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Depth Map Up-sampling with Texture Edge Feature via Sparse Representation

Depth Map Up-sampling with Texture Edge Feature via Sparse R...

引用

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

作者： Dong, Yan Lin, Chunyu Zhao, Yao Yao, Chao Hou, Jingxuan Beijing Jiaotong Univ Inst Informat Sci Beijing Peoples R China Beijing Univ Posts & Telecommun Beijing 100876 Peoples R China

ISBN: (纸本)9781509053162

Depth information has been an important part in various computer vision applications. However, the low resolution depth map captured by TOF cameras and other depth sensors cannot be directly used in visual depth perception and 3D reconstruction. Thus, this paper proposes an effective depth map up-sampling algorithm which reconstructs a high resolution depth map from a low depth map, by integrating the registered color image. Different from the other depth map up-sampling methods, we establish the correspondence between the high resolution and low resolution image pairs via image sparse representation, then extract the geometric structure component and texture component from high resolution depth map and the relevant color image, respectively. Specially, we employ a guided filter to preserve regions smooth and edges sharp. The experimental results show that the proposed depth map up-sampling method obtains quality improvement on depth map and the synthesized view compared with state-of-the-art methods.

关键词： depth map up-sampling registered color image sparse representation guided filter subjective quality

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Accurate Fingertip Detection From Binocular Mask images

Accurate Fingertip Detection From Binocular Mask Images

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

作者： Chen, Xinghao Wang, Guijin Guo, Hengkai Tsinghua Univ Dept Elect Engn Beijing 100084 Peoples R China

ISBN: (纸本)9781509053162

Accurate fingertip detection is important for hand based human computer interaction. Different from prior methods based on depth image, in this paper we propose a novel scheme for accurate fingertip detection from binocular mask images without explicitly computing the depth map. To demonstrate our proposed scheme, we build a new hand dataset containing synthetic and real binocular images. A deep convolutional neural network (CNN) is utilized as a baseline method to demonstrate the proposed scheme. The mask images are extracted from binocular images and fed into the CNN to predict the 3D positions of fingertips and palm center. Experiments show that this method achieves the mean error of 8.30mm on synthetic data and 4.64mm on real data, which is promosing for accurate 3D interaction. The proposed scheme runs at 130fps on a CPU and is promising for real-time applications.

关键词： Fingertip Detection Convolutional Neural Network Binocular Mask image

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Parallax-Aware Local Alignment for image Stitching under Large Occlusion/Disocclusion

Parallax-Aware Local Alignment for Image Stitching under Lar...

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

作者： Wang, Yangxin Li, Kai An, Ping Shen, Liquan Zou, Xuemei Shanghai Univ Sch Commun & Informat Engn Shanghai Peoples R China Minist Educ Key Lab Adv Displays & Syst Applicat Shanghai Peoples R China

ISBN: (纸本)9781509053162

This paper presents a parallax-aware image stitching approach under large occlusion/disocclusion. Different from previous research, we explore the image stitching issue in a parallax-aware perspective via local alignment. Specifically, we first label each feature point with a probability of being large parallax by developing a graph-based optimization framework. Afterwards, an integer programming model is built to pick out a group of feature matches free from parallax in a local region. Finally, by enforcing a stitching seam passing through such a locally aligned area, we are able to generate a high quality stitching result under large parallax. Experimental results demonstrate the effectiveness and superiority of the proposed parallax-aware approach.

关键词： image stitching parallax-aware local alignment occlusion/disocclusion

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Autoregressive image Interpolation via Context Modeling and Multiplanar Constraint

Autoregressive Image Interpolation via Context Modeling and ...

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

作者： Deng, Shihong Liu, Jiaying Li, Mading Yang, Wenhang Guo, Zongming Peking Univ Inst Comp Sci & Technol Beijing 100871 Peoples R China

ISBN: (纸本)9781509053162

In this paper, we propose a novel image interpolation algorithm by context-aware autoregressive (Lambda R) model and multi-planar constraint. Different from existing AR based methods which employ predetermined reference configuration to predict pixel values, the proposed method considers the anisotropic pixel dependencies in natural images and adaptively chooses the optimal prediction context by utilizing the nonlocal redundancy to interpolate pixels. Furthermore, the multiplanar constraint is applied to enhance the correlations within the estimation window by exploiting the self-similarity property of natural images. Similar patches are collected by the combination of patch-wise pixel values and the gradient information. And the inter-patch dependencies are adopted to improve the interpolation. The experimental results show that our method is effective in image interpolation and successfully decreases the artifacts nearby the sharp edges. The comparison experiments demonstrate that the proposed method can obtain better performance than other related ones in terms of both objective and subjective results.

关键词： Autoregressive(AR) context modeling image interpolation multiplanar patch similarity

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An Advanced Local Offset Matching Strategy for Object Proposal Matching

An Advanced Local Offset Matching Strategy for Object Propos...

引用

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

作者： Zhang, Ruonan Wang, Wenmin Peking Univ Shenzhen Grad Sch Sch Elect & Comp Engn Shenzhen Peoples R China

ISBN: (纸本)9781509053162

image correspondence problem is very challenging due to the complexity of real-world scenes, especially in the presence of deformation, outliers, and other intra-class variations. Semantic flow methods are used for finding image correspondences, but the prevalent ones are mainly depended on pixel level or regularly local-region sampled operation that are easily confused by image-specific details and element-specific scenes to individual objects, e.g. background, clutter, and they rarely care about the relationships between proposals. Therefore, we propose an advanced local offset matching strategy for object proposals, which is in term of naive Bayesian model, considering both appearance and the local spatial pattern with preprocessing. It not only builds reliable region relationships using object proposals, but also explores consistency between appearance and spatial pattern among proposals. In addition, two evaluation metrics, seven features are applied to experiments. The comparison and analysis, along with adequate experiments on standard benchmark, present that our proposed method effectively outperforms other matching strategies in diverse settings.

关键词： Terms image correspondence semantic flow Bayesian model object proposal proposal matching

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A novel Selective image Encryption Method Based on Saliency Detection

A novel Selective Image Encryption Method Based on Saliency ...

引用

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

作者： Wen, Wenying Zhang, Yushu Fang, Yuming Fang, Zhijun Jiangxi Univ Finance & Econ Sch Informat Technol Nanchang Jiangxi Peoples R China Southwest Univ Sch Elect & Informat Engn Chongqing Peoples R China Shenzhen Univ Coll Comp Sci & Engn Shenzhen Peoples R China Shanghai Univ Engn Sci Coll Elect & Elect Engn Shanghai Peoples R China

ISBN: (纸本)9781509053162

Salient regions usually carry important information in images. Existing feature encryption algorithms aim at extracting edge features as significant information rather than salient regions for encryption purpose. Moreover, most of them protect significant information by transforming the input image into texture-like or noise-like encrypted image which is obviously a visual sign of encrypted image, and thus can be easily attacked. In this paper, we propose a salient regions encryption scheme to generate visually meaningful ciphertext. First, salient regions are efficiently extracted by a saliency detection model in the compressed domain. Then we pre-encrypt these salient regions by a chaos-based encryption algorithm. With optical encryption theory, the pre-encrypted salient regions are finally transformed into a visually meaningful ciphertext. To the best of our knowledge, it is the first time to use salient regions as important visual information for encryption to obtain cipertext in images. The experimental results demonstrate that the salient regions can be largely hidden with the proposed method.

关键词： Saliency detection Selective encryption visually meaningful ciphertext

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image Contrast Enhancement Using Histogram Equalization with Fuzzy approach on the Neighbourhood metrics (FANMHE)

Image Contrast Enhancement Using Histogram Equalization with...

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IEEE International conference on Wireless communications, Signal processing and Networking (WiSPNET)

作者： Devi, G. Shree Rabbani, M. Munir Ahamed BS Abdur Rahman Univ Dept Comp Applicat Madras Tamil Nadu India

ISBN: (纸本)9781467393379

In this paper, a fuzzy approach on the neighbourhood metric is proposed for usage with Histogram equalization for image contrast enhancement. The Neighbourhood Metrics or local image properties are used to sub divide the large histogram bins produced by Global Histogram Equalization. Large histogram bins in the image causes visual deteriorations. We propose, a fuzzy approach to sub divide the large bins. Fuzzy Approach on Neighbourhood Metric for Histogram Equalization(FANMHE) consists of two stages. In the first stage, Gaussian membership function is used to a assign membership value for all the eight neighbouring pixels with reference to the middle pixel. The next stage Fuzzy C-Mean clustering algorithm is used to group the pixels with similar membership value. After clustering pixels with similar neighbourhood histogram equalization performed. This approach sub divides large histograms bins into sub bins giving equal weight age to all the 8 neighbouring pixels. The pixels with similar neighbouring properties considering 8 pixels are grouped into each sub bins. This provides actual and meaning grouping to divide large histogram bins into sub bins.

关键词： Histogram Equalization Fuzzy Approach Neighbourhood Contrast Enhancement

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A Novel Low Delay In-Loop Filtering WPP Process for Parallel HEVC Encoding

A Novel Low Delay In-Loop Filtering WPP Process for Parallel...

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

作者： Gu, Jiawen Han, Yuxing Wen, Jiangtao Tsinghua Univ Dept Comp Sci Beijing 10084 Peoples R China

ISBN: (纸本)9781509053162

Wave front parallel processing (WPP) is a parallelization technique that enables processing of several rows of Largest Coding Units (LCUs) in parallel. It achieves a relatively high level of parallelism with reasonable loss in compression performance. At the same time, the HEVC standard specifies two in-loop filters, namely the deblocking filter and the sample adaptive offset (SAO), to improve subjective quality as well as coding efficiency. Because SAO parameters cannot be precisely determined until the lower right deblocked samples are available, a delay of several rows is introduced to implement the filtering process. Since the reconstruction of the LCUs will not start until finish filtering, the total delay of WPP and filtering is at least two rows, which obviously influences the performance of parallel processing. In this paper, we propose a novel In Loop Filtering WPP method that reduces the row delay into four LCUs and significantly improves the parallelism with little rate-distortion (RD) performance loss. Experimental results show that the proposed algorithms can achieve up to the 2.89x speedup compared with the existing WPP method with 24-core server, where the speedup improves with the increase of core number.

关键词： In-loop filtering SAO deblocking filter wave-front parallel processing low delay

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MULTI-SCALE BLOCKS BASED image EMOTION CLASSIFICATION USING MULTIPLE INSTANCE LEARNING 23

MULTI-SCALE BLOCKS BASED IMAGE EMOTION CLASSIFICATION USING ...

引用

23rd IEEE International conference on image processing (ICIP)

作者： Rao, Tianrong Xu, Min Liu, Huiying Wang, Jinqiao Burnett, Ian Univ Technol Sydney Fac Engn & IT Sydney NSW 2007 Australia Inst Infocomm Res Singapore Singapore Chinese Acad Sci Inst Automat Beijing 100864 Peoples R China

ISBN: (纸本)9781467399616

Emotional factors usually affect users' preferences for and evaluations of images. Although affective image analysis attracts increasing attention, there are still three major challenges remaining: 1) it is difficult to classify an image into a single emotion type since different regions within an image can represent different emotions;2) there is a gap between low-level features and high-level emotions and 3) it is difficult to collect a training set of reliable emotional image content. To address these three issues, we propose an emotion classification method based on multi -scaleblocks using Multiple Instance Learning (MIL). We firstly extract blocks of an image at multiple scales using different image segmentation methods pyramid segmentation and simple linear iterative clustering (SLIC) and represent each block using the bag-of-visual words (BoVW) method. Then, to bridge the "affective gap", probabilistic latent semantic analysis (pLSA) is employed to estimate the latent topic distribution as a mid-level representation of each block. Finally, MIL, which reduces the need for exact labelling, is employed to classify the dominant emotion type of the image. Experiments carried out on three widely used datasets demonstrate that our proposed method with SLIC effectively improves the state-of-the-art results of image emotion classification 5.1% on average.

关键词： image Emotion Classification Multi-scale Multiple Instance Learning

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An Evaluation of HDR image Matching under Extreme Illumination Changes

An Evaluation of HDR Image Matching under Extreme Illuminati...

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

作者： Rana, Aakanksha Valenzise, Giuseppe Dufaux, Frederic Univ Paris Saclay Telecom Paristech CNRS LTCI Paris France

ISBN: (纸本)9781509053162

High dynamic range (HDR) imaging has potential to facilitate computer vision tasks such as image matching where lighting transformations hinder the matching performance. However, little has been done to quantify the gains with different possible HDR representations for vision algorithms like feature extraction. In this paper, we evaluate the performance of the full feature extraction pipeline, including detection and description, on ten different image representations: low dynamic range (LDR), seven different tone mapped (TM) HDR and two HDR imaging (linear and log encoded) representations. We measure the impact of using these different representations for feature matching using mean average precision (mAP) scores on four illumination change datasets. We perform feature extraction using four popular schemes in the literature: SIFT, SURF, BRISK, FREAK. With respect to previous studies, our observations confirm the advantages of HDR over conventional LDR imagery, and the fact that HDR linear values are not appropriate for vision tasks. However, HDR representations that work best for keypoint detection are not necessarily optimal when the full feature extraction is taken into account.

关键词： High dynamic range imaging lone mapping descriptors feature extraction illumination change

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