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28th SIBGRAPI Conference on graphics, Patterns and images SIBGRAPI

作者： Nogueira, Keiller Miranda, Waner O. dos Santos, Jefersson A. Univ Fed Minas Gerais Dept Comp Sci Belo Horizonte MG Brazil

ISBN: (纸本)9781467379625

The performance of image classification is highly dependent on the quality of extracted features. Concerning high resolution remote image images, encoding the spatial features in an efficient and robust fashion is the key to generating discriminatory models to classify them. Even though many visual descriptors have been proposed or successfully used to encode spatial features of remote sensing images, some applications, using this sort of images, demand more specific description techniques. Deep Learning, an emergent machine learning approach based on neural networks, is capable of learning specific features and classifiers at the same time and adjust at each step, in real time, to better fit the need of each problem. For several task, such image classification, it has achieved very good results, mainly boosted by the feature learning performed which allows the method to extract specific and adaptable visual features depending on the data. In this paper, we propose a novel network capable of learning specific spatial features from remote sensing images, with any pre-processing step or descriptor evaluation, and classify them. Specifically, automatic feature learning task aims at discovering hierarchical structures from the raw data, leading to a more representative information. This task not only poses interesting challenges for existing vision and recognition algorithms, but also brings huge opportunities for urban planning, crop and forest management and climate modelling. The propose convolutional neural network has six layers: three convolutional, two fully-connected and one classifier layer. So, the five first layers are responsible to extract visual features while the last one is responsible to classify the images. We conducted a systematic evaluation of the proposed method using two datasets: (i) the popular aerial image dataset UCMerced Land-use and, (ii) a multispectral high-resolution scenes of the brazilian Coffee Scenes. The experiments show that the pro

关键词： Deep Learning Remote Sensing Feature Learning image Classification Machine Learning High-resolution images

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An Approach to Iris Contact Lens Detection based on Deep image Representations 28

An Approach to Iris Contact Lens Detection based on Deep Ima...

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28th SIBGRAPI Conference on graphics, Patterns and images SIBGRAPI

作者： Silva, Pedro Luz, Eduardo Baeta, Rafael Menotti, David Pedrini, Helio Falcao, Alexandre Xavier Fed Univ Ouro Preto UFOP Dept Comp BR-35400000 Ouro Preto MG Brazil Univ Campinas UNICAMP Inst Comp BR-13083852 Campinas SP Brazil

ISBN: (纸本)9781467379625

Spoofing detection is a challenging task in biometric systems, when differentiating illegitimate users from genuine ones. Although iris scans are far more inclusive than fingerprints, and also more precise for person authentication, iris recognition systems are vulnerable to spoofing via textured cosmetic contact lenses. Iris spoofing detection is also referred to as liveness detection (binary classification of fake and real images). In this work, we focus on a three-class detection problem: images with textured (colored) contact lenses, soft contact lenses, and no lenses. Our approach uses a convolutional network to build a deep image representation and an additional fully-connected single layer with softmax regression for classification. Experiments are conducted in comparison with a state-of-the-art approach (SOTA) on two public iris image databases for contact lens detection: 2013 Notre Dame and IIIT-Delhi. Our approach can achieve a 30% performance gain over SOTA on the former database (from 80% to 86%) and comparable results on the latter. Since IIIT-Delhi does not provide segmented iris images and, differently from SOTA, our approach does not segment the iris yet, we conclude that these are very promising results.

关键词： Iris Biometrics Contact Lens Detection Deep Learning Convolutional Networks

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3D Modeling by Scanning Physical Modifications 28

3D Modeling by Scanning Physical Modifications

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28th SIBGRAPI Conference on graphics, Patterns and images SIBGRAPI

作者： Hattab, Ammar Taubin, Gabriel Brown Univ Sch Engn Providence RI 02912 USA

ISBN: (纸本)9781467379625

3D shape design tends to be a long and tedious process, with the design of a detailed 3D part usually requiring multiple revisions. Fabricating physical prototypes using low cost 3D fabrication technologies at intermediate stages of the design process is now a common practice, which helps the designer discover errors, and to incrementally refine the design. Most often, implementing the required changes directly in the computer model, within the 3D modeling software, is more difficult and time consuming than modifying the physical model directly using hand cutting, caving and sculpting tools, power tools, or machine tools. When one of the two models is modified, the changes need to be transferred to the other model, a process we refer to as synchronization. Changes made to the computer model can be transferred to the physical model by 3d printing a new physical model. In this paper, we address the problem of synchronizing the computer model to changes made in the physical model by 3D scanning the modified physical model, automatically detecting the changes, and updating the computer model. The proposed process comprises algorithms to: 1) register each 3D scan with a previous 3D scan and/or with the 3D representation used by the 3D modeling software;2) detect the changes (subtractive and/or additive);and 3) perform the changes on the 3D computer model.

关键词： 3D registration 3D reverse engineering 3D modeling tangible interface human-computer interaction

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Parallel randomized KD-tree forest on GPU cluster for image descriptor matching

Parallel randomized KD-tree forest on GPU cluster for image ...

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International symposium on Circuits and Systems

作者： Linjia Hu Saeid Nooshabadi Majid Ahmadi Department of Computer Science Michigan Tech Houghton MI USA Department of Electrical and Computer Engineering University of Windsor Windsor ON Canada

ISBN: (纸本)9781479953424

Many high dimensional data mining applications involve the nearest neighbor search (NNS) on a KD-tree. Randomized KD-tree forest enables fast medium and large scale NNS among high dimensional data points. In this paper, we present massively parallel algorithms for the construction of KD-tree forest, and NNS on a cluster equipped with massively parallel architecture (MPA) devices of graphical processing unit (GPU). This design can accelerate the KD-tree forest construction and NNS significantly for the signature of histograms of orientations (SHOT) 3D local descriptors by factors of up to 5.27 and 20.44, respectively. Our implementations will potentially benefit realtime high dimensional descriptors matching.

关键词： graphics processing units Vegetation Clustering algorithms Instruction sets Parallel algorithms Three-dimensional displays three-dimensional displays instruction sets Parallel algorithms graphics processing Unit Clustering algorithms vegetation dimensional data GRAPPER PICK UP Descriptors

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Architecture Exploration for Energy-Efficient Embedded Vision Applications: From General Purpose Processor to Domain Specific Accelerator

Architecture Exploration for Energy-Efficient Embedded Visio...

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IEEE computer Society Annual symposium on VLSI

作者： Maria Malik Farnoud Farahmand Paul Otto Nima Akhlaghi Tinoosh Mohsenin Siddhartha Sikdar Houman Homayoun Department of Electrical and Computer Engineering George Mason University Fairfax County Department of Computer Science and Electrical Engineering University of Maryland Baltimore County Department of Bioengineering George Mason University Fairfax County

ISBN: (纸本)9781467390408

OpenCV applications are computationally intensive tasks among computer vision algorithms. The demand for low power yet high performance real-time processing of OpenCV embedded vision applications have led to developing their customized implementations on state-of-the-art embedded processing platforms. Given the industry move to heterogeneous platforms which integrates single core or multicore CPU with on-chip FPGA accelerators and GPU accelerators, the question of what platform and what implementation, whether hardware or software, is best suited for energy-efficient processing of this class of applications is becoming important. In this paper, we seek to answer this question through a detailed hardware and software implementation of OpenCV applications and methodically measurement and comprehensive analysis of their power and performance on state-of-the-art heterogeneous embedded processing platforms. The results show that in addition to application behavior, the size of image is an important factor in deciding the efficient platform in terms of highest energy-efficiency (EDP) among hardware accelerators on FPGA and software accelerators on GPU and multicore CPUs. While hardware implementation on ZYNQ shown to be the most performance and energy-efficient for image size of 500x500 or less, software GPU implementation found to be the most efficient and achieves highest speedup for larger image sizes. In addition, while for compute intensive vision applications the gap between FPGA, CPU and GPU reduces as the size of image increases, for non-intensive applications, a large performance and EDP gap is observed between the studied platforms, as the size of the image increases.

关键词： graphics processing units Field programmable gate arrays Hardware Multicore processing Filtering algorithms

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Interactive high resolution reconstruction of 3D ultrasound volumes on the GPU

Interactive high resolution reconstruction of 3D ultrasound ...

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IEEE International symposium on Biomedical Imaging

作者： Marwan Abdellah Asem Abdelaziz Ayman Eldeib Biomedical Engineering Department Cairo University Egypt

ISBN: (纸本)9781479923519

Since its inception, ultrasound has been recognized to be one of the most significant technologies in clinical imaging. The rapid and continuous development of ultrasound scanning technologies has added another challenge of rendering relatively large scale 3D volumes with high resolution. Moreover, some applications have required particular high quality ultrasound rendering at interactive frame rate for improved interpretation of the data. Based on the vast computing power of the modern graphics processing Units (GPUs), we present an efficient ultrasound reconstruction pipeline that is capable of rendering high quality and high resolution ultrasound images at interactive frame rates for large scale volumes. The reconstruction results of our rendering pipeline have been applied on a real fetal ultrasound data. The performance benchmarks of our pipeline demonstrate its capability of rendering high quality ultrasound images for 10243 volumes with resolutions of 20482 at interactive frame rates using a recent commodity GPU.

关键词： Rendering (computer graphics) Ultrasonic imaging graphics processing units Pipelines image reconstruction image resolution Imaging

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An Adjustable Error Measure for image Segmentation Evaluation 28

An Adjustable Error Measure for Image Segmentation Evaluatio...

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28th SIBGRAPI Conference on graphics, Patterns and images SIBGRAPI

作者： Linares, Oscar Cuadros Botelho, Glenda Rodrigues, Francisco Neto, Joao Batista Univ Sao Paulo ICMC Sao Carlos SP Brazil UFT Palmas TO Brazil

ISBN: (纸本)9781467379625

Due to the subjective nature of the segmentation process, quantitative evaluation of image segmentation methods is still a difficult task. Humans perceive image objects in different ways. Consequently, human segmentations may come in different levels of refinement, ie, under- and over-segmentations. Popular segmentation error measures in the literature (Arbelaez and OCE) are supervised methods (also called empirical discrepancy methods), in which error is computed by comparing objects in segmentations with a reference (ground-truth) image produced by humans. Since reference images can be many, the key issue for a segmentation error measure is to be consistent in the presence of both under- and over-segmentation. In general, the term consistency refers to the ability of the error measure to be low, when comparing similar segmentations, or high, when faced with different segmentations, while capturing under- or over-segmentations. In this paper we propose a new object-based empirical discrepancy error measure, called Adjustable Object-based Measure (AOM). We introduce a penalty parameter which gives the method the ability to be more (or less) responsive in the presence of over-segmentation. Hence, we extend the notion of consistency so as to include the application's need in the process. Some applications require segmentation to be extremely accurate, hence under- or over-segmentation should be well penalised. Others, do not. By changing the penalty parameter, AOM can deliver more consistent results not only in reference to the under- or over-segmentation issue alone, but also according to the nature of the application. We compare our method with Arbelaez (used as standard measure in the benchmark of Berkeley Segmentation image Dataset) and OCE. Our results show that AOM not only is more consistent in the presence of over-segmentation, but is also faster to compute. Unlike Arbelaez and OCE, AOM also satisfies the metric axiom of symmetry.

关键词： error measure metric evaluation image segmentation

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Unconditionally Stable Shock Filters for image and Geometry processing

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computer graphics FORUM 2015年第5期34卷 201-210页

作者： Prada, F. Kazhdan, M. Johns Hopkins Univ Baltimore MD 21218 USA

This work revisits the Shock Filters of Osher and Rudin [OR90] and shows how the proposed filtering process can be interpreted as the advection of image values along flow-lines. Using this interpretation, we obtain an efficient implementation that only requires tracing flow-lines and re-sampling the image. We show that the approach is stable, allowing the use of arbitrarily large time steps without requiring a linear solve. Furthermore, we demonstrate the robustness of the approach by extending it to the processing of signals on meshes in 3D.

关键词： Categories and Subject Descriptors (according to ACM CCS) I.3.5 [computer graphics]: Computational Geometry and Object Modeling&mdash Hierarchy and geometric transformations I.4.3 [image processing and computer Vision]: Enhancement&mdash Sharpening and deblurring

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Passive dense stereo vision on the Myriad2 VPU

Passive dense stereo vision on the Myriad2 VPU

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IEEE Hot Chips symposium (HCS)

作者： Luca Puglia Mircea Ionică Giancarlo Raiconi David Moloney Universita degli Studi di Salerno Fisciano Campania IT Movidius Ltd. 1st Floor O'Connell Bridge House D'Olier St Dublin D02 Ireland

Presents a collection of slides covering the following topics: passive-dense stereo vision; Myriad2 VPU; direct memory access controller; DMA controller; image matching; and computer vision.

关键词： Stereo vision Cameras graphics processing units computer vision Drones Security

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A portable and unified CPU/GPU parallel implementation of surface normal generation algorithm from 3D terrain data 12th

A portable and unified CPU/GPU parallel implementation of su...

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12th International symposium on Visual Computing, ISVC 2016

作者： Wilson, Brandon Deen, Robert Tavakkoli, Alireza Computation and Advanced Visualization Engineering Lab University of Houston–Victoria VictoriaTX United States Jet Propulsion Lab California Institute of Technology PasadenaCA United States

ISBN: (纸本)9783319508313

The Multi-mission Instrument processing Lab (MIPL) is responsible for developing much of the ground support software for the Mars missions. The MIPL pipeline is used to generate several products from a one mega-pixel image within a 30min time constraint. In future missions, this time constraint will be decreased to five minutes for 20 mega-pixels images, requiring a minimum 120 times speed-up from current operational hardware and software. Moreover, any changes to the current software must preserve the source code’s maintainability and portability for future missions. Therefore, the surface normal generation software has been implemented on a Graphical processing Unit (GPU) through the use of the NVidia CUDA Toolkit and Hemi Library to allow for minimum code complexity. Several changes have been made to Hemi Library to allow for additional optimizations of the GPU code. In addition, several challenges to developing a parallelized GPU implementation of the surface normal generation algorithm are explored, while both tested and prospective solutions to these problems are described. © Springer International Publishing AG 2016.

关键词： graphics processing unit

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