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检索条件"任意字段=26th IEEE Conference on Computer Vision and Pattern Recognition, CVPR"

共 1569 条记录，以下是271-280 订阅

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Human Action recognition from RGBD Videos based on Retina Model and Local Binary pattern Features 26

Human Action Recognition from RGBD Videos based on Retina Mo...

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26th International conference on Central Europe on computer Graphics, Visualization and computer vision (WSCG)

作者： Al-Akam, Rawya Al-Darraji, Salah Paulus, Dietrich Univ Koblenz Landau Inst Computat Visualist Act Vis Grp AGAS Univ Str 1 D-56070 Koblenz Germany Univ Basrah Dept Comp Sci Basrah Iraq

ISBN: (纸本)9788086943428

Human action recognition from the videos is one of the most attractive topics in computer vision during the last decades due to wide applications development. this research has mainly focused on learning and recognizing actions from RGB and Depth videos (RGBD). RGBD is a powerful source of data providing the aligned depth information which has great ability to improve the performance of different problems in image understanding and video processing. In this work, a novel system for human action recognition is proposed to extract distinctive spatio and temporal feature vectors for presenting the spatio-temporal evolutions from a set of training and testing video sequences of different actions. the feature vectors are computed in two steps: the First step is the motion detection from all video frames by using spatio-temporal retina model. this model gives a good structuring of video data by removing the noise and illumination variation and is used to detect potentially salient areas, these areas represent the motion information of the moving object in each frame of video sequences. In the Second step, because of human motion can be seen as a type of texture pattern, the local binary pattern descriptor (LBP) is used to extract features from the spatio-temporal salient areas and formulated them as a histogram to make the bag of feature vectors. To evaluate the performance of the proposed method, the k-means clustering, and Random Forest classification is applied on the bag of feature vectors. this approach is demonstrated that our system achieves superior performance in comparison with the state-of-the-art and all experimental results are depending on two public RGBD datasets.

关键词： Action recognition RGBD videos Local Binary pattern Retina Model Random Forest

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NTIRE 2018 Challenge on Spectral Reconstruction from RGB Images 31

NTIRE 2018 Challenge on Spectral Reconstruction from RGB Ima...

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ieee/CVF conference on computer vision and pattern recognition (cvpr)

作者： Arad, Boaz Ben-Shahar, Ohad Timofte, Radu Van Gool, Luc Zhang, Lei Yang, Ming-Hsuan Xiong, Zhiwei Chen, Chang Shi, Zhan Liu, Dong Wu, Feng Lanaras, Charis Galliani, Silvano Schindler, Konrad Stiebel, Tarek Koppers, Simon Seltsam, Philipp Zhou, Ruofan El Helou, Majed Lahoud, Fayez Shahpaski, Marjan Zheng, Ke Gao, Lianru Zhang, Bing Cui, Ximin Yu, Haoyang Can, Yigit Baran Alvarez-Gila, Aitor van de Weijer, Joost Garrote, Estibaliz Galdran, Adrian Sharma, Manoj Koundinya, Sriharsha Upadhyay, Avinash Manekar, Raunak Mukhopadhyay, Rudrabha Sharma, Himanshu Chaudhury, Santanu Nagasubramanian, Koushik Ghosal, Sambuddha Singh, Asheesh K. Singh, Arti Ganapathysubramanian, Baskar Sarkar, Soumik Ben Gurion Univ Negev Beer Sheva Israel Swiss Fed Inst Technol Comp Vis Lab Zurich Switzerland Merantix Berlin Germany Katholieke Univ Leuven ESAT Leuven Belgium Polytech Univ Hong Kong Hong Kong Peoples R China Univ Calif Merced Merced CA USA Univ Sci & Technol China Hefei Anhui Peoples R China Swiss Fed Inst Technol Photogrammetry & Remote Sensing Zurich Switzerland Rhein Westfal TH Aachen Inst Imaging & Comp Vis Aachen Germany Ecole Polytech Fed Lausanne Image & Visual Representat Lab Lausanne Switzerland Chinese Acad Sci Inst Remote Sensing & Digital Earth Key Lab Digital Earth Sci Beijing 100094 Peoples R China China Univ Min & Technol Beijing Coll Geosci & Surveying Engn Beijing 100083 Peoples R China Tecnalia Derio Spain Univ Autonoma Barcelona Comp Vis Ctr Barcelona Spain INESC TEC Porto Porto Portugal CSIR CEERI Pilani Rajasthan India Iowa State Univ Ames IA 50011 USA

ISBN: (数字)9781538661000

ISBN: (纸本)9781538661000

this paper reviews the first challenge on spectral image reconstruction from RGB images, i.e., the recovery of whole-scene hyperspectral (HS) information from a 3channel RGB image. the challenge was divided into 2 tracks: the "Clean" track sought HS recovery from noiseless RGB images obtained from a known response function (representing spectrally-calibrated camera) while the "Real World" track challenged participants to recover HS cubes from JPEG-compressed RGB images generated by an unknown response function. To facilitate the challenge, the BGU Hyperspectral Image Database [4] was extended to provide participants with 256 natural HS training images, and 5+ 10 additional images for validation and testing, respectively. the "Clean" and "Real World" tracks had 73 and 63 registered participants respectively, with 12 teams competing in the final testing phase. Proposed methods and their corresponding results are reported in this review.

关键词： Image reconstruction Hyperspectral imaging Training Cameras Runtime Neural networks Databases

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To Recognize Families In the Wild: A Machine vision Tutorial 18

To Recognize Families In the Wild: A Machine Vision Tutorial

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26th ACM Multimedia conference (MM)

作者： Robinson, Joseph P. Shao, Ming Fu, Yun Northeastern Univ Boston MA 02115 USA UMass Dartmouth Dartmouth MA 02747 USA

ISBN: (纸本)9781450356657

Automatic kinship recognition has relevance in an abundance of applications. For starters, aiding forensic investigations, as kinship is a powerful cue that could narrow the search space (e.g., knowledge that the "Boston Bombers" were brothers could have helped identify the suspects sooner). In short, there are many beneficiaries that could result from such technologies: whether the consumer (e.g., automatic photo library management), scholar (e.g., historic lineage & genealogical studies), data analyzer (e.g., social-media-based analysis), investigator (e.g., cases of missing children and human trafficking- for instance, it is unlikely that a missing child found online would be in any database, however, more than likely a family member would be), or even refugees. Besides application-based problems, and as already hinted, kinship is a powerful cue that could serve as a face attribute capable of greatly reducing the search space in more general face-recognition problems. In this tutorial, we will introduce the background information, progress leading us up to these points, several current state-of-the-art algorithms spanning various views of the kinship recognition problem (e.g., verification, classification, tri-subject). We will then cover our large-scale Families In the Wild (FIW) image collection, several challenge competitions it as been used in, along with the top performing deep learning approaches. the tutorial will end with a discussion about future research directions and practical use-cases.

关键词： Visual kinship understanding family recognition deep learning familiar feature big data

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Deep Learning for Historical Cadastral Maps Digitization: Overview, Challenges and Potential 26

Deep Learning for Historical Cadastral Maps Digitization: Ov...

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26th International conference on Central Europe on computer Graphics, Visualization and computer vision (WSCG)

作者： Ignjatic, Jelena Nikolic, Bojana Rikalovic, Aleksandar Culibrk, Dubravko Inst Vojvodina Urban & Spatial Planning Zeleznicka 6-2 Novi Sad 21000 Serbia Univ Novi Sad Fac Tech Sci Trg Dositeja Obradov 6 Novi Sad 21000 Serbia

ISBN: (纸本)9788086943428

Cartographic heritage of historical cadastral maps represent remarkable geospatial data. Historical cadastral maps are generally regarded as an essential part of the land management infrastructure (buildings, streets, canals, bridges, etc.). Today these cadastral maps are still in use in a digital raster form (scanned maps). Digitization of cadastral maps is time consuming and it is a challenge for scientists and engineers to find ways to automatically convert raster into vector maps. the process of map digitization typically involves several stages: preprocessing, visual object detection and classification, vector representation postprocessing and extracting information from text. Although neural networks have had a long history of use in the domain, their applications remain limited to extracting the information from text. Recent convergence of advancements in the domains of training deep neural networks (DNN) and GPU hardware allowed DNNs to achieve state-of-the-art results in computer vision applications, beyond hand-written text recognition. this paper provides an overview of different approaches to historical cadastral maps digitization, focusing of the challenges and the potential of using deep neural networks in map digitization.

关键词： Convolutional Neural Networks Deep Neural Networks Map Digitization Map Vectorization pattern recognition

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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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Feature recognition of Face with Real-Time Variations Using Eigen Face Approach Methodology with PCA Algorithm 2nd

Feature Recognition of Face with Real-Time Variations Using ...

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2nd International conference on Intelligent Communication, Control and Devices (ICICCD)

作者： Dar, Ishfaq Gaffar Khan, Azzan Sharma, Manish Amity Univ Amity Sch Engn & Technol Noida 201303 UP India

ISBN: (纸本)9789811059032;9789811059025

Face is a composite multidimensional structure, and it needs great assessing methods for acknowledgment. Our approach regards confront acknowledgment as a two-dimensional acknowledgment issue. In this plan, confront acknowledgment is finished by foremost Component Analysis (PCA) (Biometric Technology Application Manual Volume One: Biometric Basics, National Biometric Security Project, 2008 [1, Turk and Pentland, "Face recognition using Eigen-faces", ieee conference on computer vision and pattern recognition, 1991 2]). the primary point is to execute the model (framework) for a chosen confront and separate it from a substantial number of as of now put away faces with some ongoing varieties also. the Eigen-confront approach utilizes Principal Component Analysis (PCA) calculation for the acknowledgment of pictures. It gives us efficient approach to discover the lower dimensional space. the face is characterized by Eigen-confronts which are Eigenvectors of the arrangement of confronts, which may not relate to general facial component, for example, lips, nose, and eyes. the framework (Velikiy Novgorod, "pattern recognition", the 6th international conference on Image Analysion, October 21-26, 2002 [3]) performs by anticipating pre-removed face picture onto an arrangement of face space that speaks to noteworthy varieties among known face pictures. Face will be labeled as perceived or not perceived face in the wake of coordinating with the present database. In the event that the client is new to the face acknowledgment framework, then his/her information (format) will be put away in the database else coordinated against the information (layouts) (Turk and Pentland, Face recognition using Eigen-faces, ieee conference on computer vision and pattern recognition, 1991 [4]) which is as of now put away in the database. the dimensionality lessening through PCA represents the littler face space than the preparation set of appearances and henceforth more prominent computational adap

关键词： Feature recognition Real-time variation Principal component analysis algorithm Face Sociology Vectors Databases Covariance matrix

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k-NN Sampling for Visualization of Dynamic Data Using LION-tSNE

k-NN Sampling for Visualization of Dynamic Data Using LION-t...

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International conference on High Performance Computing

作者： Bheekya Dharamsotu K. Swarupa Rani Salman Abdul Moiz C. Raghavendra Rao School of Computer and Information Sciences University of Hyderabad Hyderabad India University of Hyderabad Hyderabad India

ISBN: (纸本)9781728145365

Dimensionality reduction algorithms are often used to visualize multi-dimensional data, which are mostly non-parametric. Non-parametric methods do not provide any explicit intuition for adding new data points into an existing environment which limits the applicability of visualization for Big Data scenario. the LION-tSNE (Local Interpolation with Outlier coNtrol t-Distributed Stochastic Neighbor Embedding) method was proposed to overcome the limitations of existing techniques. the LION-tSNE algorithm uses random sampling method for tSNE model design which creates an initial visual environment then new data points are added to this environment using local-IDW(Inverse Distance Weighting) interpolation method. the randomly selected sample data often suffer from non-representativeness of the whole data which creates inconsistency in the tSNE environment. To overcome this problem two new sampling methods are proposed which are based on k-NN (k-Nearest Neighbor) graph update properties. It is empirically shown that proposed methods outperform existing LION-tSNE method with 0.5 to 2% more k-NN accuracy and results are more consistent. the study is done on five differently characterized datasets with three different initial solutions of tSNE. the proposed method results are statistically significant which is done by statistical method pairwise t-test.

关键词： Big Data data analysis data reduction data visualisation graph theory interpolation nearest neighbour methods pattern classification sampling methods statistical testing stochastic processes big data sampling methods Data visualisation Statistical tests pattern recognition Interpolation data reduction Markov chain Graph theory nearest neighbor algorithm Visualization Data analysis Stochastic Processes new data

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Efficient Solvers for Minimal Problems by Syzygy-based Reduction 30

Efficient Solvers for Minimal Problems by Syzygy-based Reduc...

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30th ieee/CVF conference on computer vision and pattern recognition (cvpr)

作者： Larsson, Viktor Astrom, Kalle Oskarsson, Magnus Lund Univ Ctr Math Sci Lund Sweden

ISBN: (纸本)9781538604571

In this paper we study the problem of automatically generating polynomial solvers for minimal problems. the main contribution is a new method for finding small elimination templates by making use of the syzygies (i.e. the polynomial relations) that exist between the original equations. Using these syzygies we can essentially parameterize the set of possible elimination templates. We evaluate our method on a wide variety of problems from geometric computer vision and show improvement compared to both handcrafted and automatically generated solvers. Furthermore we apply our method on two previously unsolved relative orientation problems.

关键词： Mathematical model computer vision Generators Eigenvalues and eigenfunctions Geometry Robustness Estimation

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Emotion recognition in Context 30

Emotion Recognition in Context

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30th ieee/CVF conference on computer vision and pattern recognition (cvpr)

作者： Kosti, Ronak Alvarez, Jose M. Recasens, Adria Lapedriza, Agata Univ Oberta Catalunya Barcelona Spain CSIRO Data61 Canberra ACT Australia MIT Cambridge MA 02139 USA

ISBN: (纸本)9781538604571

Understanding what a person is experiencing from her frame of reference is essential in our everyday life. For this reason, one can think that machines with this type of ability would interact better with people. However, there are no current systems capable of understanding in detail people's emotional states. Previous research on computer vision to recognize emotions has mainly focused on analyzing the facial expression, usually classifying it into the 6 basic emotions [11]. However, the context plays an important role in emotion perception, and when the context is incorporated, we can infer more emotional states. In this paper we present the "Emotions in Context Database" (EMOTIC), a dataset of images containing people in context in non-controlled environments. In these images, people are annotated with 26 emotional categories and also with the continuous dimensions valence, arousal, and dominance [21]. With the EMOTIC dataset, we trained a Convolutional Neural Network model that jointly analyses the person and the whole scene to recognize rich information about emotional states. With this, we show the importance of considering the context for recognizing people's emotions in images, and provide a benchmark in the task of emotion recognition in visual context.

关键词： Emotion recognition Databases Face recognition Face computer vision Psychology Image recognition

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An FPGA-based Hardware Accelerator for Scene Text Character recognition 26

An FPGA-based Hardware Accelerator for Scene Text Character ...

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26th IFIP/ieee International conference on Very Large Scale Integration, VLSI-SoC 2018

作者： De Oliveira, Luiz Antonio Barros, Edna Centro de Informática Universidade Federal de Pernambuco Recife Brazil

ISBN: (纸本)9781538647561

Scene text character recognition is a challenging task in computer vision since natural scene images usually have cluttered background and the character's size, font, orientation, texture, brightness, and alignment in the picture are variable and non-predictable. Furthermore, most systems including scene text character recognition are usually embedded in a system on a chip (SoC), which has critical requirements, such as low latency, low area, mobility, and flexibility, at the same time that they require high accuracy. In this context, in this work we propose a heterogeneous system for embedded applications with time, area and power constraints, that combines hardware and software to accelerate a technique for scene text character recognition, based on Histogram of Oriented Gradients (HOG) for feature extraction and a neural network Extreme Learning Machine (ELM) as a classifier. the system was prototyped and experimented in the Terasic embedded platform DE2i-150 and the results showed that the system has accuracy of 65.5% in the Chars74k-15 dataset and is able to process up to 11 frames per second, having a good trade-off between processing time and accuracy in embedded environments. Moreover, it occupies only 11% logic elements of the Altera Cyclone IV FPGA, enabling its use in embedded systems. © 2018 ieee.

关键词： Field programmable gate arrays (FPGA)

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