Mosaic, Rust, Brown spot, and Alternaria leaf spot are the four common types of apple leaf diseases. Early diagnosis and accurate identification of apple leaf diseases can control the spread of infection and ensure the healthy development of the apple industry. The existing research uses complex image preprocessing and cannot guarantee high recognition rates for apple leaf diseases. This paper ...

Convolutional neural networks have achieved a great success in the recent years. Although, the way to maximize the performance of the convolutional neural networks still in the beginning. Furthermore, the optimization of the size and the time that need to train the convolutional neural networks is very far away from reaching the researcher's ambition. In this paper, we proposed a new convolutio...

In this project we explored the performance of deep convolutional neural network on recognizing handwritten Chinese characters. We ran experiments on a 200-class and a 3755-class dataset using convolutional networks with different depth and filter numbers. Experimental results show that deeper network with larger filter numbers give better test accuracy. We also provide a visualization of the l...

In this paper, our rst purpose is to study the performance of gating network functions in a committee machine setting. The problem of image deblur-ring is used to test the capability of such a system. Input clustering divides the task of deblurring into several subtasks. Each subtask is performed by a projection pursuit learning network (PPLN) 1]. We use a dynamic gating structure to combine ou...

in this paper, we consider a class of column-weight two quasi-cyclic low-density paritycheck codes in which the girth can be large enough, as an arbitrary multiple of 8. then we devote a convolutional form to these codes, such that their generator matrix can be obtained by elementary row and column operations on the parity-check matrix. finally, we show that the free distance of the convolution...

For introducing the advantages of feature learning and multilayer network in the interpretation of Polarimetric synthetic aperture radar (PolSAR) image, a classification algorithm based on deep convolutional neural network is proposed, and is used for PolSAR image classification. Firstly, a special convolutional neural network (CNN) for PolSAR image is constructed, secondly, a large number of P...

Very deep convolutional neural networks (CNNs) yield state of the art results on a wide variety of visual recognition problems. A number of state of the the art methods for image recognition are based on networks with well over 100 layers and the performance vs. depth trend is moving towards networks in excess of 1000 layers. In such extremely deep architectures the vanishing or exploding gradi...

In this work we report on progress in integrating deep convolutional features with Deformable Part Models (DPMs). We substitute the Histogram-of-Gradient features of DPMs with Convolutional Neural Network (CNN) features, obtained from the top-most, fifth, convolutional layer of Krizhevsky’s network [8]. We demonstrate that we thereby obtain a substantial boost in performance (+14.5 mAP) when co...

In this paper, we propose a Layer-RNN (L-RNN) module that is able to learn contextual information adaptively using within-layer recurrence. Our contributions are three-fold: (i) we propose a hybrid neural network architecture that interleaves traditional convolutional layers with L-RNN module for learning longrange dependencies at multiple levels; (ii) we show that a L-RNN module can be seamles...