Articulated body pose estimation in computer vision is the study of algorithms and systems that recover the pose of an articulated body,which consists of joints and rigid parts using image-based observations. It is one of the longest-lasting problems in computer vision because of the complexity of the models that relate observation with pose,and because of the variety of situations in which it would be useful.
Object recognition –technology in the field of computer vision for finding and identifying objects in an image or video sequence. Humans recognize a multitude of objects in images with little effort,despite the fact that the image of the objects may vary somewhat in different view points,in many different sizes and scales or even when they are translated or rotated. Objects can even be recognized when they are partially obstructed from view. This task is still a challenge for computer vision systems. Many approaches to the task have been implemented over multiple decades.
Object detection is a computer technology related to computer vision and image processing that deals with detecting instances of semantic objects of a certain class in digital images and videos. Well-researched domains of object detection include face detection and pedestrian detection. Object detection has applications in many areas of computer vision,including image retrieval and video surveillance.
Synthetic data is information that's artificially generated rather than produced by real-world events. Typically created using algorithms,synthetic data can be deployed to validate mathematical models and to train machine learning models.
In computer science,landmark detection is the process of finding significant landmarks in an image. This originally referred to finding landmarks for navigational purposes –for instance,in robot vision or creating maps from satellite images. Methods used in navigation have been extended to other fields,notably in facial recognition where it is used to identify key points on a face. It also has important applications in medicine,identifying anatomical landmarks in medical images.
Deep learning is part of a broader family of machine learning methods,which is based on artificial neural networks with representation learning. The adjective "deep" in deep learning refers to the use of multiple layers in the network. Methods used can be either supervised,semi-supervised or unsupervised.
Convolutional neural network (CNN) is a regularized type of feed-forward neural network that learns feature engineering by itself via filters optimization. Vanishing gradients and exploding gradients,seen during backpropagation in earlier neural networks,are prevented by using regularized weights over fewer connections. For example,for each neuron in the fully-connected layer 10,000 weights would be required for processing an image sized 100 ×100 pixels. However,applying cascaded convolution kernels,only 25 neurons are required to process 5x5-sized tiles Higher-layer features are extracted from wider context windows,compared to lower-layer features.
Adversarial machine learning is the study of the attacks on machine learning algorithms,and of the defenses against such attacks. A survey from May 2020 exposes the fact that practitioners report a dire need for better protecting machine learning systems in industrial applications.
DeepDream is a computer vision program created by Google engineer Alexander Mordvintsev that uses a convolutional neural network to find and enhance patterns in images via algorithmic pareidolia,thus creating a dream-like appearance reminiscent of a psychedelic experience in the deliberately overprocessed images.
Semantic parsing is the task of converting a natural language utterance to a logical form:a machine-understandable representation of its meaning. Semantic parsing can thus be understood as extracting the precise meaning of an utterance. Applications of semantic parsing include machine translation,question answering,ontology induction,automated reasoning,and code generation. The phrase was first used in the 1970s by Yorick Wilks as the basis for machine translation programs working with only semantic representations.
Gregory D. Hager is the Mandell Bellmore Professor of Computer Science and founding director of the Johns Hopkins Malone Center for Engineering in Healthcare at Johns Hopkins University.
Michael J. Black is an American-born computer scientist working in Tübingen,Germany. He is a founding director at the Max Planck Institute for Intelligent Systems where he leads the Perceiving Systems Department in research focused on computer vision,machine learning,and computer graphics. He is also an Honorary Professor at the University of Tübingen.
In the domain of physics and probability,the filters,random fields,and maximum entropy (FRAME) model is a Markov random field model of stationary spatial processes,in which the energy function is the sum of translation-invariant potential functions that are one-dimensional non-linear transformations of linear filter responses. The FRAME model was originally developed by Song-Chun Zhu,Ying Nian Wu,and David Mumford for modeling stochastic texture patterns,such as grasses,tree leaves,brick walls,water waves,etc. This model is the maximum entropy distribution that reproduces the observed marginal histograms of responses from a bank of filters,where for each filter tuned to a specific scale and orientation,the marginal histogram is pooled over all the pixels in the image domain. The FRAME model is also proved to be equivalent to the micro-canonical ensemble,which was named the Julesz ensemble. Gibbs sampler is adopted to synthesize texture images by drawing samples from the FRAME model.
An audio deepfake is a type of artificial intelligence used to create convincing speech sentences that sound like specific people saying things they did not say. This technology was initially developed for various applications to improve human life. For example,it can be used to produce audiobooks,and also to help people who have lost their voices to get them back. Commercially,it has opened the door to several opportunities. This technology can also create more personalized digital assistants and natural-sounding text-to-speech as well as speech translation services.
Video super-resolution (VSR) is the process of generating high-resolution video frames from the given low-resolution video frames. Unlike single-image super-resolution (SISR),the main goal is not only to restore more fine details while saving coarse ones,but also to preserve motion consistency.
Self-supervised learning (SSL) is a paradigm in machine learning for processing data of lower quality,rather than improving ultimate outcomes. Self-supervised learning more closely imitates the way humans learn to classify objects.
A Vision Transformer (ViT) is a transformer that is targeted at vision processing tasks such as image recognition.
Small object detection is a particular case of object detection where various techniques are employed to detect small objects in digital images and videos. "Small objects" are objects having a small pixel footprint in the input image. In areas such as aerial imagery,state-of-the-art object detection techniques under performed because of small objects.
