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[IEEE 2018 Signal Processing: Algorithms, Architectures, Arrangements, and Applications (SPA) - Poznan, Poland (2018.9.19-2018.9.21)] 2018 Signal Processing: Algorithms, Architectures, Arrangements, and Applications (SPA) - On the influence of the image normalization scheme on texture classification accuracy
摘要： Texture can be a very rich source of information about the image. Texture analysis finds applications, among other things, in biomedical imaging. One of the widely used methods of texture analysis is the Gray Level Co-occurrence Matrix (GLCM). Texture analysis using the GLCM method is most often carried out in several stages: determination of areas of interest, normalization, calculation of the GLCM, extraction of features, and finally, the classification. Values of the GLCM based features depend on the choice of the normalization method, which was examined in this work. The normalization is necessary, since acquired images often suffer from noise and intensity artifacts. Certainly, the normalization will not eliminate these two effects, however it was demonstrated, that its application improves texture analysis accuracy. The aim of the work was to analyze the influence of different normalization methods on the discriminating ability of features estimated from the GLCM. The analysis was performed both for Brodatz textures and real magnetic resonance data. Brodatz textures were corrupted by three types of distortion: intensity nonuniformity, Gaussian noise and Rician Noise. Three types of normalizations were tested: min?max, 1?99% and +/?3σ.
关键词： normalization,classification,image processing,texture analysis,GLCM
更新于2025-09-23 15:23:52
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Enhanced image processing and archiving capabilities of magneto-optical imaging for non-destructive evaluation
摘要： In its current state, the wide acceptance of the Magneto-Optical Imaging (MOI) technique is hindered due to noise, lack of recordable results, and impossibility of data post-processing. This paper presents some add-ons made to a commercial MOI system to ease the image interpretation, archiving and reporting of the results. In addition, a few image processing techniques are also employed in an attempt to perform automatic flaw detection. The recording capability of the MOI instrument output images was addressed by digitizing the video signal in video or image files. To help with the identification of the damage location and distance between images, a rotary quadrature encoder was mounted onto the MOI scan head. The use of the encoder allowed the identification of the inspection location with respect to a reference position, such as the beginning of the scan. Moreover, it allowed saving images at fixed intervals, which were then stitched into a single image, thus simplifying the post inspection analysis process. Both live and post-inspection image processing capabilities were made available. Implemented image processing included background subtraction, de-noising, contrast adjustment and morphological operation, among others. Contrast stretching transform and background subtractions were found to be among the most powerful techniques that could be used in simplifying the image interpretation.
关键词： non-destructive evaluation,Magneto-optical imaging,image processing
更新于2025-09-23 15:23:52
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Frame-based Programming, Stream-Based Processing for Medical Image Processing Applications
摘要： This paper presents and evaluates an approach to deploy image and video processing pipelines that are developed frame-oriented on a hardware platform that is stream-oriented, such as an FPGA. First, this calls for a specialized streaming memory hierarchy and accompanying software framework that transparently moves image segments between stages in the image processing pipeline. Second, we use softcore VLIW processors, that are targetable by a C compiler and have hardware debugging capabilities, to evaluate and debug the software before moving to a High-Level Synthesis flow. The algorithm development phase, including debugging and optimizing on the target platform, is often a very time consuming step in the development of a new product. Our proposed platform allows both software developers and hardware designers to test iterations in a matter of seconds (compilation time) instead of hours (synthesis or circuit simulation time).
关键词： Image processing,FPGA,Medical imaging
更新于2025-09-23 15:23:52
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[IEEE 2018 Congreso Internacional de Innovación y Tendencias en Ingeniería (CONIITI) - Bogota, Colombia (2018.10.3-2018.10.5)] 2018 Congreso Internacional de Innovación y Tendencias en Ingeniería (CONIITI) - An Algorithm for Giardia Lamblia Detection in Digital Images Acquired Through an Optical Microscope
摘要： This work proposes an algorithm for the detection of the intestinal parasite Giardia Lamblia from digital images obtained through a digital camera and an optical microscope. Its purpose is to reduce the time of visual inspection analysis of the sample made by the specialist in laboratory. The proposed algorithm converts the acquired RGB images to the HSV colour space. First, the saturation component is filtered using a Gaussian filter, in order to reduce the noise and standardize the areas of interest. The filtered image is thresholdized using a fixed threshold value, in order to segment the objects of interest. Then, using a labelling algorithm, a filtering is performed by object size, in order to eliminate those that do not comply with the dimensions of the parasite. Therefore, the edges are highlighted by a Canny filter, to finally apply the Hough transform and detect the morphology of the object and its physical dimensions. With this information it will be possible to validate if the object satisfies the conditions of a Giardia Lamblia parasite. The proposed method achieved a specificity of 86% and a sensitivity of 67%, processing each image in an average time of less than 2 seconds. The results were obtained from a universe of 30 images.
关键词： Hough transform,Giardia lamblia,digital image processing
更新于2025-09-23 15:23:52
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Deep hybrid scattering image learning
摘要： A well-trained deep neural network is shown to gain capability of simultaneously restoring two kinds of images, which are completely destroyed by two distinct scattering medias respectively. The network, based on the U-net architecture, can be trained by blended dataset of speckles-reference images pairs. We experimentally demonstrate the power of the network in reconstructing images which are strongly di?used by glass di?user or multi-mode ?ber. The learning model further shows good generalization ability to reconstruct images that are distinguished from the training dataset. Our work facilitates the study of optical transmission and expands machine learning’s application in optics.
关键词： Image processing,di?ractive optics.,machine learning
更新于2025-09-23 15:23:52
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Visibility graphs for image processing
摘要： The family of image visibility graphs (IVG/IHVGs) have been recently introduced as simple algorithms by which scalar fields can be mapped into graphs. Here we explore the usefulness of such an operator in the scenario of image processing and image classification. We demonstrate that the link architecture of the image visibility graphs encapsulates relevant information on the structure of the images and we explore their potential as image filters. We introduce several graph features, including the novel concept of Visibility Patches, and show through several examples that these features are highly informative, computationally efficient and universally applicable for general pattern recognition and image classification tasks.
关键词： image visibility graphs,image processing,pattern recognition,graph features,visibility patches,image classification
更新于2025-09-23 15:22:29
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Hyperspectral Image Denoising Based on Spectral Dictionary Learning and Sparse Coding
摘要： Processing and applications of hyperspectral images (HSI) are limited by the noise component. This paper establishes an HSI denoising algorithm by applying dictionary learning and sparse coding theory, which is extended into the spectral domain. First, the HSI noise model under additive noise assumption was studied. Considering the spectral information of HSI data, a novel dictionary learning method based on an online method is proposed to train the spectral dictionary for denoising. With the spatial–contextual information in the noisy HSI exploited as a priori knowledge, the total variation regularizer is introduced to perform the sparse coding. Finally, sparse reconstruction is implemented to produce the denoised HSI. The performance of the proposed approach is better than the existing algorithms. The experiments illustrate that the denoising result obtained by the proposed algorithm is at least 1 dB better than that of the comparison algorithms. The intrinsic details of both spatial and spectral structures can be preserved after significant denoising.
关键词： image processing,hyperspectral image,spectral dictionary,image denoising,sparse coding
更新于2025-09-23 15:22:29
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Research on Image Restoration Algorithms Based on BP Neural Network
摘要： With the development of information transmission technology and computer technology, information acquisition mode is mainly converted from character to image nowadays. However, in the process of acquiring and transmitting images, image damage and quality decrease due to various factors. Therefore, how to restore image has become a research hotspot in the field of image processing. This paper establishes an image restoration model based on BP neural network. The simulation results show that the proposed method has made a great improvement compared with the traditional image restoration method.
关键词： image processing,BP neural network,image restoration,image denoising
更新于2025-09-23 15:22:29
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Rapid and non-invasive surface crack detection for pressed-panel products based on online image processing
摘要： Crack detection during the manufacturing process of pressed-panel products is an important aspect of quality management. Traditional approaches for crack detection of those products are subjective and expensive because they are usually performed by experienced human inspectors. Therefore, the development and implementation of an automated and accurate inspection system is required for the manufacturing process. In this article, a crack detection technique based on image processing is proposed that utilizes the images of panel products captured by a regular camera system. First, the binary panel object image is extracted from various backgrounds after considering the color factor. Edge lines are then generated from a binary image using a percolation process. Finally, crack detection and localization is performed with a unique edge-line evaluation. In order to demonstrate the capability of the proposed technique, lab-scale experiments were carried out with a thin aluminum plate. In addition, a test was performed with the panel images acquired at an actual press line. Experimental results show that the proposed technique could effectively detect panel cracks at an improved rate and speed. The experimental results also demonstrate that the proposed technique could be an extension of structural health monitoring frameworks into a new manufacturing application.
关键词： crack detection,image processing,signal processing,percolation,Non-contact sensing
更新于2025-09-23 15:22:29
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[IEEE 2018 2nd International Conference on Data Science and Business Analytics (ICDSBA) - Changsha, China (2018.9.21-2018.9.23)] 2018 2nd International Conference on Data Science and Business Analytics (ICDSBA) - Detection of Diabetic Retinopathy Images Using a Fully Convolutional Neural Network
摘要： The paper discusses the development and application of a convolutional neural network (CNN) model for digital image processing in the context of data science and business analytics. It focuses on improving the accuracy and efficiency of image classification tasks.
关键词： Image Classification,Digital Image Processing,Business Analytics,Data Science,Convolutional Neural Network
更新于2025-09-23 15:22:29