have developed an algorithm that can accurately identify objects in images or videos and can learn to recognize new objects on its own. Although other object recognition systems exist, the ...

Resizing images, either by upscaling or cropping ... This can affect the accuracy and robustness of the object recognition algorithm. To deal with occlusion, you can use methods that can match ...

these algorithms form the foundation for modern image recognition systems, allowing for tasks such as object detection, classification, segmentation, and more. As the field of computer vision ...

to evaluate the performance of object recognition algorithms, we propose a new evaluation framework by synthesizing natural scenes based on the Amsterdam Library of Object Images [1]. Here, the ...

The complexity of the image recognition task significantly influences the choice of algorithm and its configuration. Tasks can range from object detection and segmentation, where the goal is to ...

Abstract: In this work, we formulate the interaction between image segmentation and object recognition in the framework of the Expectation-Maximization (EM) algorithm. We consider segmentation as the ...

An object-recognition algorithm asked to classify a particular image, for instance, might conclude that it has a 60 percent chance of depicting a dog, but a 30 percent chance of depicting a cat.

Image recognition is done in many different ways, but many of the top techniques involve the use of supervised learning, neural networks and deep learning algorithms ... The terms image recognition ...