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检索条件"任意字段=International Conference on Computer Vision and Image Processing, CVIP 2016"

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Computation Offloading Frameworks in Mobile Cloud Computing: A Survey

Computation Offloading Frameworks in Mobile Cloud Computing:...

引用

IEEE international conference on Current Trends in Advanced Computing

作者： Shantanu Deshmukh Rinku Shah Department of Computer Engineering Vidyalankar Institute of Technology

ISBN: (纸本)9781509019373

Mobile devices are characterized by limited and non-scalable resources such as low processing power, insufficient primary memory and short battery life. Resource intensive applications such as augmented reality, artificial intelligence, artificial vision, object tracking, image processing and natural language processing are becoming popular. These applications are unable to execute effectively on resource constraint mobile devices. In the last few years considerable research has been done on Mobile Cloud Computing to extend the capabilities of mobile devices. Various models and frameworks have been proposed to offload resource intensive components of applications to cloud for efficient execution. This paper surveys recent computation offloading frameworks to determine and analyze the factors which affect the wide acceptance of these frameworks.

关键词： Computation Offloading frameworks Mobile Cloud Computing

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image Feature Matching and Its Parallelization Using OpenMP

Image Feature Matching and Its Parallelization Using OpenMP

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international Symposium on Collaborative Technologies and Systems

作者： Nupur Kohli Teng-Sheng Moh Department of Computer Science San Jose State University San Jose CA USA

Parallel Computing has been gaining interest nowadays due to physical constraints preventing frequency scaling. Therefore, in order to achieve high performance on multicore systems, programmers need to focus on parallelizing their programs. Although there are many available parallelized APIs written by experts that should improve coding, they do not automatically guarantee good performance. This paper focuses on understanding factors that help to achieve better performance. This paper specifically focuses on SIFT based feature matching for various image collections. SIFT works by extracting information from images and then later compares this information for feature matching. Since SIFT performs CPU intensive computations, it requires parallelization in order to be feasible. This paper focuses on exploring a shared memory model based API known as Open multi-processing (OpenMP) to accelerate existing serial SIFT based image matching. In this paper, we were able to achieve a speedup of ~2× by using various OpenMP features. The paper later discusses factors like scalability and speedup and how multi-threading impacts them. We also discovered the importance of different levels of parallelism and their effects on performance.

关键词： Parallel processing Multicore processing Synchronization Feature extraction Computational modeling computer vision Approximation algorithms

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A survey of video datasets for crowd density estimation

A survey of video datasets for crowd density estimation

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international conference on Global Trends in Signal processing, Information Computing and Communication (ICGTSPICC)

作者： Neeta A. Nemade V. V. Gohokar Baba College of Engineering & Technology Electronics and Communication Engg. Shri Sant Gadge Bhusawal India Electronics & Telecommunication Engg. Maharashtra Institute of Technology Kothrud Pune India

ISBN: (纸本)9781509004683

Crowd density analysis is very imperative for intellectual video surveillance to help in management and control of crowds for safety. In recent years, more and more datasets dedicated to crowd density estimation, crowd analysis and anomaly detection in crowded scenes have been created. The use of these dataset allows us to compare different crowd density estimation methods with the same input data. The survey introduced in this paper compares different crowd datasets for crowd density estimation methods.

关键词： Estimation Legged locomotion Feature extraction Cameras image edge detection Lighting computer vision

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Dynamic hand gesture recognition

Dynamic hand gesture recognition

引用

international conference on Signal and Information processing (IConSIP)

作者： Rajeshree Rokade-Shinde Jayashree Sonawane Lokmanya Tilak Callege of Engineering Koperkhairne Navi Mumbai INDIA

ISBN: (纸本)9781509015238

Recognition of dynamic gesture is one of the most challenging tasks in the computer vision. This paper deals with dynamic hand gesture (digits) recognition. The method consist of three fold novel contributions: firstly finding the flow of hand, secondly recognition technique of signs (Dynamic digits) and thirdly classification of gesture. We worked on 11 to 20 (digits) gestures. Spatiotemporal volume is not always sufficient to grasp the flow of hand movement. We proposed new method to find flow of hand for special signs. Features are extracted using proposed feature extraction algorithm and gestures are recognized. Proposed algorithm shows recognition efficiency of 94% for dynamic gestures. Time complexity of the given method is less as compared to some traditional methods.

关键词： Heuristic algorithms Algorithm design and analysis Feature extraction Classification algorithms Gesture recognition image segmentation Trajectory

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Imitation and learning of human hand gesture tasks of the 3D printed robotic hand by using artificial neural networks

Imitation and learning of human hand gesture tasks of the 3D...

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international conference on Electronics, computers and Artificial Intelligence (ECAI)

作者： Mehmet Celalettin Ergene Akif Durdu Halil Cetin Dept. of Electrical and Electronics Engineering Selcuk University Konya Turkey

In this study social learning and skill acquisition of a robotic hand via teaching and imitation was aimed. The subject of Human-Robot collaboration, which includes the theme of this paper, is a common field of experiments in our age of technology. Many disabilities can be defeated or many other things, which a human being would not be able to do, can be done with the help of this technology. As an example, a robotic hand can be a light of hope of a person who does not have a hand or wants to hold an object remotely over the internet. So that in our paper it is explained how a robotic hand can learn via imitation. In the experiment a robotic hand, which was printed by a 3D printer, was used and controlled wirelessly by a computer that recognizes human hand gesture via image processing algorithms. The communication between the computer and the robot is provided with a Bluetooth module. First of all, the image processing algorithms such as filtering and background subtraction were applied to the frames of the camera and extracted the features. Secondly, the process of teaching and testing of Artificial Neural Networks (ANNs) was made for the recognition of the hand and the gestures. After that, recognized actions were imitated by the robotic-hand hardware. Eventually, the learning of the robot via imitation was achieved with some small errors and the results are given at the end of the paper.

关键词： Neurons Artificial neural networks image processing Maximum likelihood detection Nonlinear filters computer vision

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A haze density aware adaptive perceptual single image haze removal algorithm

A haze density aware adaptive perceptual single image haze r...

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international conference on Information and Automation (ICIA)

作者： Chuanzi He Chendi Zhang Qingrong Cheng Xixiaoyi Jin Jianjun Yin Department of Electronic Engineering Fudan University Shanghai China

ISBN: (纸本)9781509041039

Haze or fog jeopardizes both environment and image quality, which degrades the quality of subsequent computer vision algorithms. Recently haze removal method in image processing makes significant progress. The existing methods usually require complicated manual parameters setting according to the variance of input. Among them, dehazing method based on dark channel prior is considered to be the most efficient one. However, the problems brought by dark channel prior method including low luminance, sky region distortion and low saturation are inevitable. The proposed dehaze method in this paper can adaptively adjust parameters settings by introducing haze density detection. Besides, the proposed method improves the original dim recovered image by adaptively adjust exposure and color saturation in YCbCr color space. Furthermore, fast guided filter is employed to refine the transmission map. The experimental results show that the proposed method performs better both objectively and subjectively.

关键词： image color analysis image restoration image segmentation Indexes Estimation Imaging Mathematical model

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HIERARCHICAL PART DETECTION WITH DEEP NEURAL NETWORKS

HIERARCHICAL PART DETECTION WITH DEEP NEURAL NETWORKS

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IEEE international conference on image processing

作者： Esteve Cervantes Long Long Yu Andrew D. Bagdanov Marc Masana Joost van de Weijer Computer Vision Center Barcelona Universitat Autonoma de Barcelona Wide Eyes Technologies

ISBN: (纸本)9781467399623

Part detection is an important aspect of object recognition. Most approaches apply object proposals to generate hundreds of possible part bounding box candidates which are then evaluated by part classifiers. Recently several methods have investigated directly regressing to a limited set of bounding boxes from deep neural network representation. However, for object parts such methods may be unfeasible due to their relatively small size with respect to the image. We propose a hierarchical method for object and part detection. In a single network we first detect the object and then regress to part location proposals based only on the feature representation inside the object. Experiments show that our hierarchical approach outperforms a network which directly regresses the part locations. We also show that our approach obtains part detection accuracy comparable or better than state-of-the-art on the CUB-200 bird and Fashionista clothing item datasets with only a fraction of the number of part proposals.

关键词： Object Recognition Part Detection Convolutional Neural Networks object recognition bounding boxes Neural network parts location

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Quaternionic Upsampling: Hyperspherical Techniques for 6 DoF Pose Tracking

Quaternionic Upsampling: Hyperspherical Techniques for 6 DoF...

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international conference on 3D Imaging, Modeling, processing, Visualization and Transmission (3DIMPVT)

作者： Benjamin Busam Marco Esposito Benjamin Frisch Nassir Navab FRAMOS Imaging Sytems Germany Computer Aided Medical Procedures Technische Universität München Germany Computer Aided Medical Procedures Johns Hopkins University US

ISBN: (纸本)9781509054084

Fast real-time tracking is an integral component of modern 3D computer vision pipelines. Despite their advantages in accuracy and reliability, optical trackers suffer from limited acquisition rates depending either on intrinsic sensor capabilities or physical limitations such as exposure time. Moreover, data transmission and image processing produce latency in the pose stream. We introduce quaternionic upsampling to overcome these problems. The technique models the pose parameters as points on multidimensional hyperspheres in (dual) quaternion space. In order to upsample the pose stream, we present several methods to sample points on geodesics and piecewise continuous curves on these manifolds and compare them regarding accuracy and computation efficiency. With the unified approach of quaternionic upsampling, both interpolation and extrapolation in pose space can be done by continuous linear variation of only one sampling parameter. Since the method can be implemented rather efficiently, pose rates of over 4 kHz and future pose predictions with an accuracy of 128 μm and 0.5° are possible in real-time. The method does not depend on a special tracking algorithm and can thus be used for any arbitrary 3 DoF or 6 DoF rotation or pose tracking system.

关键词： Quaternions Three-dimensional displays Interpolation Algebra Real-time systems Trajectory Topology

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vision based Counting of Texture-less Objects using Shape and Color Features

Vision based Counting of Texture-less Objects using Shape an...

引用

IEEE international conference on Industrial and Information Systems

作者： Nishchal K. Verma Teena Sharma Shreedharkumar D. Rajurkar Rakesh Ranjan Al Salour Indian Institute of Technology Kanpur India - 208016 Indian School of Mines (ISM) Dhanbad The BOEING Company St. Louis MO USA

Automatic object recognition for texture-less objects using computer vision is a difficult task in comparison of textured one since class discriminative information is rarely available. Herein, an algorithm to count such objects using shape and color attributes for recognition with scale, rotation and illumination invariance is proposed. Initially, the algorithm extracts shape and color features of the prototype image to find its instance in the real-time pre-processed scene image captured by the vision interface. The pre-processing is achieved by morphological boundary extraction and segmentation techniques. Color and shape features are extracted based on mean hue value and Hu-moments respectively from the obtained segments. SVM, kNN, neural network and tree-bagging are then applied for classification. Tree-bagging is found to eclipse over the other classifiers in terms of accuracy. Finally, the classified objects are counted and localized in the image by drawing bounding boxes around them. A desktop application of the proposed algorithm is also developed. To assess the performance of the proposed algorithm, experimentation has been carried out for various objects having different shapes and colors. The algorithm proved out to be robust and effective for recognition and counting of the texture-less objects.

关键词： Feature extraction Prototypes image segmentation Shape image color analysis Support vector machines image edge detection Edge detection image colour analysis Support Vector Network image segmentation Feature extraction color feature PROTOTYPE Sight Shape desktop application images Color algorithms

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Night-time vehicle detection using low exposure video enhancement and lamp detection

Night-time vehicle detection using low exposure video enhanc...

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international conference on Electronics, Information and Communications (ICEIC)

作者： Jin-Woo Park Byung Cheol Song Department of Electronic Engineering Inha University Incheon Republic of Korea

ISBN: (纸本)9781467380171

This paper proposes a vehicle detection algorithm using pre-processing and lamp detection at night-time. First, we present a vehicle detection using contrast enhancement. By applying a specific contrast enhancement to night-time images with low exposure, we can enhance salient features even at dark night-time. Next, we detect a pair of rear lamps from the pre-processed image(s). Finally, we can find forward vehicle(s) by lamp pairing. Experimental results show that the proposed algorithm provides reliable detection accuracy.

关键词： Vehicle detection Vehicles Classification algorithms image color analysis Training Advanced driver assistance systems computer vision

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