Biomedical subjects

Mubarak Shah

Publications and source records attributed to Mubarak Shah.

7 recordsLinked to original sources

PubMed2006

Determining scale and sea state from water video.

In most image processing and computer vision applications, real-world scale can only be determined when calibration information is available. Dynamic scenes further complicate most situations. However, some types of dynamic scenes provide useful information that can be used to recover real-world scale. In this paper, we focus on ocean scenes and propose a method for finding sizes in real-world units and the sea state from an uncalibrated camera. Fourier transforms in the space and time dimensions yield spatial and temporal frequency spectra. For water waves, the dispersion relation defines a square relationship between the wavelength and period of a wave. Our method applies this dispersion relation to recover the real-world scale of an ocean sequence. The sea state--including the peak wavelength and period, the wind speed that generated the waves, and the wave heights--is also determined from the frequency spectrum of the sequence combined with stochastic oceanography models. The process is demonstrated on synthetic and real sequences, validating the results with known scene geometry. This has wide applications in port monitoring and coastal surveillance.

Algorithms↗

PubMed2005

Segmentation of neighboring organs in medical image with model competition.

This paper presents a novel approach for image segmentation by introducing competition between neighboring shape models. Our method is motivated by the observation that evolving neighboring contours should avoid overlapping with each other and this should be able to aid in multiple neighboring objects segmentation. A novel energy functional is proposed, which incorporates both prior shape information and interactions between deformable models. Accordingly, we also propose an extended maximum a posteriori (MAP) shape estimation model to obtain the shape estimate of the organ. The contours evolve under the influence of image information, their own shape priors and neighboring MAP shape estimations using level set methods to recover organ shapes. Promising results and comparisons from experiments on both synthetic data and medical imagery demonstrate the potential of our approach.

Algorithms↗

PubMed2005

A noniterative greedy algorithm for multiframe point correspondence.

This paper presents a framework for finding point correspondences in monocular image sequences over multiple frames. The general problem of multiframe point correspondence is NP-hard for three or more frames. A polynomial time algorithm for a restriction of this problem is presented and is used as the basis of the proposed greedy algorithm for the general problem. The greedy nature of the proposed algorithm allows it to be used in real-time systems for tracking and surveillance, etc. In addition, the proposed algorithm deals with the problems of occlusion, missed detections, and false positives by using a single noniterative greedy optimization scheme and, hence, reduces the complexity of the overall algorithm as compared to most existing approaches where multiple heuristics are used for the same purpose. While most greedy algorithms for point tracking do not allow for entry and exit of the points from the scene, this is not a limitation for the proposed algorithm. Experiments with real and synthetic data over a wide range of scenarios and system parameters are presented to validate the claims about the performance of the proposed algorithm.

Algorithms↗

PubMed2005

Motion layer extraction in the presence of occlusion using graph cuts.

Extracting layers from video is very important for video representation, analysis, compression, and synthesis. Assuming that a scene can be approximately described by multiple planar regions, this paper describes a robust and novel approach to automatically extract a set of affine or projective transformations induced by these regions, detect the occlusion pixels over multiple consecutive frames, and segment the scene into several motion layers. First, after determining a number of seed regions using correspondences in two frames, we expand the seed regions and reject the outliers employing the graph cuts method integrated with level set representation. Next, these initial regions are merged into several initial layers according to the motion similarity. Third, an occlusion order constraint on multiple frames is explored, which enforces that the occlusion area increases with the temporal order in a short period and effectively maintains segmentation consistency over multiple consecutive frames. Then, the correct layer segmentation is obtained by using a graph cuts algorithm and the occlusions between the overlapping layers are explicitly determined. Several experimental results are demonstrated to show that our approach is effective and robust.

Algorithms↗

PubMed2005

Bayesian modeling of dynamic scenes for object detection.

Accurate detection of moving objects is an important precursor to stable tracking or recognition. In this paper, we present an object detection scheme that has three innovations over existing approaches. First, the model of the intensities of image pixels as independent random variables is challenged and it is asserted that useful correlation exists in intensities of spatially proximal pixels. This correlation is exploited to sustain high levels of detection accuracy in the presence of dynamic backgrounds. By using a nonparametric density estimation method over a joint domain-range representation of image pixels, multimodal spatial uncertainties and complex dependencies between the domain (location) and range (color) are directly modeled. We propose a model of the background as a single probability density. Second, temporal persistence is proposed as a detection criterion. Unlike previous approaches to object detection which detect objects by building adaptive models of the background, the foreground is modeled to augment the detection of objects (without explicit tracking) since objects detected in the preceding frame contain substantial evidence for detection in the current frame. Finally, the background and foreground models are used competitively in a MAP-MRF decision framework, stressing spatial context as a condition of detecting interesting objects and the posterior function is maximized efficiently by finding the minimum cut of a capacitated graph. Experimental validation of the proposed method is performed and presented on a diverse set of dynamic scenes.

Algorithms↗

PubMed2004

Virtual three-dimensional blackboard: three-dimensional finger tracking with a single camera.

We present a method for three-dimensional (3D) tracking of a human finger from a monocular sequence of images. To recover the third dimension from the two-dimensional images, we use the fact that the motion of the human arm is highly constrained owing to the dependencies between elbow and forearm and the physical constraints on joint angles. We use these anthropometric constraints to derive a 3D trajectory of a gesticulating arm. The system is fully automated and does not require human intervention. The system presented can be used as a visualization tool, as a user-input interface, or as part of some gesture-analysis system in which 3D information is important.

Journal Article↗

PubMed2004

Contour-based object tracking with occlusion handling in video acquired using mobile cameras.

We propose a tracking method which tracks the complete object regions, adapts to changing visual features, and handles occlusions. Tracking is achieved by evolving the contour from frame to frame by minimizing some energy functional evaluated in the contour vicinity defined by a band. Our approach has two major components related to the visual features and the object shape. Visual features (color, texture) are modeled by semiparametric models and are fused using independent opinion polling. Shape priors consist of shape level sets and are used to recover the missing object regions during occlusion. We demonstrate the performance of our method on real sequences with and without object occlusions.

Algorithms↗