Ik Soo Lim

Kd-Jump: a Path-Preserving Stackless Traversal for Faster Isosurface Raytracing on GPUs

Stackless traversal techniques are often used to circumvent memory bottlenecks by avoiding a stack and replacing return traversal with extra computation. This paper addresses whether the stackless traversal approaches are useful on newer hardware and technology (such as CUDA). To this end, we present a novel stackless approach for implicit kd-trees, which exploits the benefits of index-based node traversal, without incurring extra node visitation. This approach, which we term Kd-Jump, enables the traversal to immediately return to the next valid node, like a stack, without incurring extra node visitation (kd-restart). Also, Kd-Jump does not require global memory (stack) at all and only requires a small matrix in fast constant-memory. We report that Kd-Jump outperforms a stack by 10 to 20% and kd-restar t by 100%. We also present a Hybrid Kd-Jump, which utilizes a volume stepper for leaf testing and a run-time depth threshold to define where kd-tree traversal stops and volume-stepping occurs. By using both methods, we gain the benefits of empty space removal, fast texture-caching and realtime ability to determine the best threshold for current isosurface and view direction.

Key-posture extraction out of human motion data

Recent progress in 3-D capture technology has made it possible to obtain much of realistic motion data of human subjects. Being captured in high frame rates, compression or extraction of key postures out of the motion data is useful for storage, transfer and browsing among them: this can serve as an Important pre-processing for applications such as rehabilitation, ergonomics and sports physiology. This paper addresses these problems by treating the motion date as trajectory curves In a high-dimensional space and doing a novel application of a curve simplification algorithm, typically used for planar curves, to human motion data.

Construction of animation models out of captured data

This article describes a method of constructing parametric models out of captured motion and skeleton data. Casting the problem as scattered data interpolation, our work is based on a multi-step approximation for the interpolation function with motion data compressed by principal component analysis. This leads to smaller storage and faster computation than those of previous approaches based on classical methods of exact interpolation. As a result, motion models can be constructed out of a rich set of example data, but can be used for real-time applications. We demonstrate a motion model controllable by attributes including those invariant for each individual, such as age, gender, height and weight. A parametric skeleton model is also constructed and demonstrated.

Robust tracking and segmentation of human motion in an image sequence

We present a method for improving robustness in feature-based tracking of human motion. Motion flows of features estimated by a standard tracker are modified to be coherent with neighboring ones. This coherence constraint is computed based on a smooth approximation to initial motion flows computed by the tracker. With these tracking results, we demonstrate motion segmentation of different body parts in an image sequence.

Object segmentation within microscope images of palynofacies

Identification of fossil material under a microscope is the basis of micropalentology. Our task is to locate and count the pieces of inertinite and vitrinite in images of sieve sampled rock. The classical watershed algorithm oversegments the objects because of their irregular shapes. In this paper we propose a method for locating multiple objects in a black and white image while accounting for possible overlapping or touching. The method, called Centre Supported Segmentation (CSS), eliminates oversegmentation and is robust against differences in size and shape of the objects.

Curvature and the visual perception of shape: theory on information along object boundaries and the minima rule revisited.

Previous empirical studies have shown that information along visual contours is known to be concentrated in regions of high magnitude of curvature, and, for closed contours, segments of negative curvature (i.e., concave segments) carry greater perceptual relevance than corresponding regions of positive curvature (i.e., convex segments). Lately, Feldman and Singh (2005, Psychological Review, 112, 243-252) proposed a mathematical derivation to yield information content as a function of curvature along a contour. Here, we highlight several fundamental errors in their derivation and in its associated implementation, which are problematic in both mathematical and psychological senses. Instead, we propose an alternative mathematical formulation for information measure of contour curvature that addresses these issues. Additionally, unlike in previous work, we extend this approach to 3-dimensional (3D) shape by providing a formal measure of information content for surface curvature and outline a modified version of the minima rule relating to part segmentation using curvature in 3D shape.

Somoclu : An Efficient Parallel Library for Self-Organizing Maps

Somoclu is a C++ tool for training self-organizing maps on large data sets using a high-performance cluster. It builds on MPI for distributing the workload across the nodes of the cluster. It is also able to boost training by using CUDA if graphics processing units are available. A sparse kernel is included, which is useful for high-dimensional but sparse data, such as the vector spaces common in text mining workflows. The code is released under GNU GPLv3 licence.

Trustworthy dimension reduction for visualization different data sets

Abstract A new nonlinear dimension reduction (DR) method which is called Trustworthy Stochastic Proximity Embedding (TSPE) is introduced in this paper to visualize different types of data sets. TSPE overcome the main shortcomings of the DR by sending the false neighbour points to the correct locations, and preserving the neighbourhood relation to the true neighbours, which are inside the local neighbourhood. The visualization of our proposed method displays the trustworthy, useful and meaningful colours, where the objects of the image can be easily distinguished. The performances of TSPE and 20 dimension reduction methods are compared, and the efficiency of the proposed method in both visualization accuracy and computational cost is shown. The results showed the ability of our method in preserving neighbourhood relation, where they revealed more interested information. In real data set, the efficiency of the visualization of tensor images data sets by TSPE might help the specialist to make a good decision about a patient’s treatment. The comparison with experimental data set, as three dimensions of curved cylinder, showed the ability of TSPE to unfold this complex data set efficiently whilst preserving most information of the original data set.

An evaluation measure of image segmentation based on object centres

Classification of organic materials obtained from rock and drill cuttings involves finding multiple objects in the image. This task is usually approached by segmentation. The quality of segmentation is evaluated by matching the whole detected objects to a reference segmentation. We are interested in representing each object by a single reference point called the centre. This paper proposes an evaluation measure of image segmentation for such representation. We argue that measures based only on distance between obtained centres and a set of predefined centres are insufficient. The proposed measure is based on a list of desirable properties of the segmentation. The three components of the measure evaluate the under/over segmentation of the objects, the proportion of centres placed in the background rather than in objects, and the distance between the guessed and the true centres. The ability of the measure to distinguish between segmentation results of different quality is illustrated on three sets of examples including an image containing inicrofossils and pieces of inert material.

Cybermedicine Tools for Communication and Learning

The medical domain provides excellent opportunities for communication and teaching of healthcare issues using computer graphics, visualization techniques, and virtual environments. Possible applications include anatomical educational tools; patient education; diagnostic aids; virtual autopsies; planning and guidance aids; skills training; and computer augmented reality. Both clinicians and patients can benefit from the appropriate use of tools that make use of these technologies. This paper provides an overview of the state‐of‐the‐art technologies in this exciting field, including detailed examples from our research. The term cybermedicine is discussed and issues for effective cybermedicine are highlighted.