There has been a growing interest in the use of data-driven regularizers to solve inverse problems associated with computational imaging systems. The convolutional sparse representation model has recently gained atten...
详细信息
ISBN:
(纸本)9781479981311
There has been a growing interest in the use of data-driven regularizers to solve inverse problems associated with computational imaging systems. The convolutional sparse representation model has recently gained attention, driven by the development of fast algorithms for solving the dictionary learning and sparse coding problems for sufficiently large images and data sets. Nevertheless, this model has seen very limited application to tomographic reconstruction problems. In this paper, we present a model-based tomographic reconstruction algorithm using a learnt convolutional dictionary as a regularizer. The key contribution is the use of a data-dependent weighting scheme for the l(1) regularization to construct an effective denoising method that is integrated into the inversion using the Plug-and-Play reconstruction framework. Using simulated data sets we demonstrate that our approach can improve performance over traditional regularizers based on a Markov random field model and a patch-based sparse representation model for sparse and limited-view tomographic data sets.
Signal, image and Synthetic Aperture Radar imagery algorithms in recent time are used in a daily routine. Due to huge data and complexity, their processing is almost impossible in a real time. Often imageprocessing a...
详细信息
ISBN:
(纸本)9781538669792
Signal, image and Synthetic Aperture Radar imagery algorithms in recent time are used in a daily routine. Due to huge data and complexity, their processing is almost impossible in a real time. Often imageprocessingalgorithms are inherently parallel in nature, so they fit nicely into parallel architectures multicore Central processing Unit (CPU) and Graphics processing Unit GPUs. In this paper imageprocessingalgorithms were evaluated, which are capable to execute in parallel manner on several platforms CPU and GPU. All algorithms were tested in TensorFlow, which is a novel framework for deep learning, but also for imageprocessing. Relative speedups compared to CPU were given for all algorithms. TensorFlow GPU implementation can outperform multi-core CPUs for tested algorithms, obtained speedups range from 3.6 to 15 times.
Nowadays, process supervision occupies an important place in quality control, cooperative localization in mobile robotics, video and imageprocessing, and intelligent system design, to name a few. Indeed, any failure ...
详细信息
ISBN:
(纸本)9781728103808
Nowadays, process supervision occupies an important place in quality control, cooperative localization in mobile robotics, video and imageprocessing, and intelligent system design, to name a few. Indeed, any failure of such processes can reduce performance and have serious consequences. The development of statistical methods, capable of detecting and locating anomalies in these dynamic systems as quickly as possible, is of real interest. In this context, we have proposed in a previous study a reformulation of the change detection strategy using an entropy-based criterion. Our approach allowed the calculation of an adaptive threshold, unlike the Bayes criterion. In this paper, we propose an improvement of this study by introducing the use of an optimal window of observations. We validate the proposed approach to the Exponentially Weighted Moving Average (EWMA) control charts, which is a commonly used change detection technique. Our strategy is illustrated on a well-known example of the literature. Finally, this windowed entropy-based criterion allows one to design a fault-tolerant fusion methodology, which is experimentally validated from an extended Kalman filter (EKF) in collaborative mobile robotics.
Recently, software-defined satellite has become a research hotspot in the aerospace. Based on an advanced computing platform with open system architecture, researchers can upload software for specific tasks even the s...
详细信息
Segmentation network, which are widely used in various multimedia applications in recent years, are capable of precise imageprocessing in units of pixels, but have higher computational complexity compared to other co...
详细信息
ISBN:
(数字)9781728161648
ISBN:
(纸本)9781728161655
Segmentation network, which are widely used in various multimedia applications in recent years, are capable of precise imageprocessing in units of pixels, but have higher computational complexity compared to other computer vision algorithms such as classification and object detection. In this paper, we propose a technique that applies mixed-precision quantization to the existing YOLACT network, which performs accurate instance segmentation. By adaptively applying quantization according to the parameter size and the effect on the accuracy of modules in YOLACT, it is possible to significantly reduce the network size while maintaining the accuracy of YOLACT as much as possible. The experimental results show the parameter size of the entire network is reduced by 75.4% with only a negligible drop in accuracy of less than 0.1%.
In this work, we propose an extension of the Semi-Global Matching framework for three images from a triplet-stereo rig consisting of a horizontal and vertical camera pair. After calculating the matching costs separate...
详细信息
ISBN:
(数字)9781728165530
ISBN:
(纸本)9781728165547
In this work, we propose an extension of the Semi-Global Matching framework for three images from a triplet-stereo rig consisting of a horizontal and vertical camera pair. After calculating the matching costs separately for both image pairs, these are merged at cost level using cubic spline interpolation. For cost values near the left/bottom image boundaries, we propose an advanced weighting strategy. Subsequently, the fused matching can be used directly for the cost aggregation and disparity *** benefits of the proposed fusion strategy are demonstrated by an evaluation based on synthetic and real-world data. To encourage further comparisons on triple stereo algorithms, the dataset used for evaluation is made publicly available.
imageprocessing and pattern recognitions play an important role in biomedical image analysis. Using these techniques, one can aid biomedical experts to identify the microbial particles in electron microscopy images. ...
详细信息
Adaptive object selection technique based on the results of multi-threshold imageprocessing is considered. The keynote feature of the proposed approach is the use of information about the properties of the selected o...
详细信息
The report proposes an approach to the use of numerical methods based on the theory of linear algebra for processing digital information (signals) in informationcontrol systems of unmanned vehicles under conditions of...
详细信息
ISBN:
(纸本)9781728191782
The report proposes an approach to the use of numerical methods based on the theory of linear algebra for processing digital information (signals) in informationcontrol systems of unmanned vehicles under conditions of a priori uncertainty. This approach allows for a quantitative assessment of the quality of digitized signals (images), both in a system with an intrasystem reference (reference model with a priori known parameters) and for a system with an external reference (reference model with a priori unknown parameters) signals. The proposed mathematical formalization of the process of processing multi-sensory information, in contrast to the known implementation methods, implements an adaptive approach, taking into account external and internal destabilizing influences (interference), both additive and multiplicative forms of representing a useful and useless input signal, taking into account it's the physical nature of the origin. At the same time, the problem of assessing the influence of noise as an unhelpful component in the input signal continues to be relevant [3-4], especially in the sense of testing information - control systems of unmanned vehicles at the stage of their synthesis. The presented results of imitation modeling of digital imageprocessing in the MATLAB system demonstrate the adequacy of the adaptive approach to digital signal processing, taking into account external and intrasystem destabilizing factors. At the same time, simulation modeling was limited to a group of spatial noise models (for example Gauss, etc.). This limitation can be solved by additional representation of destabilizing effects by a group of spatial noise models.
For the first time experimentally investigated the use of a vision system using the technology of depth maps, infrared depth sensor, laser rangefinder, lidar to determine the relief and geometric dimensions of the obj...
详细信息
暂无评论