Computer-Vision

Object detection is one of the most popular tasks in computer vision, since it can be applied to a wide range of applications: robotics, autonomous driving or fault detection. In this post, we will try to give a brief overview of the YOLO algorithm and the components that make it work. To do that, I have classified the main components of the algorithm into three categories: Characteristics based on the model architecture, how YOLO-based models improved the performance by using a new architecture and which are the improvements made. Strategies based on the model training, such as the function loss or data augmentation. Methods for post-processing the output of the model, such as the non-maximum suppression (NMS) and the confidence threshold. Two-stage vs One-stage Detectors Before YOLO, SoTA detectors were based on a two-stage detector: the first stage is used to detect the bounding boxes, and the second stage is used to classify the bounding boxes. This kind of model is called region-based detectors, because they need the region to then run the classification. ...

Generative models are a class of machine learning that learn a representation of the data trained on and they model the data itself. Ideally, generative models should satisfy the following key requirements in a real environment: High quality samples refers to those samples that captures the underlying patterns and structures present in the data making them indistinguishable from human observers. Fast Sampling is about the efficiency of image generation and the computational overhead that can cause generative models. Mode Coverage/Diversity points out how the model is able to generate a full range of mods and diverse patterns present in the training data Fig. 1. The Generative Learning Trilemma ...

Overview Presentation on vehicle recognition and traffic reconstruction using the NebulOus cloud platform. The talk covers the application of computer vision techniques for traffic analysis and the deployment of ML models in cloud infrastructure. Key Topics Vehicle detection and tracking Traffic pattern reconstruction NebulOus cloud platform architecture Real-time processing challenges Event TechMeeting is a technical meetup in Lleida focused on emerging technologies and their practical applications.

Traditional computer vision techniques involve methods and algorithms that do not rely on deep learning or neural networks. Instead, these approaches are not data-driven and they use classical approaches to process and analyze images. So, in this post, we’ll explore three thresholding techniques! Thresholding When the task is to distinguish the background from the foreground, thresholding provides a straightforward solution. We will use this image as an example. ...