Deep learning techniques are commonly utilized to tackle various computer vision problems, including recognition, segmentation, and classification from RGB images. With the availability of a diverse range of sensors, industry-specific datasets are acquired to address specific challenges. These collected datasets have varied modalities, indicating that the images possess distinct channel numbers and pixel values that have different interpretations. Implementing deep learning methods to attain optimal outcomes on such multimodal data is a complicated procedure. To enhance the performance of classification tasks in this scenario, one feasible approach is to employ a data fusion technique. Data fusion aims to use all the available information from all sensors and integrate them to obtain an optimal outcome. This paper investigates early fusion, intermediate fusion, and late fusion in deep learning models for bulky waste image classification. For training and evaluation of the models, a multimodal dataset is used. The dataset consists of RGB, hyperspectral Near Infrared (NIR), Thermography, and Terahertz images of bulky waste. The results of this work show that multimodal sensor fusion can enhance classification accuracy compared to a single-sensor approach for the used dataset. Hereby, late fusion performed the best with an accuracy of 0.921 compared to intermediate and early fusion, on our test data.
The choice of an appropriate illumination design is one of the most important steps in creating successful machine
vision systems for automated inspection tasks. In a popular technique, multiple inspection images are captured
under angular-varying illumination directions over the hemisphere, which yields a set of images referred to as
illumination series. However, most existing approaches are restricted in that they use rather simple patterns
like point- or sector-shaped illumination patterns on the hemisphere. In this paper, we present an illumination
technique which reduces the effort for capturing inspection images for each reflectance feature by using linear
combinations of basis light patterns over the hemisphere as feature-specific illumination patterns. The key idea
is to encode linear functions for feature extraction as angular-dependent illumination patterns, and thereby to
compute linear features from the scene's reflectance field directly in the optical domain. In the experimental
part, we evaluate the proposed illumination technique on the problem of optical material type classification of
printed circuit boards (PCBs).
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