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An Advanced Local Offset Matching Strategy for Object Proposal Matching

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

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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 ...

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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 ...

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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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MODELING TEMPORAL INFORMATION USING DISCRETE FOURIER TRANSFORM FOR RECOGNIZING EMOTIONS IN USER-GENERATED VIDEOS 23

MODELING TEMPORAL INFORMATION USING DISCRETE FOURIER TRANSFO...

引用

23rd IEEE International conference on image processing (ICIP)

作者： Zhang, Haimin Xu, Min Univ Technol Sydney Fac Engn & IT GBDTC Sydney NSW 2007 Australia

ISBN: (纸本)9781467399616

With the widespread of user-generated Internet videos, emotion recognition in those videos attracts increasing research efforts. However, most existing works are based on frame level visual features and/or audio features, which might fail to model the temporal information, e.g. characteristics accumulated along time. In order to capture video temporal information, in this paper, we propose to analyse features in frequency domain transformed by discrete Fourier transform (DFT features). Frame-level features are firstly extract by a pre-trained deep convolutional neural network (CNN). Then, time domain features are transferred and interpolated into DFT features. CNN and DFT features are further encoded and fused for emotion classification. By this way, static image features extracted from a pre-trained deep CNN and temporal information represented by DFT features are jointly considered for video emotion recognition. Experimental results demonstrate that combining DFT features can effectively capture temporal information and therefore improve emotion recognition performance. Our approach has achieved a state-of-the-art performance on the largest video emotion dataset (VideoEmotion-8 dataset), improving accuracy from 51.1% to 55.6%.

关键词： video emotion recognition discrete Fourier transform convolutional neural network

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Multimodal Retinal image Registration Using Edge Map and Feature Guided Gaussian Mixture Model

Multimodal Retinal Image Registration Using Edge Map and Fea...

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

作者： Ma, Jiayi Jiang, Junjun Chen, Jun Liu, Chengyin Li, Chang Wuhan Univ Elect Informat Sch Wuhan 430072 Peoples R China China Univ Geosci Sch Comp Sci Wuhan 430074 Peoples R China China Univ Geosci Sch Automat Wuhan 430074 Peoples R China Huazhong Univ Sci & Technol Sch Elect Informat & Commun Wuhan 430074 Peoples R China

ISBN: (纸本)9781509053162

In this paper, we propose a method for multimodal retinal image registration based on feature guided Gaussian mixture model (GMM) and edge map. We extract two sets of feature points from the edge maps of two images, and formulate image registration as the estimation of a feature guided mixture of densities: a GMM is fitted to one point set, such that both the centers and local features of the Gaussian densities are constrained to coincide with the other point set. The problem is solved under a maximum-likelihood framework together with an iterative EM algorithm initialized by confident feature matches, where the image transformation is modeled by an affine function. Extensive experiments on various retinal images show the robustness of our method, which consistently outperforms other state-of-the-arts, especially when the data is badly degraded.

关键词： Retina image registration multimodal feature guided GMM

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An Efficient HEVC Multi-rate Encoding System Based on x265

An Efficient HEVC Multi-rate Encoding System Based on x265

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

作者： Cai, Chunlei Yin, Shibo Zhang, Xiaoyun Gao, Zhiyong Shanghai Jiao Tong Univ Inst Image Commun & Network Engn Shanghai Peoples R China

ISBN: (纸本)9781509053162

Multi-rate encoding using HEVC is in great demand but with huge complexity. Although x265 is an efficient HEVC encoder, it has not been optimized for multi-rate encoding and its parallelism is still not enough on multi-core processors. In this paper, we deeply investigate the coding correlation including depth and prediction mode between HEVC streams at multiple rates. Then an efficient HEVC multi-rate encoding system is proposed which consists of depth sharing and prediction modes pruning based on Naive Bayes Classification. Experimental results show that the proposed multi-rate encoding system can reduce 44.68% of encoding time with 0.016dB BD-PSNR performance improvement and much higher CPU utilization compared with independently x265 encoding multiple times.

关键词： multi-rate encoding system HEVC x265 best mode information sharing Naive Bayes Classification

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Automated Tree Detection and Density Calculation using Unmanned Aerial Vehicles

Automated Tree Detection and Density Calculation using Unman...

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

作者： Guldogan, Olcay Rotola-Pukkila, Jani Balasundaram, Uvaraj Le, Thanh-Hai Mannar, Kamal Chrisna, Taufan Mega Gabbouj, Moncef Accenture Serv Oy Helsinki Finland Accenture Pte Ltd Singapore Singapore Asia Pacific Resources Int Holdings Ltd Singapore Singapore Tampere Univ Technol Dept Signal Proc FIN-33101 Tampere Finland

ISBN: (纸本)9781509053162

Growth monitoring in tree plantation farms plays a crucial role in taking necessary actions from early stages to improve harvesting efficiency. Unmanned aerial vehicles (UAVs) provide a fast and efficient way to acquire data from large farms with difficult access. We propose a novel end-to-end system composed of multiple stages to identify areas where seeds have not grown as expected in young tree farms. The system acquires data from UAV flights to generate georeferenced orthophoto images. Next, local binary patterns, distance transform, and watershed segmentation methods are applied on images to detect trees. Finally, tree density distributions are calculated in the granularity of approximately 90 m(2) tiles. We assessed the system performance by comparing detection results against a ground truth set of over 50000 trees from 16 orthophoto images for two tree species. The median deviation was 2 trees per tile for both species, where the median tree count was 14 and 9 per tile, respectively.

关键词： Unmanned aerial vehicles image processing object detection image segmentation feature extraction

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