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Brain tumor segmentation from magnetic resonance images is a critical step for early tumor diagnosis and treatment. However, accurate and general segmentation of brain tumor is still a challenging task due to complicated characteristics of brain tumor in magnetic resonance images. To solve this problem, we proposed a novel method for brain tumor segmentation based on features of separated local square...
This study aims to meet requirement of rapid detection of the TBM (tunnel boring machine) tool's wear state. To this end, the local region-growing method based on normalized cross-correlation was proposed to detect the block slag on belt conveyor and the least-square method for ellipse fitting was employed to measure the size. Firstly, the monocular color image was decomposed into two gray-scale images...
Fully convolutional network (FCN) has been successfully applied in semantic segmentation of scenes represented with RGB images. Images augmented with depth channel provide more understanding of the geometric information of the scene in the image. The question is how to best exploit this additional information to improve the segmentation performance.,,In this paper, we present a neural network with...
A key issue in fruit export is classification and sorting for marketing. In this work image processing techniques are used to grad Thompson orange fruit. For this purpose, fourteen parameters were extracted, comprising area, eccentricity, perimeter, length/area, blue value, green value, red value, width, contrast, texture, width/area, width/length, roughness, and length. Adaptive neuro fuzzy inference...
Classification of milk duct carcinoma in the scans of diagnostic specimens is an important medical problem. Before the classification is performed, the regions of milk ducts which will be the regions of interest (ROI) should be detected. One of the approaches to such detection is to segment the image into ROIs and the remaining regions. The segmentation by clusterization with the classical K-means...
Command extraction from human beings becomes easier for a machine if it can analyze the non verbal ways of communication such as emotions. This paper focuses on improving the efficiency of extracting emotion from human facial expression images. The features that were extracted in this experiment were obtained from JAFFE (Japanese Female Facial Expression) database which includes 213 images of different...
Melasma is a widely spread skin pigmentation disease and accurate assessments of the disease severity is crucial during its treatment. Recently, several computerized methods have been developed to overcome the shortcomings of the conventional clinical assessment method. As a key step in algorithm, image segmentation has extensive impacts on the accuracy of the assessment. Currently, the optimal hybrid...
Video summarization (VS) is one of key video signal processing techniques for unmanned aerial vehicles (UAVs). Essentially VS aims at eliminating redundant frames in aerial videos (AVs) with high similarity, which is helpful for quick browsing, retrieving and efficient storage without losing important information. For VS technique, how to measure the similarity between video frames is not a trivial...
This paper identifies a MR imaging radiomics signature for prediction of overall survival (OS) in patients with glioblastoma multiforme (GBM). A fully-automatic radiomics model is presented, including automatic tumor segmentation, high-throughput features extraction, features selection, and multi-feature signature identification. The automatic GBM segmentation method employs a random forest classifier...
Most pedestrian detection algorithms only provide the object region instead of the actual body segmentation in video. For reducing the large number of redundant information and extracting a clear contour and texture feature of an up-right person, a superpixel segmentation algorithm with region correlation saliency analysis is proposed from coarse to fine cutting without any prior information. This...
In this paper, a novel spectral-spatial low-rank subspace clustering (SS-LRSC) algorithm is presented for clustering of hyperspectral images (HSI). Generally, employing the traditional LRSC framework directly cannot fully exploit the sample correlations in original spatial domain. Therefore, the proposed method utilizes a novel modulation strategy to modify the low rank representation matrix, which...
This paper presents a modification to the cNMF for unmixing where the image is first segmented and the cNMF is applied to individual segments for endmember extraction. Extracted spectral endmembers from individual segments are clustered in endmember classes to describe the entire image. The approach is compared with the global cNMF. The segmentation-based cNMF better captures subtle differences between...
Superpixel has been widely applied in hyperspectral image processing as a pre-processing step for over-segmentation. However, most superpixel algorithms are difficult to control the segmentation balance between fragmentation and accuracy. In this paper, we propose a superpixel aggregation model to cluster the over-segmentations. Based on the own importance and interrelationship of superpixels, a two-step...
Contour detection is a fundamental problem in computer vision. However, there is still a considerable disparity between detection results and actual contours. To detect object-level contours on the basis of comprehensive analysis of potential edges, we present a deep-learning-based approach with a conditional random fields (CRF) model. We obtain the initial edgemap with a VGGNet-based model, and establish...
Image noise, textureless regions, and occlusions are still problems of stereo matching. We propose a novel method to address these problems. Firstly, initial disparity map and reliability map are obtained by stereo matching in the pixel level. In this stage, the proposed approach not only imposes the photo-consistency constraint, but also explicitly associates the geometric coherence to solve the...
Today Unmanned Aerial Systems (UAS) are widely used for many applications that involve advanced payload as is found to be the case for mounted remote sensing apparatus. Remote sensing from UAS platforms is now common and the use of light and smart multi/hyper-spectral cameras has opened the field to novel applications. These sensors can operate in cloudy conditions ensuring ultra high resolution images...
Saliency detection is an important problem in many computer vision applications. As a kind of popular method, graph based manifold ranking (GMR) has been successfully used in saliency detection problem. In traditional GMR saliency detection, it involves two main stages, i.e., ranking with background queries and ranking with foreground queries. However, in GMR method, these two stages are conducted...
We present LS-ELAS, a line segment extension to the ELAS algorithm, which increases the performance and robustness. LS-ELAS is a binocular dense stereo matching algorithm, which computes the disparities in constant time for most of the pixels in the image and in linear time for a small subset of the pixels (support points). Our approach is based on line segments to determine the support points instead...
We present a holistic segmentation-free query by example word spotting technique based on template matching. We have applied this technique to a dataset of historical Arabic handwritten manuscript images. First, the documents as well as query word images are pre-processed for separating text from the noisy background and converting to their binary equivalents. Then a pixel based approach is used for...
The brain is one of the most complex and integrated organ in the human body which directs our muscle movements, our breathing and internal temperature, furthermore every imaginative sight, perception, and diagram are derived by the brain. The brain's neurons are effected by internal and external stimulations. Those stimulations might have positive and negative influence on brain activity and structure...
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