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检索条件"任意字段=Conference on Visual Communications and Image Processing 2008"

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Voice for the mute

Voice for the mute

引用

International conference on Technologies for Sustainable Development (ICTSD)

作者： Amiya Kumar Tripathy Dipti Jadhav Steffi A. Barreto Daphne Rasquinha Sonia S. Mathew School of Computing and Security Sciences Edith Cowan University Perth Australia Department of Computer Engineering Don Bosco Institute of Technology Mumbai India

ISBN: (纸本)9781479981885

Many ways of communications are used between human and computer, one of them includes using gesture which is considered to be one of the most natural ways in a virtual reality system. Because of its intuitiveness and its capability of helping the hearing impaired and speaking impaired. Hand gestures enable deaf people to communication during their daily lives rather than by speaking. A sign language is a language which, instead of using sound, uses visually transmitted gesture signs which simultaneously combine hand shapes, orientation and movement of the hands, arms, lip-patterns, body movements and facial expressions to express the speaker's thoughts. in order to bridge this gap of sign to speech, Voice fOr the Mute (VOM) aims to develop a system that will take real time images and convert them to speech with text as an intermediate taking into considerations all the limitations observed by a 2D system we will be considering only fingering spelling in our system i.e. Take input in the form of finger-spelling of alphabetic signs and providing the resultant voice output. The system will be using a webcam for the input and processing of the signs will be done using Microsoft visual Studio as an IDE and OpenCv modules. With this proposed system we aims to help the speech impaired community.

关键词： Speech Assistive technology Gesture recognition image edge detection Training Libraries Skin

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A Keyword-based Video Summarization Learning Platform with Multimodal Surrogates

A Keyword-based Video Summarization Learning Platform with M...

引用

International conference on Advanced Learning Technologies (ICALT)

作者： Wen-Hsuan Chang Jie-Chi Yang Yu-Chieh Wu Graduate Institute of Network Learning Technology National Central University Jhong Li Taiwan Department of Communications and Management Ming Chuan University Taipei Taiwan

In general, video-based learning contains rich media information, but displaying an entire video linearly is time-consuming. As an alternative, video summarization techniques extract important content provides short but informative fragments. In this paper, a video learning platform (KVSUM: Keyword-based Video Summarization) is presented that integrates image processing, text summarization, and keyword extraction techniques. Without human annotators, the learning platform can process an input video and transform it into online learning materials automatically. The video frames are first split from a given video while the transcription is used to generate the text summary and keywords. In the current study, the video surrogates are composed of extracted keywords, text and video summaries, and video frames. In other words, KVSUM is able to provide both visual and verbal surrogates. In order to validate the effect of surrogates in the KVSUM, a comparison with another video surrogate, the fast forward (FF), to evaluate learners' comprehension to video contents. Sixty undergraduate students took part in examining two different video surrogates. The experimental results show that KVSUM had a more positive effect than FF in comprehension to videos. In terms of system usage and satisfaction, KVSUM is significantly more attractive than FF to learners.

关键词： visualization Streaming media Multimedia communication Time factors Materials Reliability Databases

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BPCS Steganography for Data Security Using FPGA Implementation

BPCS Steganography for Data Security Using FPGA Implementati...

引用

International conference on communications and Signal processing

作者： Vikas S. Kait Bina Chauhan electronics & telecommunication department moze college of engineering savitribai phule university of pune

ISBN: (纸本)9781479980826

There are several techniques to conceal the secret information inside Cover objects;steganography is the one of them. Steganography is the art of invisible communication by concealing information inside other information. images are the most popular cover objects for steganography. The BPCS steganography which stands for bit-plane complexity segmentation is the technique to hide secret information in some other data (carrier) with better visual imperceptibility. This technique uses the "noise-like" regions in the bit planes of the cover image to hide secret data without deteriorating the image quality. With this technique we can hide 50-60% of secrete data in the cover image. To embed the secret information inside images requires intensive computations, and therefore, the technique is implemented in an FPGA to increase the processing speed. This work presents a hardware implementation of bit-plane complexity segmentation (BPCS) steganography technique in Xilinx Spartan 3E FPGA family. To access the bit wise data on FPGA Baud rate can cause the delay.

关键词： Bit Plane BPCS Cover image Data Hiding FPGA Steganography

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Automatic Intensity-Pair Distribution for image Contrast Enhancement

Automatic Intensity-Pair Distribution for Image Contrast Enh...

引用

3rd International Symposium on communications, Control, and Signal processing (ISCCSP 2008), vol.2

作者： Nai-Ching Wang Shen-Chuan Tai Department of Electrical Enginee National Cheng Kung University Tainan Taiwan

Intensity-pair distribution was recently proposed to enhance the contrast of an image. There are several parameters provided in that algorithm for users to control the enhancement. With a proper combination of parameters, the algorithm provides satisfying contrast enhancement without noise amplification, unnatural look, and other common drawbacks that are seen in previous work, but there is no effective method of parameter selection mentioned. In this paper, we proposed an effective criterion based on human visual system to decide a proper combination of parameters. With an appropriate combination of parameters, we can take the most advantage of that algorithm. Automation of parameter selection can easily be done by iteratively using the proposed algorithm. We presented experimental results to compare results of contrast enhancement with proper and improper combinations of parameters.

关键词： Automation Intensity-pair Contrast enhancement Histogram

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A fast approach to novelty detection in video streams using recursive density estimation

A fast approach to novelty detection in video streams using ...

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International IEEE conference on Intelligent Systems, IS

作者： Ramin Ramezani Plamen Angelov Xiaowei Zhou Intelligent Systems Research Laboratory Digital Signal Processing Research Group Department of Communication Systems Lancaster University Lancaster UK

Video-based surveillance and security become extremely important in the new, 21 st century for human safety, counter-terrorism, traffic control etc. visual novelty detection and tracking are key elements of such activities. The current state-of-the-art approaches often suffer from high computational, memory storage costs and from not being fully automated (they usually require a human operator in the loop). This paper introduces a new approach to the problem of novelty detection in video streams that is based on recursive, and therefore, computationally efficient density estimation by a Cauchy type of kernel (as opposed to the usually used Gaussian one). The idea of the proposed approach stems from the recently introduced evolving clustering approach, eClustering and is suitable for online and real-time applications in fully autonomous and unsupervised systems as a stand-alone novelty detector or for priming a tracking algorithm. The approach proposed in this paper has evolving property - it can gradually update the background model and the criteria to detect novelty by unsupervised online learning. The proposed approach is faster by an order of magnitude than the well known kernel density estimation (KDE) method for background subtraction, while having has adaptive characteristics, and does not need any threshold to be pre-specified. Recursive expressions similar to the proposed approach in this paper can also be applied to image segmentation and landmark recognition used for self-localization in robotics. If combined with a real-time prediction using Kalman filter or evolving Takagi-Sugeno fuzzy models a fast and fully autonomous tracking system can be realized with potential applications in surveillance and robotic systems.

关键词： Streaming media Recursive estimation Surveillance Humans Kernel Real time systems Robots Security Safety Traffic control

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EMERGING THREAT OF DEEP FAKE: HOW TO IDENTIFY AND PREVENT IT 22

EMERGING THREAT OF DEEP FAKE: HOW TO IDENTIFY AND PREVENT IT

引用

Proceedings of the 6th International conference on Future Networks & Distributed Systems

作者： Murooj Amer Taha Wijdan Mahood Khudhair Ahmed Mahmood Khudhur Omar Abdulkareem Mahmood Yousif I. Hammadi Riyam Shihab Ahmed Al-husseinawi Ahmed Aziz Department of Computer Engineering Techniques Bilad Alrafidain University College Iraq Department of Communications Engineering University of Diyala Iraq Department of Medical Instruments Engineering Techniques Bilad Alrafidain University College Iraq Department of International Business Management Tashkent State University of Economics Uzbekistan

ISBN: (纸本)9781450399050

Although manipulations of visual and aural media have been around for as long as there have been media, the relatively recent introduction of deep fakes has marked a turning point in the creation of fake information. Deep fakes are automated methods that allow the creation of fake information that is becoming increasingly difficult for human observers to see. These procedures are made possible by the most recent technological advancements in artificial intelligence and deep learning. Deep Learning is a powerful method that is now being implemented in a variety of industries, including natural language processing, computer vision, image processing, and machine vision. Deep fakes are created by the use of deep learning algorithms in order to synthesize and modify photos, videos, or sounds of a person, to the point that human people are unable to discern the fake from the real one. In this article, we present a workable description of deep fakes as well as an outline of the technology that lies underneath them. In order to assist people in thinking about the future of deep fakes, we outline the benefits of the deepfake as well as the potential threats. This study presents a complete analysis of deep fake methods and discusses the most effective strategies for preventing counterfeiting.

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Combination of multiple images for flash re-lightning

Combination of multiple images for flash re-lightning

引用

3rd International Symposium on communications, Control, and Signal processing (ISCCSP 2008), vol.1

作者： Sakari Alenius Radu Bilcu Tampere University of Technology Tampere Finland Nokia Research Center Tampere Finland

This paper introduces a novel way to combine multiple images of the same scene in order to improve the visual appearance. Especially, images taken with the flash often contain artifacts such as too strong flash illumination and too dark background. The nature of the ambient lightning can be combined back with other image features. Two or more flash/no-flash images are combined to a spatially adaptive mixture image that represents the essential features of the images. No assumptions are made on which images contribute to the result The lightning, shadows, and colors are a blind combination of the input images. The new method uses the principal component analysis (PCA) to find an optimal fusing factors for the images in order to compute the resulting image. The images are decomposed and processed using PCA to find out the local adaptive principal component. image decomposition prior to PCA makes it possible for both coarse and fine image features to be fused. Also other camera parameters than the flash can be varied. For instance, if images with multiple focus settings are fused by the new APC (adaptive principal combination) method, the resulting mixture image depicts an extended depth of focus.

关键词： Principal component analysis Lightning Frequency Focusing Lighting Cameras image analysis Surface texture Surface treatment Paper technology

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Design of the control circuit of automatic dryer

Design of the control circuit of automatic dryer

引用

2nd International conference on Mechanical, Electronic, Control and Automation Engineering (MECAE 2018)

作者： Biqing Li Zhao Li Hongyan Zhang School of Information and Communication Engineering Hezhou University Management Engineering Department Guangxi Vocational and Technical College of Communications

the temperature and humidity of the received signal after processing is sent to the display shows the number, when the value reaches the initial settings required the relay to control the motor, closed, the dryer to stop working, the GSM module and the remote user information exchange, automatically send text messages that have been drying clothes. The design of remote control system to overcome the distance to and understanding the working state and the password protection dryer faults, scalability and use value has a good.

关键词： GSM STC89C52 DHT11 A clothes dryer

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Deep Learning-Based Multiple Detection Techniques of Covid-19 Disease From Chest X-Ray images Using Advanced image processing Methods and Transfer Learning 1st

Deep Learning-Based Multiple Detection Techniques of Covid-1...

引用

1st Analytics Global conference, AGC 2023

作者： Hussain, Arif Basak, Rohini Mandal, Sourav Department of Informtion Technology Jadavpur University West Bengal Kolkata700098 India School of Computer Scinece and Engineering XIM University Odisha Bhubaneswar752050 India

ISBN: (纸本)9783031508141

The 2019 new coronavirus (COVID-19), which originally surfaced in the Chinese city of Wuhan in December 2019, expanded quickly, and caused a pandemic. It has created a terrible influence on daily lives, public health, and the global economy. To stop the pandemic from spreading further and to treat the affected individuals as soon as feasible, it is essential to identify positive cases as soon as possible. One of the primary issues in the present COVID-19 pandemic is the early detection and diagnosis of COVID-19, as well as the precise separation of non-COVID-19 cases at the lowest cost and in the early stages of the disease. This study compares the usage of the most recent Convolutional Neural Network (CNN), a deep learning methodology employing networks, with a transfer learning technique using VGG and ResNet. With their assistance, we created models that can recognize COVID-19 in X-Ray photos. We have applied techniques of image preprocessing using Contrast Limited Adaptive Histogram Equalization (CLAHE) and Adaptive Histogram Equalization (AHE). We built the model by applying CNN. We have also applied transfer learning techniques using visual Geometry Group (VGG16) and Residual Network (ResNet50) after image preprocessing using CLAHE and AHE. We have compared the accuracy of all models. The CLAHE technique along with ResNet50 works well as we achieve an accuracy of 0.99 It can be stated that ResNet50 with CLAHE played a very significant role in determining the covid-19 in X-ray images. © 2024, The Author(s), under exclusive license to Springer Nature Switzerland AG.

关键词： COVID-19

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Compression for visual Pattern Recognition

Compression for Visual Pattern Recognition

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3rd International Symposium on communications, Control, and Signal processing (ISCCSP 2008), vol.3

作者： Gunther Heidemann Helge Ritter Intelligent Systems Group University of Stuttgart Stuttgart Germany Neuroinformatics Group Bielefeld University Bielefeld Germany

To date, computer science solves pattern recognition problems by highly task specific algorithms. Searching for a generic, unifying principle of pattern recognition, Benedetto et al. showed that compression is a good candidate: For the domain of text, approximating the mutual information of patterns by the achievable compression factors allows to detect similarity with surprising accuracy. Here we show that this principle is much more general than expected, since the common compressor gzip is able to solve a major computer vision problem, the classification of image categories. This result is remarkable for three reasons: Firstly, the applied compression programs were never designed for pattern recognition tasks of any kind;secondly, hardly any other method is able to deal with patterns as different as text and pixel images alike without any modification;thirdly, compression solves the entire task in a single step, without any data preprocessing, feature extraction or the need for parametrization. We will discuss the theoretical background of this finding and point out the role of compression in a yet to develop general theory of pattern recognition.

关键词： Pattern recognition image coding Dictionaries Pixel Text recognition Intelligent systems Computer science Mutual information Computer vision Data preprocessing

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