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共 1683 条记录，以下是51-60 订阅

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Intelligent Scene Caching to Improve Accuracy for Energy-Constrained Embedded Vision

Intelligent Scene Caching to Improve Accuracy for Energy-Con...

引用

ieee computer society conference on computer Vision and pattern recognition Workshops (CVPRW)

作者： Benjamin Simpson Ekdeep Lubana Yuchen Liu Robert Dick University of Michigan

ISBN: (数字)9781728193601

ISBN: (纸本)9781728193618

We describe an efficient method of improving the performance of vision algorithms operating on video streams by reducing the amount of data captured and transferred from image sensors to analysis servers in a data-aware manner. The key concept is to combine guided, highly heterogeneous sampling with an intelligent Scene Cache. This enables the system to adapt to spatial and temporal patterns in the scene, thus reducing redundant data capture and processing. A software prototype of our framework running on a general-purpose embedded processor enables superior object detection accuracy (by 56%) at similar energy consumption (slight improvement of 4%) compared to an H.264 hardware accelerator.

关键词： Cameras Energy consumption Energy resolution image resolution Streaming media Servers Object detection

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2D-3D CNN based architectures for spectral reconstruction from RGB images 31

2D-3D CNN based architectures for spectral reconstruction fr...

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ieee/CVF conference on computer Vision and pattern recognition (CVPR)

作者： Koundinya, Sriharsha Sharma, Himanshu Sharma, Manoj Upadhyay, Avinash Manekar, Raunak Mukhopadhyay, Rudrabha Karmakar, Abhijit Chaudhury, Santanu CSIR CEERI Pilani 333031 Rajasthan India AcSIR Pilani 333031 Rajasthan India

ISBN: (数字)9781538661000

ISBN: (纸本)9781538661000

Hyperspectral cameras are used to preserve fine spectral details of scenes that are not captured by traditional RGB cameras that comprehensively quantizes radiance in RGB images. Spectral details provide additional information that improves the performance of numerous image based analytic applications, but due to high hyperspectral hardware cost and associated physical constraints, hyperspectral images are not easily available for further processing. Motivated by the performance of deep learning for various computer vision applications, we propose a 2D convolution neural network and a 3D convolution neural network based approaches for hyperspectral image reconstruction from RGB images. A 2D-CNN model primarily focuses on extracting spectral data by considering only spatial correlation of the channels in the image, while in 3D-CNN model the inter-channel co-relation is also exploited to refine the extraction of spectral data. Our 3D-CNN based architecture achieves very good performance in terms of MRAE and RMSE. In contrast to 3D-CNN, our 2D-CNN based architecture also achieves comparable performance with very less computational complexity.

关键词： Hyperspectral imaging image reconstruction Three-dimensional displays Kernel Feature extraction image resolution

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Stacked U-Nets for Ground Material Segmentation in Remote Sensing imagery 31

Stacked U-Nets for Ground Material Segmentation in Remote Se...

引用

ieee/CVF conference on computer Vision and pattern recognition (CVPR)

作者： Ghosh, Arthita Ehrlich, Max Shah, Sohil Davis, Larry Chellappa, Rama Univ Maryland College Pk MD 20742 USA

ISBN: (数字)9781538661000

ISBN: (纸本)9781538661000

We present a semantic segmentation algorithm for RGB remote sensing images. Our method is based on the Dilated Stacked U-Nets architecture. This state-of-the-art method has been shown to have good performance in other applications. We perform additional post-processing.by blending image tiles and degridding the result. Our method gives competitive results on the DeepGlobe dataset.

关键词： Semantics image segmentation Remote sensing Convolution Training Satellites computer architecture

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Identity Aware Synthesis for Cross Resolution Face recognition 31

Identity Aware Synthesis for Cross Resolution Face Recogniti...

引用

ieee/CVF conference on computer Vision and pattern recognition (CVPR)

作者： Singh, Maneet Nagpal, Shruti Vatsa, Mayank Singh, Richa Majumdar, Angshul IIIT Delhi New Delhi India

ISBN: (数字)9781538661000

ISBN: (纸本)9781538661000

Enhancing low resolution images via super-resolution or synthesis algorithms for cross-resolution face recognition has been well studied. Several image processing.and machine learning paradigms have been explored for addressing the same. In this research, we propose Synthesis via Hierarchical Sparse Representation (SHSR) algorithm for synthesizing a high resolution face image from a low resolution input image. The proposed algorithm learns multi-level sparse representation for both high and low resolution gallery images, along with identity aware dictionaries and a transformation function between the two representations for face identification scenarios. With low resolution test data as input, a high resolution test image is synthesized using the identity aware dictionaries and transformation, which is then used for face recognition. The performance of the proposed SHSR algorithm is evaluated on four datasets, including one real world dataset. Experimental results and comparison with seven existing algorithms demonstrate the efficacy of the proposed algorithm in terms of both face identification and image quality measures.

关键词： image resolution Dictionaries Face Face recognition image recognition Training Machine learning

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Feature Pyramid Network for Multi-Class Land Segmentation 31

Feature Pyramid Network for Multi-Class Land Segmentation

引用

ieee/CVF conference on computer Vision and pattern recognition (CVPR)

作者： Seferbekov, Selim Iglovikov, Vladimir Buslaev, Alexander Shvets, Alexey Veeva Syst D-60314 Frankfurt Germany Lyft Inc Engn Ctr Level5 Palo Alto CA 94304 USA Mapbox R&D Ctr Minsk 220030 BELARUS MIT 77 Massachusetts Ave Cambridge MA 02139 USA

ISBN: (数字)9781538661000

ISBN: (纸本)9781538661000

Semantic segmentation is in-demand in satellite imagery processing. Because of the complex environment, automatic categorization and segmentation of land cover is a challenging problem. Solving it can help to overcome many obstacles in urban planning, environmental engineering or natural landscape monitoring. In this paper, we propose an approach for automatic multi-class land segmentation based on a fully convolutional neural network of feature pyramid network (FPN) family. This network is consisted of pre-trained on imageNet Resnet50 encoder and neatly developed decoder. Based on validation results, leader-board score and our own experience this network shows reliable results for the DEEPGLOBE - CVPR 2018 land cover classification sub-challenge. Moreover, this network moderately uses memory that allows using GTX 1080 or 1080 TI video cards to perform whole training and makes pretty fast predictions.

关键词： image segmentation Satellites Training Feature extraction Convolution Semantics computer vision

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Design of a Reconfigurable 3D Pixel-Parallel Neuromorphic Architecture for Smart image Sensor 31

Design of a Reconfigurable 3D Pixel-Parallel Neuromorphic Ar...

引用

ieee/CVF conference on computer Vision and pattern recognition (CVPR)

作者： Bhowmik, Pankaj Pantho, Jubaer Hossain Asadinia, Marjan Bobda, Christophe Univ Arkansas Fayetteville AR 72701 USA

ISBN: (数字)9781538661000

ISBN: (纸本)9781538661000

Power reduction and speed-up of image processing.algorithms remain of high interest as image resolutions continue to increase. Neuromorphic-circuits are inspired by the nervous system aiming to reduce power consumption and speed-up. This paper presents a neuromorphic smart image sensor designed by the pixel-parallel 3D hierarchical architecture with an on-chip attention module. The module dynamically detects regions with relevant information and produces a feedback path to sample those regions at high speed. On the other hand, by sampling non-relevant regions with a low-speed, the sensor can reduce redundancy and enable high-performance computing by ensuring low-power operation. The image sensor is comprised of several hierarchical planes and each plane has small and independent reconfigurable computational units (XPU). In each plane, all XPUs operate in parallel with a different operating speed which gives a pixel-parallel architecture. When the raw image passes through the hierarchical planes, necessary image processing.algorithms are performed in parallel on different planes at a variable clock rate for saving power and reducing redundancy. The goal of this work is to prototype the focal plane image sensor which emulates the brain features. The results show that the prototype achieves remarkable power saving and speed-up at different stages.

关键词： computer architecture image sensors Visualization Program processors Clocks Three-dimensional displays image processing

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Latent Fingerprint image Quality Assessment Using Deep Learning 31

Latent Fingerprint Image Quality Assessment Using Deep Learn...

引用

ieee/CVF conference on computer Vision and pattern recognition (CVPR)

作者： Ezeobiejesi, Jude Bhanu, Bir Univ Calif Riverside Ctr Res Intelligent Syst Riverside CA 92521 USA

ISBN: (数字)9781538661000

ISBN: (纸本)9781538661000

Latent fingerprints are fingerprint impressions unintentionally left on surfaces at a crime scene. They are crucial in crime scene investigations for making identifications or exclusions of suspects. Determining the quality of latent fingerprint images is crucial to the effectiveness and reliability of matching algorithms. To alleviate the inconsistency and subjectivity inherent in feature markups by latent fingerprint examiners, automatic processing.of latent fingerprints is imperative. We propose a deep neural network that predicts the quality of image patches extracted from a latent fingerprint and knits them together to predict the quality of a given latent fingerprint. The proposed approach eliminates the need for manual ROI markup and manual feature markup by latent examiners. Experimental results on NIST SD27 show the effectiveness of our technique in latent fingerprint quality prediction.

关键词： Quality assessment image segmentation Feature extraction Manuals Databases Neural networks

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Jittered Exposures for image Super-Resolution 31

Jittered Exposures for Image Super-Resolution

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ieee/CVF conference on computer Vision and pattern recognition (CVPR)

作者： Li, Nianyi McCloskey, Scott Yu, Jingyi Univ Delaware Newark DE 19716 USA Honeywell ACST Golden Valley MN USA ShanghaiTech Univ Shanghai Peoples R China

ISBN: (数字)9781538661000

ISBN: (纸本)9781538661000

Camera design involves tradeoffs between spatial and temporal resolution. For instance, traditional cameras provide either high spatial resolution (e.g., DSLRs) or high frame rate, but not both. Our approach exploits the optical stabilization hardware already present in commercial cameras and increasingly available in smartphones. Whereas single image super-resolution (SR) methods can produce convincing-looking images and have recently been shown to improve the performance of certain vision tasks, they are still limited in their ability to fully recover information lost due to under-sampling. In this paper, we present a new imaging technique that efficiently trades temporal resolution for spatial resolution in excess of the sensor's pixel count without attenuating light or adding additional hardware. On the hardware front, we demonstrate that the consumer-grade optical stabilization hardware provides enough precision in its static position to enable physically-correct SR. On the algorithm front, we elaborately design the image Stabilization (IS) lens position pattern so that the SR can be efficiently conducted via image deconvolution. Compared with state-of-the-art solutions, our approach significantly reduces the computation complexity of the processing.step while still providing the same level of optical fidelity, especially on quantifiable performance metrics from optical character recognition (OCR) and barcode decoding.

关键词： Lenses Cameras Hardware Spatial resolution Optical imaging Optical sensors

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KCNN: Extremely-Efficient Hardware Keypoint Detection with a Compact Convolutional Neural Network 31

KCNN: Extremely-Efficient Hardware Keypoint Detection with a...

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ieee/CVF conference on computer Vision and pattern recognition (CVPR)

作者： Di Febbo, Paolo Dal Mutto, Carlo Tieu, Kinh Mattoccia, Stefano Aquifi Inc Palo Alto CA 94303 USA Univ Bologna Bologna Italy

ISBN: (数字)9781538661000

ISBN: (纸本)9781538661000

Keypoint detection algorithms are typically based on handcrafted combinations of derivative operations implemented with standard image filtering approaches. The early layers of Convolutional Neural Networks (CNNs) for image classification, whose implementation is nowadays often available within optimized hardware units, are characterized by a similar architecture. Therefore, the exploration of CNNs for keypoint detection is a promising avenue to obtain a low-latency implementation, also enabling to effectively move the computational cost of the detection to dedicated Neural Network processing.units. This paper proposes a methodology for effective keypoint detection by means of an efficient CNN characterized by a compact three-layer architecture. A novel training procedure is proposed for learning values of the network parameters which allow for an approximation of the response of handcrafted detectors, showing that the proposed architecture is able to obtain results comparable with the state of the art. The capability of emulating different detectors allows to deploy a variety of algorithms to dedicated hardware by simply retraining the network. A sensor-based FPGA implementation of the introduced CNN architecture is presented, allowing latency smaller than 1[ms].

关键词： Detectors Training computer architecture Hardware Field programmable gate arrays Convolution Complexity theory

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Real-Time Temporal Superpixels for Unsupervised Remote Photoplethysmography 31

Real-Time Temporal Superpixels for Unsupervised Remote Photo...

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ieee/CVF conference on computer Vision and pattern recognition (CVPR)

作者： Bobbia, Serge Luguern, Duncan Benezeth, Yannick Nakamura, Keisuke Gomez, Randy Dubois, Julien UBFC LE2I EA7508 Dijon France Honda Res Inst Japan Co Ltd 8-1 Honcho Wako Saitama Japan

ISBN: (数字)9781538661000

ISBN: (纸本)9781538661000

Segmentation is a critical step for many computer vision applications. Among them, the remote photoplethysmography technique is significantly impacted by the quality of region of interest segmentation. With the heart-rate estimation accuracy, the processing.time is obviously a key issue for real-time monitoring. Recent face detection algorithms can perform real-time processing. however for unsupervised algorithms, i.e. without any subject detection based on supervised learning, existing methods are not able to achieve real-time on regular platform. In this paper, we propose a new method to perform real-time unsupervised remote photoplethysmograhy based on efficient temporally propagated superpixels segmentation. The proposed method performs the segmentation step by implicitly identifying the superpixel boundaries. Hence, only a fraction of the image is used to perform the segmentation which reduces greatly the computational burden of the process. The segmentation quality remains comparable to state of the art methods while computational time is divided by a factor up to 8 times. The efficiency of the superpixel segmentation allow us to propose a real-time unsupervised rPPG algorithm considering frames of 640x480, RGB, at 25 frames per second on a single core platform. We obtained real-time processing.for 93% of precision at 2.5 beat per minute using our inhouse video database.

关键词： image segmentation Real-time systems Skin image color analysis Sensors Principal component analysis Iterative methods

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