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Siamese Network for Visual Tracking via Focal Loss

Siamese Network for Visual Tracking via Focal Loss

引用

IEEE International Symposium on parallel and distributed processing with Applications and IEEE International conference on Ubiquitous Computing and Communications (ISPA/IUCC)

作者： Shan Jiang Cheng Han Xiaoqiang Di Hua Li School of Computer Science and Technology ChangChun Unviersity of Science and Technology Changchun China

ISBN: (纸本)9781728152103

Visual tracking is a fundamental problem in computer vision. Lots efforts have been made in the past decades and researchers have obtained some achievements, but there are still problems exist. To get a more powerful feature representation, deep learning based methods appear layer upon layer, and most of them achieve good performance at the price of losing speed. In this paper, we propose a SiamFL network for visual tracking, which introduce the focal loss with the Siamese network. With the focal loss, the network is capable of filtering out the easy examples, leaving the hard samples to train a discriminative network. Moreover, the SiamFL network also can alleviate the class imbalance problem by properly sampling the positive and negative samples at a certain ratio. Experimental results demonstrate the SimaFL has quick convergence and also have a better performance over the baseline tracker.

关键词： computer vision image representation learning (artificial intelligence) visual tracking image representation images computer vision siamese Balance impairment Network

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Multiple License Plate Location Algorithm in Complex Scene

Multiple License Plate Location Algorithm in Complex Scene

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IEEE International Symposium on parallel and distributed processing with Applications and IEEE International conference on Ubiquitous Computing and Communications (ISPA/IUCC)

作者： YuMei Zhou MeiNi Lv ZiQing Ling DongLing Li School of Electrical and Eletronic Engineering TianHe College of GuangDong Polytechnic Normal University GuangZhou China School of Eletronic and Information Engineering WuZhou University WuZhou China

ISBN: (纸本)9781728152103

Aiming at the problem that the existing license plate location methods can't work well in locating the license plate in certain natural scenes such as low luminance, low resolution and inclination scene of vehicle, a license plate location method based on cascade classifier and convolution neural network is proposed. Firstly, the cascade classifier is used for license plate coarse location, and the convolution neural network is used for license plate precise location. Experiments show that, compared with cascade classifier, the method in this paper has higher accuracy and recall rate for target license plate location, and the method in this paper has strong adaptability to different environments.

关键词： character recognition convolutional neural nets edge detection feature extraction image colour analysis object detection traffic engineering computing License plates image colour analysis Character recognition Object detection Edge detection Feature extraction engineering computing cascades Retrieval recall

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DnM3Net: Multi-Scale & Multi-Level Shuffle-CNN Via Multi-Level Attention for image Denoising

DnM3Net: Multi-Scale & Multi-Level Shuffle-CNN Via Multi-Lev...

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IEEE International Symposium on parallel and distributed processing with Applications and IEEE International conference on Ubiquitous Computing and Communications (ISPA/IUCC)

作者： Yue Cao Jinhe He Yu Zhang Gang Lu Shigang Liu Xiaojun Wu School of Computer Science Shaanxi Normal University Xian Shaanxi Normal University Xi'an China

ISBN: (纸本)9781728152103

Recently, based on novel convolutional neural net-work architectures proposed, tremendous advances have been achieved in image denoising task. An effective and efficient multi-level network architecture for image denoising refers to restore the latent clean image from a coarser scale to finer scales and pass features through multiple levels of the model. Unfortunately, the bottleneck of applying multi-level network architecture lies in the multi-scale information from input images is not effectively captured and the fine-to-coarse feature fusion strategy to be ignored in image denoising task. To solve these problems, we propose a multi-scale & multi-level shuffle-CNN Via multi-level attention (DnM3Net), which plugs the multi-scale feature extraction, fine-to-coarse feature fusion strategy and multi-level attention module into the new network architecture in image denoising task. The advantage of this approach are two-fold: (1) It solve the multi-scale information extraction issue of multi-level network architecture, making it more effective and efficient for the image denoising task. (2) It is impressive performance because the better trade-off between denoising and detail preservation. The proposed novel network architecture is validated by applying on synthetic gaussian noise gray and RGB images. Experimental results show that the DnM3Net effectively improve the quantitative metrics and visual quality compared to the state-of-the-art denoising methods.

关键词： convolutional neural nets feature extraction Gaussian noise image colour analysis image denoising image sampling image denoising image samples image colour analysis NETWORK STRUCTURE Gaussian noise Feature extraction multi-scale hierarchical models Noise reduction

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An Integrative Approach to Robust Hand Detection Using CPM-YOLOv3 and RGBD Camera in Real Time

An Integrative Approach to Robust Hand Detection Using CPM-Y...

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IEEE International conference on Big Data and Cloud Computing (BdCloud)

作者： Muhammad Raza Vachiraporn Ketsoi Haopeng Chen School of Electronic Information and Electrical Engineering Shanghai Jiao Tong University Shanghai China

ISBN: (数字)9781728143286

ISBN: (纸本)9781728143293

Innovative methods to interact with electronic devices are attracting researchers attentions, and many applications of Human Computer Interaction have been developed in recent years. Hand detection under different illuminations and backgrounds is crucial for tasks such as, actions recognition, gestures recognition and hand-based interaction with wearable devices. Many existing methods are able to detect the human hand precisely, but the task still remains challenging due to environment complexity, different invariants, processing time, and accuracy. It is required for an application to recognize the human hand precisely for proper interaction with the wearable devices. This paper proposed a robust and a compact method for hand detection by employing you only look once (YOLO) and convolutional pos machines (CPM). This paper consists of two parts, network training and testing. In the first part, Oxford hand dataset is used to train a neural network. This dataset has various hand images under different invariants, and backgrounds. Afterwards, images are captured using a digital camera in real-time and then human hands are detected using a trained neural network. In addition, detected hands, positions and orientations are calculated accordance with defined active zone parameters and a depth sensor. Our trained neural network is dubbed as HHDNet, an abbreviation for human hand detection network. The HHDNet is tested under different environment conditions, and experimental results showed that the accuracy and the frame rate of the proposed model are superior than others state of the art methods.

关键词： image color analysis Neural networks Feature extraction Cameras Robot sensing systems Estimation Skin

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Stacked Sparse Auto-Encoder for Deep Clustering

Stacked Sparse Auto-Encoder for Deep Clustering

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IEEE International conference on Big Data and Cloud Computing (BdCloud)

作者： Jinyu Cai Shiping Wang Wenzhong Guo College of Mathematics and Computer Science Fuzhou university Fuzhou China

ISBN: (数字)9781728143286

ISBN: (纸本)9781728143293

Deep clustering attempts to capture the feature representation that benefits the clustering issue for inputs. Although the existing deep clustering methods have achieved encouraging performance in many research fields, there still presents some shortcomings, such as the lack of consideration of local structure preservation and sparse characteristics of input data. To this end, we propose the deep-stacked sparse embedded clustering method in this paper, which considers both the preservation of local structure and sparse property of inputs. The proposed network is trained to capture the feature representation for input data by the guidance of clustering loss and reconstruction loss, where the reconstruction loss prevents the corruption of feature space and guarantees the local structure preservation. Besides, some sparse parameters are added to the encoder to avoid learning of meaningless features. Through simultaneously minimizing the reconstruction loss and cluster loss, the proposed network can jointly learn the clustering oriented feature and optimize the assignment of cluster labels. Then we conduct amounts of comparative experiments, which consist of six clustering methods and four publicly available data sets. Eventually, the clustering accuracy, adjusted rand index and normalized mutual information are utilized as three evaluation metrics to provide a comparison. Comprehensive experiments validate the effectiveness of introducing sparse property and preserving local structure in our method.

关键词： Clustering methods Clustering algorithms Decoding Neural networks Computer science image reconstruction

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Appraisal of Pre-processing Techniques for Automated Detection of Diabetic Retinopathy

Appraisal of Pre-processing Techniques for Automated Detecti...

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International conference on parallel distributed and Grid Computing (PDGC)

作者： Charu Bhardwaj Shruti Jain Meenakshi Sood Department of ECE Jaypee University of Information Technology H.P India

ISBN: (纸本)9781728106489

Retinopathy is a retinal vascular disease caused by abnormal blood flow that damages the retina and it accounts for 5% of blindness worldwide. There are different causes of Retinopathy as retinopathy of prematurity, diabetic retinopathy and hypertensive retinopathy; this paper mainly focuses on pre-processing techniques employed for the detection of diabetic retinopathy. In this work, literatures reporting different methods for automated diabetic retinopathy detection in color images of retina are analyzed for their strength and shortcoming. Extensive techniques for retinal image screening have been published so far thus this paper presents the evaluation of these methods on a common database of image screening. Different image pre-processing techniques are implemented on fundus images and their performance is compared using performance metrics like mean square error, peak signal to noise ratio and root mean square error. Pre-processing steps used in this work can also be applied to other images of interest to identify and recognize the objects in an image.

关键词： Retinopathy Diabetes Histograms Retina Databases Blood vessels Biomedical imaging

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MFM: A Multi-level Fused Sequence Matching Model for Candidates Filtering in Multi-paragraphs Question-Answering 19th

MFM: A Multi-level Fused Sequence Matching Model for Candida...

引用

19th Pacific-Rim conference on Multimedia (PCM)

作者： Liu, Yang Huang, Zhen Hu, Minghao Du, Shuyang Peng, Yuxing Li, Dongsheng Wang, Xu Natl Univ Def Technol Sci & Technol Parallel & Distributed Lab Changsha Hunan Peoples R China Tongji Univ Shanghai Peoples R China

ISBN: (纸本)9783030007645;9783030007638

Text-based question-answering (QA in short) is a popular application on multimedia environments. In this paper, we mainly focus on the multi-paragraphs QA systems, which can retrieve many candidate paragraphs to feed into the extraction module to locate the answers in the paragraphs. However, according to our observations, there are no real answer in many candidate paragraphs. To filter these paragraphs, we propose a multi-level fused sequence matching (MFM in short) model through deep network methods. Then we construct a distant supervision dataset based on Wikipedia and carry out several experiments on that. Also we use another popular sequence matching dataset to test the performance of our model. Experiments show that our MFM model can outperform recent models not only on the filtering candidates in multi-paragraphs QA task but also on the sequence matching task.

关键词： Question-answering Sequence matching Fused encoding

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Investigation of image processing Techniques for Glaucoma Detection in Human Eyes

Investigation of Image Processing Techniques for Glaucoma De...

引用

International conference on parallel distributed and Grid Computing (PDGC)

作者： Shilpa Kaushal Sunil Datt Sharma Shruti Jain Dept. of Electronics & Comm. Engg. Jaypee University of Information Technology Himachal Pradesh India

ISBN: (纸本)9781728106489

Glaucoma is a disease associated with retina of eye. Presently, millions of human being is suffering from this disease. Early detection of these diseases can save the people from blindness. Therefore, various methods have been developed for its detection. In this paper, we have studied the reported methods and summarized their performance in terms of accuracy of detection.

关键词： Feature extraction Databases Optical imaging Support vector machines Transforms Retina Biomedical imaging

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Mixup-Based Acoustic Scene Classification Using Multi-channel Convolutional Neural Network 19th

Mixup-Based Acoustic Scene Classification Using Multi-channe...

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19th Pacific-Rim conference on Multimedia (PCM)

作者： Xu, Kele Feng, Dawei Mi, Haibo Zhu, Boqing Wang, Dezhi Zhang, Lilun Cai, Hengxing Liu, Shuwen Natl Univ Def Technol Sci & Technol Parallel & Distributed Lab Changsha Hunan Peoples R China Natl Univ Def Technol Coll Informat Commun Wuhan Hubei Peoples R China Natl Univ Def Technol Coll Meteorol & Oceanog Changsha Hunan Peoples R China Sun Yat Sen Univ Coll Engn Guangzhou Guangdong Peoples R China Nanjing Univ Technol Coll Comp Sci Nanjing Jiangsu Peoples R China

ISBN: (纸本)9783030007645;9783030007638

Audio scene classification, the problem of predicting class labels of audio scenes, has drawn lots of attention during the last several years. However, it remains challenging and falls short of accuracy and efficiency. Recently, Convolutional Neural Network (CNN)-based methods have achieved better performance with comparison to the traditional methods. Nevertheless, conventional single channel CNN may fail to consider the fact that additional cues may be embedded in the multi-channel recordings. In this paper, we explore the use of Multi-channel CNN for the classification task, which aims to extract features from different channels in an end-to-end manner. We conduct the evaluation compared with the conventional CNN and traditional Gaussian Mixture Model-based methods. Moreover, to improve the classification accuracy further, this paper explores the using of mixup method. In brief, mixup trains the neural network on linear combinations of pairs of the representation of audio scene examples and their labels. By employing the mixup approach for data augmentation, the novel model can provide higher prediction accuracy and robustness in contrast with previous models, while the generalization error can also be reduced on the evaluation data.

关键词： Gaussian distribution

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EIP-VIO: Edge-Induced Points Based Monocular Visual-Inertial Odometry

EIP-VIO: Edge-Induced Points Based Monocular Visual-Inertial...

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IEEE International Symposium on parallel and distributed processing with Applications and IEEE International conference on Ubiquitous Computing and Communications (ISPA/IUCC)

作者： Luoying Hao Hongjian Li Jun Cheng Qieshi Zhang Shenzhen College of Advanced Technology University of Chinese Academy of Sciences Beijing China CAS Key Laboratory of Human-Machine Intelligence-Synergy Systems Shenzhen Institutes of Advanced Technology Shenzhen China The Chinese University of Hong Kong Hong Kong China

ISBN: (纸本)9781728152103

Edge and line features boost the performance of simultaneous localization and mapping (SLAM) systems while bring more additional computational cost. In this paper, we propose an improved and practical monocular visual-inertial odometry method based on selective edge points. Focus on solving the issues of mismatching and tracking loss, the auxiliary edge is utilized to supervise the selection of features instead of using rough features directly. Features along edges are employed as stronger keypoints to steer point-selection away from less distinctive points. By combining matched edge-guided feature based method, selective sampling keypoints along edges based direct method and inertial measurement unit (IMU), the performance of absolute pose estimation is more accurate and efficient. In addition, a weight adjustment factor scheme before IMU error term is introduced in our optimization model to further reduce the influence of motion blur and less texture. Experimental results on public benchmark dataset demonstrate that the proposed method is more efficient and effective compared with the state-of-the-art methods.

关键词： distance measurement edge detection feature extraction image matching pose estimation SLAM (robots) pose estimation distance measurement image matching Edge detection Feature extraction motion blur odometry MISMATCH simultaneous localization and mapping edges Edge of a figure or solid

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