computervision is a promising domain that focuses on emerging approaches, algorithms and technologies to provide computing capability to machine to analysis visual data, such as image files, videos files and real tim...
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These systems are based on images uploaded by users. They first determine the most similar users based on the images they upload to the system. They then return the most likely image to the users based on the images l...
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ISBN:
(纸本)9781538609309
These systems are based on images uploaded by users. They first determine the most similar users based on the images they upload to the system. They then return the most likely image to the users based on the images liked by the neighbors. The most challenging problem in image-based recommneder systems is to match an image with the most similar visual words or classes based on the image's visual content. We design a system which is using Bag of words model, to solving this problem.1 BoW model is an effective model in computervision field, and we use SURF, SIFT and LBP descriptors for extracting features in our system.
Scene classification makes it easier to semantic scene understanding and aids to further processes and inference, using an assignment of pre-defined classes. Under this motive, we proposed an approach to classify indo...
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We implemented a real-time ensemble model for face detection by combining the results of YOLO v1 to v4. We used the WIDER FACE benchmark for training YOLOv1 to v4 in the Darknet framework. Then, we ensemble their resu...
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Metal cylinder is widely used as an important workpiece in machinery, its precision determines the accuracy and quality of mechanical resembling. In this paper, the feature recognition of edge burr on metal cylindrica...
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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.
With the rapid development of computer technology and the Internet era, machine vision is a new science and technology formed by the cross-integration of various disciplines such as imageprocessing theory, advanced i...
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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.
Following the great success of curriculum learning in the area of machine learning, a novel deep curriculum learning method proposed in this paper, entitled DCL, particularly for the classification of fully polarimetr...
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The encryption of images is an essential component of ensuring data security in the digital age. Delving into chaotic mappings, our study unveils their robust potential for image encryption. In this paper, we propose ...
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