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检索条件"任意字段=Conference on Image Processing - Algorithms and Systems VI"

共 15618 条记录，以下是4151-4160 订阅

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Single image Based Random-Value Impulse Noise Level Estimation Algorithm

Single Image Based Random-Value Impulse Noise Level Estimati...

引用

Computing conference

作者： Bao, Long Panetta, Karen Agaian, Sos Tufts Univ Elect & Comp Engn Medford MA 02155 USA CUNY Dept Comp Sci Staten Isl NY 10314 USA

ISBN: (纸本)9783030011772;9783030011765

image denoising is a vital and indispensable pre-process for most applied image processing systems. Having prior knowledge about the noise level is essential for optimizing denoising algorithms. However, this information most likely does not exist for real applications and is much harder to extract from a single noisy image than from multiple noisy images. For Gaussian noise, there are many accurate state-of-the-art level estimations, whereas there are only limited random valued impulse noise level estimations proposed. Moreover, the existing proposed impulse noise estimators are limited in accuracy, especially in the presence of high noise levels. This paper presents a new random-valued impulse noise level estimation (Rvi-E) algorithm using only a single image. The presented Rvi-E algorithm is based on distribution property of impulse noise pixels, on correlation among the image, and on a new linear relationship between the percentage of big-distorted noise and one of all noise. The mathematical study, computer simulations, and analysis on 347 different images using five online grayscale image databases shows that (a) the presented method is efficient, robust and reliable, (b) the presented methods show stably accurate performance across images with different contents and different levels of noise (lower than 60%), and (c) the speed performance of the proposed Rvi can be boosted by the parallel computing strategy, since the estimation utilizes a parallel framework.

关键词： Impulse noise Random-value impulse noise Noise level estimation Probability theory

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Comparison of Face Detection algorithms on Mobile Devices

Comparison of Face Detection Algorithms on Mobile Devices

引用

International conference on image and vision Computing New Zealand, IVCNZ

作者： Yishi Guo Burkhard C. Wünsche University of Auckland Auckland New Zealand

ISBN: (数字)9781728185798

ISBN: (纸本)9781728185804

Face detection is a fundamental task for many computer vision applications such as access control, security, advertisement, automatic payment, and healthcare. Due to technological advances mobile robots are becoming increasingly common in such applications (e.g. healthcare and security robots) and consequently there is a need for efficient and effective face detection methods on such platforms. Mobile robots have different hardware configurations and operating conditions from desktop applications, e.g. unreliable network connections and the need for lower power consumption. Hence results for face detection methods on desktop platforms cannot be directly translated to mobile *** compare four common face detection algorithms, viola-Jones, HOG, MTCNN and MobileNet-SSD, for use in mobile robotics using different face data bases. Our results show that for a typical mobile configuration (Nvidia Jetson TX2) Mobile-NetSSD performed best with 90% detection accuracy for the AFW data set and a frame rate of almost 10 fps with GPU acceleration. MTCNN had the highest precision and was superior for more difficult face data sets, but did not achieve real-time performance with the given implementation and hardware configuration.

关键词： Performance evaluation Graphics processing units Robot sensing systems Real-time systems Face detection Faces Robots

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UWStereoNet: Unsupervised Learning for Depth Estimation and Color Correction of Underwater Stereo imagery

UWStereoNet: Unsupervised Learning for Depth Estimation and ...

引用

IEEE International conference on Robotics and Automation (ICRA)

作者： Skinner, Katherine A. Zhang, Junming Olson, Elisabeth A. Johnson-Roberson, Matthew Univ Michigan Inst Robot Ann Arbor MI 48109 USA Univ Michigan Dept Elect Engn & Comp Sci Ann Arbor MI 48109 USA Univ Michigan Dept Naval Architecture & Marine Engn Ann Arbor MI 48109 USA

ISBN: (纸本)9781538660263

Stereo cameras are widely used for sensing and navigation of underwater robotic systems. They can provide high resolution color views of a scene;the constrained camera geometry enables metrically accurate depth estimation;they are also relatively cost-effective. Traditional stereo vision algorithms rely on feature detection and matching to enable triangulation of points for estimating disparity. However, for underwater applications, the effects of underwater light propagation lead to image degradation, reducing image quality and contrast. This makes it especially challenging to detect and match features, especially from varying viewpoints. Recently, deep learning has shown success in end-to-end learning of dense disparity maps from stereo images. Still, many state-of-the-art methods are supervised and require ground truth depth or disparity, which is challenging to gather in subsea environments. Simultaneously, deep learning has also been applied to the problem of underwater image restoration. Again, it is difficult or impossible to gather real ground truth data for this problem. In this work, we present an unsupervised deep neural network (DNN) that takes input raw color underwater stereo imagery and outputs dense depth maps and color corrected imagery of underwater scenes. We leverage a model of the process of underwater image formation, image processing techniques, as well as the geometric constraints inherent to the stereo vision problem to develop a modular network that outperforms existing methods.

关键词： image color analysis Estimation image restoration Cameras Attenuation Stereo vision Deep learning

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Smart Traffic Lights using image processing algorithms 7

Smart Traffic Lights using Image Processing Algorithms

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7th IEEE Palestinian International conference on Electrical and Computer Engineering (PICECE)

作者： Elkhatib, Mohammed M. Adwan, Alarqam I. Alsamna, Ahmed S. Abu-Hudrouss, Ammar M. Islamic Univ Elect Engn Dept Gaza Palestine

ISBN: (纸本)9781538662915

Conventional methods of traffic light systems are unable to deal with the ongoing issues surrounding congestion. The current traffic light models are not suited to tackle problems such as traffic jams, ease of access for emergency vehicles and prevention of accidents. In order to counteract these issues, we propose the 'Smart traffic light control system'. The adopted Traffic Light Controllers (TLC) in Gaza, are based on microcontroller and microprocessor. These TLCs have limitations because they use the pre-defined hardware, which is functioning according to the program that does not have the flexibility of modification on real time basis. Due to the fixed time intervals of green, orange and red signals the waiting time is more and car uses more fuel. The intended project will focus on two aspects of implementation. First and foremost, to make traffic light controlling more efficient, image processing alongside with embedded system, will be used. This system will intelligently decide when to alternate signals based on the total traffic on each road detected by cameras on each lane which will increase road capacity and traffic flow. Secondly, the minor component consists of the ability to control the traffic signals by an android application throughout the Bluetooth and wireless connection. This will be controlled by the traffic warden or police officer. The prototype of the system has been built and tested. The model is better than the current system which is limited to the Fixed Mode Traffic Light Controller. It is observed that the proposed smart Traffic Light Controller is more efficient than the conventional controller in respect of less waiting time, more distance travelled by average vehicles and efficient operation during emergency mode.

关键词： smart traffic lights embedded system Arduino applications image processing

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Foveated image processing for Faster Object Detection and Recognition in Embedded systems Using Deep Convolutional Neural Networks 8th

Foveated Image Processing for Faster Object Detection and Re...

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8th International conference on Biomimetic and Biohybrid systems

作者： Jaramillo-Avila, Uziel Anderson, Sean R. Univ Sheffield Dept Automat Control & Syst Engn Sheffield S1 3JD S Yorkshire England

ISBN: (纸本)9783030247409;9783030247416

Object detection and recognition algorithms using deep convolutional neural networks (CNNs) tend to be computationally intensive to implement. This presents a particular challenge for embedded systems, such as mobile robots, where the computational resources tend to be far less than for workstations. As an alternative to standard, uniformly sampled images, we propose the use of foveated image sampling here to reduce the size of images, which are faster to process in a CNN due to the reduced number of convolution operations. We evaluate object detection and recognition on the Microsoft COCO database, using foveated image sampling at different image sizes, ranging from 416x416 to 96x96 pixels, on an embedded GPU - an NviDIA Jetson TX2 with 256 CUDA cores. The results show that it is possible to achieve a 4x speed-up in frame rates, from 3.59 FPS to 15.24 FPS, using 416 x 416 and 128 x 128 pixel images respectively. For foveated sampling, this image size reduction led to just a small decrease in recall performance in the foveal region, to 92.0% of the baseline performance with full-sized images, compared to a significant decrease to 50.1% of baseline recall performance in uniformly sampled images, demonstrating the advantage of foveated sampling.

关键词： Pixels

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An Optimized Process of Multiscale Retinex Algorithm Based on Hardware System

An Optimized Process of Multiscale Retinex Algorithm Based o...

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International conference on Information Science and Control Engineering (ICISCE)

作者： Gangcan Sun Wenya Zhao Wanming Hao School of Information Engineering Henan Institute of Advanced Technology Zhengzhou University Zhengzhou China

This paper introduces an optimized process of Multiscale Retinex (MSR) algorithm that apply to the hardware system. Retinex theory is a constant color model derived from the Human visual System (HVS) and can obtain more stable result for image enhancement than classic algorithms. Generally, the MSR model in Retinex is more suitable for enhancing images on hardware since it has less computation than other models. On the basis of ensuring the stability of the MSR algorithm results, we further optimize its calculation process. And the key issue in the restructuring model process is how to use the simplest Gaussian Convolution Kernel (GCK) to accomplish the Multiscale Gaussian Convolution (MSGC) assignment. Moreover, we briefly introduce the video display and the algorithm processing mechanisms in the hardware application, which includes a sophisticated image processing IP (IP-IP) based on the rearranged calculation process we designed. Under the real-time condition, the hardware system based on upgraded MSR can enhance smoothly color image and embrace a perfect performance in terms of visual intuition.

关键词： visualization Convolution Computational modeling visual systems Streaming media Throughput Hardware

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LIGHT FIELD image DATASET OF SKIN LESIONS 41

LIGHT FIELD IMAGE DATASET OF SKIN LESIONS

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41st Annual International conference of the IEEE Engineering in Medicine and Biology Society (EMBC)

作者： de Farie, Sergio M. M. Filipe, Jose N. Pereira, Pedro M. M. Tavora, Luis M. N. Assuncao, Pedro A. A. Santos, Miguel O. Fonseca-Pinto, Rui Santiago, Felicidade Dominguez, victoria Henrique, Martinha Inst Telecomunicacoes Aveiro Portugal Polytech Inst Leiria ESTG Leiria Portugal Univ Coimbra DEI Coimbra Portugal Ctr Hosp Leiria Dept Dermatol Leiria Portugal

ISBN: (纸本)9781538613115

Light field imaging technology has been attracting increasing interest because it enables capturing enriched visual information and expands the processing capabilities of traditional 2D imaging systems. Dense multiview, accurate depth maps and multiple focus planes are examples of different types of visual information enabled by light fields. This technology is also emerging in medical imaging research, like dermatology, allowing to find new features and improve classification algorithms, namely those based on machine learning approaches. This paper presents a contribution for the research community, in the form of a publicly available light field image dataset of skin lesions (named SKINL2 v1.0). This dataset contains 250 light fields, captured with a focused plenoptic camera and classified into eight clinical categories, according to the type of lesion. Each light field is comprised of 81 different views of the same lesion. The database also includes the dermatoscopic image of each lesion. A representative subset of 17 central view images of the light fields is further characterised in terms of spatial information (SI), colourfulness (CF) and compressibility. This dataset has high potential for advancing medical imaging research and development of new classification algorithms based on light fields, as well as in clinically-oriented dermatology studies.

关键词： Medical dataset Skin lesion Light fields

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11th International conference on Multimedia and Network Information systems, MISSI 2018

11th International Conference on Multimedia and Network Info...

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11th International conference on Multimedia and Network Information systems, MISSI 2018

ISBN: (纸本)9783319986777

The proceedings contain 58 papers. The special focus in this conference is on Multimedia and Network Information systems. The topics include: Comparative analysis of accuracy of fuzzy clustering methods applied for image processing;an efficient method for sign language recognition from image using convolutional neural network;Audio/speech coding based on the perceptual sparse representation of the signal with DAE neural network quantizer and near-end listening enhancement;The features and functions of an effective multimedia information retrieval system (MMIR);youTube timed metadata enrichment using a collaborative approach;improving the responsiveness of geospatial web applications through client-side processing;isotone galois connections and generalized one-sided concept lattices;Transfer learning in GMDH-type neural networks;modelling of taxi dispatch problem using heuristic algorithms;modern approaches to multi-dimensional visual signals analysis;malware detection using black-box neural method;intrusion detection and risk evaluation in online transactions using partitioning methods;Prediction of autism severity level in Bangladesh using fuzzy logic: FIS and ANFIS;a framework for analyzing academic data;verifying usefulness of algorithms for WordNet based similarity sense disambiguation;comparison of paragram and glove results for similarity benchmarks;calculating optimal queries from the query relevance file;survey on neural machine translation into Polish;Multilingual and intercultural competence for ICT: Accessing and assessing electronic information in the global world;selection of most suitable secondary school alternative by multi-criteria fuzzy analytic hierarchy process;new applications of sound and vision engineering;comparison of the novel classification methods on the reuters-21578 corpus;sparse solution to inverse problem of nonlinear dimensionality reduction.

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Automatic Prostate Segmentation on 3D MRI Scans Using Convolutional Neural Networks with Residual Connections and Superpixels

Automatic Prostate Segmentation on 3D MRI Scans Using Convol...

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International conference on systems, Signals and image processing, IWSSIP

作者： Giovanni Lucca França da Silva João vitor Ferreira França Petterson Sousa Diniz Aristófanes Corrêa Silva Anselmo Cardoso de Paiva Elton Anderson Araújo de Cavalcanti Federal University of Maranhão - UFMA Applied Computing Group - NCA São Luís MA Brazil University Hospital of the Federal University of Maranhão - HUUFMA São Luís MA Brazil

ISBN: (数字)9781728175393

ISBN: (纸本)9781728175409

Automatic and reliable prostate segmentation is an essential prerequisite for assisting the diagnosis and treatment, such as guiding biopsy procedure and radiation therapy. Notwithstanding, automatic segmentation is challenging due to the lack of clear prostate boundaries owing to similar appearance of prostate and surrounding tissues and the wide variation in size and shape among different patients. Therefore, this paper proposes an automatic method for prostate segmentation on 3D MRI scans using a content-sensitive superpixels technique, deep convolutional neural networks with residual connections (ResCNN), and the particle swarm optimization (PSO) algorithm. The proposed method has been evaluated on the Prostate 3T and PROMISE12 databases, presenting a Dice similarity coefficient of 86.68 %, Jaccard index of 76.58%, relative volume difference of 3.92%, volumetric similarity of 96.69 %, sensitivity of 88.36 %, specificity of 93.43%, accuracy of 91.97% and an area under ROC curve of 90.90 %. Experimental results demonstrate the high performance-potential of the proposed method comparable to those previously published.

关键词： Magnetic resonance imaging Databases Three-dimensional displays image segmentation Training Cancer Clustering algorithms

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Evaluating perception systems for autonomous vehicles using quality temporal logic 18th

Evaluating perception systems for autonomous vehicles using ...

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18th International conference on Runtime Verification, RV 2018

作者： Dokhanchi, Adel Amor, Heni Ben Deshmukh, Jyotirmoy V. Fainekos, Georgios Arizona State University TempeAZ85281 United States University of Southern California Los AngelesCA90089 United States

ISBN: (纸本)9783030037680

For reliable situation awareness in autonomous vehicle applications, we need to develop robust and reliable image processing and machine learning algorithms. Currently, there is no general framework for reasoning about the performance of perception systems. This paper introduces Timed Quality Temporal Logic (TQTL) as a formal language for monitoring and testing the performance of object detection and situation awareness algorithms for autonomous vehicle applications. We demonstrate that it is possible to describe interesting properties as TQTL formulas and detect cases where the properties are violated. © Springer Nature Switzerland AG 2018.

关键词： Autonomous vehicles

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