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A ROBUST EYE GAZE ESTIMATION USING GEOMETRIC EYE FEATURES 3

A ROBUST EYE GAZE ESTIMATION USING GEOMETRIC EYE FEATURES

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3rd International conference on Digital Information processing, Data Mining, and Wireless communications (DIPDMWC)

作者： Jariwala, Krupa Dalal, Upena Vincent, Amal SVNIT Comp Engn Surat India SVNIT Elect Engn Surat India

ISBN: (纸本)9781467393799

Gaze estimation is the process of determining the point of gaze in the space, or the visual axis of an eye. It plays an important role in representing human attention;therefore, it can be most appropriately used in Human Computer Interaction as a means of an advance computer input. Here, the focus is to develop a gaze estimation method for Human Computer Interaction using an ordinary webcam mounted on the top of the computer screen without any additional or specialized hardware. The eye center coordinates are obtained with the geometrical eye model and edge gradients. To improve the reliability, the estimates from two eye centers are combined to reduce the noise and improve the accuracy. Facial land marking is done to identify a precise reference point on the face between the nose. The ellipse fitting and RANSAC method is used to estimate the gaze coordinates and to reject the outliers. This approach can estimate the gaze coordinates with high degree of accuracy even when significant numbers of outliers are present in the data set. Several refinements such as feedback and masking, queuing and averaging are proposed to make the system more stable and useful practically. The results show that the proposed method can be successfully applied to commercial gaze tracking systems using ordinary webcams.

关键词： eye tracking computer vision image processing

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Architecture of the parallel hierarchical network for fast image recognition

Architecture of the parallel hierarchical network for fast i...

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conference on Photonics Applications in Astronomy, communications, Industry, and High-Energy Physics Experiments

作者： Timchenko, Leonid Wojcik, Waldemar Kokriatskaia, Natalia Kutaev, Yuriy Ivasyuk, Igor Kotyra, Andrzej Smailova, Saule State Univ Transport Econ & Technol 19 Lukashevich St Kiev Ukraine Lublin Univ Technol Inst Elect & Informat Technol Nadbystrzycka 38A PL-20618 Lublin Poland Dept KIA Syst JSC Design Bur Moscow Russia D Serikbayev East Kazakhstan State Univ 69 AK Protazonov St Ust Kamenogorsk Kazakhstan

ISBN: (数字)9781510604865

ISBN: (纸本)9781510604858

Multistage integration of visual information in the brain allows humans to respond quickly to most significant stimuli while maintaining their ability to recognize small details in the image. Implementation of this principle in technical systems can lead to more efficient processing procedures. The multistage approach to image processing includes main types of cortical multistage convergence. The input images are mapped into a flexible hierarchy that reflects complexity of image data. Procedures of the temporal image decomposition and hierarchy formation are described in mathematical expressions. The multistage system highlights spatial regularities, which are passed through a number of transformational levels to generate a coded representation of the image that encapsulates a structure on different hierarchical levels in the image. At each processing stage a single output result is computed to allow a quick response of the system. The result is presented as an activity pattern, which can be compared with previously computed patterns on the basis of the closest match. With regard to the forecasting method, its idea lies in the following. In the results synchronization block, network-processed data arrive to the database where a sample of most correlated data is drawn using service parameters of the parallel-hierarchical network.

关键词： parallel-hierarchical networks Q-transformation laser image forecasting

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Mode-Dependent Transforms Based on Elliptical Model for High Efficiency Video Coding

Mode-Dependent Transforms Based on Elliptical Model for High...

引用

30th IEEE conference on visual communications and image processing (VCIP)

作者： Jia, Kaiyuan Chen, Chen Meng, Xiandong Zhu, Shuyuan Zeng, Bing Univ Elect Sci & Technol China Inst Image Proc 2006 Xiyuan Ave Chengdu Sichuan Peoples R China Hong Kong Univ Sci & Technol Dept Elect & Comp Engn Kowloon Hong Kong Peoples R China

ISBN: (纸本)9781509053162

High efficiency video coding (HEVC) defines 35 prediction modes in its intra prediction stage to signal the direction information of residual blocks. Traditionally, separable two-dimension (2-D) transforms (integer DCT and DST) are utilized in a similar manner as in the previous H.264/AVC standards. However, such 2-D transforms cannot yield the best energy compaction for a 2-D directional source where the dominating directional information is other than the horizontal or vertical one. In order to overcome this drawback, we build an elliptical model with directionality and design some non separable transforms based on the Karhunen-Loeve transform in this paper. Specifically, we derive a non-separable transform in closed-form for each intra-prediction mode and replace the default transform in HEVC. Simulation results reveal that 1.7% and 2.0% on average and up to 7.7% and 8.1% BD-rate reduction can be achieved for luma and chroma component, respectively. In the meantime, the test results show that both the encoding time and decoding time increase only about 5%.

关键词： Terms HEVC intra prediction KLT mode-dependent transform non-separable transform elliptical model

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DC Coefficient Estimation of Intra-Predicted Residuals in High Efficiency Video Coding

DC Coefficient Estimation of Intra-Predicted Residuals in Hi...

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30th IEEE conference on visual communications and image processing (VCIP)

作者： Chen, Chen Miao, Zexiang Meng, Xiandong Zhu, Shuyuan Zeng, Bing Hong Kong Univ Sci & Technol Dept Elect & Comp Engn Kowloon Hong Kong Peoples R China Univ Elect Sci & Technol China Inst Image Proc 2006 Xiyuan Ave Chengdu Sichuan Peoples R China

ISBN: (纸本)9781509053162

This paper proposes a DC coefficient estimation algorithm for intra-predicted residual blocks in the High Efficiency Video Coding (HEVC) standard. Discarding the DC coefficient in the current coding block leads to a substantial bit-saving but produces at the same time strong discontinuities between this block and its neighboring reconstructed blocks. To overcome this problem, we propose an estimation algorithm for the DC coefficient, which solves an optimal offset in a closed form in the pixel domain to recover the corresponding block edges. Test results show that our algorithm achieves 1.0% and 1.4% BD-rate reduction on average for luma and chroma as compared with HM-16.6, respectively, when the sign-bit-hiding (SBH) technique is disabled. When SDH is set on, namely under the common test condition (CTC), the BD-rate reduction drops slightly to 0.7% and 1.1% for luma and chroma, respectively. In the meantime, the test results show that both encoding time and decoding time increase only slightly (about 10%, without any special optimization on programming our proposed algorithm).

关键词： Terms HEVC video coding intra prediction coefficient estimation R-D performance

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A fuzzy logic approach for the fast approximate computation of image transforms from block JPEG DCT coefficients 20

A fuzzy logic approach for the fast approximate computation ...

引用

20th IEEE International conference on Automation, Quality and Testing, Robotics, AQTR 2016

作者： Gordan, Mihaela Meza, Serban Cislariu, Mihaela Orza, Bogdan Vlaicu, Aurel Capatina, Dorina Stoian, Ioan Department of Communications Technical University of Cluj-Napoca Cluj-Napoca Romania S.C. IPA S.A. Subsidiary CIFATT Cluj Cluj-Napoca Romania

ISBN: (纸本)9781467386906

Computationally efficient implementation of different image processing and analysis algorithms is crucial in many modern real world applications. Several modern visual analytics applications involve processing large amounts of compressed image/video data on restricted hardware resources, in a numerically efficient manner. In this context, the formulation of some image analysis/processing algorithms in the compressed image/video domain can bring significant benefits in the computational complexity reduction. An important step to image analysis, feature extraction, based on unitary image transforms is addressed in this paper, in a general form. Starting from the fact that in most popular video compression the intraframes are block discrete cosine transform (DCT) encoded, we present and propose a solution to computing any unitary image transform at pixels block level, starting from the DCT coefficients of the block. Since approximate forms of the transforms are often satisfactory, we propose a fuzzy logic system based approach to optimize the computational complexity with minimal loss of image information. The method is exemplified for the case of the Walsh transform, which shows that even the fastest implementation provides (by reconstruction) images of good quality. © 2016 IEEE.

关键词： Discrete cosine transforms

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Content Adaptive Directional Transform for High Efficiency Video Coding

Content Adaptive Directional Transform for High Efficiency V...

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30th IEEE conference on visual communications and image processing (VCIP)

作者： Xu, Long Ma, Lin Zhang, Yun Yan, Yihua Chinese Acad Sci Key Lab Solar Act Beijing 100012 Peoples R China Huawei Noahs Ark Lab Hong Kong Hong Kong Peoples R China Chinese Acad Sci Shenzhen Inst Adv Technol Shenzhen 518055 Peoples R China

ISBN: (纸本)9781509053162

HEVC is an emerging new standard for digital video compression, which is regarded as a successor to H.264/AVC standard. It still belongs to block-based hybrid video coding framework. The block patterns range from 4x4 to 64x64 blocks, and DCT is extended from 4x4 to 32x32. 2D-DCT for image is performed along the vertical and horizontal directions, so it is good at the energy compaction of residual block with vertical or horizontal edges. However, the edges are usually neither horizontal nor vertical for most cases, such as neither vertical nor horizontal intra prediction, so directional transform was explored in the past several years. In this paper, a directional transform adaptive to image content is proposed. Firstly, the prediction residual blocks are collected from coding a number of video sequences with plenty of image content. Secondly, for each kind of image content, the residual blocks are clustered to form the given number of clusters. Thirdly, each cluster contributes a transform basis after Singular Value Decomposition (SVD). The experimental results in terms of PSNR gains demonstrate the efficiency of the proposed algorithm with the comparison with the standard HM software.

关键词： Directional transform DCT KLT image content cluster

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Decompressed Video Enhancement via Accurate Regression Prior

Decompressed Video Enhancement via Accurate Regression Prior

引用

30th IEEE conference on visual communications and image processing (VCIP)

作者： Shen, Tao Zhang, Yulun Zhang, Yongbing Wang, Xingzheng Wang, Haoqian Dai, Qionghai Tsinghua Univ Grad Sch Shenzhen Shenzhen 518055 Peoples R China Tsinghua Univ Dept Automat Beijing 100084 Peoples R China

ISBN: (纸本)9781509053162

There is an increasing need for high-quality multimedia applications based on block-based hybrid video coding. Inevitably, the frame will degrade during the process of block wise intra/inter prediction, transformation, and quantization, especially when the bit rate is low. In this paper, we propose an efficient decompressed video enhancement algorithm based on the adjusted anchored neighborhood regression (A+) method. In our work, first, we learn offline linear regressors, i.e. projection matrices from the decompressed to original video frames in the training phase. For grouping anchored neighborhoods more accurately, we adopt MI-KSVD rather than KSVD to learn the dictionary. Moreover, we exploit the mutual coherence between dictionary atoms and training samples to find the nearest neighbors. Second, in the enhancement phase, we boost the quality of input decompressed videos offline by learned regression priors. To verify the robustness of our enhancement method, extensive experiments are conducted. As shown in our experimental results, the proposed enhancement method yields superior performance both objectively and subjectively.

关键词： Dictionary learning image enhancement ridge regression video coding standard video decompression

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Shot Boundary Detection Using Convolutional Neural Networks

Shot Boundary Detection Using Convolutional Neural Networks

引用

30th IEEE conference on visual communications and image processing (VCIP)

作者： Xu, Jingwei Song, Li Xie, Rong Shanghai Jiao Tong Univ Inst Image Commun & Network Engn Shanghai Peoples R China Cooperat Medianet Innovat Ctr Shanghai Peoples R China

ISBN: (纸本)9781509053162

Video shot boundary detection (SBD) is necessary for further video analysis like video retrieval and annotation. Great efforts have been made to develop SBD algorithms for speed and accuracy. Most works implement frame histogram as features to measure similarity for detection. However, when changes between consecutive shot boundaries are small and backgrounds of them are highly similar, most state-of-the-art methods miss these boundaries thus cannot achieve high accuracy of detection. In this paper we propose a novel SBD framework with Convolutional Neural Networks (CNNs). Firstly we adopt a candidate segment selection method to locate the positions of shot boundaries coarsely using adaptive thresholds and eliminate most non-boundary frames. Then CNN is implemented to extract representative features of frames in candidate segments. Finally cut and gradual transitions can be obtained by using a novel pattern-matching method based on a new similarity strategy. Experiments on TRECVID 2001 test data demonstrate that the proposed scheme outperforms the state-of-the-art methods and achieves high accuracy of detection.

关键词： Shot boundary detection adaptive thresholds convolutional neural networks cut transition detection gradual transition detection pattern matching

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Cluster-Based Cross-view Filtering for Compressed Multi-view Depth Maps

Cluster-Based Cross-view Filtering for Compressed Multi-view...

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30th IEEE conference on visual communications and image processing (VCIP)

作者： Liu, Zhen Liu, Qiong Yang, You Liu, Yuchi Jiang, Gangyi Yu, Mei Ningbo Univ Fac Informat Sci & Engn Ningbo Zhejiang Peoples R China Huazhong Univ Sci & Technol Sch Elect Informat & Commun Wuhan Hubei Peoples R China Early Warning Inst Wuhan Hubei Peoples R China

ISBN: (纸本)9781509053162

In the field of multi-view video coding. multi-view plus depth video is an important data format, but it always suffers from quantization errors, which result in obvious artifacts in consequent virtual view rendering. In this paper, we propose a cluster-based cross-view filtering (CBF) scheme for the enhancement of compressed depth maps. In this scheme, reconstructed depth information are mapped from cross-view, and this information is benefit to the proposed filter. Then in filtering one viewpoint depth map with candidate information that are selected from non-locally current and neighboring viewpoints. Specifically, in our scheme, candidates are clustered in 3D super-pixel wise rather than block wise due to cross relationship among pixels in depth maps. The experimental results show that 2.0074 dB average gain can be obtained by our scheme, which suggests that the scheme outperforms than state-of-the-art and classical filters in filtering the reconstructed depth maps.

关键词： depth image filter depth map reconstructed multi-view video coding cluster-based tilter coding distortion

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A Novel Scene Text Detection Algorithm Based On Convolutional Neural Network

A Novel Scene Text Detection Algorithm Based On Convolutiona...

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30th IEEE conference on visual communications and image processing (VCIP)

作者： Ren, Xiaohang Chen, Kai Yang, Xiaokang Zhou, Yi He, Jianhua Sun, Jun Shanghai Jiao Tong Univ Inst Image Commun & Informat Proc Shanghai 200030 Peoples R China Aston Univ Sch Engn & Appl Sci Birmingham B4 7ET W Midlands England

ISBN: (纸本)9781509053162

Candidate text region extraction plays a critical role in convolutional neural network (CNN) based text detection from natural images. In this paper, we propose a CNN based scene text detection algorithm with a new text region extractor. The so called candidate text region extractor I-MSER is based on Maximally Stable Extremal Region (MSER), which can improve the independency and completeness of the extracted candidate text regions. Design of I-MSER is motivated by the observation that text MSERs have high similarity and are close to each other. The independency of candidate text regions obtained by I-MSER is guaranteed by selecting the most representative regions from a MSER tree which is generated according to the spatial overlapping relationship among the MSERs. A multi-layer CNN model is trained to score the confidence value of the extracted regions extracted by the I-MSER for text detection. The new text detection algorithm based on I-MSER is evaluated with wide-used ICDAR 2011 and 2013 datasets and shows improved detection performance compared to the existing algorithms.

关键词： Scene Text Detection Text region extraction I-MSER Deep learning model CNN

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