140108792
Online Segmentation of Freehand Stroke by Dynamic Programming
12595607
Sketch Recognition Based on Topological Spatial Relationship
In recognition of composite graphic objects, topological spatial relationships of their components play an important role. Although most researchers focus on binary topological relationships, they cannot carry all information of the internal structure of the compound objects. Therefore, we introduce the ternary relationship, which is a complement to the binary relationship, to describe composite graphic objects. Moreover, we provide a constrained partial permutation algorithm based on both the binary and ternary topological spatial relationships to recognize the sketchy objects input by users in an online manner. Experimental results show that this approach is both efficient and effective for online composite graphics recognition in our sketch-based graphics input system–SmartSketchpad.
61733202
Parallel network RAM: effectively utilizing global cluster memory for large data-intensive parallel programs
Large scientific parallel applications demand large amounts of memory space. Current parallel computing platforms schedule jobs without fully knowing their memory requirements. This leads to uneven memory allocation in which some nodes are overloaded. This, in turn, leads to disk paging, which is extremely expensive in the context of scientific parallel computing. To solve this problem, we propose a new peer-to-peer solution called parallel network RAM. This approach avoids the use of disk and better utilizes available RAM resources. This approach will allow larger problems to be solved while reducing the computational, communication and synchronization overhead typically involved in parallel applications.
2123278
Program optimization using indexed and recursive data structures
This paper describes a systematic method for optimizing recursive functions using both indexed and recursive data structures. The method is based on two critical ideas: first, determining a minimal input increment operation so as to compute a function on repeatedly incremented input; second, determining appropriate additional values to maintain in appropriate data structures, based on what values are needed in computation on an incremented input and how these values can be established and accessed. Once these two are determined, the method extends the original program to return the additional values, derives an incremental version of the extended program, and forms an optimized program that repeatedly calls the incremental program. The method can derive all dynamic programming algorithms found in standard algorithm textbooks. There are many previous methods for deriving efficient algorithms, but none is as simple, general, and systematic as ours.
6732585
Systematic Derivation of Incremental Programs
A systematic approach is given for deriving incremental programs from non-incremental programs written in a standard functional programming language. We exploit a number of program analysis and transformation techniques and domain-specific knowledge, centered around effective utilization of caching, in order to provide a degree of incrementality not otherwise achievable by a generic incremental evaluator.
8093965
A feature-based approach for individualized human head modeling
The human head is a significant part of the human body, with which we can recognize individuals from a vast universe of populations. Since the early 1970s, considerable effort has been devoted to computeraided modeling of human head for applications varied from realistic effect in computer graphics to custom model generation in modern manufacturing industries. However, realistic head modeling is still a challenge and continues to fascinate computer graphics researchers. The greatest difficulties in human head modeling result from the extremely complex geometric form of the human head. To generate realistic individualized models, most proposed head modeling techniques use the common approach of deforming a generic model into an individualized one, based on individual head information. There are various sources to obtain individual information, e.g., anthropometric data, range data and 2D pictures. Given the individual information, the quality of the resulting individualized model depends on the quality of the generic model and the deformation technique used. In our study, we observe that the mathematical form in which the generic model is represented strongly determines the deformation effect and, thus, determines the quality of the resulting individualized models. During the past few years, the multiresolution modeling technique has been demonstrated to be a powerful tool for highly detailed sculptured object modeling. In our work, we adopt the multiresolution technique for generic head modeling and propose a feature-based deformation technique. We show that using our technique we can generate highly realistic individualized head models with speed and efficiency. We also demonstrate that our proposed technique can result in great efficiency for a wide range of downstream applications.
638941
Existence and Finiteness Conditions for Risk-Sensitive Planning: Results and Conjectures
Decision-theoretic planning with risk-sensitive planning objectives is important for building autonomous agents or decision-support systems for real-world applications. However, this line of research has been largely ignored in the artificial intelligence and operations research communities since planning with risk-sensitive planning objectives is more complicated than planning with risk-neutral planning objectives. To remedy this situation, we derive conditions that guarantee that the optimal expected utilities of the total plan-execution reward exist and are finite for fully observable Markov decision process models with non-linear utility functions. In case of Markov decision process models with both positive and negative rewards, most of our results hold for stationary policies only, but we conjecture that they can be generalized to non stationary policies.