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Insect recognition Under Natural Scenes Using R-FCN with Anchor Boxes Estimation 10th

Insect Recognition Under Natural Scenes Using R-FCN with Anc...

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10th International conference on Image and Graphics (ICIG)

作者： Pang, Hong-Wei Yang, Peipei Chen, Xiaolin Wang, Yong Liu, Cheng-Lin Chinese Acad Sci Inst Automat Natl Lab Pattern Recognit Beijing Peoples R China Univ Chinese Acad Sci Beijing Peoples R China Chinese Acad Sci Inst Zool Key Lab Zool Systemat & Evolut Beijing Peoples R China

ISBN: (纸本)9783030341206;9783030341190

Insect species recognition is an important application of computer vision in zoology and agriculture. Most of existing methods resort to hand-crafted features and traditional classifiers, which usually give poor accuracy and apply only to elaborately taken full-size pictures. In this paper, we focus on a more challenging case where the images are taken in the wild with complex backgrounds, and propose to use a deep learning based detection model to deal with it. It exploits multi-class object detection to eliminate interferences from complex backgrounds, while taking advantages of deep learning to significantly improve the performance of recognition. After evaluating several popular detection methods, R-FCN is selected as the base model. To further improve its performance, we introduce a clustering algorithm for estimation of the anchor boxes instead of using predefined ones. the experimental results on a dataset of insect images collected in the wild prove the effectiveness of our proposed method in improving both accuracy and speed.

关键词： Multi-class object detection R-FCN Deep learning Insect recognition Natural scenes

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An active contour model for medical image segmentation using a quaternion framework 18

An active contour model for medical image segmentation using...

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18th Image Processing: Algorithms and Systems conference, IPAS 2020

作者： Voronin, V. Zhdanova, M. Semenishchev, E. Zelensky, A. Agaian, S. Moscow State University of Technology "STANKIN" Moscow Russia Don State Technical University Lab. «Mathematical methods of image processing and computer vision Intelligent Systems» Gagarina 1 Rostov on Don Russia CUNY/ College of Staten Island Dept. of Computer Science New York United States

this paper presents a new method for segmenting medical images is based on Hamiltonian quaternions and the associative algebra, method of the active contour model and LPA-ICI (local polynomial approximation - the intersection of confidence intervals) anisotropic gradient. Since for segmentation tasks, the image is usually converted to grayscale, this leads to the loss of important information about color, saturation, and other important information associated color. To solve this problem, we use the quaternion framework to represent a color image to consider all three channels simultaneously when segmenting the RGB image. As a method of noise reduction, adaptive filtering based on local polynomial estimates using the ICI rule is used. the presented new approach allows obtaining clearer and more detailed boundaries of objects of interest. the experiments performed on real medical images (Z-line detection) show that our segmentation method of more efficient compared with the current state-of-art methods. © 2020, Society for Imaging Science and Technology

关键词： Adaptive filtering

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A Real-time Tracking System Used for Surface Detection

A Real-time Tracking System Used for Surface Detection

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International conference on Information Science and Control Engineering (ICISCE)

作者： Xiao Wang Bo Li Zhankuan Zhang Chinese Academy of Forestry Research Institute of Wood Industry Beijing China

In industrial production, the high speed of object on conveyor always causes motion blur when computer vision is used to achieve appearance inspection, results in difficulties in image processing. In this study, a real-time tracking system based on cam transmission was applied to eliminate the relative movement between camera and objects. the outline of the cam was specifically designed to meet the requirement of real-time tracking and simplified the motor control. To test the system, the velocities of both camera and object were collected using rotary encoders. It was found that the camera velocity would be almost equal to that of object in a certain period when cam rotated uniformly. Also, the images were captured by triggering the camera at the suitable moment, and it was shown that the object in image was very clear. Compared with conventional restoration algorithms, the proposed method resulted in clearer images without any distortion, and could save computation time, which was especially suitable for on-line inspection in industrial production such as boards.

关键词： Tracking Process control Production Inspection Cameras Distortion Real-time systems

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Learning to Predict Salient Faces: A Novel Visual-Audio Saliency Model 16th

Learning to Predict Salient Faces: A Novel Visual-Audio Sali...

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16th European conference on computer vision, ECCV 2020

作者： Liu, Yufan Qiao, Minglang Xu, Mai Li, Bing Hu, Weiming Borji, Ali National Laboratory of Pattern Recognition CASIA Beijing China AI School University of Chinese Academy of Sciences Beijing China CEBSIT Beijing China The School of Electronic and Information Engineering and Hangzhou Innovation Institute Beihang University Beijing China MarkableAI Inc. New York United States

ISBN: (纸本)9783030585648

Recently, video streams have occupied a large proportion of Internet traffic, most of which contain human faces. Hence, it is necessary to predict saliency on multiple-face videos, which can provide attention cues for many content based applications. However, most of multiple-face saliency prediction works only consider visual information and ignore audio, which is not consistent with the naturalistic scenarios. Several behavioral studies have established that sound influences human attention, especially during the speech turn-taking in multiple-face videos. In this paper, we thoroughly investigate such influences by establishing a large-scale eye-tracking database of Multiple-face Video in Visual-Audio condition (MVVA). Inspired by the findings of our investigation, we propose a novel multi-modal video saliency model consisting of three branches: visual, audio and face. the visual branch takes the RGB frames as the input and encodes them into visual feature maps. the audio and face branches encode the audio signal and multiple cropped faces, respectively. A fusion module is introduced to integrate the information from three modalities, and to generate the final saliency map. Experimental results show that the proposed method outperforms 11 state-of-the-art saliency prediction works. It performs closer to human multi-modal attention. © 2020, Springer Nature Switzerland AG.

关键词： Forecasting

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Detection and Classification of Faulty Weft threads Using Both Feature-Based and Deep Convolutional Machine Learning Methods 7th

Detection and Classification of Faulty Weft Threads Using Bo...

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7th International conference on pattern recognition Applications and Methods, ICPRAM 2018

作者： Kopaczka, Marcin Saggiomo, Marco Güttler, Moritz Kielholz, Kevin Merhof, Dorit Institute of Imaging and Computer Vision RWTH Aachen University Aachen Germany Institut für Textiltechnik RWTH Aachen University Aachen Germany

ISBN: (纸本)9783030054984

In our work, we analyze how faulty weft threads in air-jet weaving machines can be detected using image processing methods. To this end, we design and construct a multi-camera array for automated acquisition of images of relevant machine areas. these images are subsequently fed into a multi-stage image processing pipeline that allows defect detection using a set of different preprocessing and classification methods. Classification is performed using both image descriptors combined with feature-based machine learning algorithms and deep learning techniques implementing fully convolutional neural networks. To analyze the capabilities of our solution, system performance is thoroughly evaluated under realistic production settings. We show that both approaches show excellent detection rates and that by utilizing semantic segmentation acquired from a fully convolutional network we are not only able to detect defects reliably but also classify defects into different subtypes, allowing more refined strategies for defect removal. © 2019, Springer Nature Switzerland AG.

关键词： Defects

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Design of A Video-Based Vehicle Speed Measurement System - An Uncertainty Approach 7

Design of A Video-Based Vehicle Speed Measurement System - A...

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Joint 7th International conference on Informatics, Electronics & vision (ICIEV) / 2nd International conference on Imaging, vision and pattern recognition (icIVPR)

作者： Javadi, Saleh Dahl, Mattias Pettersson, Mats, I Kulesza, Wlodek J. Blekinge Inst Technol Dept Math & Nat Sci S-37435 Karlshamn Sweden Blekinge Inst Technol Dept Appl Signal Proc S-37179 Karlskrona Sweden

ISBN: (纸本)9781538651636

Speed measurement is one of the key components of intelligent transportation systems. It provides suitable information for traffic management and law enforcement. this paper presents a versatile and analytical model for a video-based speed measurement in form of the probability density function (PDF). In the proposed model, the main factors contributing to the uncertainties of the measurement are considered. Furthermore, a guideline is introduced in order to design a video-based speed measurement system based on the traffic and other requirements. As a proof of concept, the model has been simulated and tested for various speeds. An evaluation validates the strength of the model for accurate speed measurement under realistic circumstances.

关键词： Intelligent transportation systems machine vision motion analysis pattern recognition speed measurement

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Learning Discriminative and Complementary Patches for Face recognition 14

Learning Discriminative and Complementary Patches for Face R...

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14th IEEE International conference on Automatic Face and Gesture recognition (FG)

作者： Liu, Zhiwei Tang, Ming Hu, Guosheng Wang, Jinqiao Chinese Acad Sci Inst Automat Natl Lab Pattern Recognit Beijing 100190 Peoples R China Univ Chinese Acad Sci Beijing Peoples R China Universal AI Inc Visionfinity Inc ObjectEye Inc Las Vegas NV USA AnyVision Singapore Singapore Queens Univ Belfast Belfast Antrim North Ireland

ISBN: (纸本)9781728100890

the ensemble of convolutional neural networks (CNNs) has widely been used in many computer vision tasks including face recognition. Many existing ensembles of face recognition CNNs apply a two-stage pipeline to target performance improvement [10], [20], [22], [23], [29]: (1) it trains multiple CNNs separately with many face patches covering different facial areas;(2) the features derived from different models are aggregated off-line by different fusion methods. the well-known face recognition work, DeepID2 [20] trains 200 networks based on 200 arbitrarily chosen facial areas and chooses the best 25 ones to achieve impressive performance. However, it is very time-consuming to train so many networks. In addition, a brute-force like way of choosing facial patches is used without knowing which face patches are complementary and discriminative. It might be lack of generalization capability for cross-database applications. To solve that, we propose a novel end-to-end CNN ensemble architecture which automatically learns the complementary and discriminative patches for face recognition. Specifically, we propose a novel Patch Generation Engine (PGE) with Patch Search Spatial Transformer Network (PS-STN) and ROI shrunk loss to perform the patch selection process. ROI shrunk loss enlarges the distance of learned features in spatial space and feature space and learn complementary features. In order to get final aggregated feature, we use a supervised fusion module named Two Stage Discriminative Fusion Module (TSDFM) which effective to capture the global and local information and further guide the PGE to learn better patches. Extensive experiments conducted on LFW and YTF datasets show the effectiveness of our novel end-to-end ensemble method.

关键词： Face recognition

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Single Image Plankton 3D Reconstruction from Extended Depth of Field Shadowgraph 24th

Single Image Plankton 3D Reconstruction from Extended Depth ...

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24th International conference on pattern recognition (ICPR)

作者： Zelenka, Claudius Koch, Reinhard Univ Kiel Dept Comp Sci Kiel Germany

ISBN: (纸本)9783030057923;9783030057916

Marine plankton occurs in the ocean with strongly varying degrees of sparsity. For in-situ plankton measurements the shadowgraph has been established as the observation device of choice in recent years. In this paper a novel depth from defocus based approach to partially coherent 3D reconstruction of marine plankton volumes is presented. With a combination of recent advances in coherent image restoration and deep learning, we create a 3D view of the shadowgraph observation volume. For the selection of in-focus images we develop a novel training data generation technique. this kind of reconstruction was previously only possible with holographic imaging systems, which require laser illumination with high coherence, which often causes parasitic interferences on optical components and speckles. the new 3D visualization gives easily manageable data by resulting in a sharp view of each plankton together with its depth and position. Moreover, this approach allows the creation of all-in-focus images of larger observation volumes, which is otherwise impossible due to the physically limited depth of field. We show an effective increase in depth of field by a factor of 7, which allows marine researchers to use larger observation volumes and thus a much more effective observation of marine plankton.

关键词： Marine plankton Shadowgraph Image restoration

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Adversarial network for edge detection 7

Adversarial network for edge detection

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Joint 7th International conference on Informatics, Electronics and vision and 2nd International conference on Imaging, vision and pattern recognition, ICIEV-IVPR 2018

作者： Zeng, Zhiliang Yu, Ying Kin Hong Wong, Kin Department of Computer Science and Engineering Chinese University of Hong Kong Hong Kong

ISBN: (纸本)9781538651612

Edge detection is a fundamental problem in computer vision and has been explored for many decades. Due to the rapid development of machine learning techniques and their applications to image processing, there is a proliferation of neural network-based approaches to solve the edge detection problem. these methods, such as richer convolutional features for edge detection(RCF), have good performance and even outperform human beings. Most of the existing neural network-based systems use the convolutional network or its variant. they usually produce thick edges and the application of non-maximum-suppression to suppress the edge is necessary. In this paper, we explore another type of neural network called the conditional generative adversarial network (cGAN) to address the edge detection problem. cGAN is an innovative framework to do the image synthesis task. It can generate an image close to the real one. After training, our network can produce an edge map that contains more detailed information and thinner edges compared to the state-of-the-art methods that require the well-known non-maximum-suppression for post-processing. the proposed approach is able to produce a high quality edge map directly without further processing. Our solution is computation efficient. It can achieve a speed of 59 and 26 frames per second (fps) for an image resolution of [256×256×3] and [512×512×3], respectively. © 2018 IEEE.

关键词： Edge detection

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Real time traffic density measurement using computer vision and dynamic traffic control 7

Real time traffic density measurement using computer vision ...

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Joint 7th International conference on Informatics, Electronics and vision and 2nd International conference on Imaging, vision and pattern recognition, ICIEV-IVPR 2018

作者： Chowdhury, Md. Fahim Ryad Ahmed Biplob, Md. Uddin, Jia Dept. of Computer Science and Engineering BRAC University Dhaka Bangladesh

ISBN: (纸本)9781538651612

In recent times, traffic jam has become a common problem in the major cities all over the world. In this paper, we propose a dynamic traffic control system by measuring the traffic density at the intersections by real time video feeds and image processing. A video sample was collected and then Mixture of Gaussian algorithm was used for background subtraction method and for foreground detection to keep the count of the cars in each lane. the vehicles are detected by their line of centroid. A movement in centroid confirms a vehicle. the traffic lights at the intersections will change dynamically according to the conditions of traffic that will be detected from the video feeds. In between two intersections, there will be multiple cameras installed to count the number of vehicles entering and leaving each intersection. Furthermore, we restrict the vehicles to take right turns in the intersections. To validate our work, density measurement algorithm, images of live feeds and logics for traffic control are shown in this paper. © 2018 IEEE.

关键词： Mixtures

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