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We present in this work an unsupervised image classifier, which is capable of clustering images taken by an unknown number of unknown digital cameras into a number of classes, each corresponding to one camera. The classification system first extracts and enhances a sensor pattern noise (SPN) from each image, which serves as the fingerprint of the camera that has taken the image. Secondly, it applies...
In public venues, crowd size is a key indicator of crowd safety and stability. Crowding levels can be detected using holistic image features, however this requires a large amount of training data to capture the wide variations in crowd distribution. If a crowd counting algorithm is to be deployed across a large number of cameras, such a large and burdensome training requirement is far from ideal....
In this paper we present our research work in traffic sign detection and classification. Specifically we present a set of asymmetric Haar-like features that will be shown to be effective in reducing false alarm rates for traffic sign detection, and a robust multi-class traffic sign detection and classification system built based upon the stage-by-stage performance analysis of individual traffic sign...
We consider the problem of autonomously flying a helicopter in indoor environments. Navigation in indoor settings poses two major challenges. First, real-time perception and response is crucial because of the high presence of obstacles. Second, the limited free space in such a setting places severe restrictions on the size of the aerial vehicle, resulting in a frugal payload budget. We autonomously...
This paper presents a simple yet effective approach for classification of human postures by using a time-of-flight camera. We investigate and adopt linear projection techniques such as Locality Preserving Projections (LPP), Linear Discriminant Analysis (LDA) and Principal Component Analysis (PCA), which are more widespread in face recognition and other pattern recognition tasks. We analyze the relations...
We propose a human activity classification algorithm that has a distributed and lightweight implementation appropriate for wireless camera networks. With input from multiple cameras, our algorithm achieves invariance to the orientation of the actor and to the camera viewpoint. We conceptually describe how the algorithm can be implemented on a distributed architecture, obviating the need for centralized...
This paper present an economical analysis of using a digital camera imagery data to classify land use/cover in the Prai Industrial area, Penang, located in Peninsular Malaysia. The data were captured using a digital camera, Kodak DC 290 from a small light aircraft at 8000 feet altitude. This overcomes the problem of difficulty in obtaining cloud-free satellite images especially in the equatorial region...
This paper presents an algorithm based on the method of supervised machine learning and multi-keyframes to achieve markerless augmented reality (AR) application when there is a locally planar object in the scene. The main goal is to solve the problem of AR tracking in outdoor environment by only using vision and natural features. Instead of tracking fiducial markers, we track natural keypoints, during...
We present a facial feature detector for time-of-flight (TOF) cameras that extends previous work by combining a nose detector based on geometric features with a face detector. The goal is to prevent false detections outside the area of the face. To detect the nose in the image, we first compute the geometric features per pixel. We then augment these geometric features with two additional features:...
In this work we present a multi-modal video editing system for meetings, which uses graphical models for the segmentation and classification of the video modes. The task of video editing is about selecting the camera, that represents the meeting in the best way out of various available cameras. Therefore a new training structure for graphical models was developed. This is necessary for the learning...
In many driver assistance systems and autonomous driving applications, both LIDAR and computer vision (CV) sensors are often used to detect vehicles. LIDAR provides excellent range information to different objects. However, it is difficult to recognize these objects as vehicles from range information alone. On the other hand, computer vision imagery allows for better recognition, but does not provide...
The vision has many sensors responsible for capturing information that is sent to the brain. The gaze reflects its attention, intention and interest of the brain towards the outside world. Therefore, the detection of the gaze direction is a promising alternative for the simulation programs, virtual reality applications and human-machine special communication. Cheaper devices to capture images and...
Many perception problems involve datasets that are naturally comprised of multiple streams or modalities for which supervised training data is only sparsely available. In cases where there is a degree of conditional independence between such views, a class of semi-supervised learning techniques that are based on maximizing view agreement over unlabeled data has been proven successful in a wide range...
In this paper, a novel method is proposed as a solution to the problem of frontal view recognition from multiview image sequences. Our aim is to correctly identify the view that corresponds to the camera placed in front of a person, or the camera whose view is closer to a frontal one. By doing so, frontal face images of the person can be acquired, in order to be used in face or facial expression recognition...
Surveillance system involving hundreds of cameras becomes very popular. Due to various positions and orientations of camera, object appearance changes dramatically in different scenes. Traditional appearance based object classification methods tend to fail under these situations. We approach the problem by designing an adaptive object classification framework which automatically adjust to different...
Along with the development of sophisticated image processing software, it is getting easier forging a digital image but harder to detect it. It is already a problem for us to distinguish tampered photos from authentic ones. In this paper, we propose an approach based on feature fusion to detect digital image tampering. First, we extract the feature statistics that can represent the property of a camera...
Natural feature image recognition (NFIR) is camera based robotic vision system for recognition, acquisition, tracking and pose estimation of a target vehicle. This paper presents our on-going work on development of the capability of the NFIR software in recognition and acquisition for autonomous rendezvous and docking. The heart of the acquisition algorithm is a classification-based scheme. Feature...
The time-study data collected at the Station M site off the coast of central California includes high quality still-frame images taken in 1-hour time-lapse increments. The approximately 67,000 time-lapse images collected would take an unfeasible amount of time to fully analyze manually, and therefore would benefit from automated analysis. Towards this end, this work is an aid in the significant effort...
In this paper we study object recognition on a humanoid robotic head. The head is equipped with a stereo vision system with two cameras in each eye, where the cameras have lenses with different view angles. Such a system models the foveated structure of a human eye. To facilitate the pursuit of moving objects, we provide mathematical analysis that enables the robot to guide the narrow-view cameras...
This paper describes the procedure for detection and tracking of a vehicle from an on-road image sequence taken by a monocular video capturing device in real time. The main objective of such a visual tracking system is to closely follow objects in each frame of a video stream, such that the object position as well as other geometric information are always known. In the tracking system described, the...
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