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As one tool for structuring a massive volume of archived news videos based on their semantic contents, this paper proposes a method to detect scene duplicates from news videos. A scene duplicate is a pair of video segments taken at the same event from different viewpoints. Referring to the audio channel is effective to detect scene duplicates regardless of viewpoints, but it cannot be relied on when...
Retinal vessel segmentation is a fundamental aspect of the automatic retinal image analysis. The attributes of retinal blood vessels, such as width, tortuosity and branching pattern, play an important role in clinical diagnose. However, the edges of optic disk, fovea and edges of pathological areas have negative effects on vessel segmentation and few people focus on this problem. In this paper, we...
The accurate and precise segmentation of optic disc is a critical task in development of Computer Aided Diagnosis systems for analysis of colored fundus images to diagnose retinal diseases. The development of a reliable and efficient optic disc (OD) segmentation technique is a challenging task because of noise, illumination effects, blood vessels and lesions like exudates. In this paper, a novel technique...
This paper presents new time-frequency (T-F) features to improve the detection and classification of epileptic seizure activities in EEG signals. Most previous methods were based only on signal features derived from the instantaneous frequency and energies of EEG signals generated from different spectral sub-bands. The proposed features are based on T-F image descriptors, which are extracted from...
A quantitative texture analysis for discriminating GBM phenotypes in brain magnetic resonance (MR) images is proposed. GBM phenotypes captured using semi-automatic segmentation based on 3D Slicer Scripts. Segmentation was applied on the registered images considered the T1-Weighted and FLAIR sequence. Texture feature has been extracted from the gray level co-occurrence matrix (GLCM) based on GBM phenotypes...
In this work, we employ a pair wise Markov Random Field (MRF) and a Conditional Random Field (CRF) for bi-level image segmentation and denoising. For both tasks, the Ising pair wise model and the Iterative Conditional Mode (ICM) inference method are implemented, assuming the parameters of the unary and pair wise potentials are known. Experimental results demonstrate the effectiveness of the proposed...
CAPTCHAs exploit the gap in the ability between a human and a machine to understand the semantics of specific multimedia content, with vast applications in computer security. In this paper we compare two techniques in automated CAPTCHA solving for text-based CAPTCHA schemes, i.e., Classification based on the Vector Space Model (VSM) versus a popular Optical Character Recognition (OCR) engine. For...
In this paper, we describe a novel technique for the extraction of object shapes from Terahertz images using a 3D graph-cut segmentation scheme. This approach to segmentation includes images in temporal domain by creating nodes and edges between consecutive images in order to obtain improved segmentation results and compensate for the high levels of noise in the Terahertz images. The foreground and...
Idiopathic generalized epilepsy (IGE) and symptomatic generalized epilepsy (SGE) are two kinds of generalized epilepsy. In this study, we discussed the methods of automatically segmentation of MR images for patients with these two kinds of epilepsy. K-Means clustering, expectation-maximization, and fuzzy c-means algorithms were employed to perform segmentation on brain images for patients with IGE...
A biometric-based techniques emerge as the promising approach for most of the real-time applications including security systems, video surveillances, human-computer interaction and many more. Among all biométrie methods, face recognition offers more benefits as compared to others. Diagnosing human faces and localizing them in images or videos is the priori step of tracking and recognizing. But the...
In this paper, a rotation-invariant retina identification algorithm based on tessellation of frequency spectrum is developed. In this algorithm, the proposed tessellation scheme provides rotation invariant, multi resolution and optimized features with low computational for our retina identification algorithm. The proposed algorithm is structured in two parts namely feature extraction and decision...
This paper motivated to design and develops an automatic model for multi-class breast tissue segmentation in breast mammogram images. Various breast tissues are categorized by a novel texture features such as PTPSA-[Piece-wise Triangular Prism Surface Area], intensity difference and regular-intensity in mammogram images. Using CRF-[Classical Random Forest] method segmentation and classification of...
This paper presents a comparative study of Support Vector Machines (SVMs) which is classified based on melanoma imaging technique. After the preprocessing and segmentation of a set of distinct 35 images, the extracted features were Asymmetry, Border, Color, Diameter,(ABCD) Entropy and Correlations respectively. Further the resultant data was fed into five different SVM classifiers namely linear, poly,...
Image segmentation of anatomic structures is often an essential step in medical image analysis. A variety of segmentation methods have been proposed, but none provides automatic segmentation of the thigh. In magnetic resonance images of the thigh, the segmentation is complicated by factors, such as artifacts (e.g. intensity inhomogeneity and echo) and inconsistency of soft and hard tissue compositions,...
This paper presents a method of detecting and segmenting regions of interest (ROIs) of the thermal image of electrical installations. These regions are very important in diagnosing the thermal condition of electrical equipment. Due to the nature of thermal imaging, segmentation with the conventional approach will make inaccurate ROI detection, especially when qualitative approach is considered in...
In this paper, we propose a novel approach for writer identification using codebook generation based on text skeletonization.Unlike other schemes, the skeleton in this approach is segmented at its junction pixels into elementary graphic units called graphemes. The codebook is generated by clustering the graphemes according to their distributions into a predefined grid. This method has been evaluated...
This research analyzed the use of daubechies wavelet as a feature extraction and confusion matrix as the principal parameter of accuracy percentage level in neural network. Detection process began with image pre-processing, lung area segmentation, feature extraction, and training phase. Classifications of the system output consisted of normal lung, pleural effusion, and pulmonary tuberculosis. Seventy...
Although rule-based object-based classification can often perform better than the supervised approaches, its attribute selection is very time consuming and hardly transferable between different urban areas. The purpose of this study is to identify transferable rule-sets for different areas from QuickBird satellite imagery for urban areas consisting heterogeneous man-made and natural features. Object-based...
In this paper, an improved segmentation method of the pigmented skin lesions have been proposed in which to achieve the high characteristics of segmentation, converting the RGB images to U channel of YUV color space and noise reduction with fourier-domain filtering properties is done in pre-processing step. Also, Otsu thresholding and morphological reconstruction algorithms are used respectively in...
Image-guided therapy (IGT) provides an image data (CT, MRI and PET) to overcome a deficiency of sight during operation by using augmented reality thus tracking performance is important. However, it is difficult to verify the tracking performance in medical applications because makers which can detect the pose in image registration cannot be attached to the patient's bodies. This paper proposes a method...
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