5413397
Maximizing CMP throughput with mediocre cores
In this paper we compare the performance of area equivalent small, medium, and large-scale multithreaded chip multiprocessors (CMTs) using throughput-oriented applications. We use area models based on SPARC processors incorporating these architectural features. We examine CMTs with in-order scalar processor cores, 2-way or 4-way in-order superscalar cores, private primary instruction and data caches, and a shared secondary cache. We explore a large design space, ranging from processor-intensive to cache-intensive CMTs. We use SPEC JBB2000, TPC-C, TPC-W, and XML Test to demonstrate that the scalar simple-core CMTs do a better job of addressing the problems of low instruction-level parallelism and high cache miss rates that dominate Web service middleware and online transaction processing applications. For the best overall CMT performance, smaller cores with lower performance, so called "mediocre" cores, maximize the total number of CMT cores and outperform CMTs built from larger, higher performance cores.
14449649
The RASE (Rapid, Accurate Simulation Environment) for chip multiprocessors
We present RASE, a full system high performance simulation methodology for simulating complex server applications and server class chip multiprocessors enabled with fine-grain multithreading (CMTs). RASE combines application knowledge, operating system information, and data access patterns with an instruction stream from a highly-tuned, scalable steady-state benchmark [5] [22] to generate multiple representative instruction streams that can be mapped to a variety of CMT configurations. We use execution-driven simulation to generate instruction streams for M processors and store them as instruction trace files (several billion instructions per processor) that can be post-processed and augmented for larger than M processor system simulation. We use SPEC JBB2000, TPC-C, and an XML server benchmark to compare the performance estimates of RASE to a reference prototype CMT system. By varying M, we find that our trace-driven simulation methodology predicts within 5% of the instructions per cycle (IPC) of the reference hardware for the applications. Without post-processing the traces, in the best cases, the performance prediction accuracy degrades to 20-40% of the real IPC for instruction traces that require a high replication factor.
10341811
A Two-Stage Template Approach to Person Detection in Thermal Imagery
We present a two-stage template-based method to detect people in widely varying thermal imagery. The approach initially performs a fast screening procedure using a generalized template to locate potential person locations. Next an AdaBoosted ensemble classifier using automatically tuned filters is employed to test the hypothesized person locations. We demonstrate and evaluate the approach using a challenging dataset of thermal imagery
14172959
Improved sub-pixel stereo correspondences through symmetric refinement
Most dense stereo correspondence algorithms start by establishing discrete pixel matches and later refine these matches to sub-pixel precision. Traditional sub-pixel refinement methods attempt to determine the precise location of points, in the secondary image, that correspond to discrete positions in the reference image. We show that this strategy can lead to a systematic bias associated with the violation of the general symmetry of matching cost functions. This bias produces random or coherent noise in the final reconstruction, but can be avoided by refining both image coordinates simultaneously, in a symmetric way. We demonstrate that the symmetric sub-pixel refinement strategy results in more accurate correspondences by avoiding bias while preserving detail.
12006498
Computing depth under ambient illumination using multi-shuttered light
Range imaging has become a critical component of many computer vision applications. The quality of the depth data is of critical importance, but so is the need for speed. Shuttered light-pulse (SLP) imaging uses active illumination hardware to provide high quality depth maps at video frame rates. Unfortunately, current analytical models for deriving depth from SLP imagers are specific to the number of shutters and have a number of deficiencies. As a result, depth estimation often suffers from bias due to object reflectivity, incorrect shutter settings, or strong ambient illumination such as that encountered outdoors. These limitations make SLP imaging unsuitable for many applications requiring stable depth readings. This paper introduces a method that is general to any number of shutters. Using three shutters, the new method produces invariant estimates under changes in ambient illumination, producing high quality depth maps in a wider range of situations.
19186309
Using an Aligned Ontology to Process User Queries
Ontologies have been developed for a number of knowledge domains as diverse as clinical terminology, photo camera parts and micro-array gene expression data. However, processing user queries over a set of overlapping ontologies is not straightforward because they have often been created by independent groups of expertise, each of them adopting different configurations for ontology concepts. A project being carried out at the Brazilian Corporation of Agricultural Research has produced ontologies in sub-domains such as beef cattle, dairy cattle, sheep and beans, among others. This paper focuses on an alignment method for these ontologies based on Formal Concept Analysis, a data analysis technique founded on lattice theory, and a strategy for processing user queries.
18551439
An adaptive focus-of-attention model for video surveillance and monitoring
In current video surveillance systems, commercial pan/tilt/zoom (PTZ) cameras typically provide naive (or no) automatic scanning functionality to move a camera across its complete viewable field. However, the lack of scene-specific information inherently handicaps these scanning algorithms. We address this issue by automatically building an adaptive, focus-of-attention, scene-specific model using standard PTZ camera hardware. The adaptive model is constructed by first detecting local human activity (i.e., any translating object with a specific temporal signature) at discrete locations across a PTZ camera’s entire viewable field. The temporal signature of translating objects is extracted using motion history images (MHIs) and an original, efficient algorithm based on an iterative candidacy-classification-reduction process to separate the target motion from noise. The target motion at each location is then quantified and employed in the construction of a global activity map for the camera. We additionally present four new camera scanning algorithms which exploit this activity map to maximize a PTZ camera’s opportunity of observing human activity within the camera’s overall field of view. We expect that these efficient and effective algorithms are implementable within current commercial camera systems.