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Most image retargeting algorithms rely heavily on valid saliency map detection to proceed. But the inefficiency of high quality saliency map detection severely restricts applications of these image retargeting methods. In this paper, we describe a stochastic algorithm for efficient context-aware saliency map detection. Our method is a multiple level saliency map detection algorithm which integrates...
The collection of knowledge about an imaging domain usually requires input from experts on that domain. In a two-dimensional image space this input can be provided through graphic annotation of image regions that represent objects of interest. This is a necessary process which is both time-consuming and cost inefficient, especially when sequences of images are involved. In this paper we present a...
When 3D video is transmitted over lossy channels, different strategies can be adopted at the different layers of the communication protocol stack to cope with such errors, in a cross-layer operation framework. At the receiver side, effective error concealment methods are necessary to overcome the quality loss due to erroneous packets. The existing concealment techniques for conventional 2D video can...
In this study, we investigate the ability of the spectral data from a multi-angle WorldView-2 image sequence to improve classification accuracy of an urban scene. Specifically, we investigate the multi-angle reflectance, as well as two data extraction methods applied to the reflectance data, developed from thirteen images collected over downtown Atlanta, GA in Dec. 2009. These images were collected...
In recent years, there has been increased interest in characterizing and extracting 3D information from video sequences for object tracking and identification. In this paper, we propose a single view-based framework for robust estimation of height and position. In this work, 2D features of a target object is back-projected into the 3D scene space where its coordinate system is given by a rectangular...
A new method based on the Discrete Cosine Transform (DCT) and the Otsu method for blur detection in image sequences is proposed in this paper. In the first step, the standard deviation (STD) and the DCT coefficients are utilized to detect blurred and homogeneous areas in each image. Then, the Otsu method is used to calculate an adaptive threshold in each segment of the image sequence. Our experiments...
Vision-based driver assistance requires basic vision modules for stereo analysis or optic flow calculation. Driving situations change frequently, and methods need to be evaluated in the real world. The paper proposes ways for evaluating optic flow techniques by using estimated geometries for surrounding buildings or 'road furniture'. It demonstrates the value of such an evaluation by discussing four...
This paper presents a new approach which combines the Kernel Density Estimation and Trust Region algorithm for tracking objects in video sequences. Kernel density estimation (KDE) of the object's color distribution is built from the object region and used to generate a probability map for each incoming frame. Tracking is accomplished by localizing blobs in the maps. Compared with color histograms...
Object tracking is a paramount task in video surveillance systems. Although many efforts have been accomplished on object tracking during the last years more work is still needed in order to generate more robust systems. A new fuzzy method for object tracking is presented in this paper. The proposed method is composed of two Sugeno type systems with weighted average memory output functions. One of...
We introduce a fast and robust subspace-based approach to appearance-based object tracking. The core of our approach is based on Fast Robust Correlation (FRC), a recently proposed technique for the robust estimation of large translational displacements. We show how the basic principles of FRC can be naturally extended to formulate a robust version of Principal Component Analysis (PCA) which can be...
A new semantic steganographic algorithm for hiding data in the motion trajectories of Video Objects (VOs) is presented. First, a set of primitives (objects bounding-box coordinates and its centroid) are used to dene a VO. The centroid of VO is tracked in each frame and its coordinate is stored in a trajectory vector. The proposed algorithm embeds data in the VO object trajectory by presenting a relationship...
The objective is to develop a probabilistic approach for vision-based fire detection in videos. The proposed method analyzes the frame-to-frame changes of specific low-level features describing potential fire regions. These features are color, area size, surface coarseness, boundary roughness, and skewness within estimated fire regions. Because of flickering and random characteristics of fire, these...
Processing Real-Time image sequence is now possible because of advancement of technological developments in digital signal processing, wide-band communication, and high-performance VLSI. With the developments in video technology, the surveillance system can be built with some low cost gadget such as the web-camera. In this modern life with increasing number of crime rate, people in society need for...
Segmentation of moving objects in an image sequence is one of the most fundamental and crucial steps in visual surveillance applications. This paper proposes a novel and efficient method for detecting moving objects in a noisy background by using a growing self organizing map to construct the codebook. The segmentation process distinguishes between those parts of the objects which move on static and...
Background subtraction is a technique for detecting moving objects in video frames. A simple BS process involves building a model of the background and extracting regions of the foreground (moving objects) with the assumptions that the camera remains stationary and there exist no movements in the background. Video object extraction is a critical task in multimedia analysis and editing. Normally, the...
The endocardium tracking in ultrasound images is challenging due to large shape variations and the signal dropout. In this paper, we present a method to fuse multiple information sources to robustly track the endocardium. The first novelty of the method is to perform tracking in a straightened shape space, to minimize the image pattern changes caused by cardiac motions. Straightened images are used...
We present a framework for cell tracking in a highly cluttered environment in live cell imaging from mouse brain cortex. Our goal is to track cells over a long period of time for intracellular calciumion concentration in order to detect important cellular events such as neural activity and cell division. Since traditional object tracking approach such as segmentation followed by tracking is not applicable...
We report the implementation of a fully on-chip, lensless, sub-pixel resolving optofluidic microscope (SROFM) based on the super resolution algorithm. The device utilizes microfluidic flow to deliver specimens directly across a complementary metal oxide semiconductor (CMOS) sensor to generate a sequence of low-resolution (LR) projection images, where resolution is limited by the sensor's pixel size...
We present a framework for interactive tracking of protein translocations between nuclei and cytoplasm of single cells. Initially, we segment selected keyframes using a novel interactive segmentation method, which employs a constrained density weighted Nyström method for eigenvector decomposition, and the geodesic commute distance for pixel classification. Tracking is achieved by both forward and...
This paper addresses the clinically challenging problem of hairline mandibular fracture detection from a sequence of Computed Tomography (CT) images. A hairline fracture of critical clinical importance, can be easily missed due to the absence of sharp surface and contour discontinuities and the presence of intensity inhomogeneity in the CT image, if not scrutinized carefully. In this work, the 2D...
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