This paper describes a framework for learning probabilistic models of objects and scenes and for exploiting these models for tracking complex, deformable, or articulated objects in image sequences. We focus on the probabilistic tracking of people and learn models of how they appear and move in images. In particular, we learn the likelihood of observing various spatial and temporal filter respon...

This paper explores the use of the Gabor wavelet representation of an image for robust object tracking in robot vision (object grasping, gesture recognition and face tracking in human-robot interaction). For image decomposition we developed a fast non-iterative transform algorithm, in which the original image is processed with a 2D Gabor wavelet filter bank. We used the positions of the local e...

Modern research for tracking Human-Object by means of various statistical and mathematical shows a new method enriching the conventional methods. The use covariance considering the images in matrix form for collecting features is a proven approach for tracking object/image in preference to the usual histogram based object/image depiction replica frequently used in well-liked methodology. In thi...

Developing specialized software tools is essential to support studies of solar activity evolution. With new space missions such as Solar Dynamics Observatory (SDO), solar images are being produced in unprecedented volumes. To capitalize on that huge data availability, the scientific community needs a new generation of software tools for automatic and efficient data processing. In this paper a p...

This paper presents the development of a real time tracking algorithm that runs on a 1.2 GHz PC/104 computer onboard a small UAV. The algorithm uses zero mean normalized cross correlation to detect and locate an object in the image. A Kalman filter is used to make the tracking algorithm computationally efficient. Object position in an image frame is predicted using the motion model and a search...

We introduce a novel tracking technique which uses dynamic confidence-based fusion of two different information sources for robust and efficient tracking of visual objects. Mean-shift tracking is a popular and well known method used in object tracking problems. Originally, the algorithm uses a similarity measure which is optimized by shifting a search area to the center of a generated “weight i...

In this paper, we propose a novel method on tracking unit pixel width line segments for function approximation-based image coding. This method is applied prior to function approximation of line segments in image coding. Compared to conventional methods, our method overcomes the problems introduced by inaccurately tracking unit pixel width contours that appear normally in images of fine details ...

The ultimate goal of this study is to afford enhanced video object detection and tracking by eliminating the limitations which are existing nowadays. Although high performance ratio for video object detection and tracking is achieved in the earlier work it takes more time for computation. Consequently we are in need to propose a novel video object detection and tracking technique so as to minim...

This paper describes a novel image-based method for tracking robotic mechanisms and interventional devices during Magnetic Resonance Image (MRI)-guided procedures. It takes advantage of the multi-planar imaging capabilities of MRI to optimally image a set of localizing fiducials for passive motion tracking in the image coordinate frame. The imaging system is servoed to adaptively position the s...

This paper describes a new approach to the tracking of complex shapes through image sequences, that combines deformable region models and deformable contours. A new deformable region model is presented: its optimisation is based on texture correlation and is constrained by the use of a motion model, such as rigid, aane or homographic. The use of texture information (versus edge information) not...