Refinement of Individual Tree Detection Results Obtained from Airborne Laser Scanning Data for a Mixed Natural Forest

Numerous semi- and fully-automatic algorithms have been developed for individual tree detection from airborne laser-scanning data, but different rates of falsely detected treetops also accompany their results. In this paper, we proposed an approach that includes a machine learning-based refinement step to reduce the number of falsely detected treetops. The approach involves the local maxima filtering and segmentation of the canopy height model to extract different segment-level features used for the classification of treetop candidates. The study was conducted in a mixed temperate forest, predominantly deciduous, with a complex topography and an area size of 0.6 km × 4 km. The classification model’s training was performed by five machine learning approaches: Random Forest (RF), Extreme Gradient Boosting, Artificial Neural Network, the Support Vector Machine, and Logistic Regression. The final classification model with optimal hyperparameters was adopted based on the best-performing classifier (RF). The overall accuracy (OA) and kappa coefficient (κ) obtained from the ten-fold cross validation for the training data were 90.4% and 0.808, respectively. The prediction of the test data resulted in an OA = 89.0% and a κ = 0.757. This indicates that the proposed method could be an adequate solution for the reduction of falsely detected treetops before tree crown segmentation, especially in deciduous forests

Estimation of the Fundamental Frequency of the Speech Signal Compressed by MP3 Algorithm

The paper analyzes the estimation of the fundamental frequency from the real speech signal which is obtained by recording the speaker in the real acoustic environment modeled by the MP3 method. The estimation was performed by the Picking-Peaks algorithm with implemented parametric cubic convolution (PCC) interpolation. The efficiency of PCC was tested for Catmull-Rom, Greville, and Greville two- parametric kernel. Depending on MSE, a window that gives optimal results was chosen

Estimation of the Fundamental Frequency of the Speech Signal Compressed by MP3 Algorithm

The paper analyzes the estimation of the fundamental frequency from the real speech signal which is obtained by recording the speaker in the real acoustic environment modeled by the MP3 method. The estimation was performed by the Picking-Peaks algorithm with implemented parametric cubic convolution (PCC) interpolation. The efficiency of PCC was tested for Catmull-Rom, Greville, and Greville two- parametric kernel. Depending on MSE, a window that gives optimal results was chosen

An approach to the language discrimination in different scripts using adjacent local binary pattern

<p>The paper proposes a language discrimination method of documents. First, each letter is encoded with the certain script type according to its status in baseline area. Such a cipher text is subjected to a feature extraction process. Accordingly, the local binary pattern as well as its expanded version called adjacent local binary pattern are extracted. Because of the difference in the language characteristics, the above analysis shows significant diversity. This type of diversity is a key aspect in the decision-making differentiation of the languages. Proposed method is tested on an example of documents. The experiments give encouraging results.</p