SOUTHERN JOURNAL OF SCIENCES

Background: This paper presents a straightforward and intuitive method for interactive 3D reconstruction and Direct Linear Transformation (DLT) camera calibration using a single image of a structured scene with known object dimensions. The method relies on manual registration of pairs of points on both the image and the terrain, allowing for precise alignment and calibration. Aim: By utilizing this method, users can easily reconstruct 3D scenes and calibrate cameras without the need for complex algorithms or extensive computational resources. Our approach offers a user-friendly solution for 3D reconstruction and camera calibration, making it accessible to a wider audience and applicable in a range of fields such as computer vision, augmented reality, and virtual reality. Methods: This work primarily focuses on the determination of the projection matrix, which plays a crucial role in mapping 3D points onto a 2D image plane. The projection matrix encapsulates both the intrinsic parameters of the camera (such as focal length and optical center) and the extrinsic parameters (such as camera position and orientation in the world coordinate system). By accurately determining the projection matrix, we can effectively project 3D points onto the 2D image plane, enabling tasks like 3D reconstruction, camera localization, and augmented reality applications. Results: We present experimental results obtained from testing the method on an image of a known object, demonstrating its effectiveness and accuracy in producing realistic 3D reconstructions. Discussion: The method's reliance on manual registration of point pairs allows for precise alignment and calibration without the need for complex algorithms or extensive computational resources. This user-friendly approach makes 3D reconstruction and camera calibration accessible to a wider audience and applicable in various fields. Conclusions: Overall, our approach offers a practical and accessible solution for 3D reconstruction and camera calibration, expanding the potential applications in computer vision, augmented reality, and virtual reality.

Background: Dr. Bhavna, an experienced leader in education and innovation, has held various leadership positions, including head of department, dean of alumni relations, and president of the institutions innovation council. She has been instrumental in initiating the alumni cell at her previous organization and establishing world-class state-of-the-art laboratories in collaboration with the automotive sector. Aims: Dr. Bhavna's efforts aim to create an ecosystem for innovation and entrepreneurship within the institute and beyond, encouraging learners to consider entrepreneurship as a career option and fostering innovation in the electronics manufacturing domain. Methods: Dr. Bhavna's approach involves raising awareness about innovation and entrepreneurship among learners, fostering collaborations between academia and industry, and establishing world-class state-of-the-art laboratories to bridge the gap between academia and industry. Results: Dr. Bhavna's efforts have resulted in a strong alumni network, with graduates across the globe contributing to various domains and positions. She has also established world-class laboratories in collaboration with the automotive sector, fostering innovation and startups in the electronics manufacturing domain. Discussions: The Ministry of Electronics and Information Technology plays a significant role in initiating innovations and startups in the electronics manufacturing domain. They have generated a separate vertical for startups, which poses challenges to innovators. If a solution is validated, the ministry handholds the innovators, incubates their ideas, funds them, and helps them reach the market at the national and international levels. Conclusions: Dr. Bhavna's vision and efforts toward fostering an ecosystem for innovation and entrepreneurship within the institute and beyond are commendable. Her experience and achievements in various leadership roles, including as the head of the department and president of the institutions innovation council, have added significant value to her professional and personal life.

Background: in the last decades, colocalization analysis of fluorescently tagged biomolecules has proven to be a powerful approach to studying functional relationships between these biomolecules. However, in many cases, to give this analysis a biological meaning, colocalization coefficients must be tested statistically, comparing them with the colocalization expected by chance. Aim: It addressed the statistical significance of triple colocalization to distinguish real triple colocalization and classify different triple signal scenarios. Methods: we use biological and generated images of triple signal scenarios to contrast seven independent statistical facts with independent statistical tests. Three of these tests correspond to pairwise relationships (double scrambling tests), and the others correspond to triple relationships: single scrambling tests (red, green, and blue scrambling) and the triple scrambling test. The analysis and methodology proposed can be reproduced using the application developed in our laboratory. Results: In the study approach, we found true triple relationships ignored by using traditional methods of computing the statistical significance, while we could reinterpret cases of not significant triple colocalization wrongly considered as significant by traditional methods. Discussion: single scrambling tests can reveal significant triple colocalization for low levels of triple co-occurrence, even when all pairwise relationships were exclusion relationships. Moreover, on the other hand, single scrambling tests can reveal the absence of a significant triple colocalization for high levels of triple co-occurrence, even when all pairwise relationships were significant colocalization. Conclusion: all scrambling tests are useful to classify a specific scenario of a triple relationship. Dynamics like mitosis can be distinguished into their phases by triple signal relationships using these 7 independent statistical tests.