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Left ventricle(LV) segmentation is a prerequisite step of evaluation of LV structure and function, which plays an important role in the diagnosis and treatment of cardiovascular diseases. In this paper, we propose a method to segment endocardium and epicardium of LV using convolution neural network combined with active contour model and tensor voting. A fully convolution neural network (FCN) named...
We report on the results of the first visual search and rating study (N60) evaluating human gaze when assessing the realism of image composites. The effects of object identity knowledge and mismatched feature type on observers' gaze and subjective realism scores are studied. Gaze metrics used include: fixation count, fixation duration, time and duration of first fixation on target object, as well...
In this paper, we present an Optical Coherence Tomography (OCT) processing system to in vivo imaging of vascular remodeling of the skin. The main processing steps consist of vascular segmentation and vascular reconstruction. Firstly, image preprocessing algorithms, i. e., image enhancememt with mean filter and de-shadowing by projection-based strategies, are proposed for vascular segmentation. Then,...
We present a scene parsing method that utilizes global context information based on both the parametric and nonparametric models. Compared to previous methods that only exploit the local relationship between objects, we train a context network based on scene similarities to generate feature representations for global contexts. In addition, these learned features are utilized to generate global and...
The human brain consists of two hemispheres, right and left. These two hemispheres are almost symmetrical, not perfectly. However, in neurological diseases, the volumetric losses in the brain begin to deteriorate asymmetrically between the two hemispheres. This deterioration can be local or global in the brain. Symmetry deterioration can be a biomarker in the early stage diagnosis and the following...
In modern remote sensing procedures, one of the most important issues is to distinguish specific types of land coverage. Discrimination between different land coverages especially in metropolitan surveying is so important that the in front civilization projects are basically dependent to them. In this paper, an innovative image processing strategy is employed for distinguishing green lands from other...
The widespread penetration of counterfeit integrated circuits (ICs) is not only a major threat to the electronic goods supply chain, but also constitute a great threat to national security. Image processing based counterfeit IC design techniques are promising, but currently often suffer from high computational complexity and requirement of expensive image acquisition infrastructure. We describe two...
We propose, under the form of a short overview, to stress the interest of graph to encode the "topological" structure of networks hidden in images especially when applied in life sciences. We point toward existing computer science tools to extract such structural graph from images. We then illustrate different applications, such as segmentation, denoising, and simulation on practical examples...
We propose an effective optimization algorithm for a general hierarchical segmentation model with geometric interactions between segments. Any given tree can specify a partial order over object labels defining a hierarchy. It is well-established that segment interactions, such as inclusion/exclusion and margin constraints, make the model significantly more discriminant. However, existing optimization...
Superpixel segmentation of 2D image has been widely used in many computer vision tasks. However, limited to the Gaussian imaging principle, there is not a thorough segmentation solution to the ambiguity in defocus and occlusion boundary areas. In this paper, we consider the essential element of image pixel, i.e., rays in the light space and propose light field superpixel (LFSP) segmentation to eliminate...
Forest monitoring is a major concern today due to climate changes, conservation of fauna and flora and to the lack of water. Therefore, several environmental monitoring techniques have been developed and used to detect changes in the scenes. The use of SAR (synthetic aperture radar) seems appropriate to detect changes due to its independence of atmospheric and lighting conditions. The SAR change detection...
This paper presents a variational method for SAR image segmentation that unifies boundary- and region-based information into the geometric active contour model. A new Riemannian metric is introduced to construct region-based energy function derived by maximizing the geodesic distance of a new Riemannian metric in differential-geometric structure for spectral density functions. A heterogeneity indicator...
Segmentation of 3D colored point clouds is a research field with renewed interest thanks to recent availability of inexpensive consumer RGB-D cameras and its importance as an unavoidable low-level step in many robotic applications. However, 3D data's nature makes the task challenging and, thus, many different techniques are being proposed, all of which require expensive computational costs. This paper...
Semantic instance segmentation remains a challenge. We propose to tackle the problem with a discriminative loss function, operating at pixel level, that encourages a convolutional network to produce a representation of the image that can easily be clustered into instances with a simple post-processing step. Our approach of combining an offthe- shelf network with a principled loss function inspired...
High-speed confocal microscopy has shown great promise to yield insights into plant-fungal interactions by allowing for large volumes of leaf tissue to be imaged at high magnification. Currently, segmentation is performed either manually, which is infeasible for large amounts of data, or by developing separate algorithms to extract individual features within the image data. In this work, we propose...
This paper explores the feasibility of using multiframe analysis to increase the classification performance of machine learning methods for cancer detection in Volumetric Laser Endomicroscopy (VLE). VLE is a novel and promising modality for the detection of neoplasia in patients with Baretts Esophagus (BE). It produces hundreds of high-resolution, cross-sectional images of the esophagus and offers...
Object-Based Image Analysis (OBIA) has gained swift popularity in remote sensing area mainly due to the increasing availability of very high resolution imagery. Image segmentation is a major step within OBIA process. Image segmentation quality remarkably influences the subsequent image classification accuracy. It is necessary to implement advanced and robust methods to increase image segmentation...
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 propose an alternative to the common approaches to the topological segmentation in structured or unstructured environments, Contour-Based Segmentation. It is faster and equally accurate, without the need of fine tuning parameters or heuristics. During robotic exploration, we propose an incremental version that reduces the processing time by reusing the previous segmentation. Tests demonstrate the...
The article outlines the publications on measuring the field of vibration movement of the surface of the controlled object. The authors have conducted the modelling of the trace of vibration-induced image blurring with the aim of determining structural parameters that are the input data for calculating the vector components of the vibration movement of the studied material point. The article describes...
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