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

共 15687 条记录，以下是4061-4070 订阅

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An image-based Method to Predict Surface Enhanced Raman Spectroscopy Sensor Quality

An Image-based Method to Predict Surface Enhanced Raman Spec...

引用

IEEE International conference on image processing

作者： Yiming Zuo Yang Lei Steven Barcelo HP Labs Palo Alto CA USA

ISBN: (数字)9781728163956

ISBN: (纸本)9781728163963

image-based quality control is a powerful tool for nondestructive testing of product quality. Machine vision systems (MVS) often implement image-based machine learning algorithms in an attempt to match human level accuracy in detecting product defects for better efficiency and repeatability. Plasmonic sensors, such as those used in Surface Enhanced Raman Spectroscopy (SERS), present a unique challenge for image-based quality control, because in addition to obvious defects such as scratches and missing areas, subtle color changes can also indicate significant changes in sensor performance. As a further challenge, it is not straightforward for even a human expert to distinguish between high- and low-quality sensors based on these subtle color changes on the sensors. In this paper we show that by extracting image features according to the domain knowledge, we can build an image-based method that outperforms human expert prediction. This method enables automated non-destructive SERS sensor quality control and has been implemented successfully on our server.

关键词： Indexes Rough surfaces Surface roughness

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Skin lesion segmentation using convolutional neural networks with improved U-Net architecture

Skin lesion segmentation using convolutional neural networks...

引用

International conference of Signal processing and Intelligent systems (ICSPIS)

作者： Rasool Iranpoor Amir Soltany Mahboob Shakiba Shahbandegan Nasrin Baniasadi School of Electrical Engineering University of Birjand Birjand Iran School of Electrical Engineering Iran University of Science and Technology Tehran Iran

ISBN: (数字)9781728186290

ISBN: (纸本)9781728186306

The location of skin lesions is of particular importance in the diagnosis and monitoring of skin disease. For this purpose, image segmentation could be used, for which various methods, algorithms and approaches have been proposed. Lately, many convolutional neural networks (CNN) with different architectures have been effectively employed for semantic image segmentation. In this paper, a CNN with improved U-Net architecture is introduced; this architecture is used for applying image segmentation to a dermatology image dataset including images of three different skin damage types. In the proposed method, the efficiency of the architecture is significantly improved by employing a pre-trained architecture in the encoding section and replacing some of the pooling layers. Various factors affecting the network such as the function of layers and their effects on network performance are investigated. Compared to existing CNN architectures, the proposed method attains higher stability and efficiency for the given dataset. For training data, %92 accuracy and for testing data %89 accuracy has been achieved.

关键词： image segmentation Training data Skin Lesions Convolutional neural networks Task analysis Testing

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Key-Point Based Distribution Probability Optimization for Object Detection in Remote Sensing images

Key-Point Based Distribution Probability Optimization for Ob...

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International conference on Computer and Communications (ICCC)

作者： Xiaoke Zhu Yufei Wang Xiaopan Chen Danyang Li Jitong Zhang Yulong Cai School of Computer and Information Engineering Henan University Kaifeng China Henan Key Laboratory of Big Data Analysis and Processing Henan University Kaifeng China

ISBN: (纸本)9781665409513

Recently, object detection in remote sensing images has been an important topic in computer vision. As a mainstream approach to detectors, single-stage detectors usually compute the important points of the bounding box to get the location of the target, and optimize them by Focal Loss. One of the development trends for one-stage detectors is to combine with Localization Quality Estimation (LQE), which can provide accurate ranking scores, and thus bring benefits to the Non-Maximum Suppression as well as the following detection results. In this paper, we propose a one-stage detector, named Key-point based Distribution Probability Optimization (KDPO), for remote sensing object detection. Specially, we first classify and locate the object through five key points. Then, we merge the quality estimation into the class prediction vector to form a joint representation of localization quality and classification. Next, we utilize a vector to represent arbitrary distribution of box locations. The improved representations avoid the inconsistency risk and accurately describe the flexible distribution of the data. Meanwhile, we use the improved Focal Loss for the succeed optimization. The proposed method is tested on two popular aerial image datasets, including HRSC2016 and DOTA. In all experiments, our approach achieves promising performance comparing to the state-of-the-art detectors.

关键词： Location awareness Heating systems Computer vision Estimation Detectors Object detection Prediction algorithms

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A novel approach to detect outer retinal tubulation using U-Net in SD-OCT images 15

A novel approach to detect outer retinal tubulation using U-...

引用

15th International conference on Signal image Technology and Internet Based systems, SISITS 2019

作者： Megyeri, Istvan Katona, Melinda Nyul, Laszlo G. Department of Computer Algorithms and Artificial Intelligence Hungary Department of Image Processing and Computer Graphics University of Szeged Hungary

ISBN: (数字)9781728156866

ISBN: (纸本)9781728156866

Optical Coherence Tomography (OCT) has become a basic non-invasive tool in diagnosing and following different types of eye diseases. This technique can produce high-resolution cross-sectional images of retinal layers. Outer retinal tubulation (ORT) is one of the detectable biomarker by SD-OCT. ORTs defined as hyporeflective, tubular structures with hyperreflective borders or reversed within the retina and appear in many retinal diseases, including age-related macular degeneration (AMD). Our aim is to develop an automatic method that can efficiently characterize ORT biomarker. Detection of this biomarker can be challenging because of its variable size, location, and reflectivity. In this paper, we present a fully convolutional U-Net based architecture to detect ORT. The proposed approach is evaluated using a dataset annotated by ophthalmologists. One of the main challenges was the limited amount of training data that we resolve with real-time augmentation during training and using nested cross-validation. Our method achieved near human performance reaching an overall object-based recall score of 0.847 and Dice score of 0.579 on the test set. © 2019 IEEE.

关键词： Optical tomography

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A Hybrid Framework for Expediting Emergency Vehicle Movement on Indian Roads

A Hybrid Framework for Expediting Emergency Vehicle Movement...

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International conference on Innovative Mechanisms for Industry Applications (ICIMIA)

作者： Abhishek Raman S Kaushik K.V.S Rajeswara Rao Minal Moharir RV College of Engineering Bangalore INDIA Industrial Engineering and Management RV College of Engineering Bangalore INDIA Computer Science and Engineering RV College of Engineering Bangalore INDIA

ISBN: (数字)9781728141671

ISBN: (纸本)9781728141688

Unhindered and smooth movement of emergency vehicles within a city is a crucial aspect of any intelligent transport system. It is common to observe emergency vehicles such as ambulances and fire engines obstructed by traffic snarls on Indian roads, especially in the proximity of busy intersections. Existing literature primarily advocates the deployment of RFID technology to terminate the round-robin sequence of the signal system and switch the signal to green in the required direction. However, this technology has proven to be susceptible to electromagnetic interferences and also the economic feasibility is questionable. This paper proposes a model that employs real time image processing and object detection using a convolutional neural network (CNN) architecture called SSD Mobilenet. Unlike a few other architectures, SSD Mobilenet requires very limited computation, hence enabling swift detection. Furthermore, an acoustic signal (sound) processing (pitch detection) algorithm is employed to detect the sirens of emergency vehicles to nullify the potential false positives (e.g. an ambulance in a non-emergency scenario) that creep into object detection using image processing. Both algorithms work in unison, bolstering the accuracy of detection. Upon detection, the signal instantly switches to green, facilitating the expedited movement of emergency vehicles, even in high traffic conditions.

关键词： image processing Cameras Training Switches Radiofrequency identification Real-time systems Detection algorithms

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A Fast and Scalable Pipeline for Stain Normalization of Whole-Slide images in Histopathology 15th

A Fast and Scalable Pipeline for Stain Normalization of Whol...

引用

15th European conference on Computer vision (ECCV)

作者： Stanisavljevic, Milos Anghel, Andreea Papandreou, Nikolaos Andani, Sonali Pati, Pushpak Ruschoff, Jan Hendrik Wild, Peter Gabrani, Maria Pozidis, Haralampos IBM Res Zurich CH-8803 Ruschlikon Switzerland Univ Klinikum Frankfurt Senckenberg Inst Pathol D-60590 Frankfurt Germany Univ Spital Zurich Inst Pathol & Mol Pathol CH-8091 Zurich Switzerland

ISBN: (纸本)9783030110246;9783030110239

Stain normalization is one of the main tasks in the processing pipeline of computer-aided diagnosis systems in modern digital pathology. Some of the challenges in this tasks are memory and runtime bottlenecks associated with large image datasets. In this work, we present a scalable and fast pipeline for stain normalization using a state-of-the-art unsupervised method based on stain-vector estimation. The proposed system supports single-node and distributed implementations. Based on a highly-optimized engine, our architecture enables high-speed and large-scale processing of high-magnification whole-slide images (WSI). We demonstrate the performance of the system using measurements from different datasets. Moreover, by using a novel pixel-sampling optimization we show lower processing time per image than the scanning time of ultrafast WSI scanners with the single-node implementation and additional 3.44 average speed-up with the 4-nodes distributed pipeline.

关键词： Histopathological image processing Whole-slide images Stain normalization Distributed computing Color deconvolution

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Deep Learning Based Cross-Spectral Disparity Estimation For Stereo Imaging

Deep Learning Based Cross-Spectral Disparity Estimation For ...

引用

IEEE International conference on image processing

作者： Nils Genser Andreas Spruck Jürgen Seiler André Kaup Multimedia Communications and Signal Processing Friedrich-Alexander-Universität Erlangen-Nürnberg (FAU) Erlangen Germany

ISBN: (数字)9781728163956

ISBN: (纸本)9781728163963

Recently, cross-spectral stereo-camera setups found their way from special applications to mass market, especially in smartphones, automotive systems, or drones. In the following, a novel concept is introduced to bring stereo cameras and cross-spectral disparity estimation together. So far, either monomodal stereo algorithms exist that are not suitable for cross-spectral image registration, or structural template matching is applied that achieves a low quality. To overcome these limitations, a technique is proposed to synthesize arbitrary spectral components from widely available color stereo databases, and to retrain mono-modal deep learning methods. In this contribution, the estimation of spectral bands based on random processes is shown together with noise models, which also allow for a robust registration of narrowband components. The theoretical examination is completed by an extensive evaluation, including a self-manufactured cross-spectral camera setup. In comparison to state-of-the-art techniques, the end-point error is on average reduced by a factor of seven.

关键词： image color analysis Training Cameras Estimation Machine learning Photonics

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An experimental comparative performance study of demosaicing algorithms on general-purpose GPUs 19

An experimental comparative performance study of demosaicing...

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9th Balkan conference in Informatics, BCI 2019

作者： Zapryanov, Georgi Nikolova, Iva Technical University of Sofia FCST Department of Computer Systems Sofia Bulgaria

ISBN: (纸本)9781450371933

The image registration by digital still cameras and video cameras requires color filters to be posed onto the photosensitive sensors (CCD or CMOS). The filters are arranged in patterns across the face of the image sensing array. The conventional color filter array (CFA) capture only one color component at each image pixel. The missing colors in the raw sensor data are interpolated by a process called CFA interpolation or demosaicing. Quality of the full-color reconstruction process is mostly relied on demosaicing method applied. Most of the current demosaicing methods are computationally expensive and often too slow for real-time scenarios. Many industrial applications require real-time and high quality demosaicing solutions, and quite often slow image reconstruction process is a real bottleneck. The purpose of this research is to present a comparative performance study of demosaicing algorithms on general-purpose GPUs. The experimental results of CUDA-based implementations of two state-of-the-art and widely applied in practice CFA algorithms are presented. The performance efficiency is assessed and analyzed by experimental studies on a set of real photographic test images on two general-purpose graphic cards. The obtained results demonstrated the benefit of exploiting the contemporary GPUs in speeding up the demosaicing process, especially for practical applications that need to meet real-time and high-speed video processing requirements combined with high quality of the full-color image reconstruction. © 2019 Association for Computing Machinery.

关键词： Graphics processing unit

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Faster than FAST: GPU-Accelerated Frontend for High-Speed viO

Faster than FAST: GPU-Accelerated Frontend for High-Speed VI...

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2020 IEEE/RSJ International conference on Intelligent Robots and systems (IROS)

作者： Balázs Nagy Philipp Foehn Davide Scaramuzza Robotics and Perception Group University of Zurich University of Zurich and ETH Zurich Switzerland

ISBN: (数字)9781728162126

ISBN: (纸本)9781728162133

The recent introduction of powerful embedded graphics processing units (GPUs) has allowed for unforeseen improvements in real-time computer vision applications. It has enabled algorithms to run onboard, well above the standard video rates, yielding not only higher information processing capability, but also reduced latency. This work focuses on the applicability of efficient low-level, GPU hardware-specific instructions to improve on existing computer vision algorithms in the field of visual-inertial odometry (viO). While most steps of a viO pipeline work on visual features, they rely on image data for detection and tracking, of which both steps are well suited for parallelization. Especially non-maxima suppression and the subsequent feature selection are prominent contributors to the overall image processing latency. Our work first revisits the problem of non-maxima suppression for feature detection specifically on GPUs, and proposes a solution that selects local response maxima, imposes spatial feature distribution, and extracts features simultaneously. Our second contribution introduces an enhanced FAST feature detector that applies the aforementioned non-maxima suppression method. Finally, we compare our method to other state-of-the-art CPU and GPU implementations, where we always outperform all of them in feature tracking and detection, resulting in over 1000fps throughput on an embedded Jetson TX2 platform. Additionally, we demonstrate our work integrated into a viO pipeline achieving a metric state estimation at ~*** available at: https://***/uzh-rpg/vilib.

关键词： Pipelines Graphics processing units Detectors Feature extraction Throughput Real-time systems State estimation

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Modelling and Simulation of Microgrid in Grid-Connected Mode and Islanded Mode

Modelling and Simulation of Microgrid in Grid-Connected Mode...

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International conference on Electrical, Control and Instrumentation Engineering (ICECIE)

作者： Tithitip Prabaksorn R.T Naayagi Sze Sing Lee Newcaslte University (Singapore) Singapore 567739

The following topics are dealt with: inductive power transmission; diseases; wind turbines; wind power plants; battery powered vehicles; coils; genetic algorithms; global warming; battery chargers; medical image proce... 详细信息

ISBN: (数字)9781728198774

ISBN: (纸本)9781728198781

关键词： Microgrids Batteries Photovoltaic systems Fuel cells Inductors Load modeling Switching frequency

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