Search results for " Detection"
showing 10 items of 1676 documents
Lateral flow assays towards point-of-care cancer detection: A review of current progress and future trends
2020
Abstract Cancer is one of the main causes of mortality and morbidity worldwide. However, its early non-invasive detection via quantification of appropriate biomarkers can significantly reduce mortality, enhance survival, and save treatment costs. Lateral flow test strips (LFTS) are nowadays considered as the most attractive point-of-care devices for healthcare applications. However, the quantification of cancer biomarkers in body fluids suffers from some challenges including i) the necessity for multiplex analysis, ii) the development of sensitive detection systems, iii) to overcome the analysis of complex samples, at the same time, it should keep the quality assurance criteria for an accur…
Anomaly‐based intrusion detection systems: The requirements, methods, measurements, and datasets
2021
International audience; With the Internet's unprecedented growth and nations' reliance on computer networks, new cyber‐attacks are created every day as means for achieving financial gain, imposing political agendas, and developing cyberwarfare arsenals. Network security is thus acquiring increasing attention among researchers, practitioners, network architects, policy makers, and others. To defend organizations' networks from existing, foreseen, and future threats, intrusion detection systems (IDSs) are becoming a must. Existing surveys on anomaly‐based IDS (AIDS) focus on specific components such as detection mechanisms and lack many others. In contrast to existing surveys, this article co…
Detection of Duplicated Regions in Tampered Digital Images by Bit-Plane Analysis
2009
In this paper we present a new method for searching duplicated areas in a digital image. The goal is to detect if an image has been tampered by a copy-move process. Our method works within a convenient domain. The image to be analyzed is decomposed in its bit-plane representation. Then, for each bitplane, block of bits are encoded with an ASCII code, and a sequence of strings is analyzed rather than the original bit-plane. The sequence is lexicographically sorted and similar groups of bits are extracted as candidate areas, and passed to the following plane to be processed. Output of the last planes indicates if, and where, the image has been altered.
Automatic multi-seed detection for MR breast image segmentation
2017
In this paper an automatic multi-seed detection method for magnetic resonance (MR) breast image segmentation is presented. The proposed method consists of three steps: (1) pre-processing step to locate three regions of interest (axillary and sternal regions); (2) processing step to detect maximum concavity points for each region of interest; (3) breast image segmentation step. Traditional manual segmentation methods require radiological expertise and they usually are very tiring and time-consuming. The approach is fast because the multi-seed detection is based on geometric properties of the ROI. When the maximum concavity points of the breast regions have been detected, region growing and m…
Convolutional Long Short-Term Memory Network for Multitemporal Cloud Detection Over Landmarks
2019
In this work, we propose to exploit both the temporal and spatial correlations in Earth observation satellite images through deep learning methods. In particular, the combination of a U-Net convolutional neural network together with a convolutional long short-term memory (LSTM) layer is proposed. This model is applied for cloud detection on MSG/SEVIRI image time series over selected landmarks. Implementation details are provided and our proposal is compared against a standard SVM and a U-Net without the convolutional LSTM layer but including temporal information too. Experimental results show that this combination of networks exploits both the spatial and temporal dependence and provides st…
Infantile Hemangioma Detection using Deep Learning
2020
Infantile hemangiomas are the most common type of benign tumor which appear in the first weeks of life. As currently there is no robust protocol to monitor and assess the hemangioma status, this study proposes a preliminary method to detect the lesion. Therefore, in this paper we describe a hemangiomas classifier based on a linear convolutional neural network architecture. The challenge was to achieve a good classification using a relatively small internal database of 240 images from 40 different patients. The results are promising as the CNN performance evaluation showed a level of accuracy on the test set of 93.84%. Five metrics were calculated to assess the proposed model performances: a…
Towards General Purpose Object Detection: Deep Dense Grid Based Object Detection
2020
Object detection is one of the most challenging and very important branch of computer vision. Some of the challenging aspect of a detection network is the fact that an object can appear anywhere in the image, be partially occluded by another object, might appear in crowd or have greatly varying scales. Consequently, we propose a fine grained and equally spaced dense grid cells throughout an input image be responsible of detecting an object. We re-purpose an already existing deep state-of-the-art detector or classifier into deep and dense detector. Our dense object detector uses binary class encoding and hence suitable for very large multi-class object detector. We also propose a more flexib…
Artificial Intelligence in Monitoring and Diagnostics of Electrical Energy Conversion Systems
2020
Diagnostics and prognostics of electrical energy conversion systems are moving forward with the rapid development of IT and artificial intelligence possibilities. This also broadens the horizons for classical and advanced condition and operation monitoring techniques, resulting in more accurate fault detection, degradation prognosis and calculation of remaining life of energy conversion systems, utilized in every aspect and field of industry today. This paper gives an overview of the necessity for condition monitoring and diagnostics of the mentioned systems, explaining the classical and advanced techniques for diagnostics. Methodology to diagnose and prognose the energy conversion units, w…
Features extraction on complex images
2005
The accessibility of inexpensive and powerful computers has allowed true digital holography to be used for industrial inspection using microscopy. This technique allows the capture of a complex image (i.e., one containing magnitude and phase), and the reconstruction of the phase and magnitude information. Digital holograms give a new dimension to texture analysis, since the topology information can be used as an additional way to extract features. This new technique can be used to extend previous work on the image segmentation of patterned wafers for defect detection. The paper presents a comparison between the features obtained using Gabor filtering on complex images under illumination and…
High Level Modeling and Hardware Implementation of Image Processing Algorithms Using XSG
2019
International audience; Design of Systems-on-Chip has become very common especially with the remarkable advances in the field of high-level system modeling. In recent years, Matlab also offers a Simulink interface for the design of hardware systems. From a high-level specification, Matlab provides self-generation of HDL codes and/or FPGA configuration codes while providing other benefits of easy simulation. In addition, a large part of the Systems-on-Chip use at least one image processing algorithm and at the same time border detection is one of the most used algorithms. This paper presents a study and a hardware implementation of various algorithms of borders detection realized under Xilin…