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Many species in the wild exhibit a visual pattern that can be used to uniquely identify an individual. This observation has recently led to visual animal biometrics become a rapidly growing application area of computer vision. Customized software tools for animal biometrics already employ vision based techniques to recognize individuals in images taken in uncontrolled environments. However, most existing...
Omnidirectional cameras are commonly used in computer vision and robotics. Their main advantage is their wide field of view which allows them to acquire a 360 degree view of the scene with only one sensor and a single shot. However, few studies have investigated the human detection problem using this kind of cameras. In this paper, we propose to extend the conventional approach for human detection...
Light Fields capturing all light rays at every point in space and in all directions contain very rich information about the scene. This rich description of the scene enables advanced image creation capabilities, such as re-focusing or extended depth of field from a single capture. But, it yields a very high volume of data which needs compression. This paper studies the impact of Light Fields compression...
Light field imaging is recently made available to the mass market by Lytro and Raytrix commercial cameras. Thanks to a grid of microlenses put in front of the sensor, a plenoptic camera simultaneously captures several images of the scene under different viewing angles, providing an enormous advantage for post-capture applications, e.g., depth estimation and image refocusing. In this paper, we propose...
In this article we describe an approach for object detection and pose estimation from stereo RGB frames for robot manipulation in manufacturing scenarios. This solution was developed in the framework of the second challenge of the EuRoC project, and meets the need of a registration method invariant to the view perspective and robust to the structural symmetries and ambiguities of the target objects...
The paper describes a computer vision method for estimating the clinical gait metrics of walking patients in unconstrained environments. The method employs background subtraction to produce a silhouette of the subject and a randomized decision forest to detect their feet. Given the feet detections, the stride and step length, cadence, and walking speed are estimated. Validation of the system is presented...
Advances in high-accuracy measurement techniques and parallel computing systems for simulations lead to a widening gap between the rate at which data is generated and the rate at which it can be transferred and stored. In situ visualization directly tackles this issue by processing — and with this reducing — data as soon as it is generated. This allows to create, transmit and store visualizations...
With the development of surveillance cameras, person re-identification has gained much interest, however re-identifying people across cameras remains a challenging problem which not only requires a good feature description but also a reliable matching scheme. Our method can be applied with any feature and focuses on the second requirement. We propose a robust bidirectional sparse coding method that...
Appearance based person re-identification is a challenging task, specially due to difficulty in capturing high intra-person appearance variance across cameras when inter-person similarity is also high. Metric learning is often used to address deficiency of low-level features by learning view specific re-identification models. The models are often acquired using a supervised algorithm. This is not...
Due the recently increased number of survey papers, we consider important to offer an evaluation of the different types of current survey papers to assess their benefits and inform the readers regarding their categories. The field selected here is the Human Activity Recognition (HAR), although its idea can be applied to other research areas as well. Thus, a variety of survey papers are studied thoroughly...
In this paper, we propose a hybrid metric for measuring the accuracy of the estimated camera. Existing camera pose evaluation methods are mainly built upon one of the two metrics: the geometric measurement or the photometric measurement. The geometric based method inspects the relative position between source and target points, while the photometric based method utilizes the pixel intensity as assessment...
Using the spectral signature of a target by means of matching the signature with the pixels of an acquired hyperspectral image has been proven as an effective way of classifying hyperspectral pixels in most of the proposed methods in hyperspectral image analysis. A disadvantage of these methods is however to use only the spectral characteristics of pixels for detection while ignoring the spatial relations...
Person re-identification has become a hot research topic due to its importance in surveillance and forensics applications. The purpose of person re-identification is to find the same person from disjoint camera views at different time. Most of the existing methods try to identify the person by measuring the similarity of two still images from different camera views, which only uses intraimage features...
In this paper, we propose combined visual features for person re-identification. Our features are based on the multiple hand-crafted visual features. The proposed features are a combination of histogram from the RGB, YUV and HSV color channels, LBP and SIFT features. Then we use different distance metric learning methods to measure the similarity of the same persons and different persons. Experimental...
The aim of person re-identification is to match pedestrians which across disjoint camera views. Many features have been proposed to improve the re-identification accuracy. However, due to significant person appearance variations in viewpoints, poses, and illumination across different cameras, individual feature is less discriminative to represent the different person images. In this paper, we propose...
In this paper we learn patterns of activity in open urban spaces and detect activity outliers that represent events of interest. We do so utilising background suppression to flag people as foreground blobs in videos from city surveillance cameras. Since the application domain is challenging, with far-field cameras viewing scenes that vary from completely empty to very crowded, and each person in the...
Distance learning (DL) is an effective technique for person reidentification (PR-ID). DL based methods learn the distance metric by exploiting the discriminative information contained in samples. In PR-ID, different types of negative samples own different amounts of discriminative information, and impostor samples usually own more than other well separable negative samples (WSN-samples). Therefore,...
Measurement based on micro-vision is an important part of micro assembly. In order to accurately control the manipulator to complete the optical fiber assembly, it's necessary to measure the relative position of the optical fiber and the groove by the visual. This paper builds a new vision measurement system for optical fiber assembly without special markets. In this system, two microscope cameras...
A metric direct (feature-less) online monocular SLAM algorithm is proposed, which, different from the traditional monocular SLAM algorithm, can obtain the absolute scale of the world. A calibrate object is used to calculate the relative pose of the first key frame and initialize the depth map, introducing the metric scale into the reconstruction meanwhile. Highly accurate pose estimation is achieved...
In this paper, we present a hierarchical spatiotemporal blur-based approach to automatically detect contaminants on the camera lens. Contaminants adhering to camera lens corresponds to blur regions in digital image, as camera is focused on scene. We use kurtosis for a first level analysis to detect blur regions and filter them out. Next level of analysis computes lowpass energy and singular values...
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