In the data post-processing of BDS/INS integrated navigation, the high precision smoothing algorithm is researched aimed at solving the problem of precision degradation caused by the loss of lock of the BDS receiver f...
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In the data post-processing of BDS/INS integrated navigation, the high precision smoothing algorithm is researched aimed at solving the problem of precision degradation caused by the loss of lock of the BDS receiver for a long time. The RTS smoothing algorithm is analyzed when the BDS signal is interrupted, and the equations are given at the same time experimental program are designed. The results show that RTS is not only able to have a smoothing effect on the navigation solution results, but also can significantly weaken the influence of BDS loss of lock to the integrated navigation system.
Evaluating stereo reconstruction algorithms regardless of camera system and application environment is not sufficient to rate the overall performance of a stereo system. To overcome this the Stereo Evaluation Toolbox ...
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ISBN:
(纸本)9781728151021
Evaluating stereo reconstruction algorithms regardless of camera system and application environment is not sufficient to rate the overall performance of a stereo system. To overcome this the Stereo Evaluation Toolbox (SET) proposes a well-founded selection and comparison approach for stereo systems. We aim at providing one performance score evaluating the generated stereo point cloud, complementary to common benchmarks which solely evaluate stereo algorithms on provided image sets. Using images captured in the desired application environment SET measures and compares performance as interaction of modular camera-algorithm combinations inside a local application scenario. Furthermore an evaluation approach for camera-based visual simultaneous localization and mapping (SLAM) systems is presented for further analysis. We apply SET on the evaluation of different camera systems in 3D reconstruction of indoor and outdoor environments as well as on visual SLAM for person indoor navigation.
A digital watermark is an information or signal inserted into a digital content (image, audio, video, document, software) that can be used to determine its ownership, showing who created or sold it, or to ensure its i...
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ISBN:
(数字)9781728175393
ISBN:
(纸本)9781728175409
A digital watermark is an information or signal inserted into a digital content (image, audio, video, document, software) that can be used to determine its ownership, showing who created or sold it, or to ensure its integrity. Typically, the same watermark is added consistently to mark movie collections. Consequently, media with a visible watermark will be eventually susceptible to the necessity of watermark remission, operation that could be an issue. This paper presents two new and very fast algorithms for watermark detection and removal. The here considered watermarks could be blended, overlaid or added in a continuous form to the digital video on remission treatment. The first of such algorithms use a template matching approach to pre-process the blended watermarks frame-to-frame in the Hue-Saturation-Value (HSV) color space, and then improve the detection using a post-processing step. The second algorithm presents two steps as well. Initially, it estimates the structure of a translucent watermark observing the constancy of its gradient magnitude in the area corresponding to the tenth part of each video. Secondly, it uses a sliding window to detect the watermarked in the frames. The results show that watermark detection by both algorithms has high sensitivity. Moreover, they are very fast when compared with other methods especially those based on deep learning.
The infrared image features are the basis of the automatic target recognition (ATR) algorithm for seeker in the countermeasure environment. The complication of the IR countermeasure environment will inevitably lead to...
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Standard lossy image compression algorithms aim to preserve an image's appearance, while minimizing the number of bits needed to transmit it. However, the amount of information actually needed by a user for downst...
ISBN:
(纸本)9781713845393
Standard lossy image compression algorithms aim to preserve an image's appearance, while minimizing the number of bits needed to transmit it. However, the amount of information actually needed by a user for downstream tasks – e.g., deciding which product to click on in a shopping website – is likely much lower. To achieve this lower bitrate, we would ideally only transmit the visual features that drive user behavior, while discarding details irrelevant to the user's decisions. We approach this problem by training a compression model through human-in-the-loop learning as the user performs tasks with the compressed images. The key insight is to train the model to produce a compressed image that induces the user to take the same action that they would have taken had they seen the original image. To approximate the loss function for this model, we train a discriminator that tries to distinguish whether a user's action was taken in response to the compressed image or the original. We evaluate our method through experiments with human participants on four tasks: reading handwritten digits, verifying photos of faces, browsing an online shopping catalogue, and playing a car racing video game. The results show that our method learns to match the user's actions with and without compression at lower bitrates than baseline methods, and adapts the compression model to the user's behavior: it preserves the digit number and randomizes handwriting style in the digit reading task, preserves hats and eyeglasses while randomizing faces in the photo verification task, preserves the perceived price of an item while randomizing its color and background in the online shopping task, and preserves upcoming bends in the road in the car racing game.
The following topics are dealt with: learning (artificial intelligence); error statistics; probability; convolutional neural nets; radio receivers; speech recognition; relay networks (telecommunication); neural nets; ...
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ISBN:
(数字)9781728188959
ISBN:
(纸本)9781728188966
The following topics are dealt with: learning (artificial intelligence); error statistics; probability; convolutional neural nets; radio receivers; speech recognition; relay networks (telecommunication); neural nets; MIMO communication; audio signal processing.
Diffusion distance is a spectral method for measuring distance among nodes on graph considering global data structure. In this work, we propose a spec-diff-net for computing diffusion distance on graph based on approx...
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Diffusion distance is a spectral method for measuring distance among nodes on graph considering global data structure. In this work, we propose a spec-diff-net for computing diffusion distance on graph based on approximate spectral decomposition. The network is a differentiable deep architecture consisting of feature extraction and diffusion distance modules for computing diffusion distance on image by end-to-end training. We design low resolution kernel matching loss and high resolution segment matching loss to enforce the network's output to be consistent with human-labeled image segments. To compute high-resolution diffusion distance or segmentation mask, we design an up-sampling strategy by feature-attentional interpolation which can be learned when training spec-diff-net. With the learned diffusion distance, we propose a hierarchical image segmentation method outperforming previous segmentation methods. Moreover, a weakly supervised semantic segmentation network is designed using diffusion distance and achieved promising results on PASCAL VOC 2012 segmentation dataset.
images captured in rainy conditions are often corrupted by unexpected rain streaks, which severely degrade the performance of subsequent processes in outdoor computer vision systems. In this paper, we exploit the dire...
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ISBN:
(纸本)9781538662496
images captured in rainy conditions are often corrupted by unexpected rain streaks, which severely degrade the performance of subsequent processes in outdoor computer vision systems. In this paper, we exploit the directional smoothness of rain streaks for the single-image rain streaks removal and propose a convex model that uses the directional total variation (DTV) to characterize the smoothness of rain streaks in arbitrary orientations. The proposed model consists of four terms: the fidelity term, the l(1) norm for the sparsity of rain streaks, and two DTV regularization terms for the directional smoothness and the piecewise smoothness of rain streaks and rain-free backgrounds, respectively. To solve the proposed model, we develop an efficient algorithm based on the alternating direction method of multipliers (ADMM) framework. Extensive experimental results on both synthetic and real rainy images show that our method outperforms the recent state-of-the-art methods visually and quantitatively.
Due to its ill-posed nature, single image dehazing is a challenging problem. In this paper, we propose an end-to-end feature aggregation attention network (FAAN) for single image dehazing. It incorporates the idea of ...
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ISBN:
(数字)9781728163956
ISBN:
(纸本)9781728163963
Due to its ill-posed nature, single image dehazing is a challenging problem. In this paper, we propose an end-to-end feature aggregation attention network (FAAN) for single image dehazing. It incorporates the idea of attention mechanism and residual learning and can adaptively aggregate different level features. In particular, in the proposed FANN, we design a novel block structure consisting of feature attention module, smoothed dilated convolution and local residual learning. The local residual learning allows the less useful information to be bypassed through multiple skip connections. The feature attention module is designed to assign more weight to important features. The smoothed dilated convolution is adopted to enlarge the receptive field without the negative influence of gridding artifacts. The experiments on the RESIDE dataset show that the proposed approach acquires state-of-the-art performance in both qualitative and quantitative measures.
This paper offers a new feature-oriented compression algorithm for flexible reduction of data redundancy commonly found in images and videos streams. Using a combination of image segmentation and face detection techni...
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ISBN:
(数字)9781728175744
ISBN:
(纸本)9781728175751
This paper offers a new feature-oriented compression algorithm for flexible reduction of data redundancy commonly found in images and videos streams. Using a combination of image segmentation and face detection techniques as a preprocessing step, we derive a compression framework to adaptively treat `feature' and `ground' while balancing the total compression and quality of `feature' regions. We demonstrate the utility of a feature compliant compression algorithm (FC-SVD), a revised peak signal-to-noise ratio PSNR assessment, and a relative quality ratio to control artificial distortion. The goal of this investigation is to provide new contributions to image and video processing research via multi-scale resolution and the block-based adaptive singular value decomposition.
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