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Meaning and Uncertainty Inherent in Understanding images, Spatial-Taxon Hierarchy, Word Annotation and Relevant Context 17th

Meaning and Uncertainty Inherent in Understanding Images, Sp...

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17th International conference on Information processing and Management of Uncertainty in Knowledge-Based systems (IPMU)

作者： Barghout, Lauren Univ Calif Berkeley Elect Engn & Comp Sci BISC Berkeley CA 94720 USA

ISBN: (纸本)9783319914794;9783319914787

This paper explores the meaning and uncertainty inherent in (a) understanding image hierarchies;(b) describing them with words;and (c) navigating the abstraction context of the viewer. A spatial-taxon hierarchy, a standardized scene architecture, partitions an image into a foreground, subject and salient objects and/or sub-objects. The introduction starts with a thought experiment (Thought experiments, borrowed from the model-theoretic isomorphism standard of structure-mapping theory, enable readers to compare two systems thought to be similar. It's a form of inductive reasoning that expands knowledge in the face of uncertainty (Holland et al. 1986 [13]) by providing an explicit representation of how two systems are similar. Though the conclusion that the two systems do share an isomorphic structure can only be supported via various degrees of truth (fuzzy membership), it establishes its plausibility. Analogical reasoning is natural to human thought and communication making it useful for scientific papers.) based on a poem & an image landscape. The thought experiment is intended to provide analogical inference as scaffolding for the rest of the paper. The results of experimental data of human annotated spatial-taxon and corresponding word descriptions of two images are presented. The experimental results are analyzed in terms of spatial-taxon designation and the meaning & uncertainty presented by the human annotations. The results support the fuzzy spatial-taxon hierarchy of human scene perception described by other works, show that word descriptions depend on spatial-taxon designation and that long tail word distributions require unbounded possibility with semantic uncertainty (type 2 fuzzy sets) for the word counts in the probability distribution. Deep learning image recognition, Zadeh information restriction principal, Shannon's distinction between information content and semantics, customized image descriptions and fuzzy inference techniques are explored.

关键词： Segmentation Recognition Classification image labeling Semantic Spatial-taxon Fuzzy inference Possibility theory Deep learning Information theory Visual-taxometrics Machine vision Human vision Attention Visual & image cognition

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Face image Illumination processing Based on GAN with Dual Triplet Loss 1

引用

1st Chinese conference on Pattern Recognition and Computer Vision (PRCV)

作者： Ma, Wei Xie, Xiaohua Lai, Jianhuang Zhu, Junyong Sun Yat Sen Univ Guangzhou Peoples R China Guangdong Key Lab Informat Secur Technol Guangzhou Peoples R China Minist Educ Key Lab Machine Intelligence & Adv Comp Beijing Peoples R China

ISBN: (数字)9783030033385

ISBN: (纸本)9783030033385;9783030033378

It is generally known that the illumination could seriously affect the performance of face analysis algorithms. Moreover, in most practical applications, the illumination is usually uncontrolled. A number of methods have been put forward to tackle the problem of illumination variations in face images, but they always only work on facial region and need to segment faces in advance. Furthermore, many illumination processing methods only demonstrate on grayscale images and require strict alignment of face images, resulting in limited applications in the real world. In this paper, we propose a face image illumination processing method based on the Generative Adversarial Network (GAN) with dual triplet loss. Through considering the inter-domain similarity and intra-domain difference between the generated images and the real images, we put forward the dual triplet loss. At the same time, we introduce the self-similarity constraint of the images in the target illumination field. Experiments on the CMU Multi-PIE face datasets demonstrate that the proposed method preserve the facial details well when relighting. The experiment of 3D face reconstruction also verifies the effectiveness of the proposed method.

关键词： Face image Illumination processing Generative adversarial nets Dual triplet loss

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Baking Status Characterization of Baked Food image Based on Superpixel Segmentation

Baking Status Characterization of Baked Food Image Based on ...

引用

IEEE International Workshop on Imaging systems and Techniques (IST)

作者： Conghui Wang Bochuan Hou Jing Shi Jianhua Yang Boning Wu Zixing Fu Kun Fang School of Automation Northwestern Polytechnical University Xi'an China Information Security Center State Key Laboratory of Networking and Switching Technology Beijing University of Posts and Telecommunications Beijing China Ningbo Fotile Kitchen Ware Co. Ltd. Ningbo China

ISBN: (数字)9781728138688

ISBN: (纸本)9781728138695

The temperature field distribution of the oven directly affects the quality of the baked food. The traditional oven performance research mainly focuses on the heating method in the simulated oven, lacking quantitative analysis of the baked food. In this paper, the digital image processing technology is used to segment and extract different baking status of the baked food in order to qualitatively and quantitatively expresses the internal temperature field distribution of the oven. Firstly, the image of baked food is captured by high-definition digital camera. And then it will be preprocessed to obtain a denoised image with only baked food area. Thirdly, the simple linear iterative clustering (SLIC) segmentation is used to extract the different baking status. The experimental results show that the simple linear iterative clustering (SLIC) segmentation algorithm can digitally express the baking status in the form of superpixels. The proposed method can qualitatively and quantitatively reflect the distribution of the temperature field inside the oven corresponding to the baked food image, which provide a basis for further evaluation of the heat distribution field inside the oven.

关键词： image color analysis image segmentation Ovens Temperature distribution Temperature measurement Clustering algorithms Digital images

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Visual Indoor Positioning Method Using image Database 5

Visual Indoor Positioning Method Using Image Database

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5th IEEE conference on Ubiquitous Positioning, Indoor Navigation and Location-Based Services (UPINLBS)

作者： Xia, Yi Xiu, Chundi Yang, Dongkai Beihang Univ Sch Elect & Informat Engn Beijing Peoples R China

ISBN: (纸本)9781538637555

With the increasing significance and usage of ordinary location-based services, a number of technologies have been invented for indoor positioning systems over the years. Some of them, however, suffer from limited precision and high expenses. In this paper, we propose a simple visual indoor positioning method using images captured by handheld cameras, in which case, there is no need for infrastructure components. Taking advantage of Bag-of-Visual-Words model, our proposed method consists of two stages. By using image processing algorithms i. e. feature extraction, description and clustering, an image database and its visual vocabulary are prepared in the first stage, along with the corresponding indoor position information. Then, a query image is matched with the image database from stage one to get the most several similar ones, whose position will be taken to get the location estimation by the method of voting and re-classification. We conducted some experiments to demonstrate the feasibility of the visual indoor positioning method. According to the simulation results, 90% of the positioning errors are within 1.2 meters and the average positioning time is no more than 2 seconds, which turns out to achieve a balance between speed and accuracy.

关键词： visual indoor positioning Bag-of-Visual-Words image matching position estimation

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Blind image Quality Assessment for Screen Content images Based on Patch-wise Multi-order Derivatives 10

Blind Image Quality Assessment for Screen Content Images Bas...

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10th International conference on Digital image processing (ICDIP)

作者： Yang, Wenming Zeng, Zhengda Sun, Wen Liao, Qingmin Tsinghua Univ Grad Sch Shenzhen Shenzhen Key Lab Info Sci & Tech Shenzhen Engn Lab IS & DCPDept Elect Engn Shenzhen Peoples R China

ISBN: (数字)9781510622005

ISBN: (纸本)9781510622005

The Screen content images (SCIs) are images containing textual and pictorial regions, which have become more and more connected with our daily life with the widespread adoption of multimedia applications. In particular, the image quality assessment (IQA) of SCIs is important because of its good property to guide and optimize lots of image processing systems. However, the no-reference (NR) IQA algorithms receive little attention and achieve unsatisfactory performance. Hence, this paper proposes a novel no-reference IQA method based on patch-wise multi-order derivatives for SCIs. This method includes two stages: patch-wise image quality evaluation and quality pooling. The first stage focuses on learning visual quality of local regions. Two features of image patches are extracted: multi-order derivative statistics, multi-order derivative histograms, which respectively describe the global and local information of the multi-order derivatives. Then the support vector regression (SVR) is applied to measure visual quality of image patches given a set of extracted features. The second stage aims at pooling patch-wise quality to an overall quality score with weights derived from entropy of gradient information of SCIs. Experimental results show that our method obtains superior performance against state-of-the-art NR-IQA approaches on the SIQAD database of SCIs, and also achieves competitive performance against state-of-the-art FR-IQA methods for SCIs.

关键词： No-reference (Blind) image quality assessment Screen Content images (SCIs) multi-order derivatives patch-wise

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Performance of an automatic inspection system for classification of Fusarium Moniliforme damaged corn seeds by image analysis 22

Performance of an automatic inspection system for classifica...

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22nd International conference on Circuits, systems, Communications and Computers (CSCC)

作者： Daskalov, Plamen Kirilova, Eleonora Georgieva, Tzvetelina Univ Ruse Dept Automat & Mechatron 8 Studentska Str Ruse 7017 Bulgaria

This paper presents algorithms for pre-processing, feature selection and classifier design which are used for Parameter Refining in the task of development of an automatic system for recognition and grading of corn seeds with external signs of Fusarium Moniliforme disease. The abilities of several feature selection methods FDR, Scatter matrices and Stepwise Discriminant Analysis and two classification methods - Support Vector Machine (SVM) and K-Nearest Neighbours (K-NN) are investigated. Design and implementation of the system also has been showed. The system could continually present one by one positioned corn kernels to CCD camera, perform a classification procedure of captured images and discharge seeds to assigned containers. The software was developed in LabVIEW environment including image analysis and classification procedures performed using MATLAB Script. Results for total error rate of 8.4% - 7.2% from preliminary classification related to 8480 seeds from16 Bulgarian varieties and total error rate of 6.95% - 20.4% for experimental results obtained with the system during the control measurements of the seed sample are obtained.

关键词： image analysis

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Pattern Recognition Framework for Histological Slide Segmentation 1

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17th International conference on Computer Information systems and Industrial Management (CISIM)

作者： Jelen, Lukasz Kulus, Michal Jurek, Tomasz Wroclaw Univ Sci & Technol Dept Comp Engn Wybrzeze Wyspianskiego 27 PL-50370 Wroclaw Poland Wroclaw Med Univ Dept Human Morphol & Embryol Histol & Embryol Div Ul Chalubinskiego 6a PL-50368 Wroclaw Poland Wroclaw Med Univ Dept Forens Med Ul J Mikulicza Radeckiego 4 PL-50345 Wroclaw Poland

ISBN: (数字)9783319999548

ISBN: (纸本)9783319999548;9783319999531

The venous system is similar in all people, but there are some individual variations. The system is well described and compared in the literature but it seems that there is a lack of comparison on the microscopic level. In this paper we present a segmentation framework for histological image segmentation that uses a clustering approach. The described framework can be used for further comparison of the venous system at the microscopic level. For that purpose we adopted a k-means and fuzzy c-means algorithms to classify image pixels to obtain vein segmentation. The presented results are promising and achieved partitioning can further be utilized for quantitative vein comparison.

关键词： Movat FCM Segmentation KNN segmentation Fuzzy c-means k-means image processing Computer aided diagnosis Pattern Recognition Histological slide

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Real-time object detection towards high power efficiency

Real-time object detection towards high power efficiency

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Design, Automation and Test in Europe conference and Exhibition (DATE)

作者： Yu, Jincheng Guo, Kaiyuan Hu, Yiming Ning, Xuefei Qiu, Jiantao Mao, Huizi Yao, Song Tang, Tianqi Li, Boxun Wang, Yu Yang, Huazhong Tsinghua Univ Beijing Peoples R China DeePhi Technol Beijing Peoples R China

ISBN: (纸本)9783981926309

In recent years, Convolutional Neural Network (CNN) has been widely applied in computer vision tasks and has achieved significant improvement in image object detection. The CNN methods consume more computation as well as storage, so GPU is introduced for real-time object detection. However, due to the high power consumption of GPU, it is difficult to adopt CPU in mobile applications like automatic driving. The previous work proposes some optimizing techniques to lower the power consumption of object detection on mobile GPI) or FPGA. In the first Low-Power image Recognition Challenge (LPIRC), our system achieved the best result with mAP/Energy on mobile GPU platforms. We further research the acceleration of detection algorithms and implement two more systems for real-time detection on FPGA with higher energy efficiency. In this paper, we will introduce the object detection algorithms and summarize the optimizing techniques in three of our previous energy efficient detection systems on different hardware platforms for object detection.

关键词： Object detection Convolution Graphics processing units Hardware Task analysis Proposals Acceleration

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A Real-time Video Fusion processing System Based On SIFT Algorithm and FPGA

A Real-time Video Fusion Processing System Based On SIFT Alg...

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IEEE International conference on Automation, Electronics and Electrical Engineering (AUTEEE)

作者： Chaodong Ling Hongkai Qin Qing Tao Ruimin Huang Huipeng Duan Wencai Lin Huaqiao University Xiamen China

ISBN: (数字)9781728150307

ISBN: (纸本)9781728150314

In order to combine multi-views into one ultra-wide single-view for present surveillance desire, a video fusion scheme based on FPGA is proposed in this paper. The geometric transformation and image fusion algorithms used in our scheme are simulated by Matlab for verification and optimization. Then, a combination of two 640*480 video images captured by cameras is implemented in a Altera FPGA platform DE2-115. Moreover, the weighted fusion method is used to realize video fusion for better performance; and larger size of 1280*768@60Hz video images' combination are also realized in DE2-115. Experiments show that the proposed approach can realize the mosaic of two video images with a delay of 16 clock cycles, i.e. a total of 0.16us. The error induced by the fusion process is less than 1 pixel. The implementation in DE2-115 for two RGB 1280*768@60 pixel size image combination can be 1743 times faster than software implementation with a good splicing performance.

关键词： Streaming media Field programmable gate arrays Real-time systems Splicing Software Hardware image fusion

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Secure UAV-Based System to Detect and Filter Sea Objects Using image processing 16th

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16th International conference on Computer Aided systems Theory (EUROCAST)

作者： Lodeiro-Santiago, Moises Caballero-Gil, Candido Aguasca-Colomo, Ricardo Munilla-Fajardo, Jorge Ramio-Aguirre, Jorge Univ La Laguna Dept Ingn Informat & Sistemas Tenerife Spain Univ Las Palmas Gran Canaria Inst Univ SIANI Gran Canaria Spain Univ Malaga Dept Ingn Comunicac Malaga Spain Univ Politecn Madrid Dept Sistemas Informat Madrid Spain

ISBN: (纸本)9783319747187;9783319747170

In the last years irregular immigration has aroused again due to the fact of war in some regions like middle east and in continents like Africa. Because of that, in this paper we presented a novel solution based in new emerging technology to face off the immigration problem focused on the case of sea-type immigration. The presented system is composed by unmanned aerial vehicles (UAV), also known as RPAs or drones, used to fly autonomously over an area in order to detect unallocated potentials immigration boats. The method used is the image processing using combined algorithms and software like OpenCV2 to detect certain patterns in images to determinate whether it is a possible immigration boat or not. Besides, it has been included some prof of concepts to show the effectiveness of the presented system. As main results we was able to determine and detect using aerial pictures if a boat it is a boat with an 82.3% of accuracy (according to the F1 score accuracy test).

关键词： UAV image processing Patera boat Ship detection Security

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