7,797 research outputs found
Reduced Memory Region Based Deep Convolutional Neural Network Detection
Accurate pedestrian detection has a primary role in automotive safety: for
example, by issuing warnings to the driver or acting actively on car's brakes,
it helps decreasing the probability of injuries and human fatalities. In order
to achieve very high accuracy, recent pedestrian detectors have been based on
Convolutional Neural Networks (CNN). Unfortunately, such approaches require
vast amounts of computational power and memory, preventing efficient
implementations on embedded systems. This work proposes a CNN-based detector,
adapting a general-purpose convolutional network to the task at hand. By
thoroughly analyzing and optimizing each step of the detection pipeline, we
develop an architecture that outperforms methods based on traditional image
features and achieves an accuracy close to the state-of-the-art while having
low computational complexity. Furthermore, the model is compressed in order to
fit the tight constrains of low power devices with a limited amount of embedded
memory available. This paper makes two main contributions: (1) it proves that a
region based deep neural network can be finely tuned to achieve adequate
accuracy for pedestrian detection (2) it achieves a very low memory usage
without reducing detection accuracy on the Caltech Pedestrian dataset.Comment: IEEE 2016 ICCE-Berli
Medical imaging analysis with artificial neural networks
Given that neural networks have been widely reported in the research community of medical imaging, we provide a focused literature survey on recent neural network developments in computer-aided diagnosis, medical image segmentation and edge detection towards visual content analysis, and medical image registration for its pre-processing and post-processing, with the aims of increasing awareness of how neural networks can be applied to these areas and to provide a foundation for further research and practical development. Representative techniques and algorithms are explained in detail to provide inspiring examples illustrating: (i) how a known neural network with fixed structure and training procedure could be applied to resolve a medical imaging problem; (ii) how medical images could be analysed, processed, and characterised by neural networks; and (iii) how neural networks could be expanded further to resolve problems relevant to medical imaging. In the concluding section, a highlight of comparisons among many neural network applications is included to provide a global view on computational intelligence with neural networks in medical imaging
Scalable Compression of Deep Neural Networks
Deep neural networks generally involve some layers with mil- lions of
parameters, making them difficult to be deployed and updated on devices with
limited resources such as mobile phones and other smart embedded systems. In
this paper, we propose a scalable representation of the network parameters, so
that different applications can select the most suitable bit rate of the
network based on their own storage constraints. Moreover, when a device needs
to upgrade to a high-rate network, the existing low-rate network can be reused,
and only some incremental data are needed to be downloaded. We first
hierarchically quantize the weights of a pre-trained deep neural network to
enforce weight sharing. Next, we adaptively select the bits assigned to each
layer given the total bit budget. After that, we retrain the network to
fine-tune the quantized centroids. Experimental results show that our method
can achieve scalable compression with graceful degradation in the performance.Comment: 5 pages, 4 figures, ACM Multimedia 201
Data compression experiments with LANDSAT thematic mapper and Nimbus-7 coastal zone color scanner data
A case study is presented where an image segmentation based compression technique is applied to LANDSAT Thematic Mapper (TM) and Nimbus-7 Coastal Zone Color Scanner (CZCS) data. The compression technique, called Spatially Constrained Clustering (SCC), can be regarded as an adaptive vector quantization approach. The SCC can be applied to either single or multiple spectral bands of image data. The segmented image resulting from SCC is encoded in small rectangular blocks, with the codebook varying from block to block. Lossless compression potential (LDP) of sample TM and CZCS images are evaluated. For the TM test image, the LCP is 2.79. For the CZCS test image the LCP is 1.89, even though when only a cloud-free section of the image is considered the LCP increases to 3.48. Examples of compressed images are shown at several compression ratios ranging from 4 to 15. In the case of TM data, the compressed data are classified using the Bayes' classifier. The results show an improvement in the similarity between the classification results and ground truth when compressed data are used, thus showing that compression is, in fact, a useful first step in the analysis
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