Image Processing

Our team of experts can develop computer vision solutions for object detection and recognition using deep learning techniques such as convolutional neural networks (CNNs) and transfer learning.

We use popular frameworks like TensorFlow, PyTorch, and OpenCV to create accurate and efficient models that can detect and recognize objects in images.

Utilizing ML techniques like support vector machines (SVMs), k-nearest neighbors (k-NN), and decision trees, our team can also create image categorization solutions.

Applications for these systems include product recognition, satellite imaging, and medical imaging.

We may offer picture segmentation services that divide an image into several regions or interesting things. This method is commonly utilized in autonomous driving, computer vision, and medical imaging.

To develop effective and precise segmentation models, we employ methods such as the watershed algorithm, the graph cut algorithm, and U-Net architecture.