Catálogo de publicaciones - libros

Most clickstream visualization techniques display web users’ clicks by highlighting paths in a graph of the underlying web site structure. These techniques do not scale to handle high volume web usage data. Further, historical usage data is not considered. The work described in this paper differs from other work in the following aspect. Fuzzy clustering is applied to historical usage data and the result imaged in the form of a point cloud. Web navigation data from active users are shown as animated paths in this point cloud. It is clear that when many paths get attracted to one of the clusters, that particular cluster is currently “hot.” Further as sessions terminate, new sessions are incrementally incorporated into the point cloud. The complete process is closely coupled to the fuzzy clustering technique and makes effective use of clustering results. The method is demonstrated on a very large set of web log records consisting of over half a million page clicks.

Points, lines, and polygons have been the fundamental primitives in graphics. Graphics hardware is optimized to handle them in a pipeline. Other objects are converted to these primitives before rendering. Programmable GPUs have made it possible to introduce a wide class of computations on each vertex and on each fragment. In this paper, we outline a procedure to accurately draw high-level procedural elements efficiently using the GPU. The CPU and the vertex shader setup the drawing area on screen and pass the required parameters. The pixel shader uses ray-casting to compute the 3D point that projects to it and shades it using a general shading model. We demonstrate the fast rendering of 2D and 3D primitives like circle, conic, triangle, sphere, quadric, box, etc., with a combination of specularity, refraction, and environment mapping. We also show combination of objects, like Constructive Solid Geometry (CSG) objects, can be rendered fast on the GPU. We believe customized GPU programs for a new set of high-level primitives – which we call – is a way to exploit the power of GPUs and to provide interactive rendering of scenes otherwise considered too complex.

We present a reordering-based procedure for the multiresolution decomposition of a point cloud in this paper. The points are first reordered recursively based on an optimal pairing. Each level of reordering induces a division of the points into approximation and detail values. A balanced quantization at each level results in further compression. The original point cloud can be reconstructed without loss from the decomposition. Our scheme does not require local reference planes for encoding or decoding and is progressive. The points also lie on the original manifold at all levels of decomposition. The scheme can be used to generate different discrete LODs of the point set with fewer points in each at low BPP numbers. We also present a scheme for the progressive representation of the point set by adding the detail values selectively. This results in the progressive approximation of the original shape with dense points even at low BPP numbers. The shape gets refined as more details are added and can reproduce the original point set. This scheme uses a wavelet decomposition of the detail coefficients of the multiresolution decomposition. Progressiveness is achieved by including different levels of the DWT decomposition at all multiresolution representation levels. We show that this scheme can generate much better approximations at equivalent BPP numbers for the point set.

Factorization methods are used extensively in computer vision for a wide variety of tasks. Existing factorization techniques extract factors that meet requirements such as compact representation, interpretability, efficiency, dimensionality reduction . However, when the extracted factors lack interpretability and are large in number, identification of factors that cause the data to exhibit certain properties of interest is useful in solving a variety of problems. Identification of such factors or has interesting applications in data synthesis and recognition. In this paper simple and efficient methods are proposed, for identification of factors of interest from a pool of factors obtained by decomposing videos represented as tensors into their constituent low rank factors. The method is used to select factors that enable appearance based facial expression transfer and facial expression recognition. Experimental results demonstrate that the factor selection facilitates efficient solutions to these problems with promising results.

We present a newly developed algorithm for automatically segmenting videos into basic shot units. A basic shot unit can be understood as an unbroken sequence of frames taken from one camera. At first we calculate the frame difference by using the local histogram comparison, and then we dynamically scale the frame difference by Log-formula to compress and enhance the frame difference. Finally we detect the shot boundaries by the newly proposed shot boundary detection algorithm which it is more robust to camera or object motion, and many flashlight events. The proposed algorithms are tested on the various video types and experimental results show that the proposed algorithm are effective and reliably detects shot boundaries.

In this work we propose a hierarchical state-based model for representing an echocardiogram video using objects present and their dynamic behavior. The modeling is done on the basis of the different types of views like short axis view, long axis view, apical view, etc. For view classification, an artificial neural network is trained with the histogram of a of each video frame. A state transition diagram is used to represent the states of objects in different views and corresponding transition from one state to another. States are detected with the help of synthetic M-mode images. In contrast to traditional single M-mode approach, we propose a new approach named as ‘’ for the detection of states.

In this paper, we present a new approach for object removal and video completion of indoor scenes. In indoor images, the frames are not affine related. The region near the object to be removed can have multiple planes with sharply different motions. Dense motion estimation may fail for such scenes due to missing pixels. We use feature tracking to find dominant motion between two frames. The geometry of the motion of multiple planes is used to segment the motion layers into component planes. The homography corresponding to each hole pixel is used to warp a frame in the future or past for filling it. We show the application of our technique on some typical indoor videos.

Video has become an interactive medium of daily use today. However, the sheer volume of the data makes it extremely difficult to browse and find required information. Organizing the video and locating required information effectively and efficiently presents a great challenge to the video retrieval community. This demands a tool which would break down the video into smaller and manageable units called shots.

Traditional shot detection methods use pixel difference, histograms, or temporal slice analysis to detect hard-cuts and gradual transitions. However, systems need to be robust to sequences that contain dramatic illumination changes, shaky camera effects, and special effects such as fire, explosion, and synthetic screen split manipulations. Traditional systems produce false positives for these cases; i.e., they claim a shot break when there is none.

We propose a shot detection system which reduces false positives even if all the above effects are present in one sequence. Similarities between successive frames are computed by finding the correlation and is further analyzed using a wavelet transformation. A final filtering step is to use a trained Support Vector Machine (SVM). As a result, we achieve better accuracy (while retaining speed) in detecting shot-breaks when compared with other techniques.

In this paper we address the problem of temporal segmentation of videos. We present a multi-modal approach where clues from different information sources are merged to perform the segmentation. Specifically, we segment videos based on textual descriptions or commentaries of the action in the video. Such a parallel information is available for cricket videos, a class of videos where visual feature based () scene segmentation algorithms generally fail, due to lack of visual dissimilarity across space and time. With additional information from textual domain, these ambiguities could be resolved to a large extent. The video is segmented to meaningful entities or scenes, using the scene level descriptions provided by the commentary. These segments can then be automatically annotated with the respective descriptions. This allows for a semantic access and retrieval of video segments, which is difficult to obtain from existing visual feature based approaches. We also present techniques for automatic highlight generation using our scheme.

A novel object representation for tracking is proposed. The tracked object is represented as a constellation of spatially localised linear predictors which are learned on a single training image. In the learning stage, sets of pixels whose intensities allow for optimal least square predictions of the transformations are selected as a support of the linear predictor.

The approach comprises three contributions: learning object specific linear predictors, explicitly dealing with the predictor precision – computational complexity trade-off and selecting a view-specific set of predictors suitable for global object motion estimate. Robustness to occlusion is achieved by RANSAC procedure.

The learned tracker is very efficient, achieving frame rate generally higher than 30 frames per second despite the Matlab implementation.