In this paper, different image preprocessing methods are compared based on their ability to remove noise and to segment the images. Two filters, namely, Median filter and Wiener filter, and seven image segmentation me...
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ISBN:
(纸本)9781509083152
In this paper, different image preprocessing methods are compared based on their ability to remove noise and to segment the images. Two filters, namely, Median filter and Wiener filter, and seven image segmentation methods, namely, Sobel, Prewitt, Roberts, Laplacian of Gaussian (LoG), Canny edge detection, basic global thresholding (BGT) and Otsu's global thresholding (OGT) are applied on the images of eight different signal-to-noise ratios (SNRs) ranging from 2.7 to 17.8. First, the preprocessing results are qualitatively compared by visual inspection for image SNRs of 17.8 (high) and 2.7 (low). Then the effects of different preprocessing methods are quantitatively analyzed by determining the accuracy of centroid detection of circular marks using Hough transform. The quantitative comparison showed that Median filter plus BGT or OGT give better results than other methods for low SNRs, and Wiener filter plus LoG detector provided higher accuracy compared to other methods for high SNRs. The application of this work is in many areas, for example, biomedical imaging, flow diagnostics andcomputervision, where we detect the sizes or locations of circular objects in images.
image compression, which is a type of data compression applied to digital images, has been a fundamental research topic for many decades. Recent image techniques produce very large amounts of data, which may make it p...
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ISBN:
(纸本)9781450360920
image compression, which is a type of data compression applied to digital images, has been a fundamental research topic for many decades. Recent image techniques produce very large amounts of data, which may make it prohibitive to storage and communications of image data without the use of compression. However, the traditional compression methods, such as JPEG, may introduce the compression artefact problems. Recently, deep learning has achieved great success in many computervision tasks and is gradually being used in image compression. To solve the compression atrefact problem, in this paper, we present a lossy image compression architecture, which utilizes the advantages of the existing deep learning methods to achieve a high coding efficiency. We design a densely connected autoencoder structure for lossy image compression. Firstly, we design a densely autoencoder structure to get richer feature information from image which can be helpful for compression. Secondly, we design a U-net like network to decrease the distortion caused by compression. Finally, an improved binarizer is adopted to quantize the output of encoder. In low bit rate image compression, experiments show that our method significantly outperforms JPEG and JPEG2000 and can produce a better visual result with sharp edges, rich textures, and fewer artifacts.
In recent years, computervision technology has experienced a period of rapid development, and it has achieved remarkable results on core problems in various fields. At the same time, with the iterative development of...
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Underwater images are highly important for studying various oceanic objects and underwater vegetation & life. Underwater scenes are usually veiled by the light interaction with the medium: low luminosity, absorpti...
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image saliency detection helps the computer quickly analyze the surrounding environment, locate the interested objects and extract the salient regions from the background. Conventional image saliency detection algorit...
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ISBN:
(纸本)9781728151021
image saliency detection helps the computer quickly analyze the surrounding environment, locate the interested objects and extract the salient regions from the background. Conventional image saliency detection algorithms usually have high computational complexity, and the detection results seems to be less than satisfactory under complex application circumstances. In this paper, a novel image saliency detection method via color contradistinction and background similarity is proposed, which is effective. In our method, the input image is reconstructed according to block-based compressed sensing for reducing the computational complexity. Then, a weighted local contrast principle and a background similarity calculation framework are designed to obtain two different primary saliency maps. Finally, a weighted fusion strategy is used to combine the two saliency maps to get the final result which has the best detection performance. The experimental results show that the proposed method has good detection performance in terms of accuracy and running time.
In this paper, balanced two-stage residual networks (BT SRN) are proposed for single image super-resolution. The deep residual design with constrained depth achieves the optimal balance between the accuracy and the sp...
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ISBN:
(纸本)9781538607336
In this paper, balanced two-stage residual networks (BT SRN) are proposed for single image super-resolution. The deep residual design with constrained depth achieves the optimal balance between the accuracy and the speed for super-resolving images. The experiments show that the balanced two-stage structure, together with our lightweight two-layer PConv residual block design, achieves very promising results when considering both accuracy and speed. We evaluated our models on the New Trends in image Restoration and Enhancement workshop and challenge on image super-resolution (NTIRE SR 2017). Our final model with only 10 residual blocks ranked among the best ones in terms of not only accuracy (6th among 20 final teams) but also speed (2nd among top 6 teams in terms of accuracy).
One of the most fundamental features of digital image and the basic steps in imageprocessing, analysis, pattern recognition andcomputervision is the Edge of image where the preciseness and reliability of its result...
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ISBN:
(纸本)9780769539256
One of the most fundamental features of digital image and the basic steps in imageprocessing, analysis, pattern recognition andcomputervision is the Edge of image where the preciseness and reliability of its results will affect directly the comprehension machine system made for objective world. Several edge detectors have been developed in the past decades, although no single edge detectors have been developed satisfactorily enough for all application. In this paper, a new edge detection technique is proposed basis on the BP neural network. Here, it is classified the edge patterns of binary images into 16 possible types of visual patterns. In the following, After training the pre-defined edge patterns, the BP neural network is applied to correspond any type of edges with its related visual pattern. The results demonstrated that the new proposed technique provides the better results compared with traditional edge detection techniques while improved the computations complexity.
This paper introduces a method of monitoring seam center real-time, and it can overcome the environment factor effectively. The seam imageprocessing procedure including: Firstly, the seam image is tested by machine v...
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ISBN:
(纸本)9781479999613
This paper introduces a method of monitoring seam center real-time, and it can overcome the environment factor effectively. The seam imageprocessing procedure including: Firstly, the seam image is tested by machine vision system based on structured-light. Then, the laser stripe direction is divided reliably by threshold segmentation and filtering based on MATLAB. Thirdly, the seam center is tested accurately by extremum method. Lastly, the laser stripe, corresponded by welding gun, is located by averaging method. The results suggest that the discrepancy fits the welding requirement.
Traditional traffic monitoring systems are limited by the number of devices and the efficiency of location data processing and analysis, making it difficult to achieve timely perception and response to traffic conditi...
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This paper proposes a more accurate way to detect the logistics driver fatigue state. In logistics transportation vehicle driving process, there are differences between the blinking frequency and the closing time of l...
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ISBN:
(纸本)9781479999613
This paper proposes a more accurate way to detect the logistics driver fatigue state. In logistics transportation vehicle driving process, there are differences between the blinking frequency and the closing time of logistics driver's eyes. According to the difference, the logistics driver's fatigue state can be able to be detected. Using the computer, it can strengthen the processing to the image acquisition of logistics driver. And the improving the resolution of the image can provide the basis for logistics driver fatigue detection. The experimental results show: using a fuzzy detection method in this paper, can greatly improve the accuracy of detection, and overcome the drawbacks of traditional algorithm. That obtained the satisfactory results.
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