Evaluation of PCA, LDA and Fisherfaces in Appearance-based Object Detection in Thermal Infra-red Images with Incomplete Data

Abstract

Object detection in outdoor environments is a challenging task. One is not only confronted with the problem of acquiring a sufficient amount of training images, but also the issue of huge variation in the objects appearance due to changing weather and light conditions. When using appearance-based object detection algorithms, such as in this paper, dimensional reduction of input data is an integral component to reduce computational costs and improve reliability. Based on the probabilistic classification method of Gaussian classifiers this paper examines the effect different dimensional reduction approaches have on the classification performance of thermal infra-red object images with respect to incomplete training data. It is shown that in the detection task at hand, which is to find the rear end of a truck in a thermal infra-red image, a reduction approach that combines principal component analysis (PCA) and linear discriminant analysis (LDA) is less sensitive to missing data.

Image Noise Level Estimation for Rice Noise Based on Extended ELM Neural Network Training Algorithm

“…Image noise level estimation (NLE) is an important technique in computer image processing because of its huge importance in object detection [1], [2], denoising [3], [4], and super-resolution [5] and it has been studied for several decades [6], [7]. Normal single image denoising methods typically need pre-known arguments of the noise distribution model [8], [9], which has a huge impact on the performance of the denoising algorithm, so NLE becomes a key point.…”

Analysis of Image Enhancement Methods for Outdoor-Applications

“…Regarding to the steady growth of autonomous machines in industry and agriculture it always becomes more inevitably to be able to react on many environmental influences. To solve this setting of tasks camera-systems are frequently used [1][2].…”