Expert knowledge based approach for automatic sorting and packing

The automatic sorting system is presented in this work which is based on expert knowledge and high resolution visual sensor. Proposed system was tested for natural amber sorting task. Five types of amber have been explored in this research. Experimental investigation involves amber classification in three different color spaces (RGB, HSV and Grayscale). The results have shown that the highest classification accuracy is reached using the combination of the most essential features sets acquired from different color spaces

Are you ashamed? Can a gaze tracker tell?

Our aim was to determine the possibility of detecting cognitive emotion information (neutral, disgust, shameful, “sensory pleasure”) by using a remote eye tracker within an approximate range of 1 meter. Our implementation was based on a self-learning ANN used for profile building, emotion status identification and recognition. Participants of the experiment were provoked with audiovisual stimuli (videos with sounds) to measure the emotional feedback. The proposed system was able to classify each felt emotion with an average of 90% accuracy (2 second measuring interval)

Analysis of Training Data Augmentation for Diabetic Foot Ulcer Semantic Segmentation

Deep learning model training and achieved performance relies on available data. Diabetic foot ulcers and other image processing applications in the medical domain add another layer of complexity to training data collection. Data collection is troublesome and data annotation requires medical expertise. This problem is usually solved by employing training data augmentation. Although in previous research augmentation was facilitated in various ways, it is rarely evaluated or reported how much it contributes to achieved performance. The current research seeks to answer this question by applying individual photometric and geometric augmentation techniques and comparing the model performance achieved for semantic segmentation of diabetic foot ulcers. It was found that geometric augmentation techniques help achieve a better model performance when compared with photometric techniques. The model trained using an augmented dataset and applying a shear technique was found to improve segmentation results the most; the benchmark dice score was increased by 6%. An additional improvement over the benchmark was observed (a total of 6.9%) when the model was trained using data combining image sets generated by the three best-performing augmentation techniques. The highest test dice score achieved was 91%

Efficient Violence Detection in Surveillance

Intelligent video surveillance systems are rapidly being introduced to public places. The adoption of computer vision and machine learning techniques enables various applications for collected video features; one of the major is safety monitoring. The efficacy of violent event detection is measured by the efficiency and accuracy of violent event detection. In this paper, we present a novel architecture for violence detection from video surveillance cameras. Our proposed model is a spatial feature extracting a U-Net-like network that uses MobileNet V2 as an encoder followed by LSTM for temporal feature extraction and classification. The proposed model is computationally light and still achieves good results—experiments showed that an average accuracy is 0.82 ± 2% and average precision is 0.81 ± 3% using a complex real-world security camera footage dataset based on RWF-2000

ANN Hybrid Model for Forecasting Landfill Waste Potential in Lithuania

Waste management is currently a fast-growing environmental business and one of solutions to manage the huge amount of waste being generated on landfills is to use the disposed waste as an energy source. There is a major focus on energy forecasting, highlighting the importance of having reliable data on the volume and composition of municipal solid waste in landfills. However, the lack of historical data is forcing the development of machine-learning based models. This study contributes to this field by proposing a hybrid ANN-based model to forecast the total amount of landfill waste, different waste fraction and the potential for energy recovery. The proposed model includes an adaptive number of inputs adjusted to the relevant waste fraction and to the specific landfill. The obtained results substantiated that the proposed model allows for stable and accurate forecasting of recovered energy potential in cases where there is insufficient historical data. The experiments showed that the model with 12 inputs (meaning the forecast of the future value takes into account the last 12 months of data) was the most accurate in the energy forecasting task, with the lowest forecasting error in terms of mean absolute error −8.9878 gigawatt hours per year

Fast Multi-Focus Fusion Based on Deep Learning for Early-Stage Embryo Image Enhancement

Background: Cell detection and counting is of essential importance in evaluating the quality of early-stage embryo. Full automation of this process remains a challenging task due to different cell size, shape, the presence of incomplete cell boundaries, partially or fully overlapping cells. Moreover, the algorithm to be developed should process a large number of image data of different quality in a reasonable amount of time. Methods: Multi-focus image fusion approach based on deep learning U-Net architecture is proposed in the paper, which allows reducing the amount of data up to 7 times without losing spectral information required for embryo enhancement in the microscopic image. Results: The experiment includes the visual and quantitative analysis by estimating the image similarity metrics and processing times, which is compared to the results achieved by two wellknown techniques—Inverse Laplacian Pyramid Transform and Enhanced Correlation Coefficient Maximization. Conclusion: Comparatively, the image fusion time is substantially improved for different image resolutions, whilst ensuring the high quality of the fused image