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A New Few-shot Segmentation Network Based on Class Representation

A New Few-shot Segmentation Network Based on Class Represent...

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IEEE visual communications and image processing conference

作者： Yuwei Yang Fanman Meng Hongliang Li King N. Ngan Qingbo Wu School of Information and Communication Engineering University of Electronic Science and Technology of China Chengdu China

This paper studies few-shot segmentation, which is a task of predicting foreground mask of unseen classes by a few of annotations only, aided by a set of rich annotations already existed. The existing methods mainly focus the task on "how to transfer segmentation cues from support images (labeled images) to query images (unlabeled images)", and try to learn efficient and general transfer module that can be easily extended to unseen classes. However, it is proved to be a challenging task to learn the transfer module that is general to various classes. This paper solves few-shot segmentation in a new perspective of "how to represent unseen classes by existing classes", and formulates few-shot segmentation as the representation process that represents unseen classes (in terms of forming the foreground prior) by existing classes precisely. Based on such idea, we propose a new class representation based few-shot segmentation framework, which firstly generates class activation map of unseen class based on the knowledge of existing classes, and then uses the map as foreground probability map to extract the foregrounds from query image. A new two-branch based few-shot segmentation network is proposed. Moreover, a new CAM generation module that extracts the CAM of unseen classes rather than the classical training classes is raised. We validate the effectiveness of our method on Pascal VOC 2012 dataset, the value FB-IoU of one-shot and five-shot arrives at 69.2% and 70.1% respectively, which outperforms the state-of-the-art method.

关键词： image segmentation Feature extraction Training Task analysis Annotations Convolution Manuals

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Automated Cell Segmentation and Spot Detection in Fluorescence in Situ Hybridization Staining to Assess HER2 Status in Breast Cancer 26

Automated Cell Segmentation and Spot Detection in Fluorescen...

引用

26th IEEE Signal processing and communications Applications conference (SIU)

作者： Cetin, Sukru Burak Khameneh, Fariba D. Serteli, Ercan Alp Cayir, Sercan Hatipoglu, Gokhan Kamasak, Mustafa Ayalti, Samet Razavi, Salar Budancamanak, Yakup Ozsoy, Gulsah Istanbul Tech Univ Informat Inst Istanbul Turkey Istanbul Tech Univ Dept Elect & Commun Istanbul Turkey Istanbul Tech Univ Dept Comp & Informat Istanbul Turkey Virasoft ARI 1 Teknokent Istanbul Turkey

ISBN: (纸本)9781538615010

Fluorescence in situ hybridization (FISH) approach is constituted of a pair of complementary techniques for precisely detecting gene amplification and over-expression which are regarded as signs of cancer in patients. Signal detection of FISH whole slides is extremely significant as enables to detect amplification situation. However, nuclei detection and segmentation of FISH slides through microscopic images is tedious and time-consuming for pathologists to evaluate. Furthermore, FISH specimen slides provided at pathological laboratories are frequently noisy and not analyzable entirely. Therefore, traditional visual methods require more time due to fact that they are exceedingly reliant on human view. They require more time and attention in the evaluation process by pathologists. Nowadays, computer-aided FISH solutions bring radical remedies in this particular problematic area of pathology. Although computer-aided FISH solutions have many advantages, they have some drawbacks due to the variability of staining images. In this study, we present an accurate cell nuclei segmentation and signal detection methodology to detect red and green spots localized in segmented cells. The problems in the visual experiments as well as the assessment of amplification state of the evaluated cases are presented, which corresponds to the visual scoring of pathologists.

关键词： biomarker image processing cell segmentation tile-based processing fluorescence in situ hybridization (FISH) region merging

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image processing Algorithm to Detect Defects in Optical Fibers 37th

Image Processing Algorithm to Detect Defects in Optical Fibe...

引用

37th conference of the North-American-Fuzzy-Information-processing-Society (NAFIPS)

作者： Mafalda, Marcelo Welfer, Daniel De Souza Leite Cuadros, Marco Antonio Tello Gamarra, Daniel Fernando Univ Fed Santa Maria Santa Maria RS Brazil Fed Inst Espirito Santo Vitoria Brazil

ISBN: (纸本)9783319953120;9783319953113

This work proposes a system to detect visual defects in an optical fiber. Fibers of different types and with different simulated deformations were used, looking for an approximation of a real case of defect in an optical fiber. Some continuous fiber patterns were detected in images captured with a microscopic camera. The identification of these patterns was searched using different image processing techniques, such as edge detection, line detection and feature descriptors. In order to classify images of the fibers in good and defective ones, a fuzzy classifier was used. Experimental results of the algorithm are shown and is demonstrated that the proposed method helps to detect defects and classify optical fibers.

关键词： image processing Optical fiber Fuzzy logic

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Corrosion Damage Identification and Lifetime Estimation of Ship Parts using image processing 7

Corrosion Damage Identification and Lifetime Estimation of S...

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7th International conference on Computing, communications and Informatics (ICACCI)

作者： Naladala, Indukala Raju, Aparna Aishwarya, C. Koolagudi, Shashidhar G. Natl Inst Technol Karnataka Dept Comp Sci & Engn Surathkal India

ISBN: (纸本)9781538653142

Corrosion is a process that leads to early failure of ship parts, high maintenance costs and a shortened service life of the ship, as a whole. Human visual inspection is currently the most widely used method to assess corrosion. In this paper, we propose the use of image processing to determine the extent of corrosion and estimate the time period within which the ship parts have to be replaced. In the case of availability of pre-corrosion images, the histograms of the pre-corrosion and post-corrosion images are compared and their similarity is quantified as the Sum of Squared Distances (SSD) value. Our method then produces a numerical output which signifies the level of corrosion. We then correlate extent of damage and ship part replacement period. In the absence of pre-corrosion images, we classify superpixels in the post-corrosion image as undamaged or damaged with an accuracy of 92 per cent, using Random Forest classifier. We have also evaluated the performance of corrosion prevention measures such as galvanization, painting, etc on different parts of the ship, for example, parts exposed to only air and parts exposed to both saline water and air.

关键词： Corrosion Damage Identification image processing Texture Features

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Multiscale Progressive image Compression Network Guided by Learnable Just Noticeable Distortion 33

Multiscale Progressive Image Compression Network Guided by L...

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33rd IEEE International conference on visual communications and image processing (IEEE VCIP)

作者： Jin, Xin Ye, Runchun Chen, Zhibo Univ Sci & Technol China CAS Key Lab Technol Geospatial Informat Proc & Ap Hefei Anhui Peoples R China

ISBN: (纸本)9781538644584

One key challenge to the learning-based image compression is that adaptive bit allocation is crucial for compression effectiveness but can hardly be trained into a neural network. Hereby, in this work, We presents an end-to-end trainable image compression framework, named Multi-scale Progressive Network (MPN) to achieve spatially variant bit allocation and rate control through the guidance of a novel learnable just noticeable distortion (JND) map. Specifically, MPN's encoder archives multi-scale feature representation through a three-branched structure. Each branch employs an independent feature extraction strategy for the specific receptive field and merge progressively under the guidance of corresponding learnable JND maps that generated by our proposed Bit-Allocation sub-Network (BAN), which make MPN focus on the areas where attract the human visual system (HVS) and preserve more texture of the image during the compression procedure. Finally, a hybrid objective function is introduced to further make MPN more efficient and mimic the discriminative characteristics of the human visual system (HVS). Experiments show that MPN significantly outperforms traditional JPEG, JPEG 2000 and few state-of-art learning-based methods by multi-scale structural similarity (MS-SSIM) index, and has the ability to produce the much better visual result with rich textures, sharp edges, and fewer artifacts.

关键词： image compression

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DeepPalm-A Unified Framework for Personal Human Authentication 12

DeepPalm-A Unified Framework for Personal Human Authenticati...

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12th International conference on Signal processing and communications (SPCOM)

作者： Jaswal, Gaurav Kaul, Amit Nath, Ravinder Nigam, Aditya Natl Inst Technol Dept Elect Engn Hamirpur 177001 India Indian Inst Technol Sch Comp & Elect Engn Mandi 175005 Himachal Prades India

ISBN: (纸本)9781538638217

Different biometric traits have been proved to carry unique biological information of an individual. Although their use is always problem specific but the good performance of palm print represents a recent trend in this field. In this paper, we have proposed a novel generalized segmentation Network (PSegNet), that automatically categorize the palm print image obtained from multiple sensory resources and then detect the fixed size ROIs accurately. To best of our knowledge, this is the first attempt, of an end-to-end trained object detector, inspired by Deep Learning technique namely faster R-CNN (Region based Convolutional Neural Network) has been employed to detect and localize the position of palm. Then, the ROI image is well enhanced and transformed into illumination invariant visual representation. Over this, hand crafted features are patch-wise computed and the sub-patches are recursively matched using deformable dense matching technique called as Deep-Matching. The experimental results are examined on three publicly available palm print databases namely Poly-U, CASIA, and GPDS-CL1 databases. The proposed approach achieves equal error rate (EER) of 0.083 %, decidability index (DI) of 2.94 and rank-1 identification rates (CRR) of 100% that justifies the role of CNN based ROI segmentation and multi-scale Deep-Matching criterion.

关键词： image enhancement

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Block-based image Coding by Compression-Constrained Transform Domain Down-Scaling 33

Block-based Image Coding by Compression-Constrained Transfor...

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33rd IEEE International conference on visual communications and image processing (IEEE VCIP)

作者： Cui, Chang Zhu, Shuyuan Meng, Xiandong Liu, Shuaicheng Zeng, Bing Univ Elect Sci & Technol China Sch Informat & Commun Engn Chengdu Sichuan Peoples R China Hong Kong Univ Sci & Technol Dept Elect & Comp Engn Hong Kong Peoples R China

ISBN: (纸本)9781538644584

Transform domain down-scaling (TDDS) is traditionally implemented by dropping most of high-frequency components of the transformed block. Applying it to image compression can improve the compression efficiency by saving considerable bit-cost. Due to losing some necessary high-frequency information, the resulted image compressed by using the traditional TDDS-based coding often suffers a serious quality degradation. In this paper, we propose a compression-constrained TDDS and perform it on each N x N block to produce an N/2 x N/2 coefficient block for the compression. Our proposed TDDS not only guarantees a high reconstruction quality but also makes a low bit-cost for compression. We integrate it in practical image coding to build up our proposed compression scheme. Experimental results show that our proposed method demonstrates excellent coding performance when used to compress image signals.

关键词： image coding spatial domain transform domain down-scaling coefficients

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HEVC Compression Artifact Reduction with Generative Adversarial Networks

HEVC Compression Artifact Reduction with Generative Adversar...

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International conference on Wireless communications and Signal processing (WCSP)

作者： Shiqi Yu Bolin Chen Yiwen Xu Weiling Chen Zhonghui Chen Tiesong Zhao Fujian Key Lab for Intelligent Processing and Wireless Transmission of Media Information Fuzhou University Fuzhou China

Video compression technology is significant to video transmission and storage. However, compression artifacts arise in videos. Specifically, coarse quantization eliminates video details and degrades visual quality. Most of artifact reduction methods use filter processing or Mean Square Error (MSE)loss that leads to over-smoothing results. Moreover, most of the methods target to reduce single image compression artifact instead of video artifact. In this paper, we present an adversarial learning method with recurrent framework called Video Artifact Reduction Generative Adversarial Network (VRGAN). Our network contains a generator with recurrent framework that improves video consistency, a dense block that enhances receptive field for large transform unit, and a relativistic discriminator that evaluates the relationship between the generated frames and the original high-quality frames. Our VRGAN is able to generate more realistic videos. The effectiveness in reducing video compression artifacts of the method has been demonstrated qualitatively and quantitatively. The performance comparison with previous works shows the superiority of the proposed method.

关键词： Generators Convolution Gallium nitride visualization Transform coding Estimation image coding

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Multi-Label Deep Sparse Hashing 33

Multi-Label Deep Sparse Hashing

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33rd IEEE International conference on visual communications and image processing (IEEE VCIP)

作者： Liong, Venice Erin Lu, Jiwen Tan, Yap-Peng Nanyang Technol Univ Interdisciplinary Grad Sch Rapid Rich Object Search ROSE Lab Singapore Singapore Tsinghua Univ Dept Automat Beijing Peoples R China Nanyang Technol Univ Sch Elect & Elect Engn Singapore Singapore

ISBN: (纸本)9781538644584

In this paper, we propose a multi-label deep sparse hashing (MDSH) to learn compact binary codes for efficient image retrieval. Unlike most existing supervised hashing methods which only exploit pairwise or triplet-wise similarity to learn binary codes, we perform deep network training such that optimal binary codes are obtained from a sparsity-based discriminative criterion. Specifically, we learn our hashing network by solving a multi-label classification problem with a sparse cross-entropy loss which ensures that sparse probabilities can be obtained while also learning the binary codes. By doing so, our network is able to scale well with ground truth labels which are generally sparse. Experimental results on two widely used multi-label image hashing datasets are presented to show the effectiveness of our proposed approach.

关键词： hashing image retrieval deep learning

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SC-IQA: Shift compensation based image quality assessment for DIBR-synthesized views 33

SC-IQA: Shift compensation based image quality assessment fo...

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33rd IEEE International conference on visual communications and image processing (IEEE VCIP)

作者： Tian, Shishun Zhang, Lu Morin, Luce Deforges, Olivier Natl Inst Appl Sci Rennes CNRS UMR 6164 IETR 20 Ave Buttes Coesmes F-35708 Rennes 7 France

ISBN: (纸本)9781538644584

Depth-image-based-rendering (DIBR) has been used to generate the virtual views for Multi-view videos and Free-viewpoint videos. However, the quality assessment of DIBR-synthesized views is very challenging owing to the new types of distortions induced by inaccurate depth maps, dis-occlusions and image inpainting methods. There exist a large number of object shifts and geometric distortions in the synthesized view which the traditional 2D quality metrics may fail to assess. In this paper, we propose a shift compensation based image quality assessment metric (SC-IQA) for DIBR-synthesized views. Firstly, the global geometric shift is compensated roughly by an SURF + RANSAC homography approach. Then, a multi-resolution block matching method, which performs a more accurate matching, is used to precisely compensate the shift and penalize the local geometric distortion as well. In addition, a visual saliency map is also used as a weighting function. To calculate the final overall quality scores, only the worst blocks are utilized since the biggest distortions have the most effects on the overall perceptual quality. The results show that the proposed metric significantly outperforms the state-of-the-art synthesized view dedicated metrics and the conventional 2D IQA metrics.

关键词： Synthesized view Quality assessment Shift compensation Affine transform Block matching

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