In situ Microscopy (ISM) is an optical non-invasive technique to monitor cells in bioprocesses in real-time. Pichia pastoris is one of the most promising protein expression systems. This yeast combines fast growth on ...
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In situ Microscopy (ISM) is an optical non-invasive technique to monitor cells in bioprocesses in real-time. Pichia pastoris is one of the most promising protein expression systems. This yeast combines fast growth on simple media and important eukaryotic features such as glycosylation. In this work, the ISM technology was applied to Pichia pastoris cultivations for online monitoring of the cell concentration during cultivation. Different ISM settings were tested. The acquired images were analyzed with two imageprocessingalgorithms. In seven cultivations the cell concentration was monitored by the applied algorithms and offline samples were taken to determine optical density (OD) and dry cell mass (DCM). Cell concentrations up to 74 g/L dry cell mass could be analyzed via the ISM. Depending on the algorithm and the ISM settings, an accuracy between 0.3 % and 12 % was achieved. The overall results show that for a robust measurement a combination of the two described algorithms is required. (C) 2016 Elsevier B.v. All rights reserved.
A student-built unmanned aerial system (UAS) was developed by the University of Hawaii Drone Technologies team for the 2017 Association for Unmanned vehicle systems International (AUvSI) Student Unmanned Aerial System...
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A student-built unmanned aerial system (UAS) was developed by the University of Hawaii Drone Technologies team for the 2017 Association for Unmanned vehicle systems International (AUvSI) Student Unmanned Aerial System (SUAS) competition, which simulates a search-and-rescue (SAR) mission. The UAS comprises a fixed-wing airframe integrated with flight control and communication components, and is capable of autonomous waypoint navigation, in-flight data transfer, aerial image capture with onboard imageprocessing, and aerial payload delivery. The UAS is capable of executing a 30-minute SAR mission in search of a simulated lost hiker. SAR tasks include autonomously navigating to a designated area, conducting a search for alphanumeric targets over a 370,000-m~2 search area, and autonomously dropping an 8-oz care package to the lost hiker. image-processing and computer-vision algorithms can correctly sense and identify the alphanumeric targets with 75% accuracy.
The usage of video surveillance systems increases more and more every year and protecting people privacy becomes a serious concern. In this paper, we present ASePPI, an Adaptive Scrambling enabling Privacy Protection ...
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The usage of video surveillance systems increases more and more every year and protecting people privacy becomes a serious concern. In this paper, we present ASePPI, an Adaptive Scrambling enabling Privacy Protection and Intelligibility. It operates in the DCT domain within the H.264 standard. For each residual block of the luminance channel inside the region of interest, we encrypt the coefficients. Whereas encrypted coefficients appear as noise in the protected image, the DC value is dedicated to restore some of the original information. Thus, the proposed approach automatically adapts the level of protection according to the resolution of the region of interest. Comparing to existing methods, our framework provides better privacy protection with some flexibilities on the appearance of the protected version yielding better visibility of the scene for monitoring. Moreover, the impact on the source coding stream is negligible. Indeed, the results demonstrate a slight decrease in the quality of the reconstructed images and a small percentage of bits overhead.
Compressed sensing became a vital tool for image or signal reconstruction with less number of samples compared with the Nyquist rate. Among the existing algorithms for reconstruction of an image using compressed sensi...
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ISBN:
(纸本)9781509044429
Compressed sensing became a vital tool for image or signal reconstruction with less number of samples compared with the Nyquist rate. Among the existing algorithms for reconstruction of an image using compressed sensing, orthogonal matching pursuit algorithm is cost effective in terms of computational complexity. This algorithm provides a solution for overdetermined and underdetermined systems by minimizing the error functions using least square. This work concentrates on the construction of dictionary which can be used to solve the sparsity based image denoising problem. In this paper, we constructed the dictionary using least square solution subjected to thresholding conditions such as hard, soft and semi-soft. Orthogonal matching pursuit (OMP) algorithm avoids the selection of the same atom in every iteration, due to the existence of orthogonal property between the residue and the atom selected from the dictionary. Thus, OMP algorithm results in precise image reconstruction. The proposed method is validated on four standard test images, such as Lena, Boat, Barbara and Cameraman with different noises such as salt & pepper noise, Gaussian noise and speckle noise with varying the percentage of noise level from 5% to 40%. Obtained results are evaluated by the quality metric peak-to-signal-noise ratio (PSNR) and compared with the existing wavelet based sparse image denoising. The experimental evaluation shows that the proposed method is better applicable to remove the speckle noise and salt & pepper noise when compared with the existing wavelet based sparse image denoising.
Patient imaging systems, such as PET imaging systems, for example, may suffer from the introduction of artificially introduced noise. This noise is, typically, introduced during iterations of reconstruction algorithms...
标准号:
WO2017174627(A1)
Patient imaging systems, such as PET imaging systems, for example, may suffer from the introduction of artificially introduced noise. This noise is, typically, introduced during iterations of reconstruction algorithms, such as the least-squares algorithms, which attempts to recreate a 2D or a 3D image from raw acquisition information. The noise appears as "hot-spots" in the reconstructed image. Approaches to address these artefacts use filtering approaches. Typically, a least-squares reconstruction is supplemented with a penalty term, an approach known as "Relative Difference Penalty". The penalty parameter causes the reconstruction algorithm to filter more or less strongly at certain regions of the reconstruction. The present application proposes an approach which supplements the penalty term with continuous probability information about the likelihood of an edge being present in a portion of an image.
This work introduces an Artificial Neuro-Fuzzy Inference System functioning as a selector of color constancy algorithms for the enhancement of dark images. The system selects among three algorithms, the White-Patch, t...
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ISBN:
(纸本)9781509008704
This work introduces an Artificial Neuro-Fuzzy Inference System functioning as a selector of color constancy algorithms for the enhancement of dark images. The system selects among three algorithms, the White-Patch, the Gray-World and the Gray-Edge according to real content of an image. These three algorithms have been considered due to their simplicity and accurate remotion of the illuminant, further showing an outstanding color enhancement on images. The diverse image features are involved in the selection process, so the design of selector system is not a trivial task. For this reason we developed a fuzzy rule based system to model the information through simple rules. While addressing the problem of dark image enhancement this approach can handle large amount of data and is tolerant to ambiguity.
Signal reconstruction from magnitude-only measurements presents a long-standing problem in signal processing. In this contribution, we propose a phase (re)construction method for filter banks with uniform decimation a...
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Signal reconstruction from magnitude-only measurements presents a long-standing problem in signal processing. In this contribution, we propose a phase (re)construction method for filter banks with uniform decimation and controlled frequency variation. The suggested procedure extends the recently introduced phase-gradient heap integration and relies on a phase-magnitude relationship for filter bank coefficients obtained from Gaussian filters. Admissible filter banks are modeled as the discretization of certain generalized translation-invariant systems, for which we derive the phase-magnitude relationship explicitly. The implementation for discrete signals is described and the performance of the algorithm is evaluated on a range of real and synthetic signals.
image defogging is widely used in many outdoor working systems. However, owing to the Jack of enough information to solve the equation of image degradation model, existing restoration methods generally introduce some ...
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image defogging is widely used in many outdoor working systems. However, owing to the Jack of enough information to solve the equation of image degradation model, existing restoration methods generally introduce some parameters and set these values fixed. Inappropriate parameter setting will lead to difficulty in obtaining the best defogging results for different input foggy images. This letter proposes a novel defogging parameter value selection algorithm based on genetic algorithm (GA). We mainly focus on the way to select optimal parameter values for image defogging. The proposed method is applied to two representative defogging algorithms by selecting the two main parameters and optimizing them using the genetic algorithm. An assessment index of image defogging effect is used in the proposed method as the fitness function of the genetic algorithm. Thus, these parameters may be adaptively and automatically adjusted for the defogging algorithms. A comparative study and qualitative evaluation demonstrate that the better quality results are obtained by using the proposed method. (C) 2016 Elsevier B.v. All rights reserved.
Synthetic aperture radar (SAR) is a widely used technique suited for real-time and all-weather imaging of natural surfaces and artificial objects. To improve image resolution and increase accuracy of radar cross secti...
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Synthetic aperture radar (SAR) is a widely used technique suited for real-time and all-weather imaging of natural surfaces and artificial objects. To improve image resolution and increase accuracy of radar cross section estimation the problem of statistical synthesis of signal processing algorithm in SAR is solved. Proposed method allows to form images with super-resolution in azimuth and range. Synthesis is performed using modern theory of radio engineering systems statistical optimization.
Traditional background subtraction algorithms assume the camera is static and are based on simple per-pixel models of scene appearance. This leads to false detections when the camera moves. While this can sometimes be...
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Traditional background subtraction algorithms assume the camera is static and are based on simple per-pixel models of scene appearance. This leads to false detections when the camera moves. While this can sometimes be addressed by online image registration, this approach is prone to dramatic failures and long-term drift. We present a novel background subtraction algorithm designed for pan-tilt-zoom cameras that overcomes this challenge without the need for explicit image registration. The proposed algorithm automatically trains a discriminative background model, which is global in the sense that it is the same regardless of image location. Our approach first extracts multiple features from across the image and uses principal component analysis for dimensionality reduction. The extracted features are then grouped to form a Bag of Features. A global background model is then learned from the bagged features using Support vector Machine. The proposed approach is fast and accurate. Having a single global model makes it computationally inexpensive in comparison to traditional pixel-wise models. It outperforms several state-of-the-art algorithms on the CDnet 2014 pan-tilt-zoom and baseline categories and Hopkins155 dataset. In particular, it achieves an F-Measure of 75.41% on the CDnet dataset PTZ category, significantly better than the previously reported best score of 62.07%. These results show that by removing the coupling between detection model and spatial location, we significantly increase the robustness to camera motion. (C) 2016 Elsevier B.v. All rights reserved.
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