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Progress in our understanding of brain functions relies on our capability to explore the human cortical surface at a fine scale (typically 1.5 mm isotropic at 3T). For this purpose, high accuracy is required for all processing steps from image acquisition to data analysis. For group studies, the high intersubject variability of the human cortices hampers their precise registration. Based on the hypothesis...
In this paper, we describe a NAO H25 humanoid robot painter assisted by a human. The aim of this study is to reproduce the whole painting process by a humanoid robot with a vision system and fingers. The novelty of the study lies in using a human assistant in interaction with the robot and filling regions in the picture. The painting process is performed by the humanoid robot in three phases: obtaining...
Traditional biopsy procedure requires invasive tissue removal from a living subject followed by time-consuming complicatedly processing, so noninvasive in vivo virtual biopsy is a highly desired technique which own ability to obtain exhaustive tissue images without removing tissues from subjects. Some sets of in vivo virtual biopsy images provided by some healthy volunteers are processed by our cell...
Accurate 3D models of the human brain vessels can greatly help to diagnose serious diseases. Such models can be constructed by segmentation of 3D MRA images, especially the recently introduced high resolution 7T MRA. We propose a new two-step approach for fully automatic segmentation of 7T MRA images of the human cerebrovascular system. First, a 3D model-based approach is applied to segment thick...
Foreground-background segmentation is an important problem in computer vision, and it has many applications. We propose a technique of Automatic foreground-background segmentation based on depth from coded aperture. This method first calculates a coarse depth map using technique of coded aperture depth extraction, then estimate the general area of foreground. At last, in order to get the foreground,...
This work presents a robust method for realtime segmentation and tracking of moving objects using depth image sequences, which is insensitive to illumination changes. We propose a novel criterion in our quadtree split-and-merge framework and effectively solves the problem of segmenting objects in complex and cluttered scenes. We also introduce a plane estimation algorithm to cope with the indistinction...
Visual saliency detection provides an important methodology for many computer vision applications. In this paper, we propose a novel method to detect salient regions from an image. To detect pixel-level saliency, this method uses joint embedding of spatial and color cues, i.e., spatial constraint based saliency, color double-opponent saliency, and similarity distribution based saliency. Finally, a...
We have developed techniques to automatically generate personalised biomechanical models of patients' hearts based on 3D cardiac images. We demonstrate this approach using multi-slice computed tomography images. Unsupervised segmentation was performed using non-rigid image registration with a segmented image. A finite element model was automatically fitted to the segmented data of the left ventricle...
We propose a fully automatic method to segment the dentate nucleus using diffusion weighted images (DWI). Initialization of the dentate nucleus is produced by combining the information from tractography results on the diffusion tensor images (reconstructed from DWI) and b0 images. A geometric de-formable model (GDM) with generalized gradient vector flow (GGVF) is then applied on the b0 image to generate...
Increased frequency of micronuclei is positively correlated with the molecular dosimetry of genotoxic damages. The cytokinesis-block micronucleus test (CBMN test) is a well-established assay used in toxicological screening for potential genotoxic compounds. Since the method is simple and economical, CBMN assay can be employed on a large scale as a quantitative biological dosimeter. Automated detection...
Segmentation problem dates back to a couple of decades and many solutions have been proposed so far. The fact that there is still no solution with optimum performance justifies the efforts towards new studies. This study concentrates on keeping the user interaction in the loop for superior results. Proposed technique extends previous knowledge on monoscopic image segmentation to stereoscopic footage...
Color image segmentation is a critical pre-process in image processing. Also it's important in the field of computer vision and pattern recognition. In this paper, we first state some evidence in the human vision research. Not all the intensity from 0 to 255 in RGB spaces can be distinguished by human vision. So we reduce the level of the intensity in RGB space to 26,28,26 respectively, while maintaining...
Dental recognition is very important for forensic human identification, mainly regarding the mass disasters, which have frequently happened due to tsunamis, airplanes crashes, etc. Algorithms for automatic, precise, and robust teeth segmentation from radiograph images are crucial for dental recognition. In this work we propose the use of a graph-based algorithm to extract the teeth contours from panoramic...
In this paper, we propose a system to obtain a depth ordered segmentation of a single image based on low level cues. The algorithm first constructs a hierarchical, region-based image representation of the image using a Binary Partition Tree (BPT). During the building process, T-junction depth cues are detected, along with high convex boundaries. When the BPT is built, a suitable segmentation is found...
Biometric systems have been implemented on numerous public facilities that able to enhance the security system. Nowadays, fingerprint and face are the most popular biometric. In addition, emerging technology has introduced potential biometric such as hand geometry, palm print, lips, teeth and vein. However, most of this biometric requires a special device to capture it. This will added more cost to...
Biometrics systems based on ear are still in need of more investigation to make it robust and accurate. The most critical step in ear recognition is segmentation as all subsequent steps will depend on the accuracy of segmentation. In this paper, a robust ear segmentation method is suggested. The proposed method consists of a sequence of steps. First, a Biased Normalized Cuts method is applied to initiate...
In this paper, we use a segmentation method applied to the plants images analysis. The segmentation gives groups of pixels, and we propose to use merging operators to address the most relevant groups in order to improve the recognitions systems performance. In particular, we use an operator called fully reinforced. The experiments carried out show that the use of improves outcomes for structuring...
Normalised cuts algorithm requires massive similarity measurement computation for image segmentation. Since a digital camera at present has the capability to produce high resolution image, it will be inevitably that resizing image into suitable resolution at which the algorithm can perform image segmentation with minimal burden. While retaining the important features in the images, natural images...
In this paper, we propose a new region-based saliency model to simulate the human visual attention. First, we construct a pixel-level fully-connected graph representation for an image, and perform normalized cut to segment the image based on the proximity and similarity principles. After obtaining image regions, we reconstruct a region-based fully-connected graph. Based on the saliency principle “center-surround...
This paper presents a simple and effective method to compute the pixel saliency with full resolution in an image. First, the proposed method creates an image representation of four color channels through the modified computation on the basis of Itti et al.[5]. Then the most informative channel is automatically identified from the derived four color channels. Finally, the pixel saliency is computed...
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