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检索条件"任意字段=IEEE Workshop on Visualization and Machine Vision"

共 120 条记录，以下是71-80 订阅

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Generating reliable video annotations by exploiting the crowd

Generating reliable video annotations by exploiting the crow...

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ieee workshop on Applications of Computer vision (WACV)

作者： Roberto Di Salvo Concetto Spampinato Daniela Giordano Department of Electrical University of Catania Italy

ISBN: (纸本)9781509006427

In computer vision and machine learning, the availability of annotated datasets is of crucial importance for both learning and performance evaluation. However, annotating visual datasets is a tedious and error-prone task and computer vision researchers usually dedicate a large amount of their time for collecting and generating annotations, which most of the time cannot be re-used in other scenarios. In this paper, we propose a simple, but effective, interactive video object segmentation method exploiting large noisy data gathered from crowd of users while playing a web game. Experimental results, carried out on two challenging video benchmarks, show how it is possible to generate reliable object segmentations in videos with a small human effort, achieving an accuracy comparable to the one obtained with manually-labeled annotations and also outperforming state-of-the-art video object segmentation approaches.

关键词： Games Computational modeling Object segmentation Crowdsourcing Computer vision visualization Benchmark testing

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Is image super-resolution helpful for other vision tasks?

Is image super-resolution helpful for other vision tasks?

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ieee workshop on Applications of Computer vision (WACV)

作者： Dengxin Dai Yujian Wang Yuhua Chen Luc Van Gool Computer Vision Lab. ETH Zurich

ISBN: (纸本)9781509006427

Despite the great advances made in the field of image super-resolution (ISR) during the last years, the performance has merely been evaluated perceptually. Thus, it is still unclear whether ISR is helpful for other vision tasks. In this paper, we present the first comprehensive study and analysis of the usefulness of ISR for other vision applications. In particular, six ISR methods are evaluated on four popular vision tasks, namely edge detection, semantic image segmentation, digit recognition, and scene recognition. We show that applying ISR to input images of other vision systems does improve their performance when the input images are of low-resolution. We also study the correlation between four standard perceptual evaluation criteria (namely PSNR, SSIM, IFC, and NQM) and the usefulness of ISR to the vision tasks. Experiments show that they correlate well with each other in general, but perceptual criteria are still not accurate enough to be used as full proxies for the usefulness. We hope this work will inspire the community to evaluate ISR methods also in real vision applications, and to adopt ISR as a pre-processing step of other vision tasks if the resolution of their input images is low.

关键词： Image resolution Testing Standards Training machine vision Interpolation visualization

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Support vector machines with time series distance kernels for action classification

Support vector machines with time series distance kernels fo...

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ieee workshop on Applications of Computer vision (WACV)

作者： Mohammad Ali Bagheri Qigang Gao Sergio Escalera Dalhousie University University of Lar Dalhousie University Universitat de Barcelona

ISBN: (纸本)9781509006427

Despite the outperformance of Support Vector machine (SVM) on many practical classification problems, the algorithm is not directly applicable to multi-dimensional trajectories having different lengths. In this paper, a new class of SVM that is applicable to trajectory classification, such as action recognition, is developed by incorporating two efficient time-series distances measures into the kernel function. Dynamic Time Warping and Longest Common Subsequence distance measures along with their derivatives are employed as the SVM kernel. In addition, the pairwise proximity learning strategy is utilized in order to make use of non-positive semi-definite kernels in the SVM formulation. The proposed method is employed for a challenging classification problem: action recognition by depth cameras using only skeleton data; and evaluated on three benchmark action datasets. Experimental results demonstrate the outperformance of our methodology compared to the state-of-the-art on the considered datasets.

关键词： Kernel Support vector machines Time series analysis Trajectory visualization Time measurement Skeleton

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Atomic scenes for scalable traffic scene recognition in monocular videos

Atomic scenes for scalable traffic scene recognition in mono...

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ieee workshop on Applications of Computer vision (WACV)

作者： Chao-Yeh Chen Wongun Choi Manmohan Chandraker University of Texas Austin NEC Labs America

ISBN: (纸本)9781509006427

The efficacy of advance warning systems (AWS) in automobiles can be significantly enhanced by semantic recognition of traffic scenes that pose a potential danger. However, the complexity of road scenes and the need for real-time solutions pose key challenges. This paper proposes a novel framework for monocular traffic scene recognition, relying on a decomposition into high-order and atomic scenes to meet those challenges. High-order scenes carry semantic meaning useful for AWS applications, while atomic scenes are easy to learn and represent elemental behaviors based on 3D localization of individual traffic participants. Atomic scenes allow our framework to be scalable, since a few of them combine to influence prediction for a wide array of high-order scenes. We propose a novel hierarchical model that captures co-occurence and mutual exclusion relationships while incorporating both low-level trajectory features and high-level scene features, with parameters learned using a structured support vector machine. We propose efficient inference that exploits the structure of our model to obtain real-time rates. Further, we demonstrate experiments in a large-scale dataset for scene recognition that consists of challenging traffic videos of inner-city scenes with ground truth annotations of scene types and object bounding boxes, as well as state-of-the-art 3D object localization outputs. Our experiments show the advantages of our approach relative to several baselines on a novel Inner-City dataset.

关键词： Three-dimensional displays Videos Trajectory Semantics Complexity theory Atomic layer deposition visualization

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Visual recognition of paper analytical device images for detection of falsified pharmaceuticals

Visual recognition of paper analytical device images for det...

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ieee workshop on Applications of Computer vision (WACV)

作者： Sandipan Banerjee James Sweet Christopher Sweet Marya Lieberman Department of Computer Science and Engineering Department of Chemistry and Biochemistry University of Notre Dame

ISBN: (纸本)9781509006427

Falsification of medicines is a big problem in many developing countries, where technological infrastructure is inadequate to detect these harmful products. We have developed a set of inexpensive paper cards, called Paper Analytical Devices (PADs), which can efficiently classify drugs based on their chemical composition, as a potential solution to the problem. These cards have different reagents embedded in them which produce a set of distinctive color descriptors upon reacting with the chemical compounds that constitute pharmaceutical dosage forms. If a falsified version of the medicine lacks the active ingredient or includes substitute fillers, the difference in color is perceivable by humans. However, reading the cards with accuracy takes training and practice, which may hamper their scaling and implementation in low resource settings. To deal with this, we have developed an automatic visual recognition system to read the results from the PAD images. At first, the optimal set of reagents was found by running singular value decomposition on the intensity values of the color tones in the card images. A dataset of cards embedded with these reagents is produced to generate the most distinctive results for a set of 26 different active pharmaceutical ingredients (APIs) and excipients. Then, we train two popular convolutional neural network (CNN) models, with the card images. We also extract some "hand-crafted" features from the images and train a nearest neighbor classifier and a non-linear support vector machine with them. On testing, higher-level features performed much better in accurately classifying the PAD images, with the CNN models reaching the highest average accuracy of over 94%.

关键词： Drugs Image color analysis Biomedical imaging Chemicals Feature extraction visualization

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Scotopic Visual Recognition 15

Scotopic Visual Recognition

引用

ieee International Conference on Computer vision workshops

作者： Chen, Bo Perona, Pietro CALTECH Pasadena CA 91125 USA

ISBN: (纸本)9780769557205

Recognition from a small number of photons is important for biomedical imaging, security, astronomy and many other fields. We develop a framework that allows a machine to classify objects as quickly as possible, hence requiring as few photons as possible, while maintaining the error rate below an acceptable threshold. The framework also allows for a dynamic speed versus accuracy tradeoff. Given a generative model of the scene, the optimal tradeoff can be obtained from a self-recurrent deep neural network. The generative model may also be learned from the data. We find that MNIST classification performance from less than 1 photon per pixel is comparable to that obtained from images in normal lighting conditions. Classification on CIFAR10 requires 10 photon per pixel to stay within 1% the normal-light performance.

关键词： Computational modeling Erbium Error analysis Imaging Photonics Time factors visualization

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An automatic rebar splitting system based on two-level of the chain transmission

An automatic rebar splitting system based on two-level of th...

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ieee International Conference on Cyber Technology in Automation, Control, and Intelligent Systems

作者： Yichi Zhang Min Jiang Yang Wu Xiaofeng Zhou Shenyang Institute of Automation Chinese Academy of Sciences Shenyang China West Virginia University Morgantown USA Wuxi CAS Ubiquitous Information Technology R&D Center Co. Ltd Wuxi China

This paper proposed an automatic rebar splitting system counting by machine vision based on two-level transmission. Compared with other existing systems, this system significantly reduces the space requirements for installation, can use the space of rebar workshop more effectively. This system uses efficient algorithm to solve counting problems caused adhesion and sliding between rebar. By improving the sub-steel machine, this system uses the two-step rebar splitting and visual verification technology to split stacked rebar exactly. This system makes little change on production line and runs fast, accurately, easily to be maintained. It not only meets the needs of production but greatly improve the efficiency of the existing counting system, which is fit for space limited rebar workshop.

关键词： Steel Bars visualization Optical imaging machine vision

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Scotopic Visual Recognition

Scotopic Visual Recognition

引用

International Conference on Computer vision workshops (ICCV workshops)

作者： Bo Chen Pietro Perona California Institute of Technology

ISBN: (纸本)9781467397124

关键词： Photonics visualization Computational modeling Imaging Error analysis Time factors Erbium

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An Evaluation of Supervised, Novelty-Based and Hybrid Approaches to Fall Detection Using Silmee Accelerometer Data

An Evaluation of Supervised, Novelty-Based and Hybrid Approa...

引用

International Conference on Computer vision workshops (ICCV workshops)

作者： Aneta Lisowska Gavin Wheeler Victor Ceballos Inza Ian Poole Toshiba Medical Visualization Systems Europe Ltd. Edinburgh UK

Elderly people often experience a fear of falling. A reliable fall detector could increase their confidence in receiving prompt help after a fall, thus reducing their mental distress. A wearable sensors such as Toshiba's Silmee device can gather accelerometer data, which can be used to detect falls. We collected data from 20 volunteers wearing Silmee during simulated falls and activities of daily living (ADL). This gave 168 fall and 375 ADL recordings. We used these recordings in three experiments conducted to compare the performance of machine learning techniques for the detection of falls from accelerometer data. These experiments evaluate supervised methods, novelty based fall detection techniques, and finally our proposed hybrid techniques which use supervised methods for feature learning, but can be applied in the context of novelty detection. We found that the best performing supervised method was the Convolutional Neural Network (CNN) and the best performing unsupervised method was the one-class Nearest Neighbour Classifier. The best performing hybrid approach resulted from a combination of the CNN and the one-class Support Vector machine. It draws on the strengths of the CNN (appropriate feature learning) and may offer more accurate real world fall identification.

关键词： Detectors Feature extraction Support vector machines Neural networks Accelerometers Senior citizens Training

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Selective Pooling Vector for Fine-Grained Recognition

Selective Pooling Vector for Fine-Grained Recognition

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ieee workshop on Applications of Computer vision (WACV)

作者： Guang Chen Jianchao Yang Hailin Jin Eli Shechtman Jonathan Brandt Ton X. Han University of Missouri Columbia MO USA Adobe Research San Jose CA USA

ISBN: (纸本)9781479966844

We propose a new framework for image recognition by selectively pooling local visual descriptors, and show its superior discriminative power on fine-grained image classification tasks. The representation is based on selecting the most confident local descriptors for nonlinear function learning using a linear approximation in an embedded higher dimensional space. The advantage of our Selective Pooling Vector over the previous state-of-the-art Super Vector and Fisher Vector representations, is that it ensures a more accurate learning function, which proves to be important for classifying details in fine-grained image recognition. Our experimental results corroborate this claim: with a simple linear SVM as the classifier, the selective pooling vector achieves significant performance gains on standard benchmark datasets for various fine-grained tasks such as the CMU Multi-PIE dataset for face recognition, the Caltech-UCSD Bird dataset and the Stanford Dogs dataset for fine-grained object categorization. On all datasets we outperform the state of the arts and boost the recognition rates to 96.4%, 48.9%, 52.0% respectively.

关键词： Vectors Image recognition Encoding visualization Support vector machine classification Face recognition Approximation methods

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