Biomarkers in urine samples are widely used in clinical diagnosis. Involving imageprocessing and data analysis, urinalysis is very popular in hospitals because of its convenience and speediness;and the most important...
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Biomarkers in urine samples are widely used in clinical diagnosis. Involving imageprocessing and data analysis, urinalysis is very popular in hospitals because of its convenience and speediness;and the most important reason is its high accuracy rating. This paper presents colorimetric recognition for urine test device with different algorithms aiming to find a good-performance classifier. Those algorithms can train a set of data and get a model to discriminate the test data. Almost the accuracy of each classifier is beyond 92%, even 99%. Although the classifier that has highest average accurate rate of recognition is K-Nearest Neighbor, we cannot overlook the performance of Support Vector Machine, which perform best in protein test. In order to compare these eight algorithms, we use Python simulation to validate the results and show the accuracy of each classifier.
Power efficient is an important availability for various mobile devices and imageprocessing applications. The proposed probabilistic adder is to trade a lesser amount of accuracy with reduced power dissipation. In th...
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ISBN:
(纸本)9781509032433
Power efficient is an important availability for various mobile devices and imageprocessing applications. The proposed probabilistic adder is to trade a lesser amount of accuracy with reduced power dissipation. In this paper, the probabilistic adder is eliminating the some part of the carry propagation path in Least Significant Bit to reduce the power consumption and transistor count. The power consumption and probabilistic error behavior of the proposed adder is designed and compared with other adders. The low power probabilistic adders can save up to 60% power compared to conventional adders for a tolerable accuracy. When used in larger systems implementing imageprocessingalgorithms, power reserves of 40% are achievable.
Breast cancer is the most prevalent disease among women and mammography is the main test used for screening and early diagnosis. During analysis, physicians often enlarge an image to view a part in order to pick up cl...
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ISBN:
(纸本)9781509045594
Breast cancer is the most prevalent disease among women and mammography is the main test used for screening and early diagnosis. During analysis, physicians often enlarge an image to view a part in order to pick up clue and details about the disease. Interpolation is a technique used for this purpose. The interpolation has several drawbacks like producing zigzag artifacts, blurring effect and jagged image edges in the interpolated images. This paper proposes a hybrid method that combines bicubic interpolation method and undeciminated wavelet transformation that interpolates an image in a way that preserves the edge and detail region of the input. Experimental results show that the proposed method is efficient and the performance is favorable compared with the existing techniques both in visual, edge and structural quality.
Summary form only given, as follows. The complete presentation was not made available for publication as part of the conference proceedings. We developed a multi-robot testbed that can be used by educators and researc...
ISBN:
(纸本)9781538661345
Summary form only given, as follows. The complete presentation was not made available for publication as part of the conference proceedings. We developed a multi-robot testbed that can be used by educators and researchers to advance related aspects of for embedded programming and imageprocessing. The testbed is built using commercially-available off the shelf components and using open source software packages. The tutorial will provide participants an overview of multi-robot systems and technical details underpinning the development of the testbed, including: technical specifications of the m3pi mobile robot and its capabilities, programming the LPC1768 microcontroller in C/C++, the robot navigation as well the XBee-based communication libraries we developed. We show how the testbed can be used to develop and test multi-robot path planning in a grid world. We also present our initial development of robot tracking algorithms in C++ via an overhead camera using OpenCV. To illustrate the capabilities of the testbed, we demonstrate path planning in a multi-robot setting using both centralize and decentralized algorithms.
In the event of a maritime disaster, casualties need to be found and rescued promptly. imageprocessing methods could help to perform automated detection from a UAV. The main current approaches make use of multispectr...
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ISBN:
(纸本)9781509044948
In the event of a maritime disaster, casualties need to be found and rescued promptly. imageprocessing methods could help to perform automated detection from a UAV. The main current approaches make use of multispectral and thermal cameras, which can deal with lightning difficulties but are expensive and could suffer from high noise problems. This paper presents a method combining both color analysis and frequency patterns identification using an inexpensive vision camera (EO camera), and implements it through an adaptive algorithm to deal with a dynamically changing background. The method is tested successfully in different environments.
Noise is an issue for image in terms of corruption. Some noises can be automatically added with the image while capturing the image. Camera sensors and lighting factors are responsible for those. Noises that can be ad...
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ISBN:
(纸本)9781538621653
Noise is an issue for image in terms of corruption. Some noises can be automatically added with the image while capturing the image. Camera sensors and lighting factors are responsible for those. Noises that can be added with the image is known as additive noise. To get a perfect image noise reduction is very important. Edge detection, image enhancing, object detection etc. are directly related with noise reduction. Noises need to be redacted before imageprocessing. There are many well established noise reduction techniques available. Linear noise reducing algorithms are not efficient to reduce impulse noise as they are mainly focused on smoothing that makes the edges of an image blur. On the contrary, nonlinear algorithms do a great job for reducing impulse noises. In this paper, we have made a proposal which is an efficient algorithm for digital grayscale image de-noising using fuzzy logic. Our proposed algorithm uses the Fuzzy Inference System (FIS) for the membership value calculation. The noise affected pixels are determined by the FIS from the 3 x 3 neighborhood. Our method works very fine with impulse noises. The proposed algorithm returns almost the original noise free image which has a significant difference in the Peak Signal to Noise Ratio (PNSR) compared to the existing Median and Average filtering technique.
This paper introduces Salient Depth Partitioning (SDP), a depth-based region cropping algorithm devised to be easily adapted to existing detection algorithms. SDP is designed to give robots a better sense of visual at...
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ISBN:
(纸本)9781538626825
This paper introduces Salient Depth Partitioning (SDP), a depth-based region cropping algorithm devised to be easily adapted to existing detection algorithms. SDP is designed to give robots a better sense of visual attention, and to reduce the processing time of pedestrian detectors. In contrast to proposal generators, our algorithm generates sparse regions, to combat image degradation caused by robot motion, making them more suitable for real-world operation. Furthermore, SDP is able achieve real-time performance (77 frames per second) on a single processor without a GPU. Our algorithm requires no training, and is designed to work with any pedestrian detection algorithm, provided that the input is in the form of a calibrated RGB-D image. We tested our algorithm with four state-of-the-art pedestrian detectors (HOG and SVM [1], Aggregate Channel Features [2], Checkerboards [3], and R-CNN [4]), and show that it improves computation time by up to 30%, with no discernible change in accuracy.
Advances in sensor technologies, mobile positioning, IoT and wireless communications have led to development of applications for data collection, processing, analysis, mining and visualization for the purposes of real...
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ISBN:
(纸本)9781538618004
Advances in sensor technologies, mobile positioning, IoT and wireless communications have led to development of applications for data collection, processing, analysis, mining and visualization for the purposes of real time responses and decision support, but have also brought new challenges to processing large amount of data in a real time. Various software systems are developed to provide a set of methods, technologies, algorithms and index structures for effective monitoring, processing, analysis and mining of continuous flow of data and events. This paper reviews recent developments in the field of data streams processing and analysis, gives an overview of existing systems and technologies, and demonstrates development of a stream processing application based on the contemporary stream processing frameworks.
Three-dimensional reconstruction of medical images plays a vital role in clinical medicine and medical research. It serves as a powerful tool to help doctors analyze multiple 2D medical images in a 3D perspective. The...
Three-dimensional reconstruction of medical images plays a vital role in clinical medicine and medical research. It serves as a powerful tool to help doctors analyze multiple 2D medical images in a 3D perspective. The developed 3D model offers a faster and accurate approach in clinical diagnosis and therapy. Before, the development of 3D reconstruction programs it has always been a challenge to visualize in a 3D perspective but with the advent of powerful computing and graphics processing, medical imaging equipment development is on the rise. The advancement of 3D image reconstruction is not only due to the innovative hardware, but also because of the increase in open-source platforms or libraries, such as visualization toolkit (VTK), that assist on its development to provide an information visualization framework and advanced 3D modeling techniques. VTK is an object-oriented 3D graphics software program that is free to obtain and used both for open- and closed-source applications such as 3D computer graphics, imageprocessing, and visualization. In medical imaging, data from ultrasound systems, magnetic resonance imaging (MRI), and computed tomography (CT) scans can be managed in VTK. Different algorithms of imageprocessing such as color mapping, image re-slicing or resampling, iso-contouring, thresholding, and other algorithms can be utilized to make volume images from the data. Subsequently, data presentation is simplified with the data reconstruction through the use of supplemental software for analysis. VTK library contains a large number of filters, which are multi-threaded to facilitate parallel processing and interpretation. In this paper, recent technologies on the application of VTK for 3D reconstruction of biomedical images will be presented. Moreover, current methodologies will be discussed to clearly understand the different integral steps needed in image data conversion.
This paper presents a robust tracking algorithm using convolutional features. The proposed tracking algorithm consists of three steps: i) training correlation filters using features extracted by a convolutional neural...
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This paper presents a robust tracking algorithm using convolutional features. The proposed tracking algorithm consists of three steps: i) training correlation filters using features extracted by a convolutional neural network, ii) localizing the target position in the next frame by multiplying weight and correlation response of each layer, and iii) re-training correlation filters every 10-frame using the proposed position error score. Experimental results show that the proposed algorithm outperforms an existing deep learning-based tracker.
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