Evaluations of Feature Extraction Programs Synthesized by Redundancy-removed Linear Genetic Programming: A Case Study on the Lawn Weed Detection Problem
スポンサーリンク
概要
- 論文の詳細を見る
This paper presents an evolutionary synthesis of feature extraction programs for object recognition. The evolutionary synthesis method employed is based on linear genetic programming which is combined with redundancy-removed recombination. The evolutionary synthesis can automatically construct feature extraction programs for a given object recognition problem, without any domain-specific knowledge. Experiments were done on a lawn weed detection problem with both a low-level performance measure, i.e., segmentation accuracy, and an application-level performance measure, i.e., simulated weed control performance. Compared with four human-designed lawn weed detection methods, the results show that the performance of synthesized feature extraction programs is significantly better than three human-designed methods when evaluated with the low-level measure, and is better than two human-designed methods according to the application-level measure.
- 一般社団法人 情報処理学会の論文
一般社団法人 情報処理学会 | 論文
- Interest Point Detection Based on Stochastically Derived Stability
- Efficient Algorithms for Extracting Pareto-optimal Hardware Configurations in DEPS Framework
- Verification of Substitution Theorem Using HOL (プログラミング Vol.5 No.2)
- Programmable Architectures and Design Methods for Two-Variable Numeric Function Generators
- An Exact Estimation Algorithm of Error Propagation Probability for Sequential Circuits