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检索条件"任意字段=2016 Conference on Design and Architectures for Signal and Image Processing, DASIP 2016"

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Geometrid Larvae Detection Using Contour Feature

Geometrid Larvae Detection Using Contour Feature

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2016 IEEE 13th International conference on signal processing（ICSP2016）

作者： Xianmu Zheng Rongtai Cai College of Photonic and Electronic Engineering Fujian Normal University

Early detection and prediction of pests in field automatically is an important topic in modern agriculture engineering. In this paper, we design a program that can detect geometrid larvae automatically using image processing technology. Firstly, according to color characterization of larvae,we extract brown larvae candidates from image with background of green leaves. We use morphological operation to correct the candidate's regions. Then we use elliptic Fourier transform to represent the contours of larval candidates. The representation is a vector. Thereafter we compare all the possible contour vectors of the larvae with the selected larval contour vector. According to the distance between vectors, we determine whether the contour is a true larval contour. The experimental results show that our method can effectively extract geometrid larvae in tea garden images.

关键词： image processing elliptic Fourier transform pest detection geometrid larvae

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A Block Matching Algorithm for Deriving Quality-Tunable Motion Estimation Architecture

A Block Matching Algorithm for Deriving Quality-Tunable Moti...

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IEEE International conference on Industrial and Information Systems

作者： Bharat Garg G K Sharma ABV-Indian Institute of Information Technology and Management Gwalior India

Energy efficient designs are prime requirements for portable multimedia devices where compression is done for efficient data transmission and store. The video compression requires huge computations in which maximum power is consumed within motion estimator (ME). This paper presents a new VLSI architecture of ME that provides the tradeoff between quality and energy. We propose a novel triangular search block matching (TSBM) algorithm that reduces the significant amount of computations within motion estimator. The proposed TSBM algorithm computes only a few initial search points and conserves power significantly by exploiting the high correlation between consecutive frames. The sub-sampling and threshold techniques are also employed to further reduce the complexity of each search point. The proposed architecture provides improved energy efficiency with acceptable output quality. Simulation results show that proposed algorithm reduces computation complexity by 60.9% and 39.9% over the Three-step and Diamond search algorithms respectively. The sub-sampling technique reduces the sum of absolute difference computation complexity by 50%, and threshold provides further 3.2% reduction with 0.02dB PSNR degradation. Further, the proposed ME architecture provides 77.8% energy saving over the exiting architectures.

关键词： signal processing algorithms Computer architecture Algorithm design and analysis Computational complexity Measurement Motion estimation

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BM3D image denoising using heterogeneous computing platforms

BM3D image denoising using heterogeneous computing platforms

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conference on design and architectures for signal and image processing

作者： Sampsa Sarjanoja Jani Boutellier Jari Hannuksela Center for Machine Vision Research University of Oulu Oulu Finland

Noise reduction is often performed at an early stage of the image processing path. In order to keep the processing delays small in different computing platforms, it is important that the noise reduction is performed swiftly. In this paper, the block-matching and three-dimensional filtering (BM3D) denoising algorithm is implemented on heterogeneous computing platforms using OpenCL and CUDA frameworks. To our knowledge, these implementations are the first successful open source attempts to use GPU computation for BM3D denoising. The presented GPU implementations are up to 7.5 times faster than their respective CPU implementations. At the same time, the experiments illustrate general design challenges in using massively parallel processing platforms for the calculation of complex imaging algorithms.

关键词： Kernel Graphics processing units Filtering Algorithm design and analysis Wiener filters Registers

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Rain removal via shrinkage of sparse codes and learned rain dictionary

Rain removal via shrinkage of sparse codes and learned rain ...

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IEEE International conference on Multimedia and Expo Workshops (ICMEW)

作者： Chang-Hwan Son Xiao-Ping Zhang Department of Electrical and Computer Engineering Ryerson University

ISBN: (纸本)9781509015535

Recently, sparse coding and dictionary learning have been widely used for feature learning and image processing. They can also be applied to the rain removal by learning two types of rain and non-rain dictionaries, and then forcing the sparse codes of the rain dictionary to be zero vectors. However, this approach can generate edge artifacts that appear in the non-rain regions, especially around the edges of objects. Based on this observation, a new approach of shrinking the sparse codes is presented in the paper. To effectively shrink the sparse codes in the rain and non-rain regions, an error map between the input rain image and the reconstructed rain image with the learned rain dictionary is generated. Based on this error map, the sparse codes of the rain and non-rain dictionaries are used together to represent the image structures of objects and avoid the edge artifacts in the non-rain regions. In the rain regions, the correlation matrix between the rain and non-rain dictionaries is calculated and then the sparse codes corresponding to the highly correlated signal-atoms between the rain and non-rain dictionaries are shrunk together to improve the removal of the rain structures. The experimental results show that the proposed approach using the shrinkage of the sparse codes can preserve image structures and avoid the edge artifacts in the non-rain regions, while removing the rain structures in the rain regions.

关键词： Rain Dictionaries image edge detection image reconstruction Algorithm design and analysis image coding Correlation

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Hyperspectral image classification method based on orthogonal NMF and LPP

Hyperspectral image classification method based on orthogona...

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IEEE Instrumentation and Measurement Technology conference

作者： Miao Zhang Peiyuan Jia Yi Shen Fei Shen Department of Control Science and Engineering Harbin Institute of Technology Harbin China Shanghai Aerospace Control Technology Research Institute Shanghai China

ISBN: (纸本)9781467392211

In order to improve the classification accuracy of hyperspectral image, its internal relations and mutual relations should be paid attention to. This paper proposes a compound non-linear dimensionality reduction method with the help of non-negative matrix factorization (NMF) to show the relationships between samples within the same class and locality preserving projections (LPP) to show the relationships between classes. Combining with the advantages of the two algorithms, the proposed method makes data samples within a class closer and data samples between different classes farther after the projection by containing orthogonal constraints for datasets. Together with maximum-likelihood classifiers, all the methods mentioned above constitute the classification method for real hyperspectral data. Experimental results show its advantage in parameter adaption and classification accuracy.

关键词： Matrix decomposition Linear programming Hyperspectral imaging Classification algorithms Gold signal processing algorithms Algorithm design and analysis

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Breast Cancer Histopathological image Classification using Convolutional Neural Networks

Breast Cancer Histopathological Image Classification using C...

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International Joint conference on Neural Networks

作者： Fabio Alexandre Spanhol Luiz S. Oliveira Caroline Petitjean Laurent Heutte Federal University of Technology - Parana Federal University of Parana VRI Lab University of Rouen LITIS Lab Saint Etienne du Rouvray

ISBN: (纸本)9781509006212

The performance of most conventional classification systems relies on appropriate data representation and much of the efforts are dedicated to feature engineering, a difficult and time-consuming process that uses prior expert domain knowledge of the data to create useful features. On the other hand, deep learning can extract and organize the discriminative information from the data, not requiring the design of feature extractors by a domain expert. Convolutional Neural Networks (CNNs) are a particular type of deep, feedforward network that have gained attention from research community and industry, achieving empirical successes in tasks such as speech recognition, signal processing, object recognition, natural language processing and transfer learning. In this paper, we conduct some preliminary experiments using the deep learning approach to classify breast cancer histopathological images from BreaKHis, a publicly dataset available at . We propose a method based on the extraction of image patches for training the CNN and the combination of these patches for final classification. This method aims to allow using the high-resolution histopathological images from BreaKHis as input to existing CNN, avoiding adaptations of the model that can lead to a more complex and computationally costly architecture. The CNN performance is better when compared to previously reported results obtained by other machine learning models trained with hand-crafted textural descriptors. Finally, we also investigate the combination of different CNNs using simple fusion rules, achieving some improvement in recognition rates.

关键词： image classification Breast Cancer feedforward network Transfer (Psychology) Neural network Representation of data domain knowledge Machine Learning images Professional personnel image patch

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Harnessing the discriminatory strength of dictionaries

Harnessing the discriminatory strength of dictionaries

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International conference on Advances in Computing, Communications and Informatics (ICACCI)

作者： Remya R K Menon Neethu V Kini G Athira Krishnan Department of Computer Science and Applications Amrita School of Engineering Amrita Vishwa Vidyapeetham Amritapuri Kollam

ISBN: (纸本)9781509020300

Over the past few years there are many developments in the area of classification in data mining. Classification is a supervised learning method, that maps data into predefined groups or classes. Nowadays classification techniques are extensively used in different applications. In this area most of the research works are done on text, image, signal etc. The main goal of this paper is to use a dictionary-based approach to learn, represent and classify documents. We consider dictionary as a collection of documents and each document in the dictionary is represented as a collection of vectors. An algorithm is also implemented to easily locate a class specific document in the dictionary and if it is not present, update the dictionary. The existing method is based on a dictionary learning algorithm which only improves the document representation based on Singular Value Decomposition (SVD) updation. Since SVD will not be helpful for discrimination of data, so our proposed algorithm is Linear Discriminant Analysis (LDA) for learning a discriminating dictionary. On applying the proposed algorithm on well known dataset, the overall results obtained shows 90% improvement in accuracy. The advantage is that it can be used for both representation as well as classification.

关键词： Dictionaries Algorithm design and analysis Classification algorithms Sparse matrices Matching pursuit algorithms image coding signal processing algorithms

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An extendible design exploration tool for supporting approximate computing techniques

An extendible design exploration tool for supporting approxi...

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International conference on design and Technology of Integrated Systems in Nanoscale Era (DTIS)

作者： Mario Barbareschi Federico Iannucci Antonino Mazzeo Department of Electrical Engineering and Information Technologies University of Naples Federico II CeRICT scrl - Centro Regionale Information Communication Technology

Today, Approximate Computing represents one of the most important breakthrough in many scientific research areas. It exploits the inherent tolerance property of algorithms in order to perform approximate computations that have an acceptable accuracy and, at the same time, better performance than original versions. Previous work in the literature demonstrated the effectiveness of the trade-off between accuracy and performance, such as energy consumption, time and occupied area for integrated circuits, and many approximate computing methodologies were proposed. Unfortunately, introduced approaches fit in specific application domains and a general and systematic methodology to automatically define approximate algorithms is still an open challenge. Taking into account previous considerations, we make a key contribution in this direction by introducing a novel Approximate Computing methodology. Indeed, in this paper, we present an extended version of idea IDEA, a design exploration tool, which makes use of a source-to-source manipulation tool in order to apply code transformations that approximate the computation of a C/C++ algorithm. IDEA searches for configurations of a given algorithm that are characterized by an approximation error which is less than a user-defined threshold. Aiming at show the efficacy of IDEA, we configure it to find approximate variants of an algorithm by applying rounding techniques on numeric operations and we optimize two image processing algorithms, reducing the hardware overhead of their equivalent implementations.

关键词： Approximation algorithms Algorithm design and analysis signal processing algorithms Software algorithms Libraries Hardware

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Session 2: processing architectures for biomedical signal and image processing

Session 2: Processing architectures for biomedical signal an...

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conference on design and architectures for signal and image processing

作者： Pierre Langlois Polytechnique Montreal CA

Biomedical signal and image processing presents a variety of challenges in terms of the dimensionality of the data being processed, nature of the processing involved, the expected data throughput and the peculiar requirements for embedded applications. The processing involved often entails the detection of patterns of interest such as lesions in tissue and abnormal transients or rhythms in ECG or EEG data. The required data throughput can be very large in the case of multi-channel or multi-dimensional time-varying signals. While General Purpose Processor and Graphical processing Unit implementations abound, custom architectures can be necessary to accelerate processing in order for time-critical information to be available to a clinician. For embedded biomedical applications, energy efficiency severely constrains custom processor design. For implantable devices, these constraints are even more stringent. This special session includes two excellent papers that address some of these issues. Both consider the implementation of biomedical signal processing algorithms with a view toward an eventual implantable device. One paper focuses on neural signal decoders to control artificial limbs, and the other is concerned with the analysis of ECG signals, in particular the detection of QRS complexes as a pre-processing step to detect various abnormal heart conditions.

关键词： Electrocardiography image processing Throughput Implants Decoding Lesions

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INFORMATION SECURITY DISPLAY SYSTEM ON ANDROID DEVICE

INFORMATION SECURITY DISPLAY SYSTEM ON ANDROID DEVICE

引用

IEEE Region 10 conference

作者： Yuanchun Chen Ning Liu Guangtao Zhai Zhongpai Gao Ke Gu Institute of Image Communication and Information Processing Shanghai Jiao Tong University

ISBN: (纸本)9781509025985

With the development of the modern technology, there is increasing a lot of important secret information to be protected. In this paper, an information security display system based on spatial psychovisual modulation (SPVM) is introduced on the android mobile platform. SPVM is a new way of information display technology which is conceived as an interplay of signal processing, optoeletronics and psychophysics. Nowadays modern displays support very high pixel density. Meanwhile the human visual system (HVS) cannot distinguish the image signals with spatial frequency more than a certain threshold. By this idea, we can design a new information security display system by utilizing the inconformity between HVS and the modern displays' resolutions. In this system, two type-s of viewers are defined, the first viewers wearing a pair of polarized glasses are authorized to see the secret information, however the second unauthorized naked-eye viewers without glasses can only see the disguise images. Thus, the differen-t contents on the same display can be seen through different viewing devices. What is more, the increasing stereoscopic display technologies drive amounts of polarization based s-patial multiplexing available. So we choose the polarization based stereoscopic screen as the display in this information security display system. The information security display system, as a guidance and concrete implementation of SPVM, attains remarkable performance in our experiments.

关键词： Spatial psychovisual modula-tion(SPVM) information security display human visual system (HVS) display technology

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