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Efficient Embedded Deep Neural-Network-based Object Detection Via Joint Quantization and Tiling

Efficient Embedded Deep Neural-Network-based Object Detectio...

引用

ieee International Conference on Artificial intelligence Circuits and Systems (AICAS)

作者： George Plastiras Shahid Siddiqui Christos Kyrkou Theocharis Theocharides KIOS Research and Innovation Center of Excellence

ISBN: (数字)9781728149226

ISBN: (纸本)9781728149233

Embedded visual AI is a growing trend in applications requiring low latency, real-time decision support, increased robustness and security. Visual object detection, a key task in visual data analytics, has enjoyed significant improvements in terms of capabilities and accuracy due to the emergence of Convolutional Neural Networks (CNNs). However, such complex paradigms require heavy computational resources that prevent their deployment on resource-constrained devices, and in particular, impose significant constraints in possible hardware accelerators geared towards such applications. In this work therefore, we investigate how a combination of techniques can lead to efficient visual AI pipelines for resource-constrained object detection. In particular we leverage an efficient search strategy based on a combination of pre-processing mechanisms, that reduce the processing demands of deep network as a counter measure for potential accuracy reduction caused by quantization. The proposed approach enables the detection of objects in higher resolution frames using quantized models, while maintaining the accuracy of full-precision CNN-based object detectors. We illustrate the impact on the accuracy and average processing time using quantization techniques and different tiling approaches on efficient object detection architectures; as a case study, we focus on Unmanned-Aerial- Vehicles (UAVs). Through the proposed methodology, hardware accelerator demands are thereby reduced, leading to both performance benefits and associated power savings.

关键词： Quantization (signal) Detectors Object detection Visualization image resolution Real-time systems Pipelines

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An Efficient Method for Ground Maneuvering Target Refocusing Based on DPT-MFP

An Efficient Method for Ground Maneuvering Target Refocusing...

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ieee International Conference on Electronic Information and Communication Technology (ICEICT)

作者： Mingming Tian Guisheng Liao Shengqi Zhu XiongPeng He Yongjun Liu Yunpeng Li National Key Lab of Radar Signal Processing Xidian University Xi'an China School of Artificial Intelligence Xidian University Xi'an China

ISBN: (数字)9781728190457

ISBN: (纸本)9781728190464

Ground maneuvering targets may be smeared in a synthetic aperture radar (SAR) image due to the Doppler ambiguity, high-order range migration (RM) and Doppler frequency migration (DFM) caused by unknown motion parameters. To address these problems, an efficient method based on discrete polynomial-phase transform (DPT) and matched filtering process (MFP) is developed to refocus the ground maneuvering targets in this paper. Firstly, the DPT is applied to eliminate the quadratic RM, cubic RM and quadratic DFM simultaneously. Then, the matched filtering functions are constructed to remove the residual linear RM and linear DFM. At last, the target energy is well focused by performing the azimuth fast Fourier transform (FFT). The proposed algorithm has high computational efficiency and superior performance of target detection and motion parameters estimation under high signal to noise (SNR) environment. The numerical results prove the effectiveness of the proposed method.

关键词： Parameter estimation Filtering Transforms Radar polarimetry Doppler effect Synthetic aperture radar signal to noise ratio

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Fast Grouped High-order SVD Method for Poisson Noise Removal in Video Sequential images

Fast Grouped High-order SVD Method for Poisson Noise Removal...

引用

symposium of image, signal processing, and Artificial Vision (STSIVA)

作者： Jiajun Fu Jun Zhang School of Science Nanjing University of Science and Technology Nanjing P.R. China

ISBN: (数字)9781728170862

ISBN: (纸本)9781728170879

High-order singular value decomposition (HOSVD) based low-rank tensor approximation has been successfully applied to high-dimensional data with multi-linear structures such as video sequential images and hyperspectral images. In some special imaging environments such as night vision surveillance, video images are usually seriously polluted by Poisson noise, but most current low-rank tensor approximation models only consider Gaussian noise. Moreover, due to the computational limitation of traditional SVD, HOSVD usually has low computational efficiency. In this paper, a low-rank and sparse regularization based tensor approximation model is proposed for Poisson noise removal in video sequential images. Considering diversity of image components in standard tensor unfoldings, we introduce a new multi-group nuclear norm based low-rank tensor prior in this new model which can be computed by grouped SVD. Using the technique of alternating direction method of multipliers(ADMM), we develop a fast parallel algorithm to solve the new model. Numerical results demonstrate our method is 9~10 times faster than traditional HOSVD method and can achieve similar or better results.

关键词： Tensors computational modeling Surveillance Numerical models computational efficiency Standards Singular value decomposition

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DeepFake Detection on Publicly Available Datasets using Modified AlexNet

DeepFake Detection on Publicly Available Datasets using Modi...

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ieee symposium Series on computational intelligence (SSCI)

作者： Daniel Xie Prosenjit Chatterjee Zhipeng Liu Kaushik Roy Edoh Kossi North Carolina School of Science and Math Oak Ridge USA The Citadel Military College of South Carolina Charleston SC North Carolina Agricultural and Technical State University Greensboro U.S.A

ISBN: (数字)9781728125473

ISBN: (纸本)9781728125480

Deep learning has been applied successfully in many areas, including computer vision, natural language processing, cyber physical systems and big data analytics. Recently, a synthesis of deep leaning techniques has been deployed to create fake images and videos that are not easily distinguishable from the real ones; this technology is known as DeepFakes. In this paper, we looked at various DeepFakes related datasets and created a model in order to identify whether a frame of a video is fake or real. This is important as videos can be easily manipulated in a way that can spread misinformation, and that can cause major problems in the world today, especially if the videos have political implications. In order to create a model, we utilize a modified AlexNet constructed of an arrangement of 6 layers: convolution2d, max pooling, dense, flatten, activation and dropout layers. UADFV, FaceForensics++, and Celeb-DF are the 3 datasets used in this research. There are many publicly available datasets, however, we found the UADFV, FaceForensics++ and Celeb-DF to be the most convenient in how the data was formatted. All data for each dataset was organized into videos of varying classes. While the UADFV dataset is straightforward and only has 2 classes: real and fake, the FaceForensics++ dataset looks at the various kinds of video manipulations and has 5 different classes. Our model was able to achieve a 9S.73% accuracy when identifying whether a video is real or fake on the UADFV dataset, a slightly adjusted version was able to accomplish an 87.49% accuracy with the FaceForenisics++dataset, and reached 98.85% accuracy on the Celeb-df dataset.

关键词： Videos Information integrity Faces Face recognition Motion pictures Painting image segmentation

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ISBI 2019 - 2019 ieee International symposium on Biomedical Imaging

ISBI 2019 - 2019 IEEE International Symposium on Biomedical ...

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16th ieee International symposium on Biomedical Imaging, ISBI 2019

ISBN: (纸本)9781538636411

The proceedings contain 408 papers. The topics discussed include: CUDA parallelization of commit framework for efficient microstructure–informed tractography;L1 and L2 norm depth-regularized estimation of the acoustic attenuation and backscatter coefficients using dynamic programming;comparison of different tensor encoding combinations in microstructural parameter estimation;feature space extrapolation for ulcer classification in wireless capsule endoscopy images;detecting prostate cancer using a CNN-based system without segmentation;improving 3D MA-TIRF reconstruction with deconvolution and background estimation;prone to supine surface based registration workflow for breast tumor localization in surgical planning;semi-automatic cell segmentation from noisy image data for quantification of microtubule organization on single cell level;classification as a criterion to select model order for dynamic functional connectivity states in rest-fMRI data;and APCP-net: aggregated parallel cross-scale pyramid network for CMR segmentation.

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Mapping DCNN to a Three Layer Modular Architecture: A Systematic Way for Obtaining Wider and More Effective Network

Mapping DCNN to a Three Layer Modular Architecture: A System...

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ieee International Conference on signal and image processing (ICSIP)

作者： Chowdhury Chowdhury Kai Su Huitao Wang Qiangfu Zhao The University of Aizu System Intelligence Laboratory Aizu-Wakamatsu Japan

ISBN: (数字)9781728168968

ISBN: (纸本)9781728168975

We propose a modular Deep Convolutional Neural Network (DCNN) architecture which has the property of block-like design and re-usage of parameters by certain blocks. We leverage networks from the ResNet family as the backbone for our proposed architecture. The proposed network architecture is composed of three primary blocks, i) common block ii) hidden blocks and iii) output block. The common block mainly focuses on learning low-level features which are afterward fed to ρ hidden blocks at a time, where ρ is the branching factor. The hidden blocks are placed in a row-wise manner and replicated ρ times depending on the computational availability. The output block takes features as an input from the common block and all the hidden blocks during both training and testing phases. In addition, each of the hidden blocks together with the common block is a stand-alone neural network, which implies that we have ρ neural networks coupled in a single framework. During the training phase, in every single epoch, each of the stand-alone neural networks (i.e. common block + hidden block) is trained in Round Robin fashion followed by fine-tuning the whole network graph. Inference can be performed using either the whole network graph or just any sub-graph of it. To substantiate the effectiveness of our proposed neural architecture we performed experiment on CIFAR-10 and CIFAR-100 datasets.

关键词： Training Systematics Neural networks Computer architecture Network architecture Feature extraction Testing

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Welcome message from ieee Indonesia Section Chair

Welcome message from IEEE Indonesia Section Chair

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ieee International Conference on computational intelligence and Cybernetics (CyberneticsCom)

The following topics are dealt with: feature extraction; support vector machines; cooperative communication; medical signal processing; learning (artificial intelligence); finite element analysis; text analysis; image...

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Road Accident Black Spot Localisation using Morphological image processing Methods on Heatmap 18

Road Accident Black Spot Localisation using Morphological Im...

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18th ieee International symposium on computational intelligence and Informatics (CINTI)

作者： Szenasi, Sandor Felde, Imre Kertesz, Gabor Nadai, Laszlo Obuda Univ John von Neumann Fac Informat Budapest Hungary

ISBN: (纸本)9781728111179

Heatmaps are widely used by road safety engineers to visualise accident data (where heat refers to the concentration of accidents). In the subfield of road accident black spot localisation, the high number of similar black spot candidate locations makes this method unreliable and difficult to use. An additional process to convert the clusters of the heatmap into a list of black spot candidates results in an objective, comparable outcome. As we will show, the commonly used thresholding method (considering pixels above a given limit as parts of black spots) has several limitations. This paper presents a novel image processing approach to quantify the raw data of the heatmap. It is possible to give a more accurate splitting between black spot and safe areas, using the morphological function named erosion.

关键词： road safety accidents black spot heat map image processing convolution erosion

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Software Tool for Audio signal Analysis and Automatic Music Transcription 12

Software Tool for Audio Signal Analysis and Automatic Music ...

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12th ieee International symposium on Applied computational intelligence and Informatics (SACI)

作者： Chis, Lucian-Gheorghe Marcu, Marius Dragan, Florin Politehn Univ Timisoara Comp & Informat Technol Dept Timisoara Romania

ISBN: (纸本)9781538646403

Music transcription could be defined as an act of listening to a piece of music and writing down music notation for the piece. In this paper we describe our work on automatic music transcription of single instrument polyphonic music. We developed a software tool that can be used to address all the phases of automatic music transcription process.

关键词： Music Instruments Multiple signal classification Time-frequency analysis Hidden Markov models signal processing algorithms Software tools"

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Unsupervised Hyperspectral Embedding by Learning a Deep Regression Network

Unsupervised Hyperspectral Embedding by Learning a Deep Regr...

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ieee International symposium on Geoscience and Remote Sensing (IGARSS)

作者： Danfeng Hong Jing Yao Jocelyn Chanussot Xiao Xiang Zhu Remote Sensing Technology Institute (IMF) German Aerospace Center (DLR) Germany Signal Processing in Earth Observation (SiPEO) Technical University of Munich (TUM) Munich Germany Univ. Grenoble INRIA CNRS Grenoble INP LJKAlpes Grenoble France

ISBN: (数字)9781728163741

ISBN: (纸本)9781728163758

This work presents a novel hyperspectral embedding technique by learning a deep regression network in an unsupervised fashion, which aims at reducing the computational complexity and storage-costing of traditional manifold embedding methods as well as improving the representation ability of spectral signatures effectively. The proposed method attempts to learn an explicit and unified nonlinear mapping from all patch-wise correspondences of original hyperspectral data and dimension-reduced products generated by some existing manifold learning approaches. This process can be well performed by means of a deep regression model. The learned model is not only capable of locally capturing the manifold structure of the whole hyperspectral image from densely patch-based random sampling but also better applicable to high-efficient out-of-sample inference. Experimental results conducted on the real hyperspectral data demonstrate the effectiveness and superiority of the proposed hyperspectral embedding technique.

关键词： Hyperspectral imaging Manifolds Feature extraction Principal component analysis Mathematical model Object detection Earth

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