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This paper presents a metric global localization in the urban environment only with a monocular camera and the Google Street View database. We fully leverage the abundant sources from the Street View and benefits from its topo-metric structure to build a coarse-to-fine positioning, namely a topological place recognition process and then a metric pose estimation by local bundle adjustment. Our method...
Re-identification of individuals has already drawn growing attentions due to the increasing intelligent visual surveillance. Human signature is quite different over a network of cameras and most related work devotes to selecting human features without any distinction. To address the problem, we propose a novel coupled feature space learning with joint graph regularization in this paper. The proposed...
Content authoring is one of essentials of Augmented Reality (AR), which is to emplace an augmented content on a true part of a real scene in order to enhance users' visual experience. For the case of street view single 2D images, the challenge emerges because of clutter environments and unknown position and orientation related to camera pose. Although existing methods based on 2D feature point matching...
We present a framework for vision based localization for two or more multirotor aerial vehicles relative to each other. This collaborative localization technique is built upon a relative pose estimation strategy between two or more cameras with the capability of estimating accurate metric poses between each other even through fast motion and continually changing environments. Through synchronized...
We consider the question of benchmarking the performance of methods used for estimating the depth of a scene from a single image. We describe various measures that have been used in the past, discuss their limitations and demonstrate that each is deficient in one or more ways. We propose a new measure of performance for depth estimation that overcomes these deficiencies, and has a number of desirable...
This work proposes a fully automated approach for vision-based quality control of manufactured metal rods. The proposed approach is able to detect the features of the rod (holes) and calculate the curvature of the object versus specifications. The proposed algorithm utilizes a single mono-ocular image. Initial results show that the proposed algorithm can operate under various camera geometric setups,...
This paper presents background removal methods to increase the accuracy of saliency-based person re-identification. After evaluating the current global salience algorithm, we found that wrong matching appears when (1) images of different people have a similar or the same background and/or (2) salience on the backgrounds of various images are similar. To prove the maximum theoretical accuracy of the...
Recently, several effective features were proposed for person re-identification, such as Weight Histograms of Overlapping Stripes (WHOS) and Local Maximal Occurrence (LOMO), but it still need to explore new effective feature to improve the precision for person re-identification. So, in this paper, we proposed a new Dual Channel Gradient feature, which can be fused with WHOS and LOMO by directly concatenating...
Foreground detection is the classical computer vision task of segmenting out motion information from a particular scene. Foreground detection using Gaussian Mixture Models (GMM) is the famous choice. Since first time proposed, many researchers tried to improve GMM. This paper focuses on the comparative evaluation of three most famous improvements in the algorithm. The improved methods are compared...
This paper proposes a method for detecting moving objects appeared in video captured by a moving camera. The proposed method relies on dense optical flow to differentiate moving objects from static background. Whenever video taken from a static camera is used, the dense optical flow itself is sufficient to determine the moving object in the scenes. However, in a non-static camera, all pixels are moving...
This paper describes a method of image sharpness evaluation while taking into account the photographer's aesthetic intention. The main idea is utilizing a visual importance map that estimates the weight of each pixel to guild evaluating image sharpness. The visual importance map is computed automatically with a saliency detection algorithm based on global color contrast. Our technique allows to treat...
Blur plays an key part in evaluating of camera image quality. It leads to decrease of high frequency information and accordingly changes the image energy. Recent researches in quaternion singular value decomposition show that the quaternion's singular values and associated vectors can capture the distortion of color images, and thus singular values can be utilized to assess the sharpness of camera...
Due to the large amount of videos generated through several data sources, the development of efficient mechanisms for storing, indexing, retrieving and visualizing their content is a challenging task. Temporal video segmentation is the automatic process of detecting transitions in video sequences, which is a fundamental step in the analysis of video content. This work proposes and evaluates an improved...
Below we present a new method to obtain quantitative measurements in medical images of a tracked video endoscope. Such quantitative measurements can be critical biomarkers during endoscopic diagnosis, e.g. for the classification of tumors and polyps. Size-based classifications are often achieved by mere visual estimation which can be imprecise and subjective. To this day established systems of quantitative...
Results of investigation of face detection algorithms in the video sequences are presented in the paper. The recordings were made with a miniature industrial USB camera in real conditions met in three bank operating rooms. The aim of the experiments was to check the practical usability of the face detection method in the biometric bank client verification system. The main assumption was to provide...
Person reidentification is a problem of recognizing a person across non-overlapping camera views. Pose variations, illumination conditions, low resolution images, and occlusion are the main challenges encountered in reidentification. Due to the uncontrolled environment in which the videos are captured, people could appear in different poses and due to which the appearance of a person could vary significantly...
Associating groups of people across non-overlapping camera views is an important but unsolved problem. Compared with the similar person re-identification task, group re-identification introduces some new challenges, such as significant deformation in uncontrolled directions, great intra-group occlusions and so on. In this paper, we propose a novel patch matching based framework for group re-identification...
The problem of person re-identification, identifying the same person appeared in different camera views, is an important and challenging task in computer vision that has high potential application in areas like visual surveillance. In this paper we introduce a new feature fusion strategy for person reidentification that combines low-level Weighted Histograms of Overlapping Stripes (WHOS) features...
We propose a method for detecting obstacles by comparing input and reference train frontal view camera images. In the field of obstacle detection, most methods employ a machine learning approach, so they can only detect pre-trained classes, such as pedestrian, bicycle, etc. This means that obstacles of unknown classes cannot be detected. To overcome this problem, we propose a background subtraction...
Most of the existing works on person re-identification have focused on improving matching rate at top ranks. Few efforts are devoted to address the problem of efficient storage and fast search for person re-identification. In this paper, we investigate the prevailing hashing method, originally designed for large scale image retrieval, for fast person re-identification with efficient storage. We propose...
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