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Rich feature hierarchies for accurate object detection and semantic segmentation

by Ross Girshick, Jeff Donahue, Trevor Darrell, Jitendra Malik
"... Object detection performance, as measured on the canonical PASCAL VOC dataset, has plateaued in the last few years. The best-performing methods are complex en-semble systems that typically combine multiple low-level image features with high-level context. In this paper, we propose a simple and scala ..."
Abstract - Cited by 251 (23 self) - Add to MetaCart
and scalable detection algorithm that im-proves mean average precision (mAP) by more than 30% relative to the previous best result on VOC 2012—achieving a mAP of 53.3%. Our approach combines two key insights: (1) one can apply high-capacity convolutional neural net-works (CNNs) to bottom-up region proposals

Markov Random Field Segmentation of Brain MR Images

by Karsten Held, Elena Rota Kops, Bernd J. Krause, William M. Wells, III, Ron Kikinis, Hans-Wilhelm Müller-Gärtner , 1997
"... We describe a fully-automatic 3Dsegmentation technique for brain MR images. By means of Markov random fields the segmentation algorithm captures three features that are of special importance for MR images: nonparametric distributions of tissue intensities, neighborhood correlations and signal inhomo ..."
Abstract - Cited by 87 (0 self) - Add to MetaCart
We describe a fully-automatic 3Dsegmentation technique for brain MR images. By means of Markov random fields the segmentation algorithm captures three features that are of special importance for MR images: nonparametric distributions of tissue intensities, neighborhood correlations and signal

LSB neural network based segmentation of MR brain images

by Yan Li, Peng Wen, David Powers, C. Richard Clark
"... Least Square Backpropagation(LSB) algorithm is employed to train a three-layer neural network for segmentation of Magnetic Resonance(MR) brain images. The simulation results demonstrate the use of LSB neural Network as a promising method for the segmentation of multi-modal medical images. The traini ..."
Abstract - Cited by 1 (0 self) - Add to MetaCart
Least Square Backpropagation(LSB) algorithm is employed to train a three-layer neural network for segmentation of Magnetic Resonance(MR) brain images. The simulation results demonstrate the use of LSB neural Network as a promising method for the segmentation of multi-modal medical images

Fully Automatic Segmentation of the Brain in MRI

by M. Stella Atkins, Blair Mackiewich, Ken Whittall , 1998
"... A robust fully automatic method for segmenting the brain from head MR images has been developed, which works even in the presence of RF inhomogeneities. It has been successful in segmenting the brain in every slice from head images acquired from several different MRI scanners, using different resolu ..."
Abstract - Cited by 60 (4 self) - Add to MetaCart
A robust fully automatic method for segmenting the brain from head MR images has been developed, which works even in the presence of RF inhomogeneities. It has been successful in segmenting the brain in every slice from head images acquired from several different MRI scanners, using different

Learning object-class segmentation with convolutional neural networks

by Hannes Schulz, Sven Behnke - in Proceedings of the European Symposium on Artificial Neural Networks (ESANN , 2012
"... Abstract. After successes at image classification, segmentation is the next step towards image understanding for neural networks. We propose a convolutional network architecture that includes innovative elements, such as multiple output maps, suitable loss functions, supervised pretraining, multisca ..."
Abstract - Cited by 14 (4 self) - Add to MetaCart
Abstract. After successes at image classification, segmentation is the next step towards image understanding for neural networks. We propose a convolutional network architecture that includes innovative elements, such as multiple output maps, suitable loss functions, supervised pretraining

Segmentation and measurement of the cortex from 3-D MR images using coupled surfaces propagation

by Xiaolan Zeng, Lawrence H. Staib, Robert T. Schultz, James S. Duncan - IEEE Trans. Med. Imag , 1999
"... Abstract—The cortex is the outermost thin layer of gray matter in the brain; geometric measurement of the cortex helps in understanding brain anatomy and function. In the quantitative analysis of the cortex from MR images, extracting the structure and obtaining a representation for various measureme ..."
Abstract - Cited by 107 (2 self) - Add to MetaCart
Abstract—The cortex is the outermost thin layer of gray matter in the brain; geometric measurement of the cortex helps in understanding brain anatomy and function. In the quantitative analysis of the cortex from MR images, extracting the structure and obtaining a representation for various

Automatic Liver segmentation Using Vector Field Convolution and Artificial Neural Network in MRI Images

by Hassan Masoumi, Ahad Salimi, Hamidreza Sadeghi Madavani
"... Accurate liver segmentation on Magnetic Resonance Images (MRI) is a challenging task especially at sites where surrounding tissues such as spleen and kidney have densities similar to that of the liver and lesions reside at the liver edges. The first and essential step for computer aided diagnosis (C ..."
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algorithm which utilizes a contour algorithm with a Vector Field Convolution (VFC) field as its external force and perceptron neural network. By convolving the edge map generated from the image with the user-defined vector field kernel, VFC is calculated. We use trained neural networks to extract some

Deep neural networks segment neuronal membranes in electron microscopy images

by Dan C. Ciresan, Luca M. Gambardella, Alessandro Giusti, Jürgen Schmidhuber - IN NIPS , 2012
"... We address a central problem of neuroanatomy, namely, the automatic segmentation of neuronal structures depicted in stacks of electron microscopy (EM) images. This is necessary to efficiently map 3D brain structure and connectivity. To segment biological neuron membranes, we use a special type of de ..."
Abstract - Cited by 33 (1 self) - Add to MetaCart
We address a central problem of neuroanatomy, namely, the automatic segmentation of neuronal structures depicted in stacks of electron microscopy (EM) images. This is necessary to efficiently map 3D brain structure and connectivity. To segment biological neuron membranes, we use a special type

using Convolutional Neural Networks

by Dimitri Palaz, Ronan Collobert, Mathew Magimai. -doss, Dimitri Palaz, Ronan Collobert, Mathew Magimai. -doss , 2013
"... In hybrid hidden Markov model/artificial neural networks (HMM/ANN) automatic speech recognition (ASR) system, the phoneme class conditional probabilities are estimated by first extracting acoustic features from the speech signal based on prior knowledge such as, speech perception or/and speech produ ..."
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not be necessary. Motivated from these studies, in the framework of convolutional neural networks (CNNs), this paper investigates a novel approach, where the input to the ANN is raw speech signal and the output is phoneme class conditional probability estimates. On TIMIT phoneme recognition task, we study

Weakly Supervised Object Segmentation with Convolutional Neural Networks

by Pedro H. O. Pinheiro, Ronan Collobert, Pedro O. Pinheiro, Ronan Collobert , 2014
"... Can a machine learn how to segment different objects in real world images without having any prior knowledge about the delineation of the classes? In this paper, we demonstrate that this task is indeed possible. We address the problem by training a Convolutional Neural Networks (CNN) model with weak ..."
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Can a machine learn how to segment different objects in real world images without having any prior knowledge about the delineation of the classes? In this paper, we demonstrate that this task is indeed possible. We address the problem by training a Convolutional Neural Networks (CNN) model
Results 1 - 10 of 741