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Advances in Signal processing and Intelligent recognition Systems: Proceedings of third international symposium on Signal processing and Intelligent recognition Systems

引用

2017年

作者： Sabu M Thampi Sri Krishnan Juan Manuel Corchado Rodriguez Swagatam Das Michal Wozniak Dhiya Al-Jumeily

ISBN: (纸本)9783319679334

This Edited Volume gathers a selection of refereed and revised papers originally presented at the third international symposium on Signal processing and Intelligent recognition Systems (SIRS17), held on September 1316, 2017 in Manipal, India. The papers offer stimulating insights into biometrics, digital watermarking, recognition systems, image and video processing, signal and speech processing, pattern recognition, machine learning and knowledge-based systems. Taken together, they offer a valuable resource for all researchers and scientists engaged in the various fields of signal processing and related areas.

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Modeling of biologically-motivated self-learning equivalent convolutional recurrent-multilayer neural structures (BLM-SL-EC-RMNS) for image fragments clustering and recognition 10

Modeling of biologically-motivated self-learning equivalent ...

引用

10th international symposium on multispectral image processing and pattern recognition (MIPPR) - pattern recognition and Computer Vision

作者： Krasilenko, Vladimir G. Lazarev, Alexander A. Nikitovich, Diana V. Vinnitsa Social Econ Inst 86-131 Kelestskaya Str UA-21021 Vinnitsa Ukraine Vinnytsia Natl Tech Univ Vinnytsia Ukraine

ISBN: (纸本)9781510617223;9781510617216

The biologically-motivated self-learning equivalence-convolutional recurrent-multilayer neural structures (BLMSL_ECRMNS) for fragments images clustering and recognition will be discussed. We shall consider these neural structures and their spatial-invariant equivalental models (SIEMs) based on proposed equivalent two-dimensional functions of image similarity and the corresponding matrix-matrix (or tensor) procedures using as basic operations of continuous logic and nonlinear processing. These SIEMs can simply describe the signals processing during the all training and recognition stages and they are suitable for unipolar-coding multilevel signals. The clustering efficiency in such models and their implementation depends on the discriminant properties of neural elements of hidden layers. Therefore, the main models and architecture parameters and characteristics depends on the applied types of non-linear processing and function used for image comparison or for adaptive-equivalent weighing of input patterns. We show that these SL_EC_RMNSs have several advantages, such as the self-study and self-identification of features and signs of the similarity of fragments, ability to clustering and recognize of image fragments with best efficiency and strong mutual correlation. The proposed combined with learning-recognition clustering method of fragments with regard to their structural features is suitable not only for binary, but also color images and combines self-learning and the formation of weight clustered matrix-patterns. Its model is constructed and designed on the basis of recursively continuous logic and nonlinear processing algorithms and to k-average method or method the winner takes all (WTA). The experimental results confirmed that fragments with a large numbers of elements may be clustered. For the first time the possibility of generalization of these models for space invariant case is shown. The experiment for an images of different dimensions (a reference array)

关键词： self-learning equivalence-convolutional recurrent-multilayer neural structures clustering two-dimensional spatial function auto-encoding-decoding nonlinear processing

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Learning deep features with adaptive triplet loss for person reidentification 10

Learning deep features with adaptive triplet loss for person...

引用

10th international symposium on multispectral image processing and pattern recognition (MIPPR) - pattern recognition and Computer Vision

作者： Li, Zhiqiang Sang, Nong Chen, Kezhou Gao, Changxin Wang, Ruolin Huazhong Univ Sci & Technol Sch Automat Minist Educ Image Proc & Intelligent Control Key Lab Wuhan 430074 Hubei Peoples R China Wuhan Univ Sch Civil Engn Wuhan 430074 Hubei Peoples R China

ISBN: (纸本)9781510617223;9781510617216

Person reidentification (re-id) aims to match a specified person across non-overlapping cameras, which remains a very challenging problem. While previous methods mostly focus on feature extraction or metric learning, this paper makes the attempt in jointly learning both the global full-body and local body-parts features of the input persons with a multichannel convolutional neural network (CNN) model, which is trained by an adaptive triplet loss function that serves to minimize the distance between the same person and maximize the distance between different persons. The experimental results show that our approach achieves very promising results on the large-scale Market-1501 and DukeMTMC-reID datasets.

关键词： Deep learning part-based person reidentification (re-id)

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Hand Pose Estimation in Depth image using CNN and Random Forest 10

Hand Pose Estimation in Depth Image using CNN and Random For...

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10th international symposium on multispectral image processing and pattern recognition (MIPPR) - pattern recognition and Computer Vision

作者： Chen, Xi Cao, Zhiguo Xiao, Yang Fang, Zhiwen Huazhong Univ Sci & Technol Sch Automat Natl Key Lab Sci & Technol Multispectral Informat Wuhan Hubei Peoples R China Hunan Univ Humanities Sci & Technol Sch Energy & Mech Elect Engn Loudi Peoples R China

ISBN: (纸本)9781510617223;9781510617216

Thanks to the availability of low cost depth cameras, like Microsoft Kinect, 3D hand pose estimation attracted special research attention in these years. Due to the large variations in hand's viewpoint and the high dimension of hand motion, 3D hand pose estimation is still challenging. In this paper we propose a two-stage framework which joint with CNN and Random Forest to boost the performance of hand pose estimation. First, we use a standard Convolutional Neural Network (CNN) to regress the hand joints' locations. Second, using a Random Forest to refine the joints from the first stage. In the second stage, we propose a pyramid feature which merges the information flow of the CNN. Specifically, we get the rough joints' location from first stage, then rotate the convolutional feature maps (and image). After this, for each joint, we map its location to each feature map (and image) firstly, then crop features at each feature map (and image) around its location, put extracted features to Random Forest to refine at last. Experimentally, we evaluate our proposed method on ICVL dataset and get the mean error about 1 lmm, our method is also real-time on a desktop.

关键词： Hand pose estimation Convolutional Neural Network Random Forest

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Deep learning based hand gesture recognition in complex scenes 10

Deep learning based hand gesture recognition in complex scen...

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10th international symposium on multispectral image processing and pattern recognition (MIPPR) - pattern recognition and Computer Vision

作者： Ni, Zihan Sang, Nong Tan, Cheng Huazhong Univ Sci & Technol Minist Educ Image Proc & Intelligent Control Key Lab Sch Automat Wuhan 430074 Hubei Peoples R China ContextLogic Inc San Francisco CA 94104 USA

ISBN: (纸本)9781510617223;9781510617216

Recently, region-based convolutional neural networks(R-CNNs) have achieved significant success in the field of object detection, but their accuracy is not too high for small objects and similar objects, such as the gestures. To solve this problem, we present an online hard example testing(OHET) technology to evaluate the confidence of the R-CNNs' outputs, and regard those outputs with low confidence as hard examples. In this paper, we proposed a cascaded networks to recognize the gestures. Firstly, we use the region-based fully convolutional neural network(R-FCN), which is capable of the detection for small object, to detect the gestures, and then use the OHET to select the hard examples. To enhance the accuracy of the gesture recognition, we re-classify the hard examples through VGG-19 classification network to obtain the final output of the gesture recognition system. Through the contrast experiments with other methods, we can see that the cascaded networks combined with the OHET reached to the state-of-the-art results of 99.3% mAP on small and similar gestures in complex scenes.

关键词： Gesture recognition Object Detection Region-Based Fully Convolutional Neural Networks

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Feature hashing for fast image retrieval 10

Feature hashing for fast image retrieval

引用

10th international symposium on multispectral image processing and pattern recognition (MIPPR) - pattern recognition and Computer Vision

作者： Yan, Lingyu Fu, Jiarun Zhang, Hongxin Yuan, Lu Xu, Hui Hubei Univ Technol Sch Comp Sci Wuhan 430068 Hubei Peoples R China

ISBN: (纸本)9781510617223;9781510617216

Currently, researches on content based image retrieval mainly focus on robust feature extraction. However, due to the exponential growth of online images, it is necessary to consider searching among large scale images, which is very time-consuming and unscalable. Hence, we need to pay much attention to the efficiency of image retrieval. In this paper, we propose a feature hashing method for image retrieval which not only generates compact fingerprint for image representation, but also prevents huge semantic loss during the process of hashing. To generate the fingerprint, an objective function of semantic loss is constructed and minimized, which combine the influence of both the neighborhood structure of feature data and mapping error. Since the machine learning based hashing effectively preserves neighborhood structure of data, it yields visual words with strong discriminability. Furthermore, the generated binary codes leads image representation building to be of low-complexity, making it efficient and scalable to large scale databases. Experimental results show good performance of our approach.

关键词： image Retrieval Machine Learning Feature Hashing image Fingerprint Visual Words Binary code Semantic loss Mapping error

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Smoke regions extraction based on two steps segmentation and motion detection in early fire 10

Smoke regions extraction based on two steps segmentation and...

引用

10th international symposium on multispectral image processing and pattern recognition (MIPPR) - pattern recognition and Computer Vision

作者： Jian, Wenlin Wu, Kaizhi Yu, Zirong Chen, Lijuan Nanchang Hangkong Univ Sch Informat Engn Nanchang 330063 Jiangxi Peoples R China

ISBN: (纸本)9781510617223;9781510617216

Aiming at the early problems of video-based smoke detection in fire video, this paper proposes a method to extract smoke suspected regions by combining two steps segmentation and motion characteristics. Early smoldering smoke can be seen as gray or gray-white regions. In the first stage, regions of interests (ROIs) with smoke are obtained by using two step segmentation methods. Then, suspected smoke regions are detected by combining the two step segmentation and motion detection. Finally, morphological processing is used for smoke regions extracting. The Otsu algorithm is used as segmentation method and the ViBe algorithm is used to detect the motion of smoke. The proposed method was tested on 6 test videos with smoke. The experimental results show the effectiveness of our proposed method over visual observation.

关键词： Video-based Smoke Detection image enhancement Two steps segmentation ViBe motion detection

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Adaptive region constrained FCM algorithm for image segmentation with hierarchical superpixels 10

Adaptive region constrained FCM algorithm for image segmenta...

引用

10th international symposium on multispectral image processing and pattern recognition (MIPPR) - pattern recognition and Computer Vision

作者： Li, Lei Dong, Zhouli Fei, Xuan Zhang, Dexian Henan Univ Technol Coll Informat Sci & Engn Zhengzhou 450001 Henan Peoples R China

ISBN: (纸本)9781510617223;9781510617216

Spatially fuzzy c-means (FCM) clustering has been successfully applied in the field of image segmentation. However, due to the existence of noise and intensity inhomogeneity in images, most of the spatial constraint model fail to resolve misclassification problem. To further improve the segmentation accuracy, a robust spatially constrained FCM-based image segmentation method with hierarchical region information is proposed in this paper. First, two-level superpixles of the input image are generated by two classical segmentation methods, and the first level superpixels instead of the pixels are as input of FCM. Second, by considering the use of the spatial constraints with high-level superpixels, a novel membership function of the first-level superpixels is designed to overcome the impact of noise in the image and accelerate the convergence of clustering process. Through using superpixels instead of pixels and incorporating superpixel information into the spatial constraints, the proposed method can achieve highly consistent segmentation results. Experimental results on the Berkeley image database demonstrate the good performance of the proposed method.

关键词： image segmentation fuzzy clustering spatial constraint hierarchical superpixels

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A MIXTURE MODEL FOR ROBUST REGISTRATION IN KINECT SENSOR 10

A MIXTURE MODEL FOR ROBUST REGISTRATION IN KINECT SENSOR

引用

10th international symposium on multispectral image processing and pattern recognition (MIPPR) - pattern recognition and Computer Vision

作者： Peng, Li Zhou, Huabing Zhu, Shengguo HuBei Radio & TV Univ Wuhan Hubei Peoples R China Huazhong Univ Sci & Technol Sch Comp Sci & Technol Wuhan Hubei Peoples R China Hubei Prov Key Lab Intelligent Robot Wuhan Hubei Peoples R China Huazhong Inst Electroopt Wuhan Natl Lab Optoelect Wuhan Hubei Peoples R China

ISBN: (纸本)9781510617223;9781510617216

The Microsoft Kinect sensor has been widely used in many applications, but it suffers from the drawback of low registration precision between color image and depth image. In this paper, we present a robust method to improve the registration precision by a mixture model that can handle multiply images with the nonparametric model. We impose non-parametric geometrical constraints on the correspondence, as a prior distribution, in a reproducing kernel Hilbert space (RKHS). The estimation is performed by the EM algorithm which by also estimating the variance of the prior model is able to obtain good estimates. We illustrate the proposed method on the public available dataset The experimental results show that our approach outperforms the baseline methods.

关键词： EM image registration Kinect sensor

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Weighted Least Square Method for Epipolar Rectification in Semi-calibrated image 10

Weighted Least Square Method for Epipolar Rectification in S...

引用

10th international symposium on multispectral image processing and pattern recognition (MIPPR) - pattern recognition and Computer Vision

作者： Zhu, Guojia Zhou, Huabing Tao, Yiwei Wuhan Inst Technol Sch Comp Sci & Engn Wuhan 430205 Hubei Peoples R China Wuhan Inst Technol Hubei Key Lab Intelligent Robot Wuhan 430205 Hubei Peoples R China

ISBN: (纸本)9781510617223;9781510617216

The traditional method for dealing with the problem of epipolar rectification in the semi-calibrated case is to use RANdom SAmple Consensus(RANSAC), which could not get a correct parameter when exist serious mismatch points. So the weighted least square method is proposed to solve this problem. First, extracting Scale Invariant Feature Transform(SIFT) and conducting initial feature matching for image pairs. Next, according to the internal geometric relations of corresponding points, transforming the problem into a maximum likelihood estimate problem. And then, each pair of corresponding points is given weight, and the weight is regarded as a latent variable to stand for the precision of correct matching. Finally, weighted least square method and Expectation Maximization(EM) algorithm are used to estimate the latent variable and uncalibrated parameters. Experimental results show that propo-sed method could not only keep rectified precision high, but also has slighter image morphing and faster rectified velocity than state-of-the-art algorithms.

关键词： Epipolar rectification semi-calibrated latent variable weighted least square method Expectation Maximization(EM) algorithm

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