Inputs are RGB images, outputs are bounding boxes that identify regions of text. Text detection is a computer vision technique used for locating instances of text within in images. ![]() Video classification is a computer vision technique for classifying the action or content in a sequence of video frames.Īll inputs are Videos only or Video with Optical Flow data, outputs are gesture classifications and scores.Īccuracy of the classifier improves when combining optical flow and RGB data.īack to top Text Detection and Recognition Pose estimation is a computer vision technique for localizing the position and orientation of an object using a fixed set of keypoints.Īll inputs are RGB images, outputs are heatmaps and part affinity fields (PAFs) which via post processing perform pose estimation. Super Resolution (estimate a high-resolution image from a low-resolution image) This example workflow shows how a semantic segmentation map input translates to a synthetic image via a pretrained model (Pix2PixHD):ĭay-to-Dusk Dusk-to-Day Image Translation Inputs are images, outputs are translated RGB images. ![]() This technique can be extended to other image-to-image learning operations, such as image enhancement, image colorization, defect generation, and medical image analysis. Image translation is the task of transferring styles and characteristics from one image domain to another. Inputs are RGB images, outputs are pixel classifications (semantic maps), bounding boxes and classification labels. In contrast, semantic segmentation considers all objects of the same class as belonging to a single entity. ![]() Instance segmentation treats individual objects as distinct entities, regardless of the class of the objects. Instance segmentation is an enhanced type of object detection that generates a segmentation map for each detected instance of an object. This network has been trained to detect 20 objects classes from the PASCAL VOC dataset: NetworkĪpplication Specific Semantic Segmentation Models Network Inputs are RGB images, outputs are pixel classifications (semantic maps). Semantic segmentation describes the process of associating each pixel of an image with a class label, (such as flower, person, road, sky, ocean, or car). Segmentation is essential for image analysis tasks. These networks have been trained to detect specific objects for a given application. Choosing a network is generally a tradeoff between these characteristics. The most important characteristics are mean average precision (mAP), speed, and size. Pretrained object detectors have different characteristics that matter when choosing a network to apply to your problem. These models are suitable for training a custom object detector using transfer learning. These networks have been trained to detect 80 objects classes from the COCO dataset. Inputs are RGB images, the output is the predicted label, bounding box and score: The goal of object detection is to replicate this intelligence using a computer. When humans look at images or video, we can recognize and locate objects of interest within a matter of moments. ![]() Object detection is a computer vision technique used for locating instances of objects in images or videos. Comparing image classification model accuracy, speed and size. The following figure highlights these tradeoffs:įigure. The most important characteristics are network accuracy, speed, and size. Pretrained networks have different characteristics that matter when choosing a network to apply to your problem. These networks have been trained on more than a million images and can classify images into 1000 object categories. Inputs are RGB images, the output is the predicted label and score: Use them as a starting point to learn a new task using transfer learning. Pretrained image classification networks have already learned to extract powerful and informative features from natural images. Discover pretrained models for deep learning in MATLAB.
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