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Although light field data provides abundant cues for depth estimation, light field depth estimation suffers from occlusion and uncertain edges. In this paper, we propose occlusion robust light field depth estimation using segmentation guided bilateral filtering. First, we calculate refocused images from light field data using digital refocusing. Second, we perform support vector machines (SVM) classification...
This paper presents a new method of segmenting and classifying protein crystallization trial images that were collected using trace fluorescent labeling. Trace fluorescent labeling typically involves fluorescence dye that can re-emit the illumination light at other wavelengths around the principal wavelength. The captured image has a primary color channel with respect to illumination light and fluorescence...
Local community detection (or local clustering) is of fundamental importance in large network analysis. Random walk based methods have been routinely used in this task. Most existing random walk methods are based on the single-walker model. However, without any guidance, a single-walker may not be adequate to effectively capture the local cluster. In this paper, we study a multi-walker chain (MWC)...
In this paper, we use a vision-based image processing technique to propose a method of real-time one-dimensional barcode localization robust for rotation and scale. The proposed method consists of three main steps. The first step generates and analyzes an orientation histogram of input images and removes their background regions and small clutters. The second step analyzes a local entropy-based orientation...
Traversable region estimation is the fundamental enabler in autonomous navigation. In this paper, we propose a traversable region segmentation algorithm using stereo vision. We address this problem mainly in road scenes for the goal of autonomous driving. Using only geometry information, our approach has the advantages of effectiveness and robustness. The proposed approach is based on a cascaded framework...
Although image segmentation technology has achieved rapid development, threshold method is still an indispensable part in many practical applications. The most advanced methods do not perform well in the segmentation of many different types of images. Therefore, it is expected that the optimal segmentation method can be obtained for images with different modalities. In this paper, a robust threshold...
In this paper, a robust level set method is proposed for image segmentation. Traditional level set methods are sensitive to noise in images which greatly limits its application in real project. To overcome this shortcoming, the fractional order regularization and Markov random fields term are incorporated into the traditional level methods in this paper. The fractional order regularization can reveal...
Palm vein recognition has emerged as a novelty highly invariant biometric technique that is difficult to forge due to their internal nature. In this work the texture descriptors Local Binary Patterns (LBP) and Uniform Local Binary Patterns (LBPU) are analyzed as feature extraction methods for biometric verification based on palm veins. Their performance and efficiency has been studied through a multivariate...
Previous approaches on 3D shape segmentation mostly rely on heuristic processing and hand-tuned geometric descriptors. In this paper, we propose a novel 3D shape representation learning approach, Directionally Convolutional Network (DCN), to solve the shape segmentation problem. DCN extends convolution operations from images to the surface mesh of 3D shapes. With DCN, we learn effective shape representations...
A temporal superpixel algorithm based on proximity-weighted patch matching (TS-PPM) is proposed in this work. We develop the proximity-weighted patch matching (PPM), which estimates the motion vector of a superpixel robustly, by considering the patch matching distances of neighboring superpixels as well as the target superpixel. In each frame, we initialize superpixels by transferring the superpixel...
We present a conceptually simple, flexible, and general framework for object instance segmentation. Our approach efficiently detects objects in an image while simultaneously generating a high-quality segmentation mask for each instance. The method, called Mask R-CNN, extends Faster R-CNN by adding a branch for predicting an object mask in parallel with the existing branch for bounding box recognition...
We introduce a novel method for 3D object detection and pose estimation from color images only. We first use segmentation to detect the objects of interest in 2D even in presence of partial occlusions and cluttered background. By contrast with recent patch-based methods, we rely on a “holistic” approach: We apply to the detected objects a Convolutional Neural Network (CNN) trained to predict their...
In this paper we do staircase detection with a stereo vision based algorithm through NAO robot, using one of this cameras. Robot programming was implemented in Python language using ROS software. The detection algorithm is divided in two parts: line detection and depth perception. In line detection process we use Hough transform and vanishing point criteria for line segmentation. Respecting depth...
Image co-segmentation is the problem of extracting the common objects from multiple images. Foreground segmentation is always effected by the diverse objects and complex background. However, the existing methods didn't pay much attention to images' background as object, especially the similar background. To address the similar scene co-segmentation problems, a method which considers the foreground...
Closed Curve approximation is a technique to approximate a digital planar curve with piece straight line segments. The terminating point of a candidate line segment is known as pseudo point. By detecting good choice of the pseudo point on the digital planar one may be able to visibly recognize the shape of the curve. The techniques analyzed in this paper makes closed curve approximation by deleting...
The paper proposed a robust optimum thresholding method based on local intensity mapping(LIM), class uncertainty and region stability theories to segment fuzzy and noisy images. First of all, the intensities of an image would be mapped into another intensity space by LIM which could decrease the influence of noise and uneven intensity distribution. Then, the intensity-based class uncertainty is applied...
Most state-of-the-art text detection methods are specific to horizontal Latin text and are not fast enough for real-time applications. We introduce Segment Linking (SegLink), an oriented text detection method. The main idea is to decompose text into two locally detectable elements, namely segments and links. A segment is an oriented box covering a part of a word or text line, A link connects two adjacent...
Intensity inhomogeneity often occurs in real images. Local information based level set methods are comparatively effective in segmenting image with inhomogeneous intensity. However, in practice, these models suffer from local minima and high computational cost. In this paper, a novel region-based level set method based on Bregman divergence and local binary fitting, hereafter referred to as Bregman-LBF,...
We address the problem of instance-level semantic segmentation, which aims at jointly detecting, segmenting and classifying every individual object in an image. In this context, existing methods typically propose candidate objects, usually as bounding boxes, and directly predict a binary mask within each such proposal. As a consequence, they cannot recover from errors in the object candidate generation...
Automatic recognition for complex scenes from aerial images and other sensor data (e.g. LiDAR) has become an active topic in the remote sensing community. In this paper, we proposed a novel framework that utilizes higher-order CRFs (HCRFs) to capture the spatial contextual information for the RGB aerial images along with their co-registered LiDAR data (DSMs). Our proposed HCRFs framework exploits...
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