Modified Distributive Arithmetic based 2D-DWT for Hybrid (Neural Network-DWT) Image Compression

Artificial Neural Networks ANN is significantly used in signal and image processing techniques for pattern recognition and template matching Discrete Wavelet Transform DWT is combined with neural network to achieve higher compression if 2D data such as image Image compression using neural network and DWT have shown superior results over classical techniques with 70 higher compression and 20 improvement in Mean Square Error MSE Hardware complexity and power issipation are the major challenges that have been addressed in this work for VLSI implementation In this work modified distributive arithmetic DWT and multiplexer based DWT architecture are designed to reduce the computation complexity of hybrid architecture for image compression A 2D DWT architecture is designed with 1D DWT architecture and is implemented on FPGA that operates at 268 MHz consuming power less than 1

Pre-IdentifyNet: An Improved Neural Network for Image Recognition

With the rise and development of artificial intelligence, image recognition and classification technology has received more and more attention as an important branch of its research field. Among them, the introduction of deep learning networks and the construction of neural network structures not only avoid a lot of the tedious work of manual extraction, but also improve the accuracy of image recognition. Convolutional neural networks have many advantages that conventional neural networks do not have. Therefore, image classification systems based on convolutional neural networks emerge in endlessly, but there is still much room for improvement in terms of recognition accuracy and recognition speed. Based on this, this paper proposes an improved deep convolutional neural network to improve the accuracy of the network by changing a series of parameters such as the number of channels of the convolution layer, the size of the convolution kernel, the learning rate, the number of iterations, and the size of the small batch with speed. In this paper, three data sets were selected, namely sewage, animals and the Simpson Family. Comparing the improved convolutional neural network network with the existing SqueezeNet and GoogleNet. It is found that the accuracy of the network is maintained while maintaining a similar speed. Both F1-score and F1-score have been improved with a higher recognition rate and better recognition effect in image recognition classification

Spatial Pyramid Pooling in Deep Convolutional Networks for Visual Recognition

Existing deep convolutional neural networks (CNNs) require a fixed-size (e.g., 224x224) input image. This requirement is "artificial" and may reduce the recognition accuracy for the images or sub-images of an arbitrary size/scale. In this work, we equip the networks with another pooling strategy, "spatial pyramid pooling", to eliminate the above requirement. The new network structure, called SPP-net, can generate a fixed-length representation regardless of image size/scale. Pyramid pooling is also robust to object deformations. With these advantages, SPP-net should in general improve all CNN-based image classification methods. On the ImageNet 2012 dataset, we demonstrate that SPP-net boosts the accuracy of a variety of CNN architectures despite their different designs. On the Pascal VOC 2007 and Caltech101 datasets, SPP-net achieves state-of-the-art classification results using a single full-image representation and no fine-tuning. The power of SPP-net is also significant in object detection. Using SPP-net, we compute the feature maps from the entire image only once, and then pool features in arbitrary regions (sub-images) to generate fixed-length representations for training the detectors. This method avoids repeatedly computing the convolutional features. In processing test images, our method is 24-102x faster than the R-CNN method, while achieving better or comparable accuracy on Pascal VOC 2007. In ImageNet Large Scale Visual Recognition Challenge (ILSVRC) 2014, our methods rank #2 in object detection and #3 in image classification among all 38 teams. This manuscript also introduces the improvement made for this competition.Comment: This manuscript is the accepted version for IEEE Transactions on Pattern Analysis and Machine Intelligence (TPAMI) 2015. See Changelo

Correlated Noise in Deep Convolutional Neural Networks

This thesis explores one of the differences between the visual cortex and deep convolutional neural networks, namely, correlated fluctuations of neuron response strength. First, we describe the similarities and differences between biological and artificial neural networks and provide motivation for bridging the gap between the two, with a focus on correlated variability. Next, we present a regularisation method for convolutional neural networks using correlated noise. The structure of the correlations are inspired from biological observations using two factors: spatial extent and tuning similarity of neurons. We provide empirical results on improved robust image classification in the setting of occluded and corrupted images. We then move on to studying the connection between popular dataset augmentation techniques and correlated variability. We compute a smooth estimate of the correlations of neural network activity as a function of distance and kernel similarity, and show the similarity to biological correlations. Finally, we introduce a structured form of Dropout for convolutional neural networks, taking into account spatial and kernel correlations. We show improvement in image classification for the VGG architecture on the CIFAR-10 dataset