The paper refers to [2] and says that those authors proved statistical consistency. However, I am then surprised to see in section 4.3 that non-zero shrinkage is obtained (including for gamma 0) for the very simple case of modelling a N(0,I) distribution with N(0, sigma 2 I). What is going on here?? A failure of consistency would be a serious flaw in the formulation of a statistical learning criterion. Also in sec 3 (Stability and KL regularization) the authors say that at least for learning based on samples (\hat{p}_{theta}) that some regularization wrt the KL divergence is required. This clearly weakens the "purity" of the smoothed Wasserstein objective fn.

In order to address issues with manual vote counting during election procedures, this study intends to examine the viability of using advanced image processing techniques for automated voter counting. The study aims to shed light on how automated systems that utilize cutting-edge technologies like OpenCV, CVZone, and the MOG2 algorithm could greatly increase the effectiveness and openness of electoral operations. The empirical findings demonstrate how automated voter counting can enhance voting processes and rebuild public confidence in election outcomes, particularly in places where trust is low. The study also emphasizes how rigorous metrics, such as the F1 score, should be used to systematically compare the accuracy of automated systems against manual counting methods. This methodology enables a detailed comprehension of the differences in performance between automated and human counting techniques by providing a nuanced assessment. The incorporation of said measures serves to reinforce an extensive assessment structure, guaranteeing the legitimacy and dependability of automated voting systems inside the electoral sphere.

With the advancing technology, the hardware gain of computers and the increase in the processing capacity of processors have facilitated the processing of instantaneous and real-time images. Face recognition processes are also studies in the field of image processing. Facial recognition processes are frequently used in security applications and commercial applications. Especially in the last 20 years, the high performances of artificial intelligence (AI) studies have contributed to the spread of these studies in many different fields. Education is one of them. The potential and advantages of using AI in education; can be grouped under three headings: student, teacher, and institution. One of the institutional studies may be the security of educational environments and the contribution of automation to education and training processes. From this point of view, deep learning methods, one of the sub-branches of AI, were used in this study. For object detection from images, a pioneering study has been designed and successfully implemented to keep records of students' entrance to the educational institution and to perform class attendance with images taken from the camera using image processing algorithms. The application of the study to real-life problems will be carried out in a school determined in the 2022-2023 academic year.

In this tutorial, you will learn how you can process images in Python using the OpenCV library. OpenCV is a free open source library used in real-time image processing. It's used to process images, videos, and even live streams, but in this tutorial, we will process images only as a first step. Before getting started, let's install OpenCV. Now OpenCV is installed successfully and we are ready.

Learn about Supervised Learning (computer vision) with Internet of things. A unique course with growing industry demand.

One of the benefits of being proficient with Machine Learning is having a good understanding of the algorithms that run some of the wonderful features we see on our devices. When Apple, the computer device manufacturing company, released the iOS16 version, one of the new functionalities was the ability to use the default Notes app as a digital scanner, think of it as a "scanner in your palm", borrowing a similar phrase from the legendary Steve Jobs. Prior to when it was introduced, I had to use other services usually apps downloaded from the App Store for the purpose of scanning documents with my phones, some paid some free and some of the free apps come with the disadvantage of a watermark which somewhat defeats the purpose unless you subscribe to a paid version. Having worked on a number of computer vision projects, I thought, would it be possible there is some computer vision library or ML algorithm one can use to replicate what's been done in my phone? In this article, we will be using a very popular library familiar to most MLEs familiar with deep learning particular computer vision: OpenCV.

Visuotactile sensing technology is becoming more popular in tactile sensing, but the effectiveness of the existing marker detection localization methods remains to be further explored. Instead of contour-based blob detection, this paper presents a learning-based marker localization network for GelStereo visuotactile sensing called Marknet. Specifically, the Marknet presents a grid regression architecture to incorporate the distribution of the GelStereo markers. Furthermore, a marker rationality evaluator (MRE) is modelled to screen suitable prediction results. The experimental results show that the Marknet combined with MRE achieves 93.90% precision for irregular markers in contact areas, which outperforms the traditional contour-based blob detection method by a large margin of 42.32%. Meanwhile, the proposed learning-based marker localization method can achieve better real-time performance beyond the blob detection interface provided by the OpenCV library through GPU acceleration, which we believe will lead to considerable perceptual sensitivity gains in various robotic manipulation tasks.

In today's lesson, you will learn the fundamentals of thermal/mid-far infrared vision. By the end of this lesson, you'll have measured the temperature value of each pixel in a thermal image and a thermal video in a very easy way, only using Python and OpenCV. In addition, you'll be able to get the video stream from a thermal camera and the temperature values in real time if you have one of these amazing cameras on hand. To learn how to measure your first temperature value from each pixel in a thermal image, just keep reading. Before we start measuring the temperature value of each pixel, we need to understand the different basic image formats that thermal cameras/images provide.

It is recommended to run the notebook on a local server for better interactive control. This project mainly works as a proof of concept. In that case, the UI design is relatively weak, and the quality of results is not guaranteed. You may need to do prompt engineering or change the size of the selection box to get better outpainting results. Pull requests are welcome for better UI control, ideas to achieve better results, or any other improvements.