Andrew Lavin
We derive a new class of fast algorithms for convolutional neural networks using Winograd’s minimal filtering algorithms. Specifically we derive algorithms for network layers with 3×3 kernels, which are the preferred kernel size for image recognition tasks. The best of our algorithms reduces arithmetic complexity up to 4X compared with direct convolution, while using small […]
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Oliver Christen, Edwin Naroska, Alexander Micheel, Shanq-Jang Ruan
This paper deals with the design, detection and recognition of a visual marker which can be detected and recognized over a long distance in realtime on a mobile device. The solution is based on a rotationally symmetric marker, a detection process based on estimated circle and ellipse parameters and a Hidden Markov Model based approach […]
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Yuliang Pu, Jun Peng, Letian Huang, John Chen
Accurate and efficient data classification techniques are of vital importance to many problems, and are rapidly developing in recent decades. K-Nearest Neighbor algorithm (KNN), as one of the most important algorithms, is widely used in text categorization, predictive analysis, data mining and image recognition, etc. To accelerate the algorithm and to optimize the parallel implementation […]
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Carlo D'Eramo
The Deep Boltzmann Machine (DBM) has been proved to be one of the most effective machine learning generative models in discriminative tasks. They’ve been able to overcome other generative, and even discriminative models, on relatively simple tasks, such as digits recognition, and also on more complex tasks such as simple objects recognition. However, there’re only […]
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Ming Zeng, Le T. Nguyen, Bo Yu, Ole J. Mengshoel, Jiang Zhu, Pang Wu, Joy Zhang
A variety of real-life mobile sensing applications are becoming available, especially in the life-logging, fitness tracking and health monitoring domains. These applications use mobile sensors embedded in smart phones to recognize human activities in order to get a better understanding of human behavior. While progress has been made, human activity recognition remains a challenging task. […]
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William Raveane, Maria Angelica Gonzalez Arrieta
We introduce a hybrid system composed of a convolutional neural network and a discrete graphical model for image recognition. This system improves upon traditional sliding window techniques for analysis of an image larger than the training data by effectively processing the full input scene through the neural network in less time. The final result is […]
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Luis A. Alexandre
RGB-D data is getting ever more interest from the research community as both cheap cameras appear in the market and the applications of this type of data become more common. A current trend in processing image data is the use of convolutional neural networks (CNNs) that have consistently beat competition in most benchmark data sets. […]
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Syed Amer Zawad
This paper was conducted to analyze the performance benefits of parallelizing the Adaptive Weighted Sub-patterned Principle Component Analysis (Aw SP PCA) algorithm, given that the algorithm is implemented so as to retain the accuracy from its serialized version. The serialized execution of this algorithm is analyzed first and then compared against its parallel implementation, both […]
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Donald Lloyd van Blommestein
This thesis brings together productivity and risk assessments through innovative design, development and evaluation of a unique system for retrieving and analysing data. In the past, although the link between them is well-documented, these assessments have largely been dealt with as separate antagonist entities. A broad evaluation of the existing traditional and technological support systems […]
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Viragkumar N. Jagtap, Shailendra K. Mishra
Handwriting recognition is having high demand in commercial & academics. In recent years lots of good work has been done on hand written digit recognition to improve accuracy. Handwritten digit recognition system needs larger dataset and long training time to improve accuracy & reduce error rate. Training of Neural Networks for large data sets is […]
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Neelima Gogineni, C Ganga Bhavani, V S Giridhar Akula
Image recognition and segmentation techniques are playing key role in the field of image processing. Present researchers are working on the design concepts of accurate image processing. This paper explains the method for designing of accurate image processing with the help of the principle called automatic construction of tree structural image transformation and graphics processing […]
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Fu Jie Huang
In this work, we describe an application of convolutional networks to object classification and detection in images. The task of image based object recognition is surveyed in the first chapter. Its application in internet advertisement is one of the main motivations of this work. The architecture of the convolutional networks is described in details in […]
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