Low-resolution images have fewer pixels and less detail than high-resolution images, which makes it difficult for traditional object detection and recognition methods to extract features and ...

Object Detection: Recognizing and locating objects within images ... Applications: Autonomous vehicles, facial recognition, medical imaging, augmented reality, and more.

And that's where object detection comes in ... HOG is implemented in five steps- the computation of gradient, orientation binning, computation of descriptor blocks, block normalization, and finally ...

The methodology for the Automatic License Plate Detection project is designed to seamlessly integrate the steps of image preprocessing, model training, license plate detection, and optical character ...

By modeling, image preprocessing can be achieved from target localization, feature extraction, target matching, and moving estimation. Convolutional neural networks is used to model the detection and ...

The paper leverages the power of CNNs to automatically learn and extract essential features from images, enabling effective object recognition. We explore the significance of image filtering and edge ...

Object detection and recognition are an integral part of computer vision systems. In computer vision, the work begins with a breakdown of the scene into components that a computer can see and analyse.

In this project, we have used the popular ESP32-CAM module to build an Image recognition system that can identify various vegetables. We have used the Edge Impulse platform to train our model and ...