Robustnesstochangesinilluminationconditionsaswellas viewing perspectives is an important requirement formany computer vision applications. One of the key fac-ors in enhancing the robustness of dynamic scene analy-sis that of accurate and reliable means for shadow de-ection. Shadowdetectioniscriticalforcorrectobjectde-ection in image sequences. Many algorithms have beenproposed in the literature that deal with shadows. How-ever,acomparativeevaluationoftheexistingapproachesisstill lacking. In this paper, the full range of problems un-derlyingtheshadowdetectionareidenti?edanddiscussed.Weclassifytheproposedsolutionstothisproblemusingaaxonomyoffourmainclasses, calleddeterministicmodeland non-model based and statistical parametric and non-parametric. Novelquantitative(detectionanddiscrimina-ionaccuracy)andqualitativemetrics(sceneandobjectin-dependence,?exibilitytoshadowsituationsandrobustnesso noise) are proposed to evaluate these classes of algo-rithms on a benchmark suite of indoor and outdoor videosequences.
本人編寫的incremental 隨機神經元網絡算法，該算法最大的特點是可以保證approximation特性，而且速度快效果不錯，可以作為學術上的比較和分析。目前只適合benchmark的regression問題。
具體效果可參考
G.-B. Huang, L. Chen and C.-K. Siew, “Universal Approximation Using Incremental Constructive Feedforward Networks with Random Hidden Nodes”, IEEE Transactions on Neural Networks, vol. 17, no. 4, pp. 879-892, 2006.
The DHRY program performs the dhrystone benchmarks on the 8051.
Dhrystone is a general-performance benchmark test originally
developed by Reinhold Weicker in 1984. This benchmark is
used to measure and compare the performance of different
computers or, in this case, the efficiency of the code
generated for the same computer by different compilers.
The test reports general performance in dhrystones per second.
Like most benchmark programs, dhrystone consists of standard
code and concentrates on string handling. It uses no
floating-point operations. It is heavily influenced by
hardware and software design, compiler and linker options,
code optimizing, cache memory, wait states, and integer
data types.
The DHRY program is available in different targets:
Simulator: Large Model: DHRY example in LARGE model
for Simulation
Philips 80C51MX: DHRY example in LARGE model
for the Philips 80C51MC
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