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Manual inspections of glass facade of high rising buildings are expensive, time-consuming and potentially life-threatening for both inspectors and pedestrians on the street. Advances in machine learning for image/video analysis an...
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Manual inspections of glass facade of high rising buildings are expensive, time-consuming and potentially life-threatening for both inspectors and pedestrians on the street. Advances in machine learning for image/video analysis and availability of affordable unmanned aerial vehicles (UAVs) with onboard video recording and processing sensors provide opportunities for smart, safe and automatic glass facade inspections. This paper is concerned with developing an effective solution for recognizing cracked glass panels, which can be installed on board a UAV. From static 2D photographic images, the proposed solution analyzes textural patterns of smooth glass surface and crack segments, linearity of detected crack segments, geometrical characteristics of crack curvatures and the crack pixel patterns, captures these discriminative features for glass cracks using Uniform Local Binary Pattern (ULBP), histograms of linearity, geometrical curvature descriptors with fixed length connected pixel configurations, and accordingly classifies images of cracked and non-cracked glass panels using a kNN classifier. Experimental results with images of different resolutions acquired by a UAV drone in a real office building setting and images collected through Google search demonstrate that the proposed solution achieves promising results with accuracy rates in excess of 80% and even as high as 91% despite the presence of reflections.
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摘要 :
Manual inspections of glass facade of high rising buildings are expensive, time-consuming and potentially life-threatening for both inspectors and pedestrians on the street. Advances in machine learning for image/video analysis an...
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Manual inspections of glass facade of high rising buildings are expensive, time-consuming and potentially life-threatening for both inspectors and pedestrians on the street. Advances in machine learning for image/video analysis and availability of affordable unmanned aerial vehicles (UAVs) with onboard video recording and processing sensors provide opportunities for smart, safe and automatic glass facade inspections. This paper is concerned with developing an effective solution for recognizing cracked glass panels, which can be installed on board a UAV. From static 2D photographic images, the proposed solution analyzes textural patterns of smooth glass surface and crack segments, linearity of detected crack segments, geometrical characteristics of crack curvatures and the crack pixel patterns, captures these discriminative features for glass cracks using Uniform Local Binary Pattern (ULBP), histograms of linearity, geometrical curvature descriptors with fixed length connected pixel configurations, and accordingly classifies images of cracked and non-cracked glass panels using a kNN classifier. Experimental results with images of different resolutions acquired by a UAV drone in a real office building setting and images collected through Google search demonstrate that the proposed solution achieves promising results with accuracy rates in excess of 80% and even as high as 91% despite the presence of reflections.
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The border irregularity of lesions or tumors is an important sign (or feature) contributing to the prediction of the tumor malignancy. This paper is concerned with developing automatic computer vision methods for assessing and rec...
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The border irregularity of lesions or tumors is an important sign (or feature) contributing to the prediction of the tumor malignancy. This paper is concerned with developing automatic computer vision methods for assessing and recognizing thyroid nodule border irregularity from ultrasound images. Unlike many existing schemes, our methods rely on a small set of points on the nodule border marked manually by clinicians. To mitigate the absence of a fully segmented lesion boundary, we first apply the cubic-spline interpolation of the region of interest (ROI) points to approximate the lesion border and then select equal numbers of points from the approximated border using equal angular distances. We developed two complementary approaches to investigate the global (big indentations and protrusions) and local (small zigzag) irregularity features of the nodule. The first approach includes two Euclidian distances-based methods and a method inspired by Fractal Dimensions (FD). The distances-based methods facilitate the use of the interpolated border and their radial distance functions measured from ROI points to a reference point (centroid) or reference shape (Convex hull), while the FD inspired method uses interpolated border and a fitted ellipse perimeter ratio to calculate an irregularity index. The second approach facilitates the texture analysis within the constructed ribbons around the border line of different widths using feature vector of uniform local binary pattern (ULBP). We evaluate and compare the performance of our methods from the two approaches by using two datasets consisting of 395 and 100 ultrasound images of thyroid nodules collected from two hospitals and labelled by experienced radiologists respectively. The first is used as training and internal testing set, while the second is used for external testing. We shall show the viability of our methods attaining accuracy rates between 70% and 90%.
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Video compression and encryption became very essential in a secured real time video transmission. Applying both techniques simultaneously is one of the challenges where the size and the quality are important in multimedia transmis...
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Video compression and encryption became very essential in a secured real time video transmission. Applying both techniques simultaneously is one of the challenges where the size and the quality are important in multimedia transmission. In this paper we proposed a new technique for video compression and encryption. Both encryption and compression are based on edges extracted from the high frequency sub-bands of wavelet decomposition. The compression algorithm based on hybrid of: discrete wavelet transforms, discrete cosine transform, vector quantization, wavelet based edge detection, and phase sensing. The compression encoding algorithm treats the video reference and non-reference frames in two different ways. The encryption algorithm utilized A5 cipher combined with chaotic logistic map to encrypt the significant parameters and wavelet coefficients. Both algorithms can be applied simultaneously after applying the discrete wavelet transform on each individual frame. Experimental results show that the proposed algorithms have the following features: high compression, acceptable quality, and resistance to the statistical and brute-force attack with low computational processing.
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Video compression and encryption became very essential in a secured real time video transmission. Applying both techniques simultaneously is one of the challenges where the size and the quality are important in multimedia transmis...
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Video compression and encryption became very essential in a secured real time video transmission. Applying both techniques simultaneously is one of the challenges where the size and the quality are important in multimedia transmission. In this paper we proposed a new technique for video compression and encryption. Both encryption and compression are based on edges extracted from the high frequency sub-bands of wavelet decomposition. The compression algorithm based on hybrid of: discrete wavelet transforms, discrete cosine transform, vector quantization, wavelet based edge detection, and phase sensing. The compression encoding algorithm treats the video reference and non-reference frames in two different ways. The encryption algorithm utilized A5 cipher combined with chaotic logistic map to encrypt the significant parameters and wavelet coefficients. Both algorithms can be applied simultaneously after applying the discrete wavelet transform on each individual frame. Experimental results show that the proposed algorithms have the following features: high compression, acceptable quality, and resistance to the statistical and brute-force attack with low computational processing.
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Ultrasound imaging is widely used in medical diagnostics. The existence of speckle noise tends to impair ultrasound image quality, which has a negative effect on the computer-aided diagnostic pipeline. As a result, a content-prese...
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Ultrasound imaging is widely used in medical diagnostics. The existence of speckle noise tends to impair ultrasound image quality, which has a negative effect on the computer-aided diagnostic pipeline. As a result, a content-preserving noise reduction is an essential part of ultrasound image pre-processing. This paper argues that conventional one-fit-all preprocessing methods on all images irrespective of their quality and/or their content have many limitations. The paper demonstrates that the negative effects of the speckle noise are more significant in regions where solid tissues are present. Consequently, we propose an adaptive approach of using trained classification models to detect such regions within the image and targeting the speckle noise of the detected regions instead of the whole image. The detection is achieved by placing a sliding window over the image and feeding individual windows to a trained classifier. In this study, we first analyse the content of the images to identify the complexity of the speckle noise by training a linear support vector machine classifier on histogram-based measurements such as skewness and kurtosis to determine whether the image partially or fully needs pre-processing. To evaluate the effectiveness of the new adaptive pre-processing methods, a hybrid two-model solution in which the first trainable model decides if an image requires pre-processing or not and applies it respectively on the whole image. The second model takes a step further to check which parts of the images requires pre-processing and adaptively applies it using the block-based trainable system. The results, based on 138 benign and 104 malignant ovarian ultrasound images, show that the two models performed better than other state-of-the-art pre-processing techniques, which confirms the need for the adaptive system that applies pre-processing only when needed.
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摘要 :
Ultrasound imaging is widely used in medical diagnostics. The existence of speckle noise tends to impair ultrasound image quality, which has a negative effect on the computer-aided diagnostic pipeline. As a result, a content-prese...
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Ultrasound imaging is widely used in medical diagnostics. The existence of speckle noise tends to impair ultrasound image quality, which has a negative effect on the computer-aided diagnostic pipeline. As a result, a content-preserving noise reduction is an essential part of ultrasound image pre-processing. This paper argues that conventional one-fit-all preprocessing methods on all images irrespective of their quality and/or their content have many limitations. The paper demonstrates that the negative effects of the speckle noise are more significant in regions where solid tissues are present. Consequently, we propose an adaptive approach of using trained classification models to detect such regions within the image and targeting the speckle noise of the detected regions instead of the whole image. The detection is achieved by placing a sliding window over the image and feeding individual windows to a trained classifier. In this study, we first analyse the content of the images to identify the complexity of the speckle noise by training a linear support vector machine classifier on histogram-based measurements such as skewness and kurtosis to determine whether the image partially or fully needs pre-processing. To evaluate the effectiveness of the new adaptive pre-processing methods, a hybrid two-model solution in which the first trainable model decides if an image requires pre-processing or not and applies it respectively on the whole image. The second model takes a step further to check which parts of the images requires pre-processing and adaptively applies it using the block-based trainable system. The results, based on 138 benign and 104 malignant ovarian ultrasound images, show that the two models performed better than other state-of-the-art pre-processing techniques, which confirms the need for the adaptive system that applies pre-processing only when needed.
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Ultrasound imagery has been widely used for medical diagnoses. Ultrasound scanning is safe and non-invasive, and hence used throughout pregnancy for monitoring growth. In the first trimester, an important measurement is that of th...
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Ultrasound imagery has been widely used for medical diagnoses. Ultrasound scanning is safe and non-invasive, and hence used throughout pregnancy for monitoring growth. In the first trimester, an important measurement is that of the Gestation Sac (GS). The task of measuring the GS size from an ultrasound image is done manually by a Gynecologist. This paper presents a new approach to automatically segment a GS from a static B-mode image by exploiting its geometric features for early identification of miscarriage cases. To accurately locate the GS in the image, the proposed solution uses wavelet transform to suppress the speckle noise by eliminating the high-frequency sub-bands and prepare an enhanced image. This is followed by a segmentation step that isolates the GS through the several stages. First, the mean value is used as a threshold to binarise the image, followed by filtering unwanted objects based on their circularity, size and mean of greyscale. The mean value of each object is then used to further select candidate objects. A Region Growing technique is applied as a post-processing to finally identify the GS. We evaluated the effectiveness of the proposed solution by firstly comparing the automatic size measurements of the segmented GS against the manual measurements, and then integrating the proposed segmentation solution into a classification framework for identifying miscarriage cases and pregnancy of unknown viability (PUV). Both test results demonstrate that the proposed method is effective in segmentation the GS and classifying the outcomes with high level accuracy (sensitivity (miscarriage) of 100% and specificity (PUV) of 99.87%).
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摘要 :
Ultrasound imagery has been widely used for medical diagnoses. Ultrasound scanning is safe and non-invasive, and hence used throughout pregnancy for monitoring growth. In the first trimester, an important measurement is that of th...
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Ultrasound imagery has been widely used for medical diagnoses. Ultrasound scanning is safe and non-invasive, and hence used throughout pregnancy for monitoring growth. In the first trimester, an important measurement is that of the Gestation Sac (GS). The task of measuring the GS size from an ultrasound image is done manually by a Gynecologist. This paper presents a new approach to automatically segment a GS from a static B-mode image by exploiting its geometric features for early identification of miscarriage cases. To accurately locate the GS in the image, the proposed solution uses wavelet transform to suppress the speckle noise by eliminating the high-frequency sub-bands and prepare an enhanced image. This is followed by a segmentation step that isolates the GS through the several stages. First, the mean value is used as a threshold to binarise the image, followed by filtering unwanted objects based on their circularity, size and mean of greyscale. The mean value of each object is then used to further select candidate objects. A Region Growing technique is applied as a post-processing to finally identify the GS. We evaluated the effectiveness of the proposed solution by firstly comparing the automatic size measurements of the segmented GS against the manual measurements, and then integrating the proposed segmentation solution into a classification framework for identifying miscarriage cases and pregnancy of unknown viability (PUV). Both test results demonstrate that the proposed method is effective in segmentation the GS and classifying the outcomes with high level accuracy (sensitivity (miscarriage) of 100% and specificity (PUV) of 99.87%).
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In tumor diagnostics from Ultrasound scan images, the region of interest is often determined by marking the boundary of the suspect mass by experts, simply by clicking on sufficient number of tumor boundary points. To determine wh...
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In tumor diagnostics from Ultrasound scan images, the region of interest is often determined by marking the boundary of the suspect mass by experts, simply by clicking on sufficient number of tumor boundary points. To determine whether the tumor is malignant or benign, clinical experts who are trained for long time on how to interpret image information from the marked tumor region and from the surrounding area. In contrast, in designing automatic computer aided diagnosis system using both traditional and conventional machine learning, the relevant image features are generally obtained by cropping the tumor as region of interest (RoI) without considering the periphery of the tumor that might contain important discriminative information for better classification accuracy. In this work, we investigate the impact on classification accuracy of different types of tumors by the cropping strategy where the tumor area will be augmented by a proportion of the surrounding region of the ROI. The required optimal proportion need to be determined so that the cropped ROIs encapsulate information about posterior echo and shadow of the tumor in addition to internal texture and echo that has mainly been used as classification indicators. Recently proposed cropping techniques use the best fitting ellipse of the tumor and examine the proportion by which the ellipse is expanded to improve accuracy. Unfortunately, the fitting ellipse may not reflect the shape of the tumor. Here, we investigate a number of alternative approaches of cropping the ROIs using the concept of convex hull shape(s) determined from the tumor boundary points selected by radiologists. Initially, we check several expansion ratio scales of the convex hull ranging from 0.6 to 4.0 against the cropped tumor without margin. Several classification methods including handcrafted features and deep learning methods are adopted for breast and liver tumors using ultrasound images. We shall demonstrate the importance of optimal cropping for breast and liver ultrasound tumor classification. Furthermore, optimal margin depends on the cancer type and classification method as well.
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