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It is a common issue to remove undesirable objects during reconstruction. The holes left behind are then needed to be given plausible information for the integrity. To complete the missing part of a 3D map generated from stereo inputs, the task can be separated into two operations which are repairing the color image and the depth map. In this paper, we design and release a specialized stereo dataset...
A post-processing method for correcting the beam hardening artifacts in fan beam axial computed tomography is presented. The original uncorrected CT image is reconstructed using filtered backprojection algorithm. Image segmentation technique is adopted to exact the high density object from the uncorrected CT image. The original and high density image are reprojected individually. Certain correction...
We introduce Appearance-MAT (AMAT), a generalization of the medial axis transform for natural images, that is framed as a weighted geometric set cover problem. We make the following contributions: i) we extend previous medial point detection methods for color images, by associating each medial point with a local scale; ii) inspired by the invertibility property of the binary MAT, we also associate...
Everyday, an enormous number of medical images are produced by hospitals and medical imaging center for research, surgical and disease diagnostics. Therefore, compression is necessary for storing, managing and transferring these data to make storage manageable. Medical images have some parts which are more important called region of interest (ROI) with useful information for the diagnostic purpose...
Deep neural networks have advanced many computer vision tasks, because of their compelling capacities to learn from large amount of labeled data. However, their performances are not fully exploited in semantic image segmentation as the scale of training set is limited, where perpixel labelmaps are expensive to obtain. To reduce labeling efforts, a natural solution is to collect additional images from...
How much does a single image reveal about the environment it was taken in? In this paper, we investigate how much of that information can be retrieved from a foreground object, combined with the background (i.e. the visible part of the environment). Assuming it is not perfectly diffuse, the foreground object acts as a complexly shaped andfar-from-perfect mirror An additional challenge is that its...
An airborne ultrasound imaging system was developed for reflection tomography. The ultrasound transducers surround the region of interest (ROI) in an arrangement optimized for maximum coverage and homogeneous distributed image quality. In this work, we developed a workflow for automatic segmentation and classification of objects in the reconstructed images. Our workflow can be applied for varying...
An airborne ultrasound imaging system using 16 ultrasonic sensors surrounding a region of interest (ROI) was introduced in previous work. It allows reconstructing reflectivity images of multiple objects in 2D using a synthetic aperture focusing technique. The aim of this work is to automatically segment objects from these images to determine their positioning and allow classification.
The analysis of structural changes in retinal vessels is the most important part for diagnosing and detecting retinal related diseases such as diabetic retinopathy, hypertension, age-related macular degeneration (AMD) and arteriosclerotic. This paper presents a method for segmenting retinal vessels in retinal fundus image based on Frangi filter and morphological reconstruction. The proposed method...
Organizing and rearranging library books in appropriate order requires attention and care of librarians. The book indexing and organizing algorithm will detect misplaced library books and suggest a proper position to the user. It can be implemented in a smartphone or a server or an autonomous embedded system. It first segments the captured image to find proper tag area in the book. Then crops that...
We present an end-to-end, multimodal, fully convolutional network for extracting semantic structures from document images. We consider document semantic structure extraction as a pixel-wise segmentation task, and propose a unified model that classifies pixels based not only on their visual appearance, as in the traditional page segmentation task, but also on the content of underlying text. Moreover,...
Recently, researchers have made great processes to build category-specific 3D shape models from 2D images with manual annotations consisting of class labels, keypoints, and ground truth figure-ground segmentations. However, the annotation of figure-ground segmentations is still labor-intensive and time-consuming. To further alleviate the burden of providing such manual annotations, we make the earliest...
In this paper we propose a framework for spatially and temporally coherent semantic co-segmentation and reconstruction of complex dynamic scenes from multiple static or moving cameras. Semantic co-segmentation exploits the coherence in semantic class labels both spatially, between views at a single time instant, and temporally, between widely spaced time instants of dynamic objects with similar shape...
We present a method for the fast 3D face reconstruction of people wearing glasses. Our method explicitly and robustly models the case in which a face to be reconstructed is partially occluded by glasses. We propose a simple and generic model for glasses that copes with a wide variety of different shapes, colors and styles, without the need for any database or learning. Our algorithm is simple, fast...
Foreground segmentation in video sequences is a classic topic in computer vision. Due to the lack of semantic and prior knowledge, it is difficult for existing methods to deal with sophisticated scenes well. Therefore, in this paper, we propose an end-to-end two-stage deep convolutional neural network (CNN) framework for foreground segmentation in video sequences. In the first stage, a convolutional...
Accurate 3D reconstruction of human tissue is a challenge problem in medical imaging. In this paper, a novel 3D reconstruction method of human brain MRI images is proposed based on the segmentation of human tissue. First, we propose a novel region-based growing algorithm to get points of an MRI image. Then, the moving cubes algorithm is used to reconstruct the accurate 3D object model. Further, in...
We present a novel technique for fast and accurate reconstruction of depth images from 3D point clouds acquired in urban and rural driving environments. Our approach focuses entirely on the sparse distance and reflectance measurements generated by a LiDAR sensor. The main contribution of this paper is a combined segmentation and upsampling technique that preserves the important semantical structure...
Electrical Impedance Tomography (EIT) and Ultrasound Transmission Tomography (UTT) are widely used in industrial process detection and medical diagnosis. EIT has a higher sensitivity near the edge of the sensitive domain, while the UTT has a higher sensitivity near the center of the sensitive domain. Based on the point, high accuracy image reconstruction is feasible by fusing the EIT and UTT together...
Deep learning offers new tools to improve our understanding of many important scientific problems. Neutrinos are the most abundant particles in existence and are hypothesized to explain the matter-antimatter asymmetry that dominates our universe. Definitive tests of this conjecture require a detailed understanding of neutrino interactions with a variety of nuclei. Many measurements of interest depend...
Several methods are exploited to watermark digital images as a safety measure for storing information, but an attacker can destroy the information by cropping a segment of the watermarked image. In recent years, numerous schemes were proposed that reduce the impact of such attacks. A new method has been proposed to confront cropping attack that is carried out using two sudoku tables. In this method,...
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