May 2, 2016 - sults in many computer vision fields. .... In computer vision, CNNs are most extensively stud- ied in the context of .... repair the artifacts. Network ...
Abstractâ Crowd people counting in crowded scenes is a very ... other, this paper proposes extended Convolutional Neural ... the extended CNN (multi-task CNN framework) for attaining .... layer. Scale-aware model is more complex than the.
using convolutional coding and modulation using BPSK to transmit through channel and added AWGN, the received signal is demodulated and decoded using ...
arXiv:1402.0608v1 [cs.IT] 4 Feb 2014. 1. Variable-length compression allowing errors (extended). Victoria Kostina, Yury Polyanskiy, Sergio Verdú. Abstract.
The input to an algorithm for coding a text is a list of characters. A theory of ... a piece of text and coding the resulting text is done in time linear in the length of the.
Dense blocks are either brow or bcol, depending on the alignment ..... [4] R. Barrett, M. Berry, T. F. Chan, J. Demmel, J. M. Donato, J. Don- garra, V. Eijkhout, R.
transforms, namely RGBâYUV, RGBâYCbCr, are analysed. To explain the necessity of using these color transforms first we should explain basics about color ...
Key words: convolutional neural networks, image compression, JPEG, .... modifications made to the architecture of a traditional deep convolutional neural.
Software and Hardware Requirements - Windows Systems .......................................
...13. Software Installation . ...... Page 35 ...... includes proprietary information
used by Olympus software. Scanalytics ...... Martin, L. C. and B. K. J
Density and friability. The bulk density was determined by pouring pellets into a previously weighed 10 ml graduated glass cylinder. TABLE I - Composition of ...
... and the rotation matrix R2. This allows us to define the pixel positions p1 and p2 in both image planes by λ1p1 = [K1|03] Pw ,. (1) λ2p2 = [K2|03]. [. R2 âR2C2.
Applying 3D animation methods to 3D video compression is not reasonable because 3D video has several different features which make it difficult to apply these ...
Paul Shelley, Xiaobo Li, Bin Han, âA hybrid quantization scheme for image compressionâ, University of Alberta, 2003. [7]. Martha R. Quispe-Ayala, Krista ...
Final submission for Carnegie Mellon University Senior. Thesis .... on audio data or motion capture data, it is not ex- ..... lab library GPUmat (GPyou group, 2010).
Running Title: Deep Learning for Knee MRI Segmentation. Page 2 of 34. 10 E. Doty St. ...... 3D Convolutional Neural Network for Brain Tumor. Segmentation. 27.
John C. Platt. 2. Chris J. C. Burges. 2. 1. University of Washington ..... Second Edition, 1999, Prentice-Hall. [6] E. B. Baum & F. Wilczek, âSupervised Learning of ...
pre-trained deep neural networks were employed, including. AlexNet and ... node detection on CT, brain segmentation, and assessing dia- betic retinopathy ...
Sep 17, 2015 - it. Being able to detect the mood of the driver could help to detect the level of attention, so that ..... hand the eyes of the woman are wide open.
The best schemes for text compression use large models to help them predict which characters will .... algorithm for converting a dictionary ... ferred to as a string, file, text, or input. They ..... free model is often used in conjunction with. Huf
We show that the standard image compression algorithms are not suitable for ... method must be employed in order to accomplish the image transfer .... For decompression of a JPEG file, the reverse processes are performed ..... nonlinear space-domain
Image data is by its nature multidimensional and tend to take up a lot of space ...
The fundamental task of image compression is to reduce the amount of data.
Jul 2, 2011 - [3] Joel E. Cohen and Uriel G. Rothblum. Nonnegative ranks, decompositions, and factorizations of nonnega- tive matrices. Linear Algebra and ...
Text compression is a kind of data compression optimized for text (i.e., based on
a ... Text compression can be faster or simpler than general data compression ...
Aug 3, 2015 - in Equation 1, where xi â Rd represents the d di- mensional word representation for the i-th word in. arXiv:1507.01839v2 [cs.CL] 3 Aug 2015 ...
Index TermsâCompression, Deep Learning, Convolutional. Neural Networks ... accelerators, and optimized algorithms to reduce the number of compute ...
Integrated Systems Laboratory, ETH Zurich, Switzerland — {cavigelli, benini}@iis.ee.ethz.ch
Abstract—After the tremendous success of convolutional neural networks in image classification, object detection, speech recognition, etc., there is now rising demand for deployment of these compute-intensive ML models on tightly power constrained embedded and mobile systems at low cost as well as for pushing the throughput in data centers. This has triggered a wave of research towards specialized hardware accelerators. Their performance is often constrained by I/O bandwidth and the energy consumption is dominated by I/O transfers to off-chip memory. We introduce and evaluate a novel, hardware-friendly compression scheme for the feature maps present within convolutional neural networks. We show that an average compression ratio of 4.4× relative to uncompressed data and a gain of 60% over existing method can be achieved for ResNet-34 with a compression block requiring
