This paper explores a data augmentation approach for images of rigid bodies, particularly focusing on electrical equipment and analogous industrial objects. By leveraging manufacturer-provided datasheets containing precise equipment dimensions, we employed straightforward algorithms to generate synthetic images, permitting the expansion of the training dataset from a potentially unlimited viewpoint. In scenarios lacking genuine target images, we conducted a case study using two well-known detectors, representing two machine-learning paradigms: the Viola-Jones (VJ) and You Only Look Once (YOLO) detectors, trained exclusively on datasets featuring synthetic images as the positive examples of the target equipment, namely lightning rods and potential transformers. Performances of both detectors were assessed using real images in both visible and infrared spectra. YOLO consistently demonstrates F1 scores below 26% in both spectra, while VJ\'s scores lie in the interval from 38% to 61%. This performance discrepancy is discussed in view of paradigms\' strengths and weaknesses, whereas the relatively high scores of at least one detector are taken as empirical evidence in favor of the proposed data augmentation approach.

BACKGROUND: Vertebral artery injury is a devastating potential complication of C1-2 posterior fusion. Intraoperative navigation can reduce the risk of neurovascular complications and improve screw placement accuracy. However, the use of intraoperative computed tomography (CT) increases radiation exposure and operative time, and it is unable to image vascular structures. The Machine-vision Image Guided Surgery (MvIGS) system uses optical topographic imaging and machine vision software to rapidly register using preoperative imaging. The authors presented the first report of intraoperative navigation with MvIGS registered using a preoperative CT angiogram (CTA) during C1-2 posterior fusion.
METHODS: MvIGS can register in seconds, minimizing operative time with no additional radiation exposure. Furthermore, surgeons can better adjust for abnormal vertebral artery anatomy and increase procedure safety.
CONCLUSIONS: CTA-guided navigation generated a three-dimensional reconstruction of cervical spine anatomy that assisted surgeons during the procedure. Although further study is needed, the use of intraoperative MvIGS may reduce the risk of vertebral artery injury during C1-2 posterior fusion.

To solve the problem of low precision of pearl shape parameters\' measurement caused by the mutual contact of batches of pearls and the error of shape sorting, a method of contacting pearls\' segmentation based on the pit detection was proposed. Multiple pearl images were obtained by backlit imaging, the quality of the pearl images was improved through appropriate preprocessing, and the contacted pearl area was extracted by calculating the area ratio of the connected domains. Then, the contour feature of the contact area was obtained by edge tracking to establish the mathematical model of the angles between the edge contour points. By judging the angle with a threshold of 60° as the candidate concave point, a concave point matching algorithm was introduced to get the true concave point, and the Euclidean distance was adopted as a metric function to achieve the segmentation of the tangent pearls. The pearl shape parameters\' model was established through the pearl contour image information, and the shape classification standard was constructed according to the national standard. Experimental results showed that the proposed method produced a better segmentation performance than the popular watershed algorithm and morphological algorithm. The segmentation accuracy was above 95%, the average loss rate was within 4%, and the sorting accuracy based on the shape information was 94%.

To achieve efficient and preventive measures against salmon lice (Lepeophtheirus salmonis Krøyer, 1838) infestation, a better understanding of behavioral patterns of the planktonic life stages is key. To investigate light responses in L. salmonis copepodites, a non-intrusive experimental system was designed to measure behavioral responses in a 12.5-l volume using machine vision technology and methodology. The experimental system successfully tracked the collective movement patterns of the sea lice population during exposure to different light stimuli emitted from alternating zones in the system. This system could further be used to study behavioral responses to different physical cues of various developmental stages of sea lice or other zooplankton.