there’s the class-agnostic nature of the task. In contrast to the related task of semantic segmentation, which typically restricts its outputs to a fixed set of categories, our model is designed to handle arbitrary objects. To achieve this, we ...
such asMask R-CNN, evaluate thousands of anchor-based RoIs before forwarding hundreds of top-ranked proposals to the second stage. We instead constrain the number of RoIs in the original DETR model by an order of magnitude (from its default configuration of 100), and yet obtain ...
We present an auxiliary learning task for the problem of neuron segmentation in electron microscopy volumes. The auxiliary task consists of the prediction of local shape descriptors (LSDs), which we combine with conventional voxel-wise direct neighbor af
In colorectal cancer (CRC), artificial intelligence (AI) can alleviate the laborious task of characterization and reporting on resected biopsies, including polyps, the numbers of which are increasing as a result of CRC population screening programs ongoi
Lyu: Maxi-Min Margin Machine: Learning Large Margin Classifiers Locally and Globally. IEEE Trans. Neural Networks 19(2): 260-272 (2008) [4] Haiqin Yang, Zenglin Xu, Jieping Ye, Irwin King, Michael R. Lyu: Efficient Sparse Generalized Multiple Kernel Learning. IEEE Trans. Neural Networks ...
Player detection and ball detection in football matches using image processing(opencv). pythonopencvmachine-learningvideocomputer-visiondetectionimage-processingimage-classificationimage-recognitionopencv-libraryopencv-pythonplayer-videoopencv2opencv3-pythonimagesegmentationfootball-detection ...
resulting in a two-channel signal. The database provides signal region labels generated by an automated expert system [2]. This example aims to use a deep learning solution to provide a label for every ECG signal sample according to the region where the sample is located. This process of la...
So far in this book, we have looked at a variety of machine learning architectures but used them to solve only one type of problem—that of classifying (or regressing) an entire image. In this chapter, we discuss three new vision problems: object detection, instance segmentation, and whole-...
Instance segmentation is a deep learning-driven computer vision task that predicts exact pixel-wise boundaries for each individual object instance in an image.
As it was mentioned in Section 4.2, there are two major types of machine learning approaches that can be used: supervised learning and unsupervised learning approaches. There are various supervised algorithms such as those based on the inference of decision trees (Podgorelec, Kokol, Stiglic, & ...