This paper addresses the problem of image based localization. The goal is to find quickly and accurately the relative pose from a query taken from a stereo camera and a map obtained using visual SLAM which contains po...
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ISBN:
(纸本)9781728180687
This paper addresses the problem of image based localization. The goal is to find quickly and accurately the relative pose from a query taken from a stereo camera and a map obtained using visual SLAM which contains poses and 3D points associated to descriptors. In this paper we introduce a new method that leverages the stereo vision by adding geometric information to visual descriptors. This method can be used when the vertical direction of the camera is known (for example on a wheeled robot). This new geometric visual descriptor can be used with several image based localization algorithms based on visual words. We test the approach with different datasets (indoor, outdoor) and we show experimentally that the new geometricvisual descriptor improves standard image based localization approaches.
This paper presents a new filtering scheme for the removal of impulsive noise in multichannel images. It is based on estimating the probability density function for image pixels in a filtering window by means of the k...
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ISBN:
(纸本)0819450235
This paper presents a new filtering scheme for the removal of impulsive noise in multichannel images. It is based on estimating the probability density function for image pixels in a filtering window by means of the kernel density estimation method. The filtering algorithm itself is based on the comparison of pixels with their neighborhood in a sliding filter window. The quality of noise suppression and detail preservation of the new filter is measured quantitatively in terms of the standard image quality criteria. The filtering results obtained with the new filter show its excellent ability to reduce noise while simultaneously preserving fine image details.
This paper presents a deep learning-based audio-in-image watermarking scheme. Audio-in-image watermarking is the process of covertly embedding and extracting audio watermarks on a cover-image. Using audio watermarks c...
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ISBN:
(纸本)9781728185514
This paper presents a deep learning-based audio-in-image watermarking scheme. Audio-in-image watermarking is the process of covertly embedding and extracting audio watermarks on a cover-image. Using audio watermarks can open up possibilities for different downstream applications. For the purpose of implementing an audio-in-image watermarking that adapts to the demands of increasingly diverse situations, a neural network architecture is designed to automatically learn the watermarking process in an unsupervised manner. In addition, a similarity network is developed to recognize the audio watermarks under distortions, therefore providing robustness to the proposed method. Experimental results have shown high fidelity and robustness of the proposed blind audio-in-image watermarking scheme.
image retrieval is a two steps process: 1) indexing, in which a set or a vector of features summarizing the properties of each image in the database, is computed and stored;and 2) retrieval, in which the features of t...
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ISBN:
(纸本)0819450235
image retrieval is a two steps process: 1) indexing, in which a set or a vector of features summarizing the properties of each image in the database, is computed and stored;and 2) retrieval, in which the features of the query image are extracted and compared with the others in the database. The database images are then ranked in order of their similarity. We introduce an innovative image retrieval strategy, the Dynamic Spatial Chromatic Histogram, which makes it possible to take into account spatial information in a flexible way without greatly adding to computation costs. Our preliminary results on a database of about 3000 images show that the proposed indexing and retrieval strategy is a powerful approach.
In this paper a novel approach to the problem of edge preserving noise reduction in color images is proposed and evaluated. The new algorithm is based on the combined forward and backward anisotropic diffusion with in...
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ISBN:
(纸本)0819450235
In this paper a novel approach to the problem of edge preserving noise reduction in color images is proposed and evaluated. The new algorithm is based on the combined forward and backward anisotropic diffusion with incorporated time dependent cooling process. This method is able to efficiently remove image noise, while preserving and even enhancing image edges. The proposed algorithm can be used as a first step of different techniques, which are based on color. shape-and spatial location information.
Content based image retrieval has gained considerable attention in nowadays as a very useful tool in a plethora of applications. Web has become the most important application, because over 70% of it is devoted to imag...
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ISBN:
(纸本)0819450235
Content based image retrieval has gained considerable attention in nowadays as a very useful tool in a plethora of applications. Web has become the most important application, because over 70% of it is devoted to images, and looking for a specific image is a really daunting task. The vast majority of these images are JPEG compressed. An extensive study of eighteen similarity measures used for image retrieval has been conducted and the corresponding results are reported in the present communication. The energy histograms of the low frequency DCT coefficients have been used as the feature space for similarity testing. Query-by-image-example was used in all tests.
This paper proposes a new method for the detection of moving cast shadows in natural video sequences. The variations of illumination generate in a shadow area are modelized assuming that the source light is fixed and ...
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ISBN:
(纸本)0819450235
This paper proposes a new method for the detection of moving cast shadows in natural video sequences. The variations of illumination generate in a shadow area are modelized assuming that the source light is fixed and unique, and that the surface on which the shadow is projected is plane and Lambertian. A local and global matching of this model is then done on the current image in order to obtain a first detection of the moving shadow areas. This matching process is based on a reference image which is assumed to contain no moving shadow. A spatio-temporal follow up of the obtained areas is applied in order to remove false detection. The proposed segmentation method was tested and validated on real video sequences.
In our work, we introduce the block significance measure reflecting the property of the blocks to be shared by pixels from different image regions. The blocks B with higher significance value C(B) belong to the fields...
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ISBN:
(纸本)0819450235
In our work, we introduce the block significance measure reflecting the property of the blocks to be shared by pixels from different image regions. The blocks B with higher significance value C(B) belong to the fields of region contrast and thus tend to attract more attention in terms of the Human visual System (HVS). The computation of C(B) is based on the execution of a region merging procedure and determination of the first partitioning containing a region completely covering a block B. The introduced measure is related to the flat/texture/edge block classification but reflects global image properties and differs from the latter. We study how one may incorporate the measure in the DCT-based based image/video compression. Experimental results are presented.
Learning-based compression systems have shown great potential for multi-task inference from their latent-space representation of the input image. In such systems, the decoder is supposed to be able to perform various ...
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ISBN:
(纸本)9781728185514
Learning-based compression systems have shown great potential for multi-task inference from their latent-space representation of the input image. In such systems, the decoder is supposed to be able to perform various analyses of the input image, such as object detection or segmentation, besides decoding the image. At the same time, privacy concerns around visual analytics have grown in response to the increasing capabilities of such systems to reveal private information. In this paper, we propose a method to make latent-space inference more privacy-friendly using mutual information-based criteria. In particular, we show how organizing and compressing the latent representation of the image according to task-specific mutual information can make the model maintain high analytics accuracy while becoming less able to reconstruct the input image and thereby reveal private information.
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