Medical image enhancement is an effective tool to improve visual quality of digital medical images. However, conventional linear image enhancement methods often suffers from problems such as over-enhancement and noise...
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ISBN:
(纸本)9781509018970
Medical image enhancement is an effective tool to improve visual quality of digital medical images. However, conventional linear image enhancement methods often suffers from problems such as over-enhancement and noise sensitivity. In this paper, we study nonlinear arithmetic frameworks designed to solve the common problems of linear enhancement methods, namely, LIP, PLIP and GLIP. We also introduce nonlinear unsharp masking algorithms based on the logarithmic imageprocessing models for medical image enhancement. Experiments are conducted to evaluate and compare the performance of the methods.
In the present period of IT and correspondence innovation, use of video based data is expanding enormously. Efficient algorithms are very highly demanded Detection of scene text and caption text in the video in the ar...
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In this paper we propose a dictionary learning method that builds an overcomplete dictionary that is computationally efficient to manipulate, i.e., sparse approximation algorithms have sub-quadratic computationally co...
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In this paper we propose a dictionary learning method that builds an overcomplete dictionary that is computationally efficient to manipulate, i.e., sparse approximation algorithms have sub-quadratic computationally complexity. To achieve this we consider two factors (both to be learned from data) in order to design the dictionary: an orthonormal component made up of a fixed number of fast fundamental orthonormal transforms and a sparse component that builds linear combinations of elements from the first, orthonormal component. We show how effective the proposed technique is to encode image data and compare against a previously proposed method from the literature. We expect the current work to contribute to the spread of sparsity and dictionary learning techniques to hardware scenarios where there are hard limits on the computational capabilities and energy consumption of the computer systems.
With the development of computer technology, digital imageprocessing technologies have been applied to many areas of real life, blurred image restoration also has a rapid development, which gives a certain basis and ...
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With the development of computer technology, digital imageprocessing technologies have been applied to many areas of real life, blurred image restoration also has a rapid development, which gives a certain basis and conditions in the bridge crack detection. Aiming at the blurred crack image generated by camera shake, the paper studies the motion blur image restoration algorithms, and explores the parameter estimation methods of motion blur, where the direction and scale of the blur kernel function are estimated from the spectrum of the blurred image. The paper uses different image quality evaluation standards to compare the output, which can choose the best results and gets a more accurate point spread function. This method can obtain clearer crack images and provide more accurate crack information for bridge project research.
image segmentation is a vital task in imageprocessing/computer vision. However, no universally accepted quality measure exists for evaluating the performance of various segmentation algorithms or even different param...
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image segmentation is a vital task in imageprocessing/computer vision. However, no universally accepted quality measure exists for evaluating the performance of various segmentation algorithms or even different parameterizations of the same algorithm. This paper proposes a new segmentation evaluation measure, based on the fusion of HOG and Harris features, thus we call it the H2. It exploits local shape, corner and edge information to evaluate the similarity between a given segmentation and its respective ground truth, and thus belongs to the category of supervised evaluation measures. The results obtained from our experiments show accuracy of up to 95% for the H2.
Pre-processing steps are critical in automated image analysis systems developed to aid in diagnosis of skin lesion images. The main areas of concern include, but are not limited to, hair on the skin, variations in ill...
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Pre-processing steps are critical in automated image analysis systems developed to aid in diagnosis of skin lesion images. The main areas of concern include, but are not limited to, hair on the skin, variations in illumination and skin tone, and alignment of successive skin images. These artifacts can partially or completely obstruct a lesion being analyzed causing errors in classification or diagnosis. This paper focuses on an independent quantitative evaluation of an open source hair removal algorithm [1]. The different input parameters to the algorithm were tested to determine their optimal values. Percent error and signal to noise ratio are utilized as the error metrics for the experimental results. Other essential pre-processing steps are considered and provided at the end of this paper.
Space field has experienced vigorous advancement with respect to evolution of vision system, image storage and processing. Real time imageprocessing has become one of the most important tools for navigation and landi...
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ISBN:
(纸本)9781538630051
Space field has experienced vigorous advancement with respect to evolution of vision system, image storage and processing. Real time imageprocessing has become one of the most important tools for navigation and landing for planetary and lunar missions. Information of horizontal velocity with high accuracy will be required to do accurate pin point landing. For the testability of such a system as well as to have the understanding of Lander dynamics, prior landing image sequences are required to initially testing the algorithm [1]. This paper deals with FPGA based processing on image sequence to find the relative velocity and also implements image Storage in a microSD card. Landing sequence is stored in SD card and post landing they are downloaded. These images provide a very useful information about lighting, lander dynamics to fine tune algorithm for future mission. Besides this image data serves the testability during various phases of testing during development.
In this paper is introduced a facial recognition system, based on mathematical methods, developed to act as the presence of students identifier in a classroom. It uses a recognition process in which seeks to extract r...
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In this paper is introduced a facial recognition system, based on mathematical methods, developed to act as the presence of students identifier in a classroom. It uses a recognition process in which seeks to extract relevant information from an image, to encode and compare them with another images of faces stored in an image database. Such image information representing a set of characteristics showing the variations between images of the obtained faces and contained in the image database. The Facial Recognition System consists of two processing modules: a training phase and a test phase, applied to a group of students in order to verify the utility of this algorithms for people recognizing.
This paper deals with real time segmentation of traffic images using a Mask R-CNN model. The aim is to improve the performance of real time image segmentation, so that it can be effective even with noisy images captur...
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This paper deals with real time segmentation of traffic images using a Mask R-CNN model. The aim is to improve the performance of real time image segmentation, so that it can be effective even with noisy images captured by traffic cameras. The approach developed here comprises of image preprocessing, object detection followed by segmentation. Mask R-CNN model not only segments the image, but also surrounds the image with bounding boxes and assigns class names to the individual objects e.g. Car, Truck, Bus, Bicycle, Person etc. The model is trained with the annotated MS COCO Training dataset. To improve the performance of Mask R-CNN over noisy images, here pre-processingalgorithms like Non Local Means (NLM) filter denoising and Median filter denoising are used. The testing is carried out on a subset of MS COCO Test dataset which comprises of only traffic images. The improved performance is demonstrated using parameters: increased correct object detections and corresponding confidence value, reduced incorrect object detections and corresponding confidence value, and an overall enhanced segment mask area accuracy.
Various Internet of Things (IoT) and Industry 4.0 use cases, such as city-wide monitoring or machine control, require low-latency distributed processing of continuous data streams. This fact has boosted research on ma...
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Various Internet of Things (IoT) and Industry 4.0 use cases, such as city-wide monitoring or machine control, require low-latency distributed processing of continuous data streams. This fact has boosted research on making Stream processing Frameworks (SPFs) IoT-ready, meaning that their cloud and IoT service management mechanisms (e.g., task placement, load balancing, algorithm selection) need to consider new requirements, e.g., ultra low latency due to physical interactions. The algorithm selection problem refers to selecting dynamically which internal logic a deployed streaming task should use in case of various alternatives, but it is not sufficiently supported in current SPFs. To the best of our knowledge, this work is the first to add this capability to SPFs. Our solution is based on i) architectural extensions of typical SPF middleware, ii) a new schema for characterizing algorithmic performance in the targeted context, and iii) a streaming-specific optimization problem formulation. We implemented our solution as an extension to Apache Storm and demonstrate how it can reduce stream processing latency by up to a factor of 2.9 in the tested scenarios.
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