Parvaneh Saeedi

A physical map of the mouse genome

A physical map of a genome is an essential guide for navigation, allowing the location of any gene or other landmark in the chromosomal DNA. We have constructed a physical map of the mouse genome that contains 296 contigs of overlapping bacterial clones and 16,992 unique markers. The mouse contigs were aligned to the human genome sequence on the basis of 51,486 homology matches, thus enabling use of the conserved synteny (correspondence between chromosome blocks) of the two genomes to accelerate construction of the mouse map. The map provides a framework for assembly of whole-genome shotgun sequence data, and a tile path of clones for generation of the reference sequence. Definition of the human–mouse alignment at this level of resolution enables identification of a mouse clone that corresponds to almost any position in the human genome. The human sequence may be used to facilitate construction of other mammalian genome maps using the same strategy.

Vision-based 3-D trajectory tracking for unknown environments

This paper describes a vision-based system for 3-D localization of a mobile robot in a natural environment. The system includes a mountable head with three on-board charge-coupled device cameras that can be installed on the robot. The main emphasis of this paper is on the ability to estimate the motion of the robot independently from any prior scene knowledge, landmark, or extra sensory devices. Distinctive scene features are identified using a novel algorithm, and their 3-D locations are estimated with high accuracy by a stereo algorithm. Using new two-stage feature tracking and iterative motion estimation in a symbiotic manner, precise motion vectors are obtained. The 3-D positions of scene features and the robot are refined by a Kalman filtering approach with a complete error-propagation modeling scheme. Experimental results show that good tracking and localization can be achieved using the proposed vision system.

Three-Dimensional Polygonal Building Model Estimation From Single Satellite Images

This paper introduces a novel system for automatic detection and height estimation of buildings with polygonal shape roofs in singular satellite images. The system is capable of detecting multiple flat polygonal buildings with no angular constraints or shape priors. The proposed approach employs image primitives such as lines, and line intersections, and examines their relationships with each other using a graph-based search to establish a set of rooftop hypotheses. The height (mean height from rooftop edges to the ground) of each rooftop hypothesis is estimated using shadows and acquisition geometry. The potential ambiguities in identification of shadows in an image and the uncertainty in identifying true shadows of a building have motivated for a fuzzy logic-based approach that estimates buildings heights according to the strength of shadows and the overlap between identified shadows in the image and expected shadows according to the building profile. To reduce the time complexity of the implemented system, a maximum number of eight sides for polygonal rooftops is assumed. Promising experimental results verify the effectiveness of the presented system with overall mean shape accuracy of 94% and mean height error of 0.53 m on QuickBird satellite (0.6 m/pixel) imageries.

Robust Weighted Graph Transformation Matching for Rigid and Nonrigid Image Registration

This paper presents an automatic point matching algorithm for establishing accurate match correspondences in two or more images. The proposed algorithm utilizes a group of feature points to explore their geometrical relationship in a graph arrangement. The algorithm starts with a set of matches (including outliers) between the two images. A set of nondirectional graphs is then generated for each feature and its K nearest matches (chosen from the initial set). Using the angular distances between edges that connect a feature point to its K nearest neighbors in the graph, the algorithm finds a graph in the second image that is similar to the first graph. In the case of a graph including outliers, the algorithm removes such outliers (one by one, according to their strength) from the graph and re-evaluates the angles until the two graphs are matched or discarded. This is a simple intuitive and robust algorithm that is inspired by a previous work. Experimental results demonstrate the superior performance of this algorithm under various conditions, such as rigid and nonrigid transformations, ambiguity due to partial occlusions or match correspondence multiplicity, scale, and larger view variation.

Automatic Rooftop Extraction in Nadir Aerial Imagery of Suburban Regions Using Corners and Variational Level Set Evolution

Building profile extraction from aerial imagery constitutes a key element in numerous geospatial applications. Rooftop detection has been addressed through a variety of approaches that are, however, rarely capable of coping with conditions such as arbitrary illumination, variant reflections, and complex building profiles. This paper proposes a new method for extracting 2-D rooftop footprints from nadir aerial imagery through a fully automatic approach that handles arbitrary illumination, variant reflections, and complex building profiles without shape priors. The proposed method combines the strength of energy-based approaches with distinctiveness of corners. Corners are assessed using multiple color and color-invariance spaces. A rooftop outline is generated from selected corner candidates and further refined to fit the best possible boundaries through level-set curve evolution that is enhanced via a mean squared error map. Experimental results confirm the ability of the presented system to effectively extract rooftop profiles with an overall average shape accuracy of 84%, correctness of 94%, completeness of 92 %, and quality of 88%.

Moving Region Segmentation From Compressed Video Using Global Motion Estimation and Markov Random Fields

In this paper, we propose an unsupervised segmentation algorithm for extracting moving regions from compressed video using global motion estimation (GME) and Markov random field (MRF) classification. First, motion vectors (MVs) are compensated from global motion and quantized into several representative classes, from which MRF priors are estimated. Then, a coarse segmentation map of the MV field is obtained using a maximum a posteriori estimate of the MRF label process. Finally, the boundaries of segmented moving regions are refined using color and edge information. The algorithm has been validated on a number of test sequences, and experimental results are provided to demonstrate its advantages over state-of-the-art methods.

A Model-Based Validation Scheme for Organ Segmentation in CT Scan Volumes

In this study, we propose a novel approach for accurate 3-D organ segmentation in the CT scan volumes. Instead of using the organs prior information directly in the segmentation process, here we utilize the knowledge of the organ to validate a large number of potential segmentation outcomes that are generated by a generic segmentation process. For this, an organ space is generated based on the principal component analysis approach using which the fidelity of each segment to the organ is measured. We detail applications of the proposed method for the 3-D segmentation of human kidney and liver in computed tomography scan volumes. For evaluation, the public database of the MICCAIs 2007 grand challenge workshop has been incorporated. Implementation results show an average Dice similarity measure of 0.90 for the segmentation of the kidney. For the liver segmentation, the proposed algorithm achieves an average volume overlap error of 8.7% and an average surface distance of 1.51 mm.

Automatic building detection in aerial and satellite images

Automatic creation of 3D urban city maps could be an innovative way for providing geometric data for varieties of applications such as civilian emergency situations, natural disaster management, military situations, and urban planning. Reliable and consistent extraction of quantitative information from remotely sensed imagery is crucial to the success of any of the above applications. This paper describes the development of an automated roof detection system from single monocular electro-optic satellite imagery. The system employs a fresh approach in which each input image is segmented at several levels. The border line definition of such segments combined with line segments detected on the original image are used to generate a set of quadrilateral rooftop hypotheses. For each hypothesis a probability score is computed that represents the evidence of true building according to the image gradient field and line segment definitions. The presented results demonstrate that the system is capable of detecting small gabled residential rooftops with variant light reflection properties with high positional accuracies.

3D motion tracking of a mobile robot in a natural environment

This paper presents a vision-based tracking system suitable for autonomous robot vehicle guidance. The system includes a head with three on-board CCD cameras, which can be mounted anywhere on a mobile vehicle. By processing consecutive trinocular sets of precisely aligned and rectified images, the local 3D trajectory of the vehicle in an unstructured environment can be tracked. First, a 3D representation of stable features in the image scene is generated using a stereo algorithm. Next, motion is estimated by trading matched features over time. The motion equation with 6-DOF is then solved using an iterative least squares fit algorithm. Finally, a Kalman filter implementation is used to optimize the world representation of scene features.

Robust region-based background subtraction and shadow removing using color and gradient information

In this paper, a novel algorithm for foreground detection and shadow removal is presented. The proposed method employs a region-based approach by processing two foregrounds resulted from gradient-and color-based background subtraction methods. The performance of the system is compared against conventional approaches for five indoor and outdoor video sequences. Experimental results confirm that the detection rate exceeds 90%, and the robustness is greatly improved.