The Infona portal uses cookies, i.e. strings of text saved by a browser on the user's device. The portal can access those files and use them to remember the user's data, such as their chosen settings (screen view, interface language, etc.), or their login data. By using the Infona portal the user accepts automatic saving and using this information for portal operation purposes. More information on the subject can be found in the Privacy Policy and Terms of Service. By closing this window the user confirms that they have read the information on cookie usage, and they accept the privacy policy and the way cookies are used by the portal. You can change the cookie settings in your browser.
This paper addresses the problem of absolute visual ego-localization of an autonomous vehicle equipped with a monocular camera that has to navigate in an urban environment. The proposed method is based on a combination of: 1) a Hidden Markov Model (HMM) exploiting the spatio-temporal coherency of acquired images and 2) learnt metrics dedicated to robust visual localization in complex scenes, such...
Inspired by the effectiveness of global priors for 2D saliency analysis, this paper aims to explore those particular to RGB-D data. To this end, we propose two priors, which are the normalized depth prior and the global-context surface orientation prior, and formulate them in the forms simple for computation. A two-stage RGB-D salient object detection framework is presented. It first integrates the...
Vision-based localization on robots and vehicles remains unsolved when extreme appearance change and viewpoint change are present simultaneously. The current state of the art approaches to this challenge either deal with only one of these two problems; for example FAB-MAP (viewpoint invariance) or SeqSLAM (appearance-invariance), or use extensive training within the test environment, an impractical...
In this study, taxonomy of recursive edge-aware filters (REAF) is provided, with the introduction of new approaches to the state-of-the-art. The one tap recursive filters are classified according to recursion rate calculation, recursion type and the unification of reverse directions. In that manner, eight types of edge-aware recursive filters are defined, where only three of them are addressed in...
Image registration for stack-based HDR photography is challenging. If not properly accounted for, camera motion and scene changes result in artifacts in the composite image. Unfortunately, existing methods to address this problem are either accurate, but too slow for mobile devices, or fast, but prone to failing. We propose a method that fills this void: our approach is extremely fast—under 700ms...
In this work, we present a multiple server fingerprint verification scheme that provides enhanced template security by eliminating several known vulnerabilities of the fuzzy vault scheme. We secure templates from adversarial attacks in honest-but-curious server scenarios by utilizing commutative encryption in which the raw fingerprint template is never used in matching or storage. In this system,...
Of increasing interest to the computer vision community is to recognize egocentric actions. Conceptually, an egocentric action is largely identifiable by the states of hands and objects. For example, “drinking soda” is essentially composed of two sequential states where one first “takes up the soda can”, then “drinks from the soda can”. While existing algorithms commonly use manually defined states...
We propose a novel approach to segment hand regions in egocentric video that requires no manual labeling of training samples. The user wearing a head-mounted camera is prompted to perform a simple gesture during an initial calibration step. A combination of color and motion analysis that exploits knowledge of the expected gesture is applied on the calibration video frames to automatically label hand...
We present a fast and accurate 3D hand tracking method which relies on RGB-D data. The method follows a model based approach using a hierarchical particle filter variant to track the model's state. The filter estimates the probability density function of the state's posterior. As such, it has increased robustness to observation noise and compares favourably to existing methods that can be trapped...
We develop an approach for detection of ruins of livestock enclosures in alpine areas captured by high-resolution remotely sensed images. These structures are usually of approximately rectangular shape and appear in images as faint fragmented contours in complex background. We address this problem by introducing a new rectangularity feature that quantifies the degree of alignment of an optimal subset...
In order to exploit the currently continuous streams of massive, multi-temporal, high-resolution remote sensing datasets there is an emerging need to address efficiently the image registration and change detection challenges. To this end, in this paper we propose a modular, scalable, metric free single shot change detection/registration method. The approach exploits a decomposed interconnected graphical...
The development of fully autonomous seafaring vessels has enormous implications to the world's global supply chain and militaries. To obey international marine traffic regulations, these vessels must be equipped with machine vision systems that can classify other ships nearby during the day and night. In this paper, we address this problem by introducing VAIS, the world's first publicly available...
High-resolution and accurate Digital Elevation Model (DEM) generation from satellite imagery is a challenging problem. In this work, a stereo 3-D reconstruction framework is outlined that is applicable to nonstereoscopic satellite image pairs that may be captured by different satellites. The orthographic height maps given by stereo reconstruction are compared to height maps given by a multiview approach...
Feature and metric researchings are two vital aspects in person re-identification. Metric learning seems to have gained extra advantage over feature in recent evaluations. In this paper, we explore the neglected potential of feature designing for re-identification. We propose a novel and efficient person descriptor, which is motivated by traditional spatiogram and covariance descriptors. The spatiogram...
This paper tackles the problem of estimating non-rigid human 3D shape and motion from image sequences taken by uncalibrated cameras. Similar to other state-of-the-art solutions we factorize 2D observations in camera parameters, base poses and mixing coefficients. Existing methods require sufficient camera motion during the sequence to achieve a correct 3D reconstruction. To obtain convincing 3D reconstructions...
This paper presents a subject centric group feature for person re-identification. Our approach is inspired by the observation that people often tend to walk alongside others or in a group. We argue that co-travelers' information, including geometry and visual cues, can reduce the re-identification ambiguity and lead to better accuracy, compared to approaches that rely only on visual cues. We introduce...
Flow analysis of crowd and traffic videos is an important video surveillance task. In this work, we propose an algorithm for long-term flow segmentation and dominant flow extraction in traffic videos. Each flow segment is a temporal sequence of image segments indicating the motion of a vehicle in the camera view. This flow segmentation is done in the framework of Conditional Random Fields using motion...
In this paper, we propose a method for real-time anomaly detection and localization in crowded scenes. Each video is defined as a set of non-overlapping cubic patches, and is described using two local and global descriptors. These descriptors capture the video properties from different aspects. By incorporating simple and cost-effective Gaussian classifiers, we can distinguish normal activities and...
Despite recent advances in computer vision, image categorization aimed at recognizing the semantic category of an image such as scene, objects or actions remains one of the most challenging tasks in the field. However, human gaze behavior can be harnessed to recognize different classes of actions for automated image understanding. To quantify the spatio-temporal information in gaze we use segments...
Set the date range to filter the displayed results. You can set a starting date, ending date or both. You can enter the dates manually or choose them from the calendar.