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IEEE International Conference on Electronics, Circuits and systems (ICECS)

作者： P. Grybos R. Kleczek P. Kmon A. Krzyzanowska P. Otfinowski R. Szczygiel M. Zoladz AGH University of Science and Technology Krakow Poland

This paper presents a readout integrated circuit of pixel architecture called MPIX (Multithreshold PIXels), designed for CdTe pixel detectors used in high-energy X-ray imaging applications. The MPIX IC of the area of $9.6\ \text{mm} \times 20.3\ \text{mm}$ is designed in a CMOS 130 nm process. The IC core is a matrix of $96\times 192$ square-shaped pixels of $100\ \mu \mathrm{m}$ pitch. Each pixel contains an analog front-end, four independently working discriminators, and four 12-bit ripple counters. Such pixel architecture allows photon processing one by one and selecting the X-ray photons according to their energy (X-ray color imaging). The MPIX chips are bump-bonded to pixel CdTe sensor with a pitch of $100\ \mu \mathrm{m}$ and thickness of $750\ \mu \mathrm{m}$ . These hybrid detectors (sensor bump-bonded to readout integrated circuit) are characterized by test pulses and X-ray radiation. To match the different range of applications the MPIX chip has 8 possible different gain settings. In the high gain mode, the chip can operate with Xray photons up to 46 keV and has the Equivalent Noise Charge (ENC) of 123 el. rms. In the low gain mode, the ENC is equal to 192 el. rms, and the chip can process the X-ray photons of energy up to 154 keV. The matrix of 18432 pixels has a very good uniformity: globally set threshold has an effective pixel to pixel offset spread of 1 mV (for the full threshold range up to 800 mV). The power consumption per pixel is $80\ \mu \mathrm{W}/\text{pixel}$ . Additionally, to deal with charge sharing effects in a thick semiconductor pixel sensor, Multithreshold Pattern Recognition algorithms are implemented in the MPIX IC.

关键词： Integrated circuits Power demand II-vi semiconductor materials image color analysis Radiation detectors Detectors Pattern recognition

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A fast numerical algorithm for the 2D non-separable linear canonical transform based on a decomposition of the ABCD matrix 6

A fast numerical algorithm for the 2D non-separable linear c...

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Conference on Holography - Advances and Modern Trends vi

作者： Zhao, Liang Wan, Min Li, Qing Liu, Sannuya Sheridan, John T. Healy, John J. Cent China Normal Univ NERCEL NELEBD Wuhan Hubei Peoples R China Univ Coll Dublin Sch Elect & Elect Engn Lab IoE2 Dublin 4 Ireland

ISBN: (数字)9781510627277

ISBN: (纸本)9781510627277

The two-dimensional non-separable linear canonical transform (2D-NS-LCT) can model a wide range of paraxial optical systems. Digital algorithms to calculate the 2D-NS-LCTs are of great interested in both light propagation modeling and digital signal processing. We have previously reported that the transform of a 2D image with rectangular sampling grid generally results in a parallelogram output sampling grid, thus complicating further calculations. One possible solution is to use interpolation techniques. However, it usually leads to poor calculation speed and reduced accuracy. To alleviate this problem, we previously proposed a unitary algorithm by choosing an advantageous sampling rate related to the system parameters. In this paper, a fast algorithm is further proposed based on a novel matrix decomposition, which can significantly improve the efficiency of the numerical approximations.

关键词： Digital holography ABCD transforms Numerical approximation and analysis Discrete optical signal processing

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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-...

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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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image processing and machine learning applications in mining industry: Mine 4.0

Image processing and machine learning applications in mining...

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Intelligent systems and Advanced Computing Sciences (ISACS),International Conference on

作者： Hamid Ouanan El Hassan Abdelwahed National School of Applied Sciences Sultan Moulay Slimane University Beni Mellal Morocco CSEHS Mohammed VI Polytechnic University Marrakesh Morocco

ISBN: (数字)9781728148137

ISBN: (纸本)9781728148144

Recently, image processing (IP) and Machine learning (ML) algorithms have been successfully used in a wide variety of industry sectors. In this paper, we first provide mining engineers with the state of the art about IP and ML applications in the mining industry. furthermore, we propose a real time application for froth image analysis in the context of mineral processing. Finally, we propose some future directions, including suggestions on mining data collection and development of an exploration tool based on IP and ML algorithms by taking advantage of cloud computing technology to analyze geoscience data in order identify target areas with high prospectively potential.

关键词： Minerals Machine learning algorithms Data mining Machine learning Machine vision Materials processing

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Computer vision-Based Model for Moisture Marks Detection and Recognition in Subway Networks

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JOURNAL OF COMPUTING IN CIviL ENGINEERING 2018年第2期32卷 1-13页

作者： Dawood, Thikra Zhu, Zhenhua Zayed, Tarek Concordia Univ Dept Bldg Civil & Environm Engn 1515 St Catherine St W Montreal PQ H3G 2W1 Canada

Moisture marks (wet areas) are significant defects that may develop on the surfaces of subway structures as a result of water leakage through soil. The detection and assessment of moisture marks are predominantly conducted on the basis of visual inspection (vi) methods, which are known to be costly, labor-intensive, and error-prone. The objective of this paper is to develop an integrated model based on image processing techniques and artificial intelligence to automate consistent moisture marks detection and numerical representation of the distress in subway networks. The integrated model comprises sequential processors that automatically detect moisture marks on concrete surfaces, and artificial neural networks (ANNs) for moisture marks identification and quantification. First, red-green-blue (RGB) images are preprocessed by means of spatial domain filters to denoise the image and enhance the crucial clues associated with moisture marks. Second, a moisture detector is streamlined with a set of morphological algorithms to detect wet areas. Third, the area percentage and severity of moisture marks are measured using the ANN model in conjunction with cross-entropy optimization function. The integrated model was validated through 165 images. Regarding the moisture marks detection algorithm, the recall, precision, and accuracy attained were 93.2, 96.1, and 91.5%, respectively. The mean and standard deviation of error percentage in moisture marks region extraction were 12.2 and 7.9%, respectively. Also, the ANN model was able to satisfactorily quantify the moisture marks area with an average validity of 96%. The integrated model is a decision support tool, expected to assist infrastructure managers and civil engineers in their future plans and decision making. (c) 2017 American Society of Civil Engineers.

关键词： image processing Concrete infrastructure Subway systems Moisture marks Condition assessment visual inspection Artificial neural networks

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Impact of Camera vibration Frequencies on image Noise for Unmanned Aerial System Applications

Impact of Camera Vibration Frequencies on Image Noise for Un...

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2018 IEEE Southeastcon, Southeastcon 2018

作者： Huang, Wei-Hao Daniel Otero, Luis Otero, Carlos E. Moyou, Marka Department of Engineering Systems Florida Institute of Technology MelbourneFL United States Department of Electrical and Computer Engineering Florida Institute of Technology MelbourneFL United States

ISBN: (纸本)9781538661338

In the transportation systems engineering field, there is a fast growing interest to use small unmanned aerial systems (UAS) for structural inspections. Of particular importance is the development of image processing algorithms to process UAS collected data for transportation infrastructure (e.g., bridges) defect detection/classification purposes. Results from preliminary experiments using image data collected with a UAS have shown the presence of significant image noise, which negatively impacts the efficiency of algorithms to detect/classify structural defects. This research study presents an ongoing effort to understand the relationship between UAS camera vibrations to image noise. Ultimately, the objective is to identify maximum camera vibration frequencies that will result in acceptable image noise levels for effective detection and classification of defects. © 2018 IEEE.

关键词： Cameras

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Real-time Localization and Navigation in an Indoor Environment Using Monocular Camera for visually Impaired 5

Real-time Localization and Navigation in an Indoor Environme...

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5th International Conference on Industrial Engineering and Applications (ICIEA)

作者： Ramesh, Kruthika Nagananda, S. N. Ramasangu, Hariharan Deshpande, Rohini MS Ramaiah Univ Appl Sci Bangalore Karnataka India

ISBN: (纸本)9781538657485

The aim of this research is to design an intelligent system that addresses the problem of real-time localization and navigation of visually impaired (vi) in an indoor environment using a monocular camera. systems that have been developed so far for the vi use either many cameras (stereo and monocular) integrated with other sensors or use very complex algorithms that are computationally expensive. In this research work, a computationally less expensive integrated system has been proposed to combine imaging geometry, visual Odometry (VO), Object Detection (OD) along with Distance-Depth (D-D) estimation algorithms for precise navigation and localization by utilizing a single monocular camera as the only sensor. The developed algorithm is tested for both standard Karlsruhe and indoor environment recorded datasets. Tests have been carried out in real-time using a smartphone camera that captures image data of the environment as the person moves and is sent over Wi-Fi for further processing to the MATLAB software model running on an Intel i7 processor. The algorithm provides accurate results on real-time navigation in the environment with an audio feedback about the person's location. The trajectory of the navigation is expressed in an arbitrary scale. Object detection based localization is accurate. The D-D estimation provides distance and depth measurements up to an accuracy of 94-98%.

关键词： visual odometry monocular camera optical flow KLT tracker visual SLAM image processing pattern recognition

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Long Term Safety Area Tracking (LT-SAT) with online failure detection and recovery for robotic minimally invasive surgery

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MEDICAL image ANALYSIS 2018年 45卷 13-23页

作者： Penza, Veronica Du, Xiaofei Stoyanov, Danail Forgione, Antonello Mattos, Leonardo S. De Momi, Elena Politecn Milan Dept Elect Informat & Bioengn Pzza L Da Vinci 32 I-20133 Milan Italy Ist Italiano Tecnol Dept Adv Robot Via Morego 30 I-16163 Genoa Italy UCL Ctr Med Image Comp Dept Comp Sci London England Osped Niguarda Ca Granda Pzza Osped Maggiore 3 I-20162 Milan Italy

Despite the benefits introduced by robotic systems in abdominal Minimally Invasive Surgery (MIS), major complications can still affect the outcome of the procedure, such as intra-operative bleeding. One of the causes is attributed to accidental damages to arteries or veins by the surgical tools, and some of the possible risk factors are related to the lack of sub-surface visibilty. Assistive tools guiding the surgical gestures to prevent these kind of injuries would represent a relevant step towards safer clinical procedures. However, it is still challenging to develop computer vision systems able to fulfill the main requirements: (i) long term robustness, (ii) adaptation to environment/object variation and (iii) real time processing. The purpose of this paper is to develop computer vision algorithms to robustly track soft tissue areas (Safety Area, SA), defined intra-operatively by the surgeon based on the real-time endoscopic images, or registered from a pre-operative surgical plan. We propose a framework to combine an optical flow algorithm with a tracking-by-detection approach in order to be robust against failures caused by: (i) partial occlusion, (ii) total occlusion, (iii) SA out of the field of view, (iv) deformation, (v) illumination changes, (vi) abrupt camera motion, (vii), blur and (viii) smoke. A Bayesian inference-based approach is used to detect the failure of the tracker, based on online context information. A Model Update Strategy (MUpS) is also proposed to improve the SA re-detection after failures, taking into account the changes of appearance of the SA model due to contact with instruments or image noise. The performance of the algorithm was assessed on two datasets, representing ex-vivo organs and in-vivo surgical scenarios. Results show that the proposed framework, enhanced with MUpS, is capable of maintain high tracking performance for extended periods of time (similar or equal to 4 min - containing the aforementioned events) with high precisi

关键词： Long-term tissue tracking Tracking failure detection Model Update Strategy Robotic minimally invasive surgery

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Cloud-based Platform for Computer vision Applications 17

Cloud-based Platform for Computer Vision Applications

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International Conference on Smart Digital Environment (ICSDE)

作者： Mahmoudi, Sidi Ahmed El Adoui, Mohammed Belarbi, Mohammed Amin Larhmam, Mohammed Amine Lecron, Fabian Univ Mons Mons Belgium

ISBN: (纸本)9781450352819

During last years, images and videos have become widely used in many daily applications. Indeed, they can come from cameras, smartphones, social networks of from medical devices. Generally, these images and videos are used for illustrating people or objects (cars, trains, planes, etc.) in many situations such as airports, train stations, public areas, sport events, hospitals, etc. Thus, image and video processing algorithms have got increasing importance, they are required from various computer visions applications such as motion tracking, real time event detection, database (images and videos) indexation and medical computer aided diagnosis methods. In this paper, we propose a cloud platform that integrates the above-mentioned methods, which are generally developed with popular open source image and video processing libraries (OpenCV(1), OpenGL(2), ITK3, VTK4, etc.). Theses modules are automatically integrated and configured in the cloud application. Thus, the platform users will have access to different computer vision techniques without the need to download, install and configure the corresponding software. Each guest can select the required application, load its data and get the output results in a safe and simple way. The cloud platform can handle the variety of Operating systems and programming languages (C++, Java, Python, etc.). Experimentations were conducted within two kinds of applications. The first represents medical methods such as image segmentation in MR images, 3D image reconstruction from 2D radiographs, left ventricle segmentation and tracking from 2D echocardiography. The second kind of applications is related to video processing such as face, people and cars tracking, and abnormal event detection in crowd videos.

关键词： Cloud computing image and video processing video surveillance medical image analysis

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A Novel ant colonies approach to medical image segmentation 14

A Novel ant colonies approach to medical image segmentation

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14th International Multi-Conference on systems, Signals & Devices (SSD)

作者： Selmani, Anissa Seddik, Hassene ben Braiek, Ezzedine ENSIT CEREP Dept Elect Engn Tunis Tunisia

ISBN: (纸本)9781538631751

Prostate cancer is becoming a threat to humanity. Today, the diagnosis of diseases is still be realized mostly by manual methods. Nevertheless, this traditional process is inefficient and not accurate. Its precision depends on the operator's expertise. Thus, applying machine learning algorithms for malignant cells detection and counting remains a significant purpose in medical image analysis research. In this paper, we apply a modified ACO algorithm to measure the rate of cell growth of cancer's patient automatically due to segmentation and counting process. The proposed method was applied on several medical images obtained from MRI-guided prostate biopsies. The robustness of this idea was showed by comparison with hand-labeled obtained segmentation results.

关键词： image processing ACO segmentation Prostate cancer cells detection cells couting

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