Intelligent access to digital video: Informedia project

Carnegie Mellon's Informedia Digital Video Library project will establish a large, on-line digital video library featuring full-content and knowledge-based search and retrieval. Intelligent, automatic mechanisms will be developed to populate the library. Search and retrieval from digital video, audio, and text libraries will take place via desktop computer over local, metropolitan, and wide-area networks. The project's approach applies several techniques for content-based searching and video-sequence retrieval. Content is conveyed in both the narrative (speech and language) and the image. Only by the collaborative interaction of image, speech, and natural language understanding technology is it possible to successfully populate, segment, index, and search diverse video collections with satisfactory recall and precision. This collaborative interaction approach uniquely compensates for problems of interpretation and search in error-ridden and ambiguous data sets. The authors have focused the work on two corpuses. One is science documentaries and lectures, the other is broadcast news content with partial closed-captions. Further work will continue to improve the accuracy and performance of the underlying processing as well as explore performance issues related to Web-based access and interoperability with other digital video resources     

Rose-red color oxynitride : A visible light photocatalyst to hydrogen production

Rose-red color Nb₂Zr₆O_17-xN_x oxynitride photocatalyst was synthesized by thermal ammonolysis of Nb₂Zr₆O₁₇ at 1073 K. TEM image of Nb₂Zr₆O_17-xN_x sample showed prismatic pseudo orthorhombic shaped particles with clear edges and an average particle size in the range of 80–90 nm. The photocatalytic activities of the oxynitride samples were studied for hydrogen production by H₂S splitting under visible light irradiation. The oxynitride Nb₂Zr₆O_17-xN_x gave the quantum yield of 13.5% in the production of hydrogen from decomposition of hydrogen sulfide under visible light irradiation.     

Reconstruction of a Scene with Multiple Linearly Moving Objects

In this paper we describe an algorithm to recover the scene structure, the trajectories of the moving objects and the camera motion simultaneously given a monocular image sequence. The number of the moving objects is automatically detected without prior motion segmentation. Assuming that the objects are moving linearly with constant speeds, we propose a unified geometrical representation of the static scene and the moving objects. This representation enables the embedding of the motion constraints into the scene structure, which leads to a factorization-based algorithm. We also discuss solutions to the degenerate cases which can be automatically detected by the algorithm. Extension of the algorithm to weak perspective projections is presented as well. Experimental results on synthetic and real images show that the algorithm is reliable under noise.     

A sequential factorization method for recovering shape and motionfrom image streams

We present a sequential factorization method for recovering the three-dimensional shape of an object and the motion of the camera from a sequence of images, using tracked features. The factorization method originally proposed by Tomasi and Kanade (1992) produces robust and accurate results incorporating the singular value decomposition. However, it is still difficult to apply the method to real-time applications, since it is based on a batch-type operation and the cost of the singular value decomposition is large. We develop the factorization method into a sequential method by regarding the feature positions as a vector time series. The new method produces estimates of shape and motion at each frame. The singular value decomposition is replaced with an updating computation of only three dominant eigenvectors, which can be performed in O(P²) time, while the complete singular value decomposition requires O(FP²) operations for an F×P matrix. Also, the method is able to handle infinite sequences, since it does not store any increasingly large matrices. Experiments using synthetic and real images illustrate that the method has nearly the same accuracy and robustness as the original method     

A Closed-Form Solution to Non-Rigid Shape and Motion Recovery

Recovery of three dimensional (3D) shape and motion of non-static scenes from a monocular video sequence is important for applications like robot navigation and human computer interaction. If every point in the scene randomly moves, it is impossible to recover the non-rigid shapes. In practice, many non-rigid objects, e.g. the human face under various expressions, deform with certain structures. Their shapes can be regarded as a weighted combination of certain shape bases. Shape and motion recovery under such situations has attracted much interest. Previous work on this problem (Bregler, C., Hertzmann, A., and Biermann, H. 2000. In Proc. Int. Conf. Computer Vision and Pattern Recognition; Brand, M. 2001. In Proc. Int. Conf. Computer Vision and Pattern Recognition; Torresani, L., Yang, D., Alexander, G., and Bregler, C. 2001. In Proc. Int. Conf. Computer Vision and Pattern Recognition) utilized only orthonormality constraints on the camera rotations (rotation constraints). This paper proves that using only the rotation constraints results in ambiguous and invalid solutions. The ambiguity arises from the fact that the shape bases are not unique. An arbitrary linear transformation of the bases produces another set of eligible bases. To eliminate the ambiguity, we propose a set of novel constraints, basis constraints, which uniquely determine the shape bases. We prove that, under the weak-perspective projection model, enforcing both the basis and the rotation constraints leads to a closed-form solution to the problem of non-rigid shape and motion recovery. The accuracy and robustness of our closed-form solution is evaluated quantitatively on synthetic data and qualitatively on real video sequences.     

Editorial

International Journal of Computer Vision -     

A semi-Markov model for mitosis segmentation in time-lapse phase contrast microscopy image sequences of stem cell populations

We propose a semi-Markov model trained in a max-margin learning framework for mitosis event segmentation in large-scale time-lapse phase contrast microscopy image sequences of stem cell populations. Our method consists of three steps. First, we apply a constrained optimization based microscopy image segmentation method that exploits phase contrast optics to extract candidate subsequences in the input image sequence that contains mitosis events. Then, we apply a max-margin hidden conditional random field (MM-HCRF) classifier learned from human-annotated mitotic and nonmitotic sequences to classify each candidate subsequence as a mitosis or not. Finally, a max-margin semi-Markov model (MM-SMM) trained on manually-segmented mitotic sequences is utilized to reinforce the mitosis classification results, and to further segment each mitosis into four predefined temporal stages. The proposed method outperforms the event-detection CRF model recently reported by Huh as well as several other competing methods in very challenging image sequences of multipolar-shaped C3H10T1/2 mesenchymal stem cells. For mitosis detection, an overall precision of 95.8% and a recall of 88.1% were achieved. For mitosis segmentation, the mean and standard deviation for the localization errors of the start and end points of all mitosis stages were well below 1 and 2 frames, respectively. In particular, an overall temporal location error of 0.73 ± 1.29 frames was achieved for locating daughter cell birth events.     

High-resolution terrain map from multiple sensor data

The authors present 3-D vision techniques for incrementally building an accurate 3-D representation of rugged terrain using multiple sensors. They have developed the locus method to model the rugged terrain. The locus method exploits sensor geometry to efficiently build a terrain representation from multiple sensor data. The locus method is used to estimate the vehicle position in the digital elevation map (DEM) by matching a sequence of range images with the DEM. Experimental results from large-scale real and synthetic terrains demonstrate the feasibility and power of the 3-D mapping techniques for rugged terrain. In real world experiments, a composite terrain map was built by merging 125 real range images. Using synthetic range images, a composite map of 150 m was produced from 159 images. With the proposed system, mobile robots operating in rugged environments can build accurate terrain models from multiple sensor data