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Segmenting a structural magnetic resonance imaging (MRI) scan is an important pre-processing step for analytic procedures and subsequent inferences about longitudinal tissue changes. Manual segmentation defines the current gold standard in quality but is prohibitively expensive. Automatic approaches are computationally intensive, incredibly slow at scale, and error prone due to usually involving many...
In this paper we propose a new method for scene representation and recognition based on the concept of Region Subspaces. Each image is pre-segmented into semantically meaningful regions and local features are extracted at different scales from each such region. The Region Subspaces are the low-dimensional linear subspaces calculated from the set of local features inside each region. We also define...
Superpixel based methods have recently shown success in scene segmentation and labeling. In scene labeling, a superpixel algorithm is used first to segment the image into visually consistent small regions; then several feature descriptors are computed and classification is performed for each superpixel. In this paper, Kernel Codebook Encoding (KCB) of superpixel features is proposed. In KCB feature...
K-means is a compute-intensive iterative algorithm, each iteration consists of two steps data assignment and K centroids recalculation. In order to accelerate the compute-intensive portions of k-means, the data assignment and K centroids recalculation steps are offloaded to the GPU in parallel. Only the initialization and convergence tests steps are performed by the CPU. In addition this new version...
Recent progress in Thermal and infrared Non-Destructive Testing (IRNDT) in different fields have provided interesting defect detection solutions. Principal Component Analysis (PCA) based K-means clustering have been successfully introduced and used in many clustering applications. However, PCA suffers from being relatively more sensitive to the noise due to having a linear transformation. On the other...
Image procession algorithms for compensation of scattered radiation influence in X-ray imaging were proposed, studied and optimized by numerical simulations. The algorithms include scattering estimation by convolution (superposition) technique, estimation of kernel functions by Monte-Carlo (MC) simulations, determination the optimal number and shape of kernel functions and images segmentation. Determination...
Co-saliency detection aims at finding the common salient objects in multiple images. In this paper, we introduce a new co-saliency detection model, which includes two main parts: co-salient seed selection using the inter-object recurrence cues from multiple images and saliency label propagation using partially absorbing random walk. With the guidance of co-salient seeds, salient objects are individually...
In this work we address the problem of blind deblurring using a single space-variantly defocused image containing text. We estimate both the all-in-focus image and the blur map corresponding to the space-variant point spread function of the finite aperture camera. Since this problem is highly ill-posed we exploit a recently proposed technique [1] to obtain an initial estimate of the space-variant...
Breast cancer is the foremost cause of morbidity and mortality among womenfolk. India has 17% of world's population suffering from breast cancer. World Health Organization's International agency for Research on Cancer (IARC) estimates that more than 4,00,000 women die every year due to breast cancer. Thus early identification of breast cancer plays a vital role in reducing the mortality rate. Medical...
Image segmentations, a branch of image processing is developing various approaches to analyze, process and extract meaningful data from abnormalities. One such abnormality is brain tumor which nowadays is an active research area in field of image processing. In order to identify tumor, MRI images are studied which contains meaningful information, which if demonstrated technically i.e with the help...
Conventional unsupervised image segmentation methods use color and geometric information and apply clustering algorithms over pixels. They preserve object boundaries well but often suffer from over-segmentation due to noise and artifacts in the images. In this paper, we contribute on a preprocessing step for image smoothing, which alleviates the burden of conventional unsupervised image segmentation...
In this paper, we address the problem of detecting and segmenting partial image blur from a single input image. Instead of assuming particular image priors or requiring additional user annotation, we propose a novel learning framework which jointly solves the tasks of blur kernel estimation and image blur segmentation, so that partial image blur can be automatically separated from the remaining parts...
Deep convolutional neural networks (DCNNs) have been employed in many computer vision tasks with great success due to their robustness in feature learning. One of the advantages of DCNNs is their representation robustness to object locations, which is useful for object recognition tasks. However, this also discards spatial information, which is useful when dealing with topological information of the...
This paper introduces automatic framework brain tumor detection, which detects and classify brain tumor in MR imaging. The proposed framework brain tumor detection is an important tool to detect the tumor and differentiate between patients that diagnosis as certain brain tumor and probable brain tumor due to its ability to measure regional changes features in the brain that reflect disease progression...
This paper concerns multiphase piecewise smooth image segmentation with intensity inhomogeneities. Traditional methods based on the Mumford–Shah (MS) model require solving complicated diffusion equations evolving in irregular subdomains, leading to significant difficulties in efficient and accurate segmentation, especially in multiphase scenarios. In this paper, we propose a general framework to modify...
X-ray imaging based inspection is an established non-destructive method for automatic detection of internal defects such as blow-hole, cold fill, shrinkage and foreign object inclusions in aluminium castings. For online inspection of casting components in the production line, X-ray imaging system needs to be integrated with dedicated image processing methods especially developed for automatic flaw...
This paper presents a region-based relaxed multiple kernel collaborative representation method for the spatial-spectral classification of hyperspectral images. The proposed method consists of three steps. In the first step, a multiscale method achieved by extending a superpixel segmentation algorithm is designed to capture the spatial-spectral information of hyperspectral images. For each scale, a...
Segmentation of MR images is more important and is an essential process in resolving the human tissues, especially at the time of clinical analysis. Brain tissue is explicitly complex and it consists of three normal main tissues named White Matter (WM), Gray Matter (GM) and Cerebral Spinal Fluid (CSF) and abnormal tissues like tumor and edema. These normal and abnormal tissues can be detected using...
Soft computing in the field of agriculture science is being employed with computer vision techniques in order to detect the diseases in crops to increase the overall yield. A Modified Rotation Kernel Transformation(MRKT) based directional feature extraction scheme is presents to resolve the issues occurring due to shape, color or other deceptive features during plant disease recognition. The MRKT...
In most cases image distortions modelled by convolution and additive white noise have unknown model parameters, such as convolution kernel (point spread function — PSF) and noise power. Different methods of blind deconvolution which iteratively approximate PSF use some initial kernel estimation; their performance is sufficiently dependent on the precision of that estimate. Modelling initial PSF as...
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