Accelerometers and gyroscopes embedded in mobile devices have shown great potential for non-obtrusive gait biometrics by directly capturing a user's characteristic locomotion. Despite the success in gait analysis under controlled experimental settings using these sensors, their performance in realistic scenarios is unsatisfactory due to data dependency on sensor placement. In practice, the placement of mobile devices is unconstrained. In this paper, we propose a novel gait representation for accelerometer and gyroscope data which is both sensor-orientation-invariant and highly discriminative to enable high-performance gait biometrics for real-world applications. We also adopt the i-vector paradigm, a state-of-the-art machine learning technique widely used for speaker recognition, to extract gait identities using the proposed gait representation. Performance studies using both the naturalistic McGill University gait dataset and the Osaka University gait dataset containing 744 subjects have shown dominant superiority of this novel gait biometrics approach compared to existing methods.

We propose a method for object tracking using prototype-based deformable template models. To track an object in an image sequence, we use a criterion which combines two terms: the frame-to-frame deviations of the object shape and the fidelity of the modeled shape to the input image. The deformable template model utilizes the prior shape information which is extracted from the previous frames along with a systematic shape deformation scheme to model the object shape in a new frame. The following image information is used in the tracking process: 1) edge and gradient information: the object boundary consists of pixels with large image gradient, 2) region consistency: the same object region possesses consistent color and texture throughout the sequence, and 3) interframe motion: the boundary of a moving object is characterized by large interframe motion. The tracking proceeds by optimizing an objective function which combines both the shape deformation and the fidelity of the modeled shape to the current image (in terms of gradient, texture, and interframe motion). The inherent structure in the deformable template, together with region, motion, and image gradient cues, makes the proposed algorithm relatively insensitive to the adverse effects of weak image features and moderate amounts of occlusion.

We present a method to automatically localize captions in JPEG compressed images and the I-frames of MPEG compressed videos. Caption text regions are segmented from background images using their distinguishing texture characteristics. Unlike previously published methods which fully decompress the video sequence before extracting the text regions, this method locates candidate caption text regions directly in the DCT compressed domain using the intensity variation information encoded in the DCT domain. Therefore, only a very small amount of decoding is required. The proposed algorithm takes about 0.006 second to process a 240/spl times/350 image and achieves a recall rate of 99.17 percent while falsely accepting about 1.87 percent nontext DCT blocks on a variety of MPEG compressed videos containing more than 2,300 I-frames.

We present a method to automatically locate captions in MPEG video. Caption text regions are segmented from the background using their distinguishing texture characteristics. This method first locates candidate text regions directly in the DCT compressed domain, and then reconstructs the candidate regions for further refinement in the spatial domain. Therefore, only a small amount of decoding is required. The proposed algorithm achieves about 4.0% false reject rate and less than 5.7% false positive rate on a variety of MPEG compressed video containing more than 42,000 frames.

We propose a novel method for object tracking using prototype-based deformable template models. To track an object in an image sequence, we use a criterion which combines two terms: the deviation of the object shape from its shape in the previous frame, and the fidelity of the detected shape to the input image. Shape and gradient information are used to track the object. We have also used the consistency between corresponding object regions throughout the sequence to help in trading the object of interest. Inter-frame motion is also used to track the boundary of moving objects. We have applied the algorithm to a number of image sequences from different sources. The inherent structure in the deformable template, together with region, motion, and image gradient cues, make the algorithm relatively insensitive to the adverse effects of weak image features and moderate partial occlusion.

Retrieval efficiency and accuracy are two important issues in designing a content-based database retrieval system. We propose a new image database retrieval method based on shape information. This system achieves both the desired efficiency and accuracy using a two-stage hierarchy: in the first stage, simple and easily computable statistical shape features are used to quickly browse through the database to generate a moderate number of plausible retrievals; in the second stage, the outputs from the first stage are screened using a deformable template matching process to discard spurious matches. We have tested the algorithm using hand drawn queries on a trademark database containing 1,100 images. Each retrieval takes a reasonable amount of computation time. The top most retrieved image from the system agrees with that obtained by human subjects, but there are significant differences between the top 10 retrieved images by our system and that provided by human subjects. This demonstrates the need for developing shape features that are better able to capture human perceptual similarity of shapes.

We propose a general object localization and retrieval scheme based on object shape using deformable templates. Prior knowledge of an object shape is described by a prototype template which consists of the representative contour/edges, and a set of probabilistic deformation transformations on the template. A Bayesian scheme, which is based on this prior knowledge and the edge information in the input image, is employed to find a match between the deformed template and objects in the image. Computational efficiency is achieved via a coarse-to-fine implementation of the matching algorithm. Our method has been applied to retrieve objects with a variety of shapes from images with complex background. The proposed scheme is invariant to location, rotation, and moderate scale changes of the template.

There is a substantial interest in retrieving images from a large database using the textual information contained in the images. An algorithm which will automatically locate the textual regions in the input image will facilitate this task; the optical character recognizer can then be applied to only those regions of the image which contain text. We present a method for automatically locating text in complex color images. The algorithm first finds the approximate locations of text lines using horizontal spatial variance, and then extracts text components in these boxes using color segmentation. The proposed method has been used to locate text in compact disc (CD) and book cover images, as well as in the images of traffic scenes captured by a video camera. Initial results are encouraging and suggest that these algorithms can be used in image retrieval applications.

We propose a new texture-based page segmentation algorithm which automatically extracts the text, halftone, and line-drawing regions from input greyscale document images. This approach utilizes a neural network to train a set of masks which is optimal for discriminating the three main texture classes in the page segmentation problem: halftone, background, and text and line-drawing regions. The test and line-drawing regions are further discriminated based on connectivity analysis. We have applied the algorithm to successfully segment English and Chinese document images. We also demonstrate that the masks can perform language separation (English/Chinese) when appropriately trained.

This paper presents two new methods for detecting edges in millimeter-wave radar images. The first method is based on a deformable template model of edge shapes and a random field model of the millimeter-wave imaging process. The second method is similar to the first, except that the imaging model component is replaced by one that is based on the magnitude and direction of the image gradient. Experimental results are shown to illustrate the advantages of using these methods over traditional edge detectors.

This paper presents a new method for representing the spatial information present in digital grey-tone images. The method is based on using multi-resolution decompositions (MRDs) and Markov random fields (MRFs) concurrently. A given image is represented by a MRD of it, along with an optimally estimated set of Gaussian MRF (GMRF) parameters. Since the GMRF parameters are very small in number, this addition to the usual MRD results in only a small increase in the number of bits in the representation. It is shown, however, that such a minor addition helps when reconstructing the (given) original image from its MRD. Experimental results are presented to illustrate the usefulness of this new method.>