Surprising but true: according to market research, customers prefer apples with a maximum diameter of 75 to 80 mm 🍏 Now you know 🙂 People would obviously struggle to accurately evaluate fruits’ size with their naked eyes. In contrast, computer vision (CV) systems can measure the precise diameter of an apple in the blink of an eye, literally. CV systems can collect and process a variety of parameters, including size, weight, shape, texture, color, and much more.
If you want to dig into Computer Vision (CV) but have no idea where to start, this beginner guide is for you. Here we recommend some sources which will come in handy for learning and understanding both the computer vision and deep learning basics.
Fiducial markers are widely used in various applications like robot navigation, logistics, augmented reality. Fig. 1. Applications of fiducial markers Advantages are obvious High contrast Simple code generation Resistance to extremal angles However, when we deal with a large number of markers, real-time recognition becomes challenging, especially on embedded devices with low power CPUs on-board.
The task of accurate cell segmentation is essential for cellular biology and single-cell analysis, as well as for studying biological processes as a whole. In biomedical image processing, this includes reconstruction of microscopy images, foreground segmentation, cell detection, cellular compartments and organelles segmentation. Despite the tremendous progress in microscopy cell imaging and numerous segmentation methods, accurate segmentation and reliable characterization of cells remain challenging tasks that usually require problem-specific tailoring of algorithms.
Currently, the number of cars in the world is well over 1 billion. It is no wonder that one of the most common computer vision tasks is the effective control of these vehicles through automatic number plate recognition (ANPR) systems.
Our task was to develop the algorithm for the automatic road detection in radar images. The challenge was that the radar images are a bit different from the optical ones. In particular, in the case of synthetic aperture radar (SAR), the image formation process is accomplished via coherent processing of the received signals backscattered from the Earth surface. As a result, the multiplicative speckle noise appears in the SAR images.