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检索条件"任意字段=2017 Conference on Design and Architectures for Signal and Image Processing, DASIP 2017"

共 891 条记录，以下是321-330 订阅

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design of image Steganography using LSB XOR Substitution Method 6

Design of Image Steganography using LSB XOR Substitution Met...

引用

IEEE International conference on Communication and signal processing (ICCSP)

作者： Arun, Chandni Murugan, Senthil Amrita Univ Amrita Vishwa Vidyapeetham Amrita Sch Engn Dept Elect & Commun Engn Amritapuri India

ISBN: (纸本)9781509038008

This paper is mainly investigates the design of image steganography, where steganography is depicted in different ways. Steganography was first initiated from Greek, although ancient people made a conception to communicate without the knowledge of the third person. Steganography is virtually categorized into 2 words where "stegano/stego" indicate as "covered" (where the messages are being hidden) and "graphy" indicate as writing. Here the method is used for describing the design is the LSB XOR substitution method whereas it improves the security usage. It is one of the more appropriate and simplest method among the other function. It is a method of data hiding method that is been used in favor of security purposes.. This paper is also attached to give a brief idea through algorithm using encryption and decryption technique which showcase the security betterment. Here we have a random 8 bit secret key that initially XOR with the RGB colors which leads to hold the embedding of data sharing and then after replace the LSB of pixel we get extracted data which is an actual message from encoding method.. It has the storage capacity to hide a large number of characteristics than the existing features of data hiding and also data sharing. The Modern steganography is used to provide a developed security on mobiles devices such as ensuring a security on the android and check and verify the latest operating system to add up some betterment in the device.

关键词： image Steganography (least significant bit) LSB method Cryptography Encryption Decryption PSNR (peak to signal noise ratio) OPAP (Optimum pixel adjustment procedure)

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Loop overhead reduction techniques for coarse grained reconfigurable architectures 20

Loop overhead reduction techniques for coarse grained reconf...

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20th Euromicro conference on Digital System design (DSD)

作者： Vadivel, Kanishkan Wijtvliet, Mark Jordans, Roel Corporaal, Henk Eindhoven Univ Technol Dept Elect Engn Eindhoven Netherlands Radboud Univ Nijmegen Dept Astrophys Radboud RadioLab IMAPP POB 9010 NL-6500 GL Nijmegen Netherlands

ISBN: (纸本)9781538621462

Due to their flexibility and high performance, Coarse Grained Reconfigurable Array (CGRA) are a topic of increasing research interest. However, CGRAs also have the potential to achieve very high energy efficiency in comparison to other reconfigurable architectures when hardware optimizations are applied. Some of these optimizations are common for more traditional processors but can also lead to large efficiency gains for reconfigurable architectures. This paper investigates three hardware based loop optimization techniques that can significantly improve the energy efficiency of CGRAs. The three techniques are evaluated on processing kernels from the image processing domain as well as an industrial computer vision application. Energy consumption and area estimates are obtained using a CGRA synthesized with a commercial 40nm library. For the three applied techniques (zero-overhead loop accelerator, single-cycle loop support, and loop buffers) the simulation results show overall energy gains of 6.8% for zero-overhead loop support, 13.2% for ZOLA combined with single-cycle loop support and 18.3% for a combination of all optimizations.

关键词： loop support energy efficiency reconfigurable architectures CGRA

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Segmentation of Blood Vessels in 2D Retinal images with Graphical processing Units 25

Segmentation of Blood Vessels in 2D Retinal Images with Grap...

引用

25th signal processing and Communications Applications conference (SIU)

作者： Akca, Zulbiye Sen, Balia Akca, Eren Ankara Yildirim Beyazit Univ Bilgisayar Muhendisligi Bolumu Ankara Turkey Ankara Univ Bilgisayar Muhendisligi Bolumu Ankara Turkey

ISBN: (纸本)9781509064946

Hypertensive Retinopathy (HR) cause serious damage to human body if not diagnosed early. Therefore, the early diagnosis of this anomaly is very important and complicated process. The most prominent aspect of HR is that the thickness of the blood vessels on the retina change. This change can be calculated with Arteriovenous ratio (AVR). Just before AVR calculation, the veins must be found and extracted on the retina image using some image processing techniques. In this study, the detection of vessels which have a very important place in the diagnosis of HR was performed on GPUs. The retina images taken from the DRIVE database were used as test data and the vessels were successfully detected in a much shorter time and without compromising sensitivity.

关键词： hypertensive retinopathy diagnosis image processing gpu cuda

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Hardware/software co-design of H.264/AVC encoders for multi-core embedded systems

Hardware/software co-design of H.264/AVC encoders for multi-...

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conference on design and architectures for signal and image processing

作者： Tiago Dias Nuno Roma Leonel Sousa INESC-ID Lisbon ISEL-PI Lisbon Technical University of Lisbon IST Lisboa Portugal

This paper presents a multi-core H.264/AVC encoder suitable for implementations in small and medium complexity embedded systems. The proposed structure results from an efficient hardware/software co-design methodology, where the encoder software application is highly optimized and structured in a very modular and efficient manner, so as to allow its most complex and time consuming operations to be offloaded to dedicated hardware accelerators. The considered methodology adopts a simple and efficient core interconnection mechanism to easily allow the inclusion and the removal of such optimized processing cores. Experimental results obtained with the implementation in a Virtex4 FPGA of an H.264/AVC encoder using an ASIP IP core as a ME hardware accelerator have proven the advantages of this methodology. For the considered system, speedup factors greater than 15 were obtained with a very modest increase of the involved hardware resources.

关键词： Hardware Multicore processing Registers Automatic voltage control IP networks Embedded systems

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High level design space exploration of RVC codec specifications for multi-core heterogeneous platforms

High level design space exploration of RVC codec specificati...

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conference on design and architectures for signal and image processing

作者： Christophe Lucarz Ghislain Roquier Marco Mattavelli Ecole Polytechnique Fédérale de Lausanne Lausanne Switzerland

Nowadays, the design flow of complex signal processing embedded systems starts with a specification of the application by means of a large and sequential program (usually in C/C++). As we are entering in the multicore era, sequential programs are no longer the most appropriate way to specify algorithms targeted to run on several processing units. The new ISO/MPEG Reconfigurable Video Coding (RVC) standard is proposing a new paradigm for specifying and designing complex signal processing systems. The RVC standard enables specifying new codecs by assembling blocks, or so called Functional Units (FUs) from a standard Video Tool Library (VTL). Flexibility, reusability, and modularity are the key features of RVC. This new way of specifying algorithms clearly simplifies the task of designing future video coding applications by allowing software and hardware reuse across multiple video coding standards. Specifications are provided in the form of an actor and dataflow-based language called CAL. Although the RVC standard does not imply any specific implementation design flow, it is an appropriate starting point for targeting multiple processing units platforms. This paper describes a new model-driven design flow which considers both algorithm and architecture to map RVC codec specifications onto heterogeneous and multi-core systems.

关键词： Algorithm design and analysis Processor scheduling Transform coding Software Throughput signal processing algorithms Measurement

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A new algorithm for realization of FIR filters using multiple constant multiplications

A new algorithm for realization of FIR filters using multipl...

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conference on design and architectures for signal and image processing

作者： Mohsen Amiri Farahani Eduardo Castillo-Guerra Bruce G. Colpitts Department of Electrical and Computer engineering University of New Brunswick Canada

This paper presents a new common subexpression elimination (CSE) algorithm to realize FIR filters based on multiple constant multiplications (MCMs). This algorithm shares the maximum number of partial terms amongst minimal signed digit (MSD)-represented coefficients. It modifies the iterated matching (ITM) algorithm to share more partial terms in MCMs, which yields a significant logic and, consequently, chip area savings. The employment of the proposed algorithm results in efficient realizations of FIR filters with a fewer number of adders compared to the conventional CSE algorithms. Experimental results demonstrate a reduction up to 22% in the complexity of FIR filters over some conventional CSE algorithms. The proposed algorithm also addresses challenges encountered in resource-constrained applications, which require banks of high-order filters, such as in real-time distributed optical fiber sensor.

关键词： Adders Finite impulse response filter Algorithm design and analysis Pattern matching Filtering algorithms Optical fiber filters Filtering theory

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MULTI-SCALE CONVOLUTIONAL NEURAL NETWORKS FOR CROWD COUNTING 24

MULTI-SCALE CONVOLUTIONAL NEURAL NETWORKS FOR CROWD COUNTING

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24th IEEE International conference on image processing (ICIP)

作者： Zeng, Lingke Xu, Xiangmin Cai, Bolun Qiu, Suo Zhang, Tong South China Univ Technol Sch Elect & Informat Engn Guangzhou Guangdong Peoples R China

ISBN: (纸本)9781509021758

Crowd counting on static images is a challenging problem due to scale variations. Recently deep neural networks have been shown to be effective in this task. However, existing neural-networks-based methods often use the multi-column or multi-network model to extract the scale-relevant features, which is more complicated for optimization and computation wasting. To this end, we propose a novel multi-scale convolutional neural network (MSCNN) for single image crowd counting. Based on the multi-scale blobs, the network is able to generate scale-relevant features for higher crowd counting performances in a single-column architecture, which is both accuracy and cost effective for practical applications. Complemental results show that our method outperforms the state-of-the-art methods on both accuracy and robustness with far less number of parameters.

关键词： Multi-scale CNN scale-relevant architectures crowd counting

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A Novel Optimally Gamma Corrected Intensity Span Maximization Approach for Dark image Enhancement 22

A Novel Optimally Gamma Corrected Intensity Span Maximizatio...

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2017 22nd International conference on Digital signal processing (DSP)

作者： Singh, H. Kumar, A. Balyan, L. K. Singh, G. K. Indian Inst Informat Technol Design & Mfg Jabalpur 482005 India Indian Inst Technol Roorkee 247667 Uttar Pradesh India

ISBN: (纸本)9781538618950

In this paper, a highly adaptive swarm intelligence inspired optimally gamma corrected intensity coverage maximization approach has been proposed for quality enhancement of dark and low contrast remotely sensed images. Various image enhancement techniques have been proposed till date, but in case of dark images, most of them are suffering from saturation effects in higher intensity regions along with information loss due to it;and hence, not appreciated very well. Also, most of the available techniques are unable to utilize the entire allowable intensity range, fully and fruitfully. Here, an optimal gamma value set which itself has been derived through optimal weighting of linearly stretched and interim equalized image so that the allowed range can be maximally occupied. Also, highly qualified objective function has been drafted for employing swarm intelligence over a two-dimensional search space. The proposed approach is found highly capable to recover the vast amount of information, along with contrast and sharpness enhancement when compared with other state-of-the-art methodologies, for remotely-sensed as well as general images.

关键词： sub-exposure clipping gamma correction remotely-sensed images image quality enhancement

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Scene Labeling using Kernel Codebook Encoding 25

Scene Labeling using Kernel Codebook Encoding

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25th signal processing and Communications Applications conference (SIU)

作者： Ates, Hasan F. Sunetci, Sercan Isik Univ Elekt Elekt Muhendisligi Bolumu Istanbul Turkey

ISBN: (纸本)9781509064946

Superpixel based methods have recently shown success in scene segmentation and labeling. In scene labeling, a superpixel algorithm is used first to segment the image into visually consistent small regions;then several feature descriptors are computed and classification is performed for each superpixel. In this paper, Kernel Codebook Encoding (KCB) of superpixel features is proposed. In KCB feature vectors are mapped to multiple codewords in a soft manner, instead of the usual hard quantization. The weights assigned to the codewords are determined by a kernel distance function. KCB method is used for encoding of SIFT features in SuperParsing image parsing algorithm. The developed approach is tested on the SIFT Flow dataset consisting of 2,688 images and 33 classes, and achieves 2.7% increase in parsing accuracy over SuperParsing.

关键词： image parsing superpixel feature encoding

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DISCRIMINATIVE CANONICAL CORRELATION ANALYSIS NETWORK FOR image CLASSIFICATION 24

DISCRIMINATIVE CANONICAL CORRELATION ANALYSIS NETWORK FOR IM...

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24th IEEE International conference on image processing (ICIP)

作者： Gatto, Bernardo B. dos Santos, Eulanda M. Univ Fed Amazonas Inst Comp ICOMP Manaus Amazonas Brazil

ISBN: (纸本)9781509021758

Recently, convolutional neural network has attracted an increasing amount of attention in machine learning and computer vision areas, improving the performance of several related applications. Currently, many deep learning network architectures such as principal component analysis network (PCANet), linear discriminant analysis network (LDANet) and canonical correlation analysis network (CCANet) have been proposed for object and face classification. These architecture solutions have demonstrated high efficiency, with a simple implementation, providing a fast prototyping of efficient image classification applications. However, these solutions take advantage of filters that may not extract high discriminative features in more complicated computer vision problems (e.g. containing high degree of overlap between the distributions of the data). To generate more discriminative information, we introduce a discriminative canonical correlation network (DCCNet), that employs filters constructed from discriminative canonical correlations analysis (DCC). Learning filters from DCC ensures that the network will produce discriminative features, generating more representative and discriminative information. We demonstrate the applicability of DCCNet through experiments on four datasets.

关键词： discriminative canonical correlations analysis PCA-based network image classification

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