472 research outputs found
Joint Visual Denoising and Classification using Deep Learning
Visual restoration and recognition are traditionally addressed in pipeline
fashion, i.e. denoising followed by classification. Instead, observing
correlations between the two tasks, for example clearer image will lead to
better categorization and vice visa, we propose a joint framework for visual
restoration and recognition for handwritten images, inspired by advances in
deep autoencoder and multi-modality learning. Our model is a 3-pathway deep
architecture with a hidden-layer representation which is shared by multi-inputs
and outputs, and each branch can be composed of a multi-layer deep model. Thus,
visual restoration and classification can be unified using shared
representation via non-linear mapping, and model parameters can be learnt via
backpropagation. Using MNIST and USPS data corrupted with structured noise, the
proposed framework performs at least 20\% better in classification than
separate pipelines, as well as clearer recovered images. The noise model and
the reproducible source code is available at
{\url{https://github.com/ganggit/jointmodel}}.Comment: 5 pages, 7 figures, ICIP 201
Word Recognition with Deep Conditional Random Fields
Recognition of handwritten words continues to be an important problem in
document analysis and recognition. Existing approaches extract hand-engineered
features from word images--which can perform poorly with new data sets.
Recently, deep learning has attracted great attention because of the ability to
learn features from raw data. Moreover they have yielded state-of-the-art
results in classification tasks including character recognition and scene
recognition. On the other hand, word recognition is a sequential problem where
we need to model the correlation between characters. In this paper, we propose
using deep Conditional Random Fields (deep CRFs) for word recognition.
Basically, we combine CRFs with deep learning, in which deep features are
learned and sequences are labeled in a unified framework. We pre-train the deep
structure with stacked restricted Boltzmann machines (RBMs) for feature
learning and optimize the entire network with an online learning algorithm. The
proposed model was evaluated on two datasets, and seen to perform significantly
better than competitive baseline models. The source code is available at
https://github.com/ganggit/deepCRFs.Comment: 5 pages, published in ICIP 2016. arXiv admin note: substantial text
overlap with arXiv:1412.339
Biografo: An integrated tool for forensic writer identification
The final publication is available at Springer via http://dx.doi.org/10.1007/978-3-319-20125-2_17The design and performance of a practical integrated tool for writer identification in forensic scenarios is presented. The tool has been designed to help forensic examiners along the complete identification process: from the data acquisition to the recognition itself, as well as with the management of large writer-related databases. The application has been implemented using JavaScript running over a relational database which provides the whole system with some very desirable and unique characteristics such as the possibility to perform all type of queries (e.g., find individuals with some very discriminative character, find a specific document, display all the samples corresponding to one writer, etc.), or a complete control over the set of parameters we want to use in a specific recognition task (e.g., users in the database to be used as control set, set of characters to be used in the identification, size of the ranked list we want as final result, etc.). The identification performance of the tool is evaluated on a real-case forensic database showing some very promising results.This work has been partially supported by the Spanish Dirección General de la Guardia Civil, and projects Contexts (S2009/TIC-1485) from CAM, Bio-Challenge (TEC2009-11186) from Spanish MICINN, BBfor2 (ITN-2008-238803) from the European Commision, and Cátedra UAM-Telefónica
Multi-Channel Scheduling with Optimal Spectrum Channel Hole Filling (MCS-OSHF) for Cognitive Radio Wireless Networks
In this study, a contemporary method of scheduling algorithm has been proposed for working on scheduling of varying size data-frames transmission in CR based wireless networks. The objective of the proposed model is to achieve maximum throughput, and also reduction of loss of dataframes in the transmission. Some of the key elements that are considered in the development of the model are optimal bandwidth and idle channel availability. Using the three level hierarchical approach, the scheduling strategy is constructed. The optimal idle channel allocation, allocation with considerable transmission intervals allocation and optimal multiple channels models are considered at respective levels in the hierarchy in the proposed algorithm. The proposed model while tested under simulated environment in comparison to the other two bench marking models, the outcome depicts that the process is more efficient and supports in improving the overall process of scheduling of data-frames as per the desired objectives of the model
On Discrete Wrapped Cauchy Model
Modeling angular data throws many challenges in practical situations. Good number of circular / semicircular models are developed for modeling continuous circular / angular data. Scant attention was paid in analysis of discrete angular data, in particular, construction of discrete angular models for fitting angular data is not touched so far. Hence an attempt is made to develop method for constructing Discrete l - axial models and study their population characteristics.Keywords: China insurance industry, Foreign fund, Challenge DOI: 10.7176/MTM/9-4-02 Publication date: April 30th 2019
Computational Intelligence In Digital Forensics: Forensic Investigation And Applications
The Series "Studies in Computational Intelligence" publishes new development and advances in the various areas of computational intelligence - quickly and with a high quality. The intent is to cover the theory, applications, and design methods of computational intelligence, as embedded in the fields of engineering, computer science, physics and life science, as well as the methodologies behind them. The series contains monographs, lecture notes and edited volumes in computational intelligence spanning the areas of neural networks, connectionist systems, genetic algorithms, evolutionary computation, artificial intelligence, cellular automata, self-organizing systems, soft computing, fuzzy systems, and hybrid intelligent systems. Of particular value to both the contributors and the readership are the short publication timeframe and the world-wide distribution, which enable both wide and rapid dissemination of research output
- …