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IEEE Signal processing and communications Applications (SIU)

作者： Göksu Erdoğan Bora Çelikkale Aykut Erdem Erkut Erdem Hacettepe Universitesi Ankara Ankara TR

visual summarization problems are complex intrinsically. Because definition of a summary have some ambiguity and only one correct summary does not exist. A good summary consists of two main properties in general which are (i) coverage and (ii) diversity. A good summary should have high coverage. On the other words summary should consist of key events and concepts for given set. At the same time a good summary should also be diverse, i.e it should not consist of similar events and concepts. In addition to these two main properties, intrinsic properties such as quality, emotions, popularity of images increase in importance depending on prevalence of social media applications. We proposed an automatic summarization method which considers intrinsic properties of images in addition to coverage and diversity for personal image collection summarization. This developed method is compared with summaries of different personal image collections which are collected by crowdsourcing and it is observed that the taking intrinsic properties into account improves the summaries.

关键词： Kernel Crowdsourcing Flickr Histograms Media Cultural differences Facebook

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A novel edge detection and pattern recognition algorithm based on beamlet theory for a vision-based wheeled mobile robot

A novel edge detection and pattern recognition algorithm bas...

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International conference on communications and Signal processing

作者： S. Soumya V. Geethapriya S. Shankar Bharathi Department of EEE Rajalakshmi Engineering College Thandalam Chennai TIFAC Core in Machine Vision Rajalakshmi Engineering College Thandalam Chennai

Number of strategies have been deployed to make Wheeled Mobile Robots (WMRs) independent or with limited dependence on human assistance/intervention. In order to enhance their mobility, improvements have been made in their pattern recognition and path planning strategies. Vision-based approach is preferred over stereopsis-based, laser-range finders and GPS-based approaches on account of higher resolution and longer ranges. Vision-Based Wheeled Mobile Robot (VB-WMR) uses an active vision system where the observer is in motion. The goal of this project is to provide a novel algorithm aimed to detect and recognize direction indicators on sign boards placed on the course of the WMR. Here visual processing is devoted to noting the salient components like the edges of patterns on sign boards rather than processing the entire image. The proposed algorithm is based on the beamlet theory, which retains directional information besides the location and scale and performs well under noisy conditions. The developed algorithm in MATLAB is later to be incorporated with the VB-WMR.

关键词： Mobile robots Pattern recognition image edge detection Mobile communication Laser theory

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Chain Code-Based Occupancy Map Coding for Video-Based Point Cloud Compression

Chain Code-Based Occupancy Map Coding for Video-Based Point ...

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IEEE visual communications and image processing (VCIP)

作者： Runyu Yang Ning Yan Li Li Dong Liu Feng Wu CAS Key Laboratory of Technology in Geo-Spatial Information Processing and Application System University of Science and Technology of China Hefei China

ISBN: (数字)9781728180687

ISBN: (纸本)9781728180694

In video-based point cloud compression (V-PCC), occupancy map video is utilized to indicate whether a 2-D pixel corresponds to a valid 3-D point or not. In the current design of V-PCC, the occupancy map video is directly compressed losslessly with High Efficiency Video Coding (HEVC). However, the coding tools in HEVC are specifically designed for natural images, thus unsuitable for the occupancy map. In this paper, we present a novel quadtree-based scheme for lossless occupancy map coding. In this scheme, the occupancy map is firstly divided into several coding tree units (CTUs). Then, the CTU is divided into coding units (CUs) recursively using a quadtree. The quadtree partition is terminated when one of the three conditions is satisfied. Firstly, all the pixels have the same value. Secondly, the pixels in the CU only have two kinds of values and they can be separated by a continuous edge whose endpoints lie on the side of the CU. The continuous edge is then coded using chain code. Thirdly, the CU reaches the minimum size. This scheme simplifies the design of block partitioning in HEVC and designs simpler yet more effective coding tools. Experimental results show significant reduction of bit-rate and complexity compared with the occupancy map coding scheme in V-PCC. In addition, this scheme is also very efficient to compress the semantic map.

关键词： Encoding Three-dimensional displays image coding Semantics Markov processes Geometry Copper

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End-to-End Learned Lossy Dynamic Point Cloud Attribute Compression

End-to-End Learned Lossy Dynamic Point Cloud Attribute Compr...

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IEEE International conference on image processing

作者： Dat Thanh Nguyen Daniel Zieger Marc Stamminger André Kaup Chair of Multimedia Communications and Signal Processing Chair of Visual Computing Friedrich-Alexander-Universität Erlangen-Nürnberg (FAU) Erlangen Germany

ISBN: (数字)9798350349399

ISBN: (纸本)9798350349405

Recent advancements in point cloud compression have primarily emphasized geometry compression while comparatively fewer efforts have been dedicated to attribute compression. This study introduces an end-to-end learned dynamic lossy attribute coding approach, utilizing an efficient high-dimensional convolution to capture extensive inter-point dependencies. This enables the efficient projection of attribute features into latent variables. Subsequently, we employ a context model that leverage previous latent space in conjunction with an auto-regressive context model for encoding the latent tensor into a bitstream. Evaluation of our method on widely utilized point cloud datasets from the MPEG and Microsoft demonstrates its superior performance compared to the core attribute compression module Region-Adaptive Hierarchical Transform method from MPEG Geometry Point Cloud Compression with $38.1 \%$ Bjontegaard Delta-rate saving in average while ensuring a low-complexity encoding/decoding.

关键词： Point cloud compression Geometry image coding Tensors Convolution Transform coding Transforms

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Analysis and synthesis of 3-D face image sequence based on model-based image coding scheme with an Internet browser

Analysis and synthesis of 3-D face image sequence based on m...

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IEEE International conference on image processing

作者： Min Chul Park N.P. Chandrasiri T. Naemura M. Kaneko H. Harashima Department of Informatics and Communication Engineering University of Tokyo Bunkyo Tokyo Japan Course in Elcctronic Eng University of Electro-Communications Japan

In this paper, an efficient analysis method for global head motion, and a synthesis method of 3-D face scenes using model-based image coding techniques on the Internet browser are presented. By applying the techniques of model-based image coding in facial image sequence, human face or anthropomorphic agents can be synthesized with real facial motion on the internet browser using VRML and/or JAVA. While typical model-based image coding produces synthetic face scenes in 2-D space at the receiver terminals, the proposed scheme can produce synthetic face scenes in 3-D space interactively supporting some visual standard. This enables users to synthesize 3-D face scenes at any terminals. In this sense, it improves the portability of synthesis in model-based image coding. To speed up the computation in the analysis process, a new method that generates referential frame dynamically is proposed. To produce a 3-D face model which is suitable for the purpose of application, an idea of automatic face modeling using different 3-D face models is presented. Experimental results show the usefulness of applying the techniques of model-based image coding to the analysis of facial motion and synthesis of 3-D face scene on the internet browser.

关键词： image analysis image sequence analysis image sequences Layout image coding Internet image motion analysis Motion analysis Head Humans

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Adaptive Kernelized Evidence C-Means Clustering Combining Spatial Information for Noisy image Segmentation

Adaptive Kernelized Evidence C-Means Clustering Combining Sp...

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Natural Language processing (ICNLP), International conference on

作者： Lan Rong Mi Haowen Qu Na Zhao Feng Yu Haiyan Zhang Lu School of Communications and Information Engineering Xi’an University of Posts and Telecommunications Xi’an China

Although the evidence c-means clustering (ECM) has the capability to process uncertain information, it is not suitable for noisy image segmentation, because the spatial information of pixels is not considered. To solve the problem, an adaptive kernelized evidence c-means clustering combining spatial information for noisy image segmentation algorithm is proposed. Firstly, an adaptive noise distance that can be iteratively updated is constructed using the local information of the pixels. Secondly, to improve the classification performance, an adaptive kernel function is proposed to measure the distance between the pixel and the cluster center. Simultaneously, the original, local and non-local information of pixels are introduced adaptively into the objective function to enhance the robustness to noise. In the iteration, the noise cluster is automatically recovered using the recovery factor constructed by the gray and spatial information of neighborhood pixels. Finally, the credal partition is transformed into a fuzzy partition by pignistic transformation, the classification of pixel be determined by the maximum membership principle. Experiments on synthetic images and real images demonstrate that the proposed algorithm has strong noise suppression ability. visual effects and evaluation indexes verify the effectiveness of the proposed algorithm for noisy image segmentation.

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Recent Advances in End-to-End Learned image and Video Compression

Recent Advances in End-to-End Learned Image and Video Compre...

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IEEE visual communications and image processing (VCIP)

作者： Wen-Hsiao Peng Hsueh-Ming Hang National Chiao Tung University Taiwan

ISBN: (数字)9781728180687

ISBN: (纸本)9781728180694

The DCT-based transform coding technique was adopted by the international standards (ISO JPEG, ITU H.261/264/265, ISO MPEG-2/4/H, and many others) for nearly 30 years. Although researchers are still trying to improve its efficiency by fine-tuning its components and parameters, the basic structure has not changed in the past two *** deep learning technology recently developed may provide a new direction for constructing a high-compression image/video coding system. Recent results, particularly from the Challenge on Learned image Compression (CLIC) at CVPR, indicate that this new type of schemes (often trained end-to-end) may have good potential for further improving compression *** the first part of this tutorial, we shall (1) summarize briefly the progress of this topic in the past 3 or so years, including an overview of CLIC results and JPEG AI Call-for-Evidence Challenge on Learning-based image Coding (issued in early 2020). Because Deep Neural Network (DNN)-based image compression is a new area, several techniques and structures have been tested. The recently published autoencoder-based schemes can achieve similar PSNR to BPG (Better Portable Graphics, H.265 still image standard) and has superior subject quality (e.g., MSSSIM), especially at the very low bit rates. In the second part, we shall (2) address the detailed design concepts of image compression algorithms using the autoencoder structure. In the third part, we shall switch gears to (3) explore the emerging area of DNN-based video compression. Recent publications in this area have indicated that end-to-end trained video compression can achieve comparable or superior rate-distortion performance to HEVC/H.265. The CLIC at CVPR 2020 also created for the first time a new track dedicated to P-frame coding.

关键词： image coding Transform coding Standards Video compression Streaming media MPEG standards High efficiency video coding

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Multi-modal fusion with particle filter for speaker localization and tracking

Multi-modal fusion with particle filter for speaker localiza...

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International conference on Multimedia Technology

作者： Michael Heuer Ayoub Al-Hamadi Bernd Michaelis Andreas Wendemuth Institute for Electronics Signal Processing and Communications Otto-von-Guericke University of Magdeburg Germany

ISBN: (纸本)9781612847719

This paper describes a methodology for fusing multimodal data meaningful together, in order to detect and track a speaker with a conventional sensor setup. We use Gaussian mixtures to combine the sensor information within a particle filter, such that a single speaker can be identified in the presence of multiple visual observations. The major advantages are design considerations that let the system perform in real time, while using an easily extensible framework. Besides, we highly reduce noise which gives us a more dependable prediction. Results illustrate the localization estimations in a two- and a three-person scenario.

关键词： Particle filters image color analysis Skin Feature extraction Streaming media Computational modeling image segmentation

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Satellite image processing Using Discrete Cosine Transform and Singular Value Decomposition

Communications in Computer and Information Science

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communications in Computer and Information Science 2011年 205 M4D卷 277-290页

作者： Ashish, B.K. Kumar, A. Padhy, P.K. PDPM Indian Institute of Information Technology Design and Manufacturing Jabalpur 482005 MP India

ISBN: (纸本)9783642240546

In this paper, a novel contrast enhancement technique for contrast enhancement of a low-contrast satellite image has been proposed based on the singular value decomposition (SVD) and discrete cosine transform (DCT). The singular value matrix represents the intensity information of the given image and any change on the singular values change the intensity of the input image. The proposed technique converts the image into the SVD-DCT domain and after normalizing the singular value matrix;the enhanced image is reconstructed by using inverse DCT. The visual and quantitative results suggest that the proposed SVD-DCT method clearly shows the increased efficiency and flexibility of the proposed method over the exiting methods such as the histogram equalization, gamma correction and SVD-DWT based techniques. © Springer-Verlag Berlin Heidelberg 2011.

关键词： Discrete cosine transforms

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Automatic extraction of important objects for an MPEG-7 compliant video database system

Automatic extraction of important objects for an MPEG-7 comp...

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IEEE Signal processing and communications Applications (SIU)

作者： Muhammet Bastan Ugur Gudukbay Ozgur Ulusoy Bilgisayar Muhendisligi Bolumu Bilkent Universitesi Ankara Turkey

We describe a method to automatically extract video objects, which are important for object-based indexing of videos in an MPEG-7 compliant video database system. Most of the existing salient object detection approaches detect visually conspicuous image structures, while our method aims to find regions that may be important for indexing in a video database system. Our method works on a shot basis. We first segment each frame to obtain homogeneous regions in terms of color and texture. Then, we extract a set of local and global color, shape, texture and motion features for each region. Finally, the regions are classified as being salient or non-salient using SVMs trained on a few hundreds of example regions. Experimental results from news video segments show that the proposed method is more effective in extracting the important regions in terms of human visual perception.

关键词： Transform coding conferences Pattern recognition Support vector machines Computer vision image segmentation Database systems

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