The ability to detect the horizon on a real-time basis in full-motion video is an important capability to aid and facilitate real-time processing of full-motion videos for the purposes such as object detection, recogn...
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ISBN:
(纸本)9781628410136
The ability to detect the horizon on a real-time basis in full-motion video is an important capability to aid and facilitate real-time processing of full-motion videos for the purposes such as object detection, recognition and other video/image segmentation applications. In this paper, we propose a method for real-time horizon detection that is designed to be used as a front-end processing unit for a real-time marine object detection system that carries out object detection and tracking on full-motion videos captured by ship/harbor-mounted cameras, Unmanned Aerial Vehicles (UAVs) or any other method of surveillance for Maritime Domain Awareness (MDA). Unlike existing horizon detection work, we cannot assume a priori the angle or nature (for e.g. straight line) of the horizon, due to the nature of the application domain and the data. Therefore, the proposed real-time algorithm is designed to identify the horizon at any angle and irrespective of objects appearing close to and/or occluding the horizon line (for e.g. trees, vehicles at a distance) by accounting for its non-linear nature. We use a simple two-stage hierarchical methodology, leveraging color-based features, to quickly isolate the region of the image containing the horizon and then perform a more fine-grained horizon detection operation. In this paper, we present our real-time horizon detection results using our algorithm on real-world full-motion video data from a variety of surveillance sensors like UAVs and ship mounted cameras confirming the real-time applicability of this method and its ability to detect horizon with no a priori assumptions.
In this paper, we propose a new face hallucination algorithm based on Locally Linear Embedding and Local Correlation method (LC-LLE). The LC-LLE algorithm is an improved locally linear embedding (LLE) algorithm by com...
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Existing support vector regression (SVR) based image super-resolution (SR) methods always utilize single layer SVR model to reconstruct source image, which are incapable of restoring the details and reduce the reconst...
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ISBN:
(纸本)9781479928941
Existing support vector regression (SVR) based image super-resolution (SR) methods always utilize single layer SVR model to reconstruct source image, which are incapable of restoring the details and reduce the reconstruction quality. In this paper, we present a novel image SR approach, where a multi-layer SVR model is adopted to describe the relationship between the low resolution (LR) image patches and the corresponding high resolution (HR) ones. Besides, considering the diverse content in the image, we introduce pixel-wise classification to divide pixels into different classes, such as horizontal edges, vertical edges and smooth areas, which is more conductive to highlight the local characteristics of the image. Moreover, the input elements to each SVR model are weighted respectively according to their corresponding output pixel's space positions in the HR image. Experimental results show that, compared with several other learning-based SR algorithms, our method gains high-quality performance.
The traditional Fuzzy C-Means (FCM) clustering algorithm is usually based on the image intensity, so the segmentation results are unsatisfactory when the images are impacted by noise. Considering this shortcoming, in ...
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The traditional Fuzzy C-Means (FCM) clustering algorithm is usually based on the image intensity, so the segmentation results are unsatisfactory when the images are impacted by noise. Considering this shortcoming, in this paper the FCM objective function is improved by adding two kinds of spatial information: the relative position information and the intensity information of the neighborhood. Moreover, Quantum Immune Clone algorithm (QICA) is used to optimize the spatial impact factors in the objective function. The proposed algorithm has been tested in synthetic and real synthetic aperture radar (SAR) images segmentation. Experimental results demonstrate that the proposed algorithm is feasible and effective, and it can lead to higher accuracy.
Tone-mapping technique which can convert high dynamic range (HDR) to low dynamic range (LDR) images provides accurately visualization of HDR images on standard LDR displays. Most of the existing tone-mapping method co...
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Tone-mapping technique which can convert high dynamic range (HDR) to low dynamic range (LDR) images provides accurately visualization of HDR images on standard LDR displays. Most of the existing tone-mapping method could not realize real time processing while preserving good visualization. Utilizing an adaptive three-section lookup table, this paper proposes an effective, high quality, real time technique to convert 12-bit images to 8-bit image which can preserve abundant details and high contrast simultaneously. Experiment results show that this method can improve the weak signals of the image greatly, and the low luminance details can be observed distinctly on an 8-bit monitor.
In this paper, we propose a novel method for hand segmentation. To improve the robustness to the wide range of hand appearances and illuminations, we segment the hand area with a superpixels based method instead of a ...
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In this paper, we propose a novel method for hand segmentation. To improve the robustness to the wide range of hand appearances and illuminations, we segment the hand area with a superpixels based method instead of a general color model. With the exploitation of the distribution of hand pixels in color space, a distance metric learning stage is designed to promote the segmentation performance. This stage makes the points in hand areas more concentrate and pulls away from the points of background in color space. The comparisons with several widely used algorithms are made on both public available and our own datasets. The experimental results show the superior performance of our method.
Mechanical fault diagnosis is important to reduce unscheduled machine downtime and avoid catastrophic accidents. It is significant to extract incipient fault and compound fault features as early as possible, which is ...
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Mechanical fault diagnosis is important to reduce unscheduled machine downtime and avoid catastrophic accidents. It is significant to extract incipient fault and compound fault features as early as possible, which is a complex and challenging task that requests advanced analytical methods with high reliability, high accuracy and high efficiency. Compound fault features are mutually coupled in dynamic signals from the complex system. Weak features of incipient faults are always submersed in background noises. Multiwavelet transform is a remarkable development of wavelet transform, which uses vector scaling functions and wavelet functions. Multiwavelets possess the property of orthogonality, symmetry, compact support and high vanishing moments simultaneously. These advantages promote the development of multiwavelets and their applications in mechanical fault diagnosis in the past decades. This paper attempts to summarize the recent development of multiwavelet transform and its applications in mechanical fault diagnosis. First, the history of wavelets and multiwavelets is introduced. Second, the necessity and the overview of preprocessing methods for multiwavelets are summarized. Third, the advantages of multiwavelets and improvements of different generation multiwavelets are addressed. Fourth, different algorithms of these multiwavelet transforms and their flow charts are presented. Fifth, engineering applications of multiwavelets in mechanical fault diagnosis are investigated. This review also describes a simulation experiment and three application examples which provide a better understanding of different generation multiwavelets for compound fault detection. Finally, existent problems and prospects of further researches are discussed. It is expected that this review will construct an image of the contributions of different generation multiwavelets and link the current frontiers with engineering applications for readers interested in this field.
An effect of weather is very critical in outdoor camera surveillance ***,we focus on bad weather by snow and we propose an algorithm of detecting snowfall from surveillance camera *** proposed algorithm increases a co...
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An effect of weather is very critical in outdoor camera surveillance ***,we focus on bad weather by snow and we propose an algorithm of detecting snowfall from surveillance camera *** proposed algorithm increases a contrast of an image by haze removal.A contrast of hazy image is reduced by haze color of *** several haze colors exist in an image,the degree of contrast degradation is different for each haze *** deal with this problem,the proposed method performs segmentation of an image for every haze color and estimates the airlight,the transmission,and the weight of dehaze in each segmentation area *** of haze removal algorithms require a tone curve correction as post ***,it is very difficult to select an optimal tone curve correction for any *** deal with this problem,we propose a novel algorithm that does not require any tone curve *** general haze removal algorithm treats only haze ***,in the field of outdoor camera surveillance,it's desirable to realize the algorithm which can be applied to various weather *** algorithm can detect snowfall at high speed and stably not only in bad weather but also in good weather.
How to design a low-cost, reliable and real-time target recognition system with large amount of data has become a hot topic in the area of imageprocessing. However, Edge detection has played an important role in targ...
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How to design a low-cost, reliable and real-time target recognition system with large amount of data has become a hot topic in the area of imageprocessing. However, Edge detection has played an important role in target recognition system. The threshold of traditional canny edge detection algorithm must be setting by human, and has a large number of calculations. In order to overcome the shortcomings of the traditional Canny algorithm, proposing an adaptive threshold edge detection algorithm, and realizing it by hardware. This paper will introduce the implementation of the common low-level imageprocessing algorithm in the FPGA, including color space convert module, edge extraction algorithms module, Hough transform module. The results of the experiment indicate that to realize the large amount of calculation of imageprocessing by FPGA hardware logic, not only improves the effect of imageprocessing, but also has high real-time!
Binary imageprocessing is a powerful tool in many image and video applications. A reconfigurable processor is presented for binary imageprocessing in this paper. The processor's architecture is a combination of ...
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Binary imageprocessing is a powerful tool in many image and video applications. A reconfigurable processor is presented for binary imageprocessing in this paper. The processor's architecture is a combination of a reconfigurable binary processing module, input and output image control units, and peripheral circuits. The reconfigurable binary processing module, which consists of mixed-grained reconfigurable binary compute units and output control logic, performs binary imageprocessing operations, especially mathematical morphology operations, and implements related algorithms more than 200 f/s for a 1024 x 1024 image. The periphery circuits control the whole imageprocessing and dynamic reconfiguration process. The processor is implemented on an EP2S180 field-programmable gate array. Synthesis results show that the presented processor can deliver 60.72GOPS and 23.72 GOPS/mm(2) at a 220-MHz system clock in the SMIC 0.18-mu m CMOS process. The simulation and experimental results demonstrate that the processor is suitable for real-time binary imageprocessing applications.
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