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3D gesture-based control system using processing open source software

3D gesture-based control system using processing open source...

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international conference on Open Source Software Computing (OSSCOM)

作者： Abdel-Mehsen Ahmad Roba Al Majzoub Ola Charanek Department of Computer and Communications Engineering Lebanese International University (LIU) Bekaa Lebanon

ISBN: (纸本)9781509045815

The following paper discusses the design and implementation of a 3D gesture-based control system that can substitute any complex human-machine interface with a simpler and more familiar one. This system that can be used by any user disregarding his/her background (age, education, health, language etc....), making it dependent on nothing more than the movements of the user's hands, thus allowing them to be free from all hardware while interfacing, and making the system surpass in its simplicity other systems that include held or touched interfaces. It utilizes the Kinect sensor's image input parts (IR projector and IR camera) for computer vision along with an open source software called processing for reception and analysis of input data to decision making on what actions and steps to perform, and even for communicating with the database. A MySQL database will be responsible for holding the information of the system and to handle them (adding, deleting, retrieving, etc....). Even though the system will be designed for a library to display information about the books available in it, however the proposed system design will be broad enough to allow its implementation in many other domains, making people's lives more efficient and innovative. Implementation steps are listed along with a detailed explanation of how the system works. The simulation results showing the system performance in different environment conditions and some screenshots are also displayed.

关键词： Sprites (computer) Libraries Three-dimensional displays Control systems Two dimensional displays Sensors Thumb

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A Modular Approach for Cloth Modelling in Virtual Fitting Room

A Modular Approach for Cloth Modelling in Virtual Fitting Ro...

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IEEE international conference on Industrial and Information Systems

作者： Yashwanth Kanduri Sanugula Durga Prasad Computer Science & Engineering National Institute of Technology Warangal Telangana India

The application is an augmented virtual reality application where the user is recognized at close range and can virtually try clothes. The unique point about this application is the fact that there'll be image processing and computer vision used to determine size of shirt and waist and so on of a person using the Kinect V2 perceptual computing kit rather than just allowing the user to try on virtual clothes. These clothes can be showcased online among the friends or can be directly used for printing and manufacturing the shirt using CO_2 laser from a robotic arm which is out of scope of this paper. The modelling of cloth which is supposed to be augmented on the virtual human body is being designed with competent geometric algorithms rather than just relying on Gaming or Motion Control Frameworks. The results are tested on latest Kinect V2 sensor which provides user a compelling experience to opt for. The paper initially overviews the major technical components for complete virtual try-on system, followed by elucidating several key challenges such as calibration of Kinect and estimation of measurements for individual subjects like outfits, etc. Eventually we discuss key details of the implementation using Kinect V2. Quality of these steps is the key to achieve seamless try-on experience for users.

关键词： Skin image color analysis Elbow Thumb Streaming media Biological system modeling Biological system modeling image colour analysis Streaming Video game player Thumb fitting room Elbow modular approach Textile fabrics skin Dress shirts Clothing Virtual Consumers

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A novel fingerprint classification method based on deep learning

A novel fingerprint classification method based on deep lear...

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international conference on Pattern Recognition

作者： Ruxin Wang Congying Han Tiande Guo School of Mathematical Science University of Chinese Academy of Sciences (UCAS) Beijing School of Mathematical Science Key Laboratory of Big Data Mining and Knowledge Management UCAS

ISBN: (纸本)9781509048489

Fingerprint classification is an effective technique for reducing the candidate numbers of fingerprints in the stage of matching in automatic fingerprint identification system (AFIS). In recent years, deep learning is an emerging technology which has achieved great success in many fields, such as image processing, computer vision. In this paper, we have a preliminary attempt on the traditional fingerprint classification problem based on the new depth neural network method. For the four-class problem, only choosing orientation field as the classification feature, we achieve 91.4% accuracy using the stacked sparse autoencoders (SAE) with three hidden layers in the NIST-DB4 database. And then two classification probabilities are used for fuzzy classification which can effectively enhance the accuracy of classification. By only adjusting the probability threshold, we get the accuracy of classification is 96.1% (setting threshold is 0.85), 97.2% (setting threshold is 0.90) and 98.0% (setting threshold is 0.95) with a single layer SAE. Applying the fuzzy method, we obtain higher accuracy.

关键词： Machine learning Databases Feature extraction Training Fingerprint recognition Neural networks image reconstruction

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Towards Robust Ego-Centric Hand Gesture Analysis for Robot Control

Towards Robust Ego-Centric Hand Gesture Analysis for Robot C...

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2016 IEEE international conference on Signal and image processing

作者： Hongyong Song Weijiang Feng Naiyang Guan Xuhui Huang Zhigang Luo Institute of Software College of Computer National University of Defense Technology Department of Computer Science and Technology College of Computer National University of Defense Technology Science and Technology on Parallel and Distributed Processing Laboratory National University of Defense Technology

Wearable device with an ego-centric camera would be the next generation device for human-computer interaction such as robot *** gesture is a natural way of egocentric human-computer *** this paper, we present an ego-centric multi-stage hand gesture analysis pipeline for robot control which works robustly in the unconstrained environment with varying *** particular, we first propose an adaptive color and contour based hand segmentation method to segment hand region from the egocentric *** then propose a convex U-shaped curve detection algorithm to precisely detect positions of *** parallelly, we utilize the convolutional neural networks to recognize hand *** on these techniques, we combine most information of hand to control the robot and develop a hand gesture analysis system on an i Phone and a robot arm platform to validate its *** result demonstrates that our method works perfectly on controlling the robot arm by hand gesture in real time.

关键词： ego-centric vision hand detection and segmentation fingertips detection hand gesture recognition robot control human-computer interaction

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Application of transfer learning in RGB-D object recognition

Application of transfer learning in RGB-D object recognition

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international conference on Advances in Computing, Communications and Informatics (ICACCI)

作者： Abhishek Kumar S. Nithin Shrivatsav G. R. K. S. Subrahmanyam Deepak Mishra School of Electrical Engineering Vellore Institute of Technoloey Vellore TN India National Institute of Technology Calicut Calicut Kerala IN Indian Institute of Space Science and Technology Thiruvananthapuram Kerala IN Dept. of Avionics lIST Thiruvananthapuram KL India

ISBN: (纸本)9781509020300

In this work, we apply Transfer Learning for a Multimodal Deep learning network for fast and robust object recognition using RGB-D dataset. The ability for a network to train quickly and recognize objects robustly is very important in the field of Robotics. The Multimodal deep learning network avoids time-consuming hand-crafted features and makes use of a RGB-D architecture for robust object recognition. Our architecture has two important features. First, it makes use of both RGB and Depth information of an image to recognize it. To achieve this, our architecture has two CNN processing streams, one for RGB modality and the other for the depth modality. This enables the network to achieve higher accuracy than normal single stream RGB network. We encoded the depth image into colour image before passing it into the CNN stream. The other important feature is the speed of training and improving the accuracy further. To achieve this, we made use of Transfer learning. Firstly we trained a CNN network with 10 classes of different objects and then we transfer the parameters to RGB and depth CNN network. This enables the network to train faster and also achieve higher accuracy for a given number of epochs.

关键词： Training computer architecture Convolution Neural networks computer vision Streaming media Detectors

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A combined feature representation of deep feature and hand-crafted features for person re-identification

A combined feature representation of deep feature and hand-c...

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IEEE international conference on Progress in Informatics and Computing (PIC)

作者： Youjiao Li Li Zhuo Xiaochen Hu Jing Zhang Signal & Information Processing Laboratory Beijing University of Technology Beijing China Department of Computer Science and Technology Shandong University of Technology Zibo China Collaborative Innovation Center of Electric Vehicles in Beijing Beijing China

ISBN: (纸本)9781509034857

Person re-identification is one of the hot topics in computer vision. How to design a robust feature representation to identify pedestrians is a key problem for person re-identification. In this paper, a feature representation based on Multi-Statistics Cascade on Pyramid (MSCP) is proposed for person re-identification. The MSCP feature is composed of deep PCA network feature and hand-crafted features of Local Maximal Occurrence (LOMO) feature and color correlogram. MSCP can characterize the pedestrian images precisely from both global and local views. The Cross-view Quadratic Discriminant Analysis (XQDA) is employed to learn the distance metric of MSCP features. And then a novel re-identification method based on MSCP and XQDA is achieved. Experimental results on VIPeR Dataset demonstrate that our proposed method can achieve superior identification performance compared with six state-of-art methods.

关键词： Feature extraction image color analysis Measurement Histograms Principal component analysis Robustness Lighting

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Real-time monocular obstacle avoidance using Underwater Dark Channel Prior

Real-time monocular obstacle avoidance using Underwater Dark...

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IEEE/RSJ international conference on Intelligent Robots and Systems

作者： Paulo Drews Emili Hernandez Alberto Elfes Erickson R. Nascimento Mario Campos Autonomous Systems Laboratory Data61-CSIRO Brisbane Australia Computer Vision and Robotics Laboratory of the Dep. de Ciencia da Computacao Univ. Federal de Minas Gerais - UFMG Belo Horizonte Brazil

ISBN: (纸本)9781509037636

In this paper we propose a new vision-based obstacle avoidance strategy using the Underwater Dark Channel Prior (UDCP) that can be applied to any Unmanned Underwater Vehicle (UUV) equipped with a simple monocular camera and minimal on-board processing capabilities. For each incoming image, our method first computes a relative depth map to estimate the obstacles nearby. Then, the map is segmented and the most promising Region of Interest (RoI) is identified. Finally, an escape direction is computed within the RoI and a control action is performed accordingly to avoid the obstacles. We tested our approach on a video sequence in a natural environment and compared it against a state-of-the-art method showing better performance, specially in light changing conditions. We also provide online results on a low-cost Remotely Operated Vehicle (ROV) in a controlled environment.

关键词： Obstacle avoidance control action Video sequences Underwater Collision avoidance Controlled Environment Natural environment monocular camera region of interest

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Cooperative multi-scale Convolutional Neural Networks for person detection

Cooperative multi-scale Convolutional Neural Networks for pe...

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international Joint conference on Neural Networks (IJCNN)

作者： Markus Eisenbach Daniel Seichter Tim Wengefeld Horst-Michael Gross Ilmenau University of Technology Neuroinformatics and Cognitive Robotics Lab Ilmenau Germany

ISBN: (纸本)9781509006212

Robust person detection is required by many computer vision applications. We present a deep learning approach, that combines three Convolutional Neural Networks to detect people at different scales, which is the first time that a multi-resolution model is combined with deep learning techniques in the pedestrian detection domain. The networks learn features from raw pixel information, which is also rare for pedestrian detection. Due to the use of multiple Convolutional Neural Networks at different scales, the learned features are specific for far, medium, and near scales respectively, and thus, the overall performance is improved. Furthermore, we show, that neural approaches can also be applied successfully for the remaining processing steps of classification and non-maximum suppression. The evaluation on the most popular Caltech pedestrian detection benchmark shows that the proposed method can compete with state of the art methods without using Caltech training data and without fine tuning. Therefore, it is shown that our method generalizes well on domains it is not trained on.

关键词： image resolution Noise measurement

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Stereo-vision based autonomous underwater navigation — The platform SARSTION

Stereo-vision based autonomous underwater navigation — The ...

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international Bhurban conference on Applied Sciences & Technology, IBCAST

作者： Sheheryar Mehmood Aadil Jaleel Choudhry Hina Anwar Saad Mahmood Amir Ali Khan NUST School of Electrical Engineering and Computer Science (SEECS) Islamabad Pakistan

Underwater robotics has been the center of attention for a long time due to diverse multi-disciplinary applications that it addresses. Conventionally, the detection and navigation has been governed by the SONAR based systems. While, SONAR offers unmatched capability for long range operation, its performance is compromised for detection of short range objects. In this regard, an alternate solution based on image processing appears promising in short range applications. Stereo-vision based systems are now regularly deployed for surface applications with huge success. However, the underwater deployment of these stereo-vision based systems is still an open problem. In the current work, we present an underwater autonomous platform, navigated by stereo-vision based algorithm. Initially, we demonstrate the distinct edge that the stereo-vision enjoys over SONAR in terms of its low cost, small size and low energy requirements. We present a working prototype of a Semi-Autonomous Sub-Aquatic Robot for Surveillance using Stereo-vision (SARSTION). SARSTION provides 6 degrees of freedom, using propellers, 4 for horizontal motion and 2 for vertical motion and is administered by an elaborate control system based on various positional feedback sensors. The main contribution of the current work is in the design of a novel simplistic navigational algorithm. The two acquired images from a front looking stereo-camera are used to compute a depth map whose intensity represents the relative distance of an object from the camera. The intensities are then equated to compute the relative physical distances of different objects from the camera. This information coupled with the approximations of relative object sizes and the data from the positional feedback sensors are then used to carefully alter the yaw of the mechanical assembly to maneuver the robot. The camera can also be used to provide live feed, depending upon its usage. We present some promising results illustrating the successful op

关键词： Cameras Sonar navigation Steel Robot vision systems Sonar Mathematical model

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Big Data processing With Application to image Super-Resolution

Big Data Processing With Application to Image Super-Resoluti...

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international conference on Industrial Technology and Management Science(ITMS 2015)

作者： Xiangjun Meng Baiqing Diao Lipeng Zhu Guangwei Gao Song Deng State Grid Shandong Electric Power Company China Electric Power Research Institute Institute of Advanced Technology Nanjing University of Posts and Telecommunications

Learning based face hallucination methods have received much attention in recent years. As opposed to the existing methods, where the input image(patch) matrix is first converted into vectors before combination coefficients calculation, this paper proposes a novel matrix based regression model for directly combination coefficients calculation to preserve the structural information of the input matrix. For each lowresolution local patch matrix, its combination coefficients over the same position image patch matrices in training images can be computed. Then the corresponding high-resolution patch matrix can be obtained. Experiments conducted on the FERET face dataset indicate that our method could outperform other state-ofthe-art algorithms in terms of both vision and quantity.

关键词： Face hallucination Position-patch Matrix regression

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