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Human activity recognition by fusion of RGB, depth, and skeletal data 2nd

Human activity recognition by fusion of RGB, depth, and skel...

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2nd international conference on computer vision and image processing, cvip 2017

作者： Khaire, Pushpajit Imran, Javed Kumar, Praveen Department of Computer Science and Engineering Visvesvaraya National Institute of Technology Nagpur India Department of Computer Science and Engineering Indian Institute of Technology Roorkee India

ISBN: (纸本)9789811078941

A significant increase in research of human activity recognition can be seen in recent years due to availability of low-cost RGB-D sensors and advancement of deep learning algorithms. In this paper, we augmented our previous work on human activity recognition (Imran et al., IEEE international conference on advances in computing, communications, and informatics (ICACCI), 2016) [1] by incorporating skeletal data for fusion. Three main approaches are used to fuse skeletal data with RGB, depth data, and the results are compared with each other. A challenging UTD-MHAD activity recognition dataset with intraclass variations, comprising of twenty-seven activities, is used for testing and experimentation. Proposed fusion results in accuracy of 95.38% (nearly 4% improvement over previous work), and it also justifies the fact that recognition improves with an increase in number of evidences in support. © 2018, Springer Nature Singapore Pte Ltd.

关键词： Musculoskeletal system

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Software for Detection of Objects while Video Sequences Handling

Software for Detection of Objects while Video Sequences Hand...

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international conference on Mathematics, Modeling and Simulation Technologies and Applications

作者： Iakov Korovin Maxim Khisamutdinov Donat Ivanov Southern Federal University

ISBN: (纸本)9781713800361

In this paper we describe a software implementation of computer vision methods for highlighting contrasting objects in noisy images.A user-friendly graphical interface is *** software program is intended for reading video sequences files,their processing in order to seek and recognize objects,present in a *** data handling the discovered objects' characteristics are stored in XML format files.

关键词： Detection Blobs detector image stabilization Optical flow Software computer software Video sequences Optic Flow computer programs software implementation Object detection XML format images computer vision

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A computer vision approach for lung cancer classification using FNAC-based cytological images 1

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2nd international conference on computer vision and image processing, cvip 2017

作者： Dholey, Moumita Sarkar, Atasi Maity, Maitreya Giri, Amita Sadhu, Anup Chaudhury, Koel Das, Soumen Chatterjee, Jyotirmoy School of Medical Science and Technology Indian Institute of Technology Kharagpur KharagpurWest Bengal721302 India Calcutta National Medical College and Hospital KolkataWest Bengal700014 India EKO Diagnostic Centre Medical College KolkataWest Bengal700073 India

ISBN: (数字)9789811078989

ISBN: (纸本)9789811078972

Lung cancer represents malignant tumour having uncontrolled lung cell growth/proliferation. It can be diagnosed by invasive and non-invasive diagnostic approaches. One of the most effective and accurate approach is Papanicolaou (Pap)-stained cell cytology from fine needle aspiration cytology (FNAC). The manual assessment of cytopathology slides under light microscopy is time-consuming and suffers from feature ambiguities including inter-observer variability. To overcome such problems, the automated cytological analysis is the need of time. This study presents an automated computer vision approach to identify and classify cancerous cell present in microscopic images of Pap smear. The proposed methodology follows colour normalization, image filtering, nucleus segmentation and classification of segmented cells. The nucleus is segmented using the Random Walker with K-means clustering method. The post-processing is carried out on the segmented images to delineate joined nucleus and to remove unwanted regions. Subsequently, multiple nuclear features, i.e. colour, texture and geometric attributes are extracted from each segmented nucleus. After that, a comparative study on supervised classifier selection for the extracted features was adopted towards improving classification accuracy for distinguishing nucleus of non-small cell and small cell lung cancer. Artificial neural network performs best with sensitivity of 97.58 %, specificity of 97.6 %, accuracy of 97.46 %. © Springer Nature Singapore Pte Ltd. 2018.

关键词： computer vision

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Action recognition from optical flow visualizations 2nd

Action recognition from optical flow visualizations

引用

2nd international conference on computer vision and image processing, cvip 2017

作者： Gupta, Arpan Balan, M. Sakthi Department of Computer Science and Engineering The LNM Institute of Information Technology Jaipur India

ISBN: (纸本)9789811078941

Optical flow is an important computer vision technique used for motion estimation, object tracking and activity recognition. In this paper, we study the effectiveness of the optical flow feature in recognizing simple actions by using only their RGB visualizations as input to a deep neural network. Feeding only the optical flow visualizations, instead of the raw multimedia content, ensures that only a single motion feature is used as a classification criterion. Here, we deal with human action recognition as a multi-class classification problem. In order to categorize an action, we train an AlexNet-like Convolutional Neural Network (CNN) on Farneback optical flow visualization features of the action videos. We have chosen the KTH data set, which contains six types of action videos, namely walking, running, boxing, jogging, hand-clapping and hand-waving. The accuracy obtained on the test set is 84.72%, and it is naturally less than the state of the art since only a single motion feature is used for classification, but it is high enough to show the effectiveness of optical flow visualization as a good distinguishing criterion for action recognition. The AlexNet-like CNN was trained in Caffe on two NVIDIA Quadro K4200 GPU cards, while the Farneback optical flow features were calculated using OpenCV library. © 2018, Springer Nature Singapore Pte Ltd.

关键词： Optical flows

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Phone based Video processing Aimed at Outdoor Guidance for the Visually Impaired 18

Phone based Video Processing Aimed at Outdoor Guidance for t...

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2nd international conference on Information System and Data Mining (ICISDM)

作者： Langenkamp, Stephen P. P. C. Abid, Muhammad Rizwan Florida Polytech Univ Lakeland FL 33805 USA

ISBN: (纸本)9781450363549

This paper discusses an outdoor guidance system that can be implemented on a smartphone. The purpose of such a system would be to assist the blind or visually impaired. The system will be able to detect the ground, find obstacles, and notify the user when the obstacle is close enough to constitute a tripping hazard. This work uses a novel semantic segmentation technique and proposes an alternate method for positioning the phone for better fall prevention techniques.

关键词： computer vision Handicap Assistance Phone Application image Segmentation Video Analysis Floor Detection Fall Prevention

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vision based system for surface roughness characterisation of milled surfaces using speckle line images 2

Vision based system for surface roughness characterisation o...

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2nd international conference on Advances in Mechanical Engineering (ICAME)

作者： Suhail, S. I. Mohamed Ali, J. Mahashar Jailani, H. Siddhi Murugan, M. BS Abdur Rahman Crescent Inst Sci & Technol Dept Mech Engn Chennai 600048 Tamil Nadu India VIT Sch Mech Engn Vellore Campus Vellore 632014 Tamil Nadu India

In the recent years, many breakthroughs are made in the image processing Technology, particularly in areas like satellite imaging and biometrics. This paper presents an experimental approach of vision based surface roughness measuring system using speckle images produced by a line laser beam on milled surfaces. A CCD Camera, line laser source and white light were used for capture the images of milled surfaces. A signal vector was obtained from image pixel intensity and it was processed using MATLAB software. Mean and standard deviation are the two parameters used to characterise the image signal vector. The roughness of the specimens, particularly the Arithmetic mean slope (R-da) are computed using a stylus instrument. From the experiments, it is found that mean intensity of the signal vector of the speckle line images correlate well with R-da values of the surface roughness. Hence, the mean image intensity value of speckle line images has a strong potential for the online characterisation of surfaces.

关键词： Surface roughness milling computer vision system image processing speckle images statistical parameters

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Flexible image Acquisition Service for Distributed Robotic Systems 2

Flexible Image Acquisition Service for Distributed Robotic S...

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2nd IEEE international conference on Robotic Computing (IRC)

作者： Semeniuta, Oleksandr Falkman, Petter NTNU Norwegian Univ Sci & Technol Dept Mfg & Civil Engn Gjovik Norway Chalmers Univ Technol Dept Elect Engn Gothenburg Sweden

ISBN: (纸本)9781538646519

The widespread use vision systems in robotics introduces a number of challenges related to management of image acquisition and image processing tasks, as well as their coupling to the robot control function. With the proliferation of more distributed setups and flexible robotic architectures, the workflow of image acquisition needs to support a wider variety of communication styles and application scenarios. This paper presents FxIS, a flexible image acquisition service targeting distributed robotic systems with event-based communication. The principal idea a FxIS is in composition of a number of execution threads with a set of concurrent data structures, supporting acquisition from multiple cameras that is closely synchronized in time, both between the cameras and with the request timestamp.

关键词： computer vision Concurrent programming Distributed systems GigE vision image acquisition Service oriented architecture

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Object proposals-based significance map for image retargeting 1

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2nd international conference on computer vision and image processing, cvip 2017

作者： Patel, Diptiben Raman, Shanmuganathan Electrical Engineering Indian Institute of Technology Gandhinagar GandhinagarGujarat382355 India

ISBN: (数字)9789811078989

ISBN: (纸本)9789811078972

Diverse aspect ratios of display devices require adaptation of the image content to be displayed on them. image retargeting pertains to changing the size of an image to adapt to the aspect ratio and spatial resolution of the display device. This is achieved while preserving the salient or important information and thereby reducing visible artifacts in the retargeted image. Seam carving techniques remove or insert least energy seams, one pixel wide paths from top to bottom or left to right of an image, iteratively in order to achieve the target display size. These techniques highly depend on an energy measure of a pixel in an image. Here, we propose a novel technique of defining energy of a pixel, also known as significance map, using object proposal boxes with the probability of objects being present. Object proposal boxes are modeled using the Gaussian kernels. The generation of significance map can be executed on an average in just over a second for a given image. We show that the proposed significance map is able to capture salient region(s) of an image having different textured background and different noise levels. The significance map can be used with an image retargeting algorithm such as seam carving to achieve desired results. Quantitative assessment and comparison with the other state-of-the-art image retargeting techniques show that the proposed energy measure yields the best results for image retargeting using seam carving. © Springer Nature Singapore Pte Ltd. 2018.

关键词： Pixels

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Hybrid Design of Real-Time image processing Techniques for Multiple Biomedical Applications

Hybrid Design of Real-Time Image Processing Techniques for M...

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2nd international conference on Recent Advancement in computer, Communication and Computational Sciences (RACCCS)

作者： Paul, Ria Shandilya, Rahul Sharma, R. K. Natl Inst Technol Kurukshetra Sch VLSI Design & Embedded Syst Kurukshetra Haryana India

ISBN: (纸本)9789811073861;9789811073854

The computer vision has become an indispensable part in the fields of biomedical application and laboratory research in which images are processed and analyzed. In this article, we have presented a non-invasive real-time biomedical image processing design. The aim of the current research is to propose a hybrid design by using techniques such as watershed, Eulerian Video Magnification and morphological filters for multiple biomedical applications like detection of brain tumor, lung cancer, gallbladder stone, cataract, and measurement of pulse rate and its implementation using MATLAB. The proposed design can process different category of images and manipulate them to visualize and understand the defects clearly that is not visible to the human eyes.

关键词： Biomedical image processing Watershed Eulerian Video Magnification Non-invasive

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Deep Learning Approach for image Classification 2

Deep Learning Approach for Image Classification

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2nd international conference on Data Science and Business Analytics (ICDSBA)

作者： Panigrahi, Santisudha Nanda, Anuja Swamkar, Tripti Siksha O Anusandhan Dept Comp Sci & Engn Bhubaneswar India Siksha O Anusandhan Dept Elect & Elect Engn Bhubaneswar India Siksha O Anusandhan Dept Comp Applicat Bhubaneswar India

ISBN: (纸本)9781538684313

As of late, deep learning has gained remarkable growth in various fields, for example, computer vision and natural language processing. Contrasted with conventional machine learning strategies, deep learning has a robust learning capacity and can improve utilization of datasets for feature extraction. In view of its practicability, deep learning turns out to be increasingly mainstream for many researchers to do research works. In this paper we mainly focus on the optimization of different parameters of convolutional neural network of deep learning for classifying 8000 labelled natural images of cat and dog. First the convolutional neural network is trained to learn features then ANN binary classifier is used for classification. Various level of optimization have been done to improve the performance level of the network and finally, we achieved the best classification accuracy of 88.31%.

关键词： deep learning machine learning neural network convolutional neural network

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