early detection of cancer when the cancer is most treatable an...

Possibilities for performing stochastic simulations on the analog and fully parallelized Cellular Neural Network Universal Machine (CNN-UM) are investigated. By using a chaotic cellular automaton perturbed with the natural noise of the CNN-UM chip, a realistic binary random number generator is built. As a specific example for Monte Carlo type simulations, we use this random number generator and...

Since its seminal publication in 1988, the Cellular Neural Network (CNN) (Chua & Yang, 1988) paradigm have attracted research community’s attention, mainly because of its ability for integrating complex computing processes into compact, real-time programmable analogic VLSI circuits (Rodríguez et al., 2004). Unlike cellular automata, the CNN model hosts nonlinear processors which, from analogic ...

CNN can automatically learn discriminative local features and give superior performance than hand-crafted features in various applications such as image classification, semantic segmentation and object detection. CNN has also been applied to MRI brain image analysis and achieved state-of-the-art results for brain tumor region segmentation [3, 4], stroke lesion segmentation [4], and mircobleeds ...

The centrosomin (cnn) gene encodes a protein associated with mitotic centrosomes in Drosophila melanogaster and is a target of homeotic gene regulation. Here we report that CNN is an essential component of the centrosome. Loss of zygotic cnn expression disrupts the development of the second midgut constriction, the gastric caeca, and the nervous system. Embryos that lack maternal as well as zyg...