Rice seed image classification based on HOG descriptor with missing values imputation

Rice is a primary source of food consumed by almost half of world population. Rice quality mainly depends on the purity of the rice seed. In order to ensure the purity of rice variety, the recognition process is an essential stage. In this paper, we firstly propose to use histogram of oriented gradient (HOG) descriptor to characterize rice seed images. Since the size of image is totally random and the features extracted by HOG can not be used directly by classifier due to the different dimensions. We apply several imputation methods to fill the missing data for HOG descriptor. The experiment is applied on the VNRICE benchmark dataset to evaluate the proposed approach

A real-time defect detection in printed circuit boards applying deep learning

Inspection of defects in the printed circuit boards (PCBs) has both safety and economic significance in the 4.0 industrial manufacturing. Nevertheless, it is still a challenging problem to be studied in-depth due to the complexity of the PCB layouts and the shrinking down tendency of the electronic component size. In this paper, a real-time automated supervision algorithm is proposed to test the PCBs quality among different scenarios. The density of the PCBs layout and the complexity on the surface are analyzed based on deep learning and image feature extraction algorithms. To be more detailed, the ORB feature and the Brute-force matching method are utilized to match perfectly the input images with the PCB templates. After transferring images by aiding the RANSAC algorithm, a hybrid method using modern computer vision algorithms is developed to segment defective areas on the PCBs surface. Then, by applying the enhanced Residual Network –50, the proposed algorithm can classify the groove defects on the surface mount technology electronic components which minimum size up to 1x3 mm. After the training process, the proposed system is capable to categorize various types of overproduced, recycled, and cloned PCBs. The speed of the quality testing operation maintains at a high level with an average precision rate up to 96.29 % in case of good brightness conditions. Finally, the computational experiments demonstrate that the proposed system based on deep learning can obtain superior results and it outperforms several existing works in terms of speed, precision, and robustnes

Evaluating the Potential of Drone Swarms in Nonverbal HRI Communication

Human-to-human communications are enriched with affects and emotions, conveyed, and perceived through both verbal and nonverbal communication. It is our thesis that drone swarms can be used to communicate information enriched with effects via nonverbal channels: guiding, generally interacting with, or warning a human audience via their pattern of motions or behavior. And furthermore that this approach has unique advantages such as flexibility and mobility over other forms of user interface. In this paper, we present a user study to understand how human participants perceived and interpreted swarm behaviors of micro-drone Crazyflie quadcopters flying three different flight formations to bridge the psychological gap between front-end technologies (drones) and the human observers' emotional perceptions. We ask the question whether a human observer would in fact consider a swarm of drones in their immediate vicinity to be nonthreatening enough to be a vehicle for communication, and whether a human would intuit some communication from the swarm behavior, despite the lack of verbal or written language. Our results show that there is statistically significant support for the thesis that a human participant is open to interpreting the motion of drones as having intent and to potentially interpret their motion as communication. This supports the potential use of drone swarms as a communication resource, emergency guidance situations, policing of public events, tour guidance, etc

Towards smart assistants in two-party collaboration

Ph.DDOCTOR OF PHILOSOPH