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Edge-Aware Monocular Dense Depth Estimation with Morphology

Edge-Aware Monocular Dense Depth Estimation with Morphology

引用

international conference on pattern recognition

作者： Zhi Li Xiaoyang Zhu Haitao Yu Qi Zhang Yongshi Jiang Institute of Automation Chinese Academy of Sciences

Dense depth maps play an important role in Computer Vision and AR (Augmented Reality). For CV applications, a dense depth map is the cornerstone of 3D reconstruction allowing real objects to be precisely displayed in the computer. And Dense depth maps can handle correct occlusion relationships between virtual content and real objects for better user experience in AR. However, the complicated computation limits the development of computing dense depth maps. We present a novel algorithm that produces low latency, spatio-temporally smooth dense depth maps using only a CPU. The depth maps exhibit sharp discontinuities at depth edges in low computational complexity ways. Our algorithm obtains the sparse SLAM reconstruction first, then extracts coarse depth edges from a down-sampled RGB image by morphology operations. Next, we thin the depth edges and align them with image edges. Finally, an effective initialization scheme and an improved optimization solver are adopted to accelerate convergence. We evaluate our proposal quantitatively and the result shows improvements on the accuracy of depth map with respect to other state-of-the-art and baseline techniques.

关键词： Three-dimensional displays Simultaneous localization and mapping Image edge detection Morphology Estimation User experience pattern recognition

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UHRSNet: A Semantic Segmentation Network Specifically for Ultra-High-Resolution Images

UHRSNet: A Semantic Segmentation Network Specifically for Ul...

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international conference on pattern recognition

作者： Lianlei Shan Minglong Li Xiaobin Li Yang Bai Ke Lv Bin Luo Si-Bao Chen Weiqiang Wang University of Chinese Academy of Sciences Beijing China MOE Key Lab of Signal Processing and Intelligent Computing School of Computer Science and Technology Anhui University Hefei China

Semantic segmentation is a basic task in computer vision, but only limited attention has been devoted to the ultra-high-resolution (UHR) image segmentation. Since UHR images occupy too much memory, they cannot be directly put into GPU for training. Previous methods are cropping images to small patches or downsampling the whole images. Cropping and downsampling cause the loss of contexts and details, which is essential for segmentation accuracy. To solve this problem, we improve and simplify the local and global feature fusion method in previous works. Local features are extracted from patches and global features are from downsampled images. Meanwhile, we propose one new fusion called local feature fusion for the first time, which can make patches get information from surrounding patches. We call the network with these two fusions ultra-high-resolution segmentation network (UHRSNet). These two fusions can effectively and efficiently solve the problem caused by cropping and downsampling. Experiments show a remarkable improvement on Deepglobe dataset [1].

关键词： Training Image segmentation Computer vision Semantics Graphics processing units Feature extraction pattern recognition

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Network Intrusion Detection System Using soft computing Technique-Fuzzy Logic Versus Neural Network: A Comparative Study

Network Intrusion Detection System Using Soft Computing Tech...

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international conference on Cognitive Informatics and soft computing (CISC)

作者： Mishra, Srinivas Pradhan, Sateesh Kumar Rath, Subhendu Kumar Biju Patnaik Univ Technol Dept Comp Sci & Engn Rourkela Odisha India Utkal Univ Dept Comp Sci Bhubaneswar Odisha India Biju Patnaik Univ Technol Rourkela Odisha India

ISBN: (纸本)9789811514517;9789811514500

Security of a data framework is its significant property, particularly today, when PCs are interconnected by means of web. Since no framework can be completely secure, the opportune and precise recognition of interruptions is essential. For this reason, intrusion detection systems (IDS) were planned. An interruption identification framework's principle aspiration is to sort exercises of a framework into two key classes: standard and suspicious exercises. Most IDS business devices are abuse frameworkswith principle-based master framework structure. Be that as it may, these strategies are less effective when assault qualities shift from inherent marks. In such manner, a scope of pre-preparing practices, for example, information mining, neural systems, Petri nets, state change outlines, hereditary calculations, choice trees and fluffy-based rationales is worked out. In this paper, we have considered distinctive fluffy methodologies for interruption identification framework utilizing fuzzy set hypothesis, and we dissect fuzzy standard, expressly for peculiarity-based assault finding. For structure inconsistency framework, neural systems likewise can be utilized, on the grounds that they can figure out how to segregate the ordinary and anomalous conduct of a framework from models. Along these lines, they offer a promising strategy for structure oddity frameworks. Fake neural systems give the possibility to recognize and arrange organized action dependent on constrained, fragmented and nonlinear information sources. We have completed a similar report between two delicate registering procedures utilization of fluffy in the event that principles versus use of the feed forward neural network prepared by back-propagation calculation for interruption location. Reenactment result demonstrates that the feed forward neural network prepared by back-propagation calculation for interruption location yields better outcome which distinguishes the interruptions precisely and is well re

关键词： Intrusion detection system Fuzzy logic Fuzzy if-then rule Back-propagation neural network Feed forward neural network soft computing

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MA-LSTM: A Multi-Attention Based LSTM for Complex pattern Extraction

MA-LSTM: A Multi-Attention Based LSTM for Complex Pattern Ex...

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international conference on pattern recognition

作者： Jingjie Guo Kelang Tian Kejiang Ye Cheng-Zhong Xu Sun Yat-sen University Shenzhen Institutes of Advanced Technology Chinese Academy of Sciences State Key Lab of IoTSC Faculty of Science and Technology University of Macau

With the improvement of data volume, computing power and algorithms, deep learning has achieved rapid development and showing excellent performance. Recently, many deep learning models are proposed to solve the problems in different areas. A recurrent neural network (RNN) is a class of artificial neural networks where connections between nodes form a directed graph along a temporal sequence. This allows it to exhibit temporal dynamic behavior, which makes it applicable to tasks such as handwriting recognition or speech recognition. However, the RNN relies heavily on the automatic learning ability to update parameters that concentrate on the data flow but seldom considers the feature extraction capability of the gate mechanism. In this paper, we propose a novel architecture to build the forget gate which is generated by multiple bases. Instead of using the traditional single-layer fully-connected network, we use a Multiple Attention (MA) based network to generate the forget gate which refines the optimization space of gate function and improve the granularity of the recurrent neural network to approximate the map in the ground truth. Due to the benefit of MA structure on the gate mechanism, the proposed MA-LSTM model achieves better feature extraction capability than other known models.

关键词： Deep learning Handwriting recognition Recurrent neural networks Computer architecture Speech recognition Logic gates Predictive models

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Talking Face Generation via Learning Semantic and Temporal Synchronous Landmarks

Talking Face Generation via Learning Semantic and Temporal S...

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international conference on pattern recognition

作者： Aihua Zheng Feixia Zhu Hao Zhu Mandi Luo Ran He Anhui Provincial Key Laboratory of Multimodal Cognitive Computation School of Computer Science and Technology Anhui University Heifei China Center for Research on Intelligent Perception and Computing (CRIPAC) National Laboratory of Pattern Recognition (NLPR) CASIA Beijing China Center for Excellence in Brain Science and Intelligence Technology CAS Beijing China

ISBN: (纸本)9781728188089;9781728188096

Given a speech clip and facial image, the goal of talking face generation is to synthesize a talking face video with accurate mouth synchronization and natural face motion. Recent progress has proven the effectiveness of the landmarks as the intermediate information during talking face generation. However, the large gap between audio and visual modalities makes the prediction of landmarks challenging and limits generation ability. This paper proposes a semantic and temporal synchronous landmark learning method for talking face generation. First, we propose to introduce a word detector to enforce richer semantic information. Then, we propose to preserve the temporal synchronization and consistency between landmarks and audio via the proposed temporal residual loss. Lastly, we employ a U-Net generation network with adaptive reconstruction loss to generate facial images for the predicted landmarks. Experimental results on two benchmark datasets LRW and GRID demonstrate the effectiveness of our model compared to the state-of-the-art methods of talking face generation.

关键词： Learning systems Visualization Face recognition Semantics Mouth Detectors Benchmark testing

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Plant Disease recognition and Crop Recommendation System using Deep Learning

Plant Disease Recognition and Crop Recommendation System usi...

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international conference on Computational Science and Technology (ICCST)

作者： Sreeraj. R Prayagdev. E Vishnu Murali Jaefer Akhilkumar K S Dept. of computer science and engineering Universal Engineering College Thrissur Kerala India

ISBN: (纸本)9781665476560

Agriculture is the foundation of our country. India, the world's second-largest producer of agricultural goods, generates over 280 million tonnes annually, which accounts for more than 15% of its GDP. A.A.R.C.S. (Advanced Agricultural Recommendation and Classification System) is a flutter-based farmer-friendly Mobile Application. One of the features of the proposed system is the crop recommendation feature that recommends correct crop by considering parameters like potassium, phosphorous and nitrogen contents, temperature, rainfall, and humidity. The second feature is seedling classification (using transfer learning), to identify the plants at an early stage. And as the third feature, the system also uses transfer learning to teach the model to detect common plant diseases. The above two models accept the image as an input and recognize and classify the image into the corresponding class. The proposed system uses edge-computing technology (Deep-Learning based model inside the mobile application) which simply means “taking action on data as near to the source as possible rather than in a central, remote data center, to reduce latency and bandwidth use”, i.e. the classification will be done on-device itself, thus overcoming the need of high latency and high bandwidth network links. The application also features a common platform to list the products of the farmers and tools and lands for rent service by providing the respective contact numbers of the owners. There will also be an encrypted community chat option which will be implemented using network sockets. The proposed system also contains an admin dashboard that provides infographics about users, common diseases, chat usage, push notification service and its analytics, etc. The mobile application will be the data source for disease-based analytics.

关键词： Productivity Plant diseases Scientific computing soft sensors Sockets Transfer learning Crops

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Online pattern recognition of Time-series Gas Sensor Data with Adaptive 2D-CNN Ensemble

Online Pattern Recognition of Time-series Gas Sensor Data wi...

引用

IEEE international Workshop on Intelligent Data Acquisition and Advanced computing Systems: Technology and Applications

作者： Vishakha Pareek Santanu Chaudhury Sanjay Singh CSIR - Central Electronics Engineering Research Institute Pilani India Academy of Scientific and Innovative Research (AcSIR) Ghaziabad India Indian Institute of Technology Delhi India

ISBN: (纸本)9781665426060

Intelligent sensor system design demands the efficient pattern recognition method that uses raw time-series sensor data to identify the target gas with fast and accurate results. The presented work uses gas sensor array response (open sampling setting) for 10 bio-marker gases. The responses are obtained at different concentrations. We propose a 2D convolution neural network (2D-CNN) based adaptive ensemble network for gas identification. The Spatio-temporal correlation of sensor array responses inspired us to design deep-learning-based gas identification networks. The network uses raw time-series gas sensor array data and identifies the target gas mixtures with improved accuracy despite sensor drift. Experimental results show that the proposed methods are an effective technique with identification accuracy approximately 91% for identifying gas mixture for smart sensor system application. The proposed method outperforms and provides higher identification accuracy than comparable various machine learning and deep learning methods.

关键词： Deep learning Gases Adaptive systems Neural networks Data acquisition pattern recognition Gas detectors

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Illustration and Identification of Skin Lesions in Dermoscopic Dataset Using pattern recognition Techniques 5th

Illustration and Identification of Skin Lesions in Dermoscop...

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5th international conference on Intelligent, Interactive Systems and Applications, IISA 2020

作者： Haider, Syeda Tooba Hussain, Syed Basharat Batool, Amreen Rokia, Laraba Selsabil Mohsan, Syed Agha Hassnain Sadiq, Muhammad Abubakar Saleem, Mazahir North China Electric Power University Beijing China Tiangong University Tianjin China East China University of Science and Technology Shanghai China Ocean College Zhejiang University Hangzhou China Beijing China Islamabad Pakistan

ISBN: (纸本)9783030637835

This paper proposes a generic method of description of skin lesion types and recognition of Melanocytic lesions in Dermoscopic images. Scientific judgments show that cancerous lesions demonstrate various characteristics such as streaks, texture or pigmented networks as compared to normal or benign lesions. Our aim is to confirm those judgments by carrying out proper segmentation, features extraction and classification. Segmentation phase of PH2 Public dataset was done by means of active contours. Features extraction stage included implementation of Local Binary patterns and its extension to study textural features more deeply. Finally, K nearest neighbor and Support vector machine were used for classification. This choice of methods is motivated by scientific importance of differential configurationally structures in the melanomas. The experiment of the offered algorithm is done on both sets of images, manually segmented (segmentation done by physicians followed by Features extraction and classification done by us) and automatic segmented images (all the steps performed by us) using performance metrics of sensitivity, specificity and Accuracy. The highest result of the system was attained by manually segmented images, i.e. SE = 89.2, SP = 84, Accuracy = 89.2. Whereas, images with automated segmentation showed SE = 89.3, SP = 75.4, Accuracy = 88.2. © 2021, The Author(s), under exclusive license to Springer Nature Switzerland AG.

关键词： Classification (of information)

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Towards micro-expression recognition through pyramid of uniform temporal local binary pattern features 18th

Towards micro-expression recognition through pyramid of unif...

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Joint conferences on 18th international conference on Intelligent Systems Design and Applications, ISDA 2018 and 10th World Congress on Nature and Biologically Inspired computing , NaBIC 2018

作者： Ben Abdallah, Taoufik Guermazi, Radhouane Hammami, Mohamed Faculty of Economics and Management MIR@CL University of Sfax Sfax Tunisia Saudi Electronic University Riyadh Saudi Arabia Faculty of Sciences MIR@CL University of Sfax Sfax Tunisia

ISBN: (纸本)9783030166564

Compared to macro-expressions, recognizing micro-expres-sions is more challenging due to low intensity and their brief duration. To deal with this issue, the present paper proposes a facial micro-expression recognition approach based on the pyramid of uniform Temporal Local Binary pattern (PTLBPu2) features for describing the appearance motion changes in time through video stream. Unlike the majority of approaches that use a high dimensional feature space, the proposed approach is based on a low dimensional space with only 83 features. Compared to the most recent facial micro-expression recognition approaches, our approach proves its effectiveness with an accuracy rate reaching 66.40% on CasmeÂ II dataset. A study of the ability of a macro-expression model to recognize micro-expression shows that it is more efficient to recognize certain micro-expressions than others. © Springer Nature Switzerland AG 2020.

关键词： Local binary pattern

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Heatmap-Based Method for Estimating Drivers' Cognitive Distraction 19

Heatmap-Based Method for Estimating Drivers' Cognitive Distr...

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19th IEEE international conference on Cognitive Informatics and Cognitive computing (ICCI*CC)

作者： Musabini, Antonyo Chetitah, Mounsif InnoCoRe Valeo Comfort & Driving Assistance Bobigny France

ISBN: (纸本)9781728195940

In order to increase road safety, among the visual and manual distractions, modern intelligent vehicles need also to detect cognitive distracted driving (i.e., the driver's mind wandering). In this study, the influence of cognitive processes on the driver's gaze behavior is explored. A novel image-based representation of the driver's eye-gaze dispersion is proposed to estimate cognitive distraction. Data are collected on open highway roads, with a tailored protocol to create cognitive distraction. The visual difference of created shapes shows that a driver explores a wider area in neutral driving compared to distracted driving. Support vector machine (SVM)-based classifiers are trained, and 85.2% of accuracy is achieved for a two-class problem, even with a small dataset. Thus, the proposed method has the discriminative power to recognize cognitive distraction using gaze information. Finally, this work details how this image-based representation could he useful for other cases of distracted driving detection.

关键词： cognitive distraction distracted driving eyegaze affective computing human-centered artificial intelligence machine learning computer vision pattern recognition

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