In the recent days, wide range of research has been carried out on visual enhancement of under images in submarine and military operations to discover submerged structural designing and sea floor exploration. But, div...
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ISBN:
(纸本)9781538695333
In the recent days, wide range of research has been carried out on visual enhancement of under images in submarine and military operations to discover submerged structural designing and sea floor exploration. But, diving in the deep ocean for a long time has increased the difficulties for analysis of underwater images. Further, other factors such as scattering resulting from presence of particles inside the water and blurring effects reduce the quality of images being captured by underwater optic camera. There are several algorithms have been introduced to improve the visual quality of deep water images. Therefore, in this project, a novel algorithm based on bidirectional Empirical Mode Decomposition (BEMD) to enhance the visual quality of the underwater images will be implemented and comparison of data with conventional enhancement technique will be illustrated. The implementation will be done using MATLAB software.
Semantic background subtraction (SBS) has been shown to improve the performance of most background subtraction algorithms by combining them with semantic information, derived from a semantic segmentation network. Howe...
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ISBN:
(数字)9781728163956
ISBN:
(纸本)9781728163963
Semantic background subtraction (SBS) has been shown to improve the performance of most background subtraction algorithms by combining them with semantic information, derived from a semantic segmentation network. However, SBS requires high-quality semantic segmentation masks for all frames, which are slow to compute. In addition, most state-of-the-art background subtraction algorithms are not real-time, which makes them unsuitable for real-world applications. In this paper, we present a novel background subtraction algorithm called Real-Time Semantic Background Subtraction (denoted RT-SBS) which extends SBS for real-time constrained applications while keeping similar performances. RT-SBS effectively combines a real-time background subtraction algorithm with high-quality semantic information which can be provided at a slower pace, independently for each pixel. We show that RT-SBS coupled with ViBe sets a new state of the art for real-time background subtraction algorithms and even competes with the non real-time state-of-the-art ones. Note that we provide python CPU and GPU implementations of RT-SBS at https://***/cioppaanthony/rt-sbs.
image segmentation is a necessary method in imageprocessing. It is nothing but partitioned an image into several parts called segments. It has applications like image compression;because of this type of application, ...
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Aiming at the task requirements of a small fast-moving test vehicle with small space, short flight support interval and real-time task evaluation, at the same time, the future flight test is faced with many test param...
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ISBN:
(数字)9781728160672
ISBN:
(纸本)9781728160689
Aiming at the task requirements of a small fast-moving test vehicle with small space, short flight support interval and real-time task evaluation, at the same time, the future flight test is faced with many test parameters, wide distribution of test systems, and high real-time data requirements. To solve the rapid acquisition test evaluation data in a specific environment, we design distributed system, data analysis and fusion technology, embedded parallel computing and other technologies to realize the parallel computing and real-time processing on airborne data. The calculated data results are directly telemetry or recorded for real-time monitoring and rapid assessment of missions. The design meets the requirements of rapid real-time evaluation during the flight test and improves the flight test efficiency.
The existing local stereo matching algorithms have some problems such as high noise sensitivity, low matching accuracy in occlusion regions, and long matching time, while the traditional Minimum Spanning Tree (MST) al...
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ISBN:
(数字)9781728165172
ISBN:
(纸本)9781728165189
The existing local stereo matching algorithms have some problems such as high noise sensitivity, low matching accuracy in occlusion regions, and long matching time, while the traditional Minimum Spanning Tree (MST) algorithm can simplify the cost aggregation step and improve the matching accuracy. However, for texture-less regions and edge regions, the matching effect of MST is still not ideal. In order to overcome these shortcomings, a stereo matching algorithm based on Improved Minimum Spanning Tree (IMST) is proposed in this paper. Firstly, based on the MST algorithm, our method transforms the original image into the image of minimum spanning tree, the cost aggregation of each tree node can be represented by the weight value and the matching cost of this node, and verifies the simplicity of the MST algorithm; Secondly, based on bilateral filtering, the grey similarity factor is redesigned to enhance the adaptive ability of the algorithm to the noise standard deviation to optimize the aggregation cost based on the MST; Thirdly, a separable bilateral filter is used to improve the processing speed while preserving the edge-preserving effect of the algorithm in the disparity refinement phase. Finally, based on the Middlebury Stereo Datasets, the pro-posed algorithm is compared with the Minimum Spanning Tree algorithm (MST) algorithm in three aspects: disparity map effect, matching accuracy and processing time. Compared with other algorithms, the proposed algorithm has better matching accuracy and shorter processing time.
India is the cultivating country and our country is the biggest maker in agricultural products. So, we have to classify and exchange our agricultural products. Manual arranging is tedious and it requires works. The au...
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ISBN:
(数字)9781728151977
ISBN:
(纸本)9781728151984
India is the cultivating country and our country is the biggest maker in agricultural products. So, we have to classify and exchange our agricultural products. Manual arranging is tedious and it requires works. The automatic grading system requires less time for grading of the agricultural products. imageprocessing technique is helpful in examination and evaluating the products. In this paper we proposed a vegetable disease detection system for recognizing diseased vegetables. Here we utilize the imageprocessing system for reviewing the vegetables. Vegetables are recognized dependent on their features. The features are color, shape, size, texture. We extract these features utilizing algorithms to distinguish the vegetables. We develop a recognition system for 2D input images. The main aim of this work is detecting infected vegetable based on features with K-means clustering algorithm. Algorithm includes three main steps namely enhancement, segmentation and classification. Vegetable samples are collected as images from high resolution camera and data acquisition is carried out for database preparation. The image segmentation process is based on pixel of the image and is applied to get the segmented and infected vegetables using K-Means Clustering algorithm. The image classification is based on Support Vector Machine (SVM) which perform supervised leaning for classification.
In this paper, an improved algorithm of error diffusion is provided, which is better than the traditional error diffusion algorithm in processingimage boundary, contour and texture details. It can make the outline of...
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Robust kernel adaptive filtering (KAF) has been extensively studied to identify nonlinear system corrupted by the heavy-tailed impulsive noise, whereas few KAF algorithm is reported to concern on the light-tailed sub-...
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ISBN:
(数字)9781728144801
ISBN:
(纸本)9781728144818
Robust kernel adaptive filtering (KAF) has been extensively studied to identify nonlinear system corrupted by the heavy-tailed impulsive noise, whereas few KAF algorithm is reported to concern on the light-tailed sub-Gaussian noise. This paper proposes a new kernel normalized least mean high order error (KNLMHOE) algorithm in the presence of sub-Gaussian noise. More importantly, the convergence condition on the mean stability of KNLMHOE is also provided. The KNLMHOE algorithm is simple to implement and exhibits significant performance improvement compared with several state-of-the-art KAF algorithms, which is confirmed by simulations.
The article describes the new handbook printed in 2019 and devoted to the theoretical fundamentals and practical implementations of digital audiovisual systems as well as principles and examples of its metrological su...
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ISBN:
(数字)9781728175287
ISBN:
(纸本)9781728175294
The article describes the new handbook printed in 2019 and devoted to the theoretical fundamentals and practical implementations of digital audiovisual systems as well as principles and examples of its metrological support. The main parts of the book are briefly presented.
In this paper, we investigate how to detect intruders with low latency for Active Authentication (AA) systems with multiple-users. We extend the Quickest Change Detection (QCD) framework to the multiple-user case and ...
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ISBN:
(数字)9781728163956
ISBN:
(纸本)9781728163963
In this paper, we investigate how to detect intruders with low latency for Active Authentication (AA) systems with multiple-users. We extend the Quickest Change Detection (QCD) framework to the multiple-user case and formulate the Multiple-user Quickest Intruder Detection (MQID) algorithm. Furthermore, we extend the algorithm to the data-efficient scenario where intruder detection is carried out with fewer observation samples. We evaluate the effectiveness of the proposed method on two publicly available AA datasets on the face modality.
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