This paper presents a novel method for segmenting functional and anatomical structures simultaneously. The proposed method unifies domains of anatomical and functional images (PET-CT), represents them in a product lattice, and performs simultaneous delineation of regions based on a random walk image segmentation. In addition, we propose a simple yet efficient object/background seed localization...

We analyze the proposed tracklet co-selection method based on the setting without knowing any prior knowledge on the Youtube-Objects dataset. We first evaluate the importance of facility location F(A) and unary terms U(A) in the submodular function. We show both the intersection-over-union (overlap) ratio for semantic segmentation and the average precision (AP) for classification in Table 1 und...

In this paper, we present an interactive approach for shape co-segmentation via label propagation. Our intuitive approach is able to produce error-free results and is very effective at handling out-of-sample data. Specifically, we start by over-segmenting a set of shapes into primitive patches. Then, we allow the users to assign labels to some patches and propagate the label information from th...

normal 0 false false false en-us x-none fa background:  regarding the importance of right diagnosis in medical applications, various methods have been exploited for processing medical images solar. the method of segmentation is used to analyze anal to miscall structures in medical imaging. objective:  this study describes a new method for brain magnetic resonance image (mri) segmentation via a ...

In this extended abstract we review our works [1, 2] on fine-grained visual classification (FGVC) and present the most recent results of our classification pipeline. In particular, we focus on the importance of the foreground segmentation, and show that accurate segmentation of training images is highly beneficial for the accuracy of classification at test time. We demonstrate the merit of rela...

The analysis of how groups of animals move collectively is the focus of much current research. This paper offers a Bayesian model of animal co-movement based on a mixture of Gaussian processes.

In the field of mesh segmentation, co-segmentation techniques achieve state-of-the-art performance; however, the segmentation results rely on the shape descriptors used in the segmentation process. In this paper, we propose a novel type of descriptor called the “volumetric shape context” (VSC). For each triangle in the mesh, the VSC describes the distribution of the shape’s volume relative to t...

In this paper, a modified segmentation algorithm for printed Farsi words is presented. This algorithm is based on a previous work by Azmi that uses the conditional labeling of the upper contour to find the segmentation points. The main objective is to improve the segmentation results for low quality prints. To achieve this, various modifications on local baseline detection, contour labeling an...

background: accurate brain tissue segmentation from magnetic resonance (mr) images is an important step in analysis of cerebral images. there are software packages which are used for brain segmentation. these packages usually contain a set of skull stripping, intensity non-uniformity (bias) correction and segmentation routines. thus, assessment of the quality of the segmented gray matter (gm), ...

PURPOSE A new approach to the segmentation of 3D CT images is proposed in an attempt to provide texture-based segmentation of organs or disease diagnosis. 3D extension of Haralick texture features was studied calculating co-occurrences of all voxels in a small cubic region around the voxel. RESULTS For verification, the proposed method was tested on a set of abdominal 3D volumes of patients. ...