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Facial expressions are fundamental to interpersonal communication, including social interaction, and allow people of different ages, cultures, and languages to quickly and reliably convey emotional information. Historically, facial expression research has followed from discrete emotion theories, which posit a limited number of distinct affective states that are represented with specific patterns of facial action. Much less work has focused on dimensional features of emotion, particularly positive and negative affect intensity. This is likely, in part, because achieving inter-rater reliability for facial action and affect intensity ratings is painstaking and labor-intensive. We use computer-vision and machine learning (CVML) to identify patterns of facial actions in 4,648 video recordings of 125 human participants, which show strong correspondences to positive and negative affect intensity ratings obtained from highly trained coders. Our results show that CVML can both (1) determine the importance of different facial actions that human coders use to derive positive and negative affective ratings when combined with interpretable machine learning methods, and (2) efficiently automate positive and negative affect intensity coding on large facial expression databases. Further, we show that CVML can be applied to individual human judges to infer which facial actions they use to generate perceptual emotion ratings from facial expressions.

Citation: Haines N, Southward MW, Cheavens JS, Beauchaine T, Ahn W-Y (2019) Using computer-vision and machine learning to automate facial coding of positive and negative affect intensity. PLoS ONE 14(2): e0211735.

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Using a well-validated method of emotion induction and both computer-vision measurement of discrete facial actions and continuous measures of positive and negative affect intensity, we (1) identified specific correspondences between perceived emotion intensity and discrete facial AUs, and (2) developed a reliable, valid, and efficient method of automatically measuring the separable dimensions of positive and negative affect intensity. Based on previous work on subjective valence intensity using facial EMG, we hypothesized that CVML would identify AUs 12 and 4 as of the most important AUs for positive and negative affect intensity, respectively. Additionally, we hypothesized that the effects of AUs 12 and 4 on positive and negative affect intensity would depend on the activation of other AUs, and that these interactions could be probed with interpretable machine learning methods. Importantly, data used to train and validate our CVML models were collected from a commonly-used psychological task and contained 4,648 video-recorded, evoked facial expressions from 125 human subjects across multiple task instructions. Our findings shed light on the mechanisms of valence recognition from facial expressions and point the way to novel research applications of large-scale emotional facial expression coding.

Computer vision deals with how computers extract meaningful information from images or videos. It has a wide range of applications, including reverse engineering, security inspections, image editing and processing, computer animation, autonomous navigation, and robotics.

The field of computer vision keeps evolving and becoming more impactful thanks to constant technological innovations. As time goes by, it will offer increasingly powerful tools for researchers, businesses, and eventually consumers.

A vehicle license plate scanner in computer vision is a type of computer vision application that can be used to identify plates and read their numbers. This technology is used for a variety of purposes, including law enforcement, identifying stolen vehicles, and tracking down fugitives.

A more sophisticated vehicle license plate scanner in computer vision can scan, read and identify hundreds, even thousands of cars per minute with 99% accuracy from distances up to half a mile away in heavy traffic conditions on highways and city streets. This project is very useful in many cases.

Another useful alternative therapy in cocaine rehab is equine therapy. Horses are beneficial in a therapy setting because they are seen as non-judgmental and are good at mirroring the emotions and attitudes of the people who care for them. If the individual is stressed or scared, the horse will reflect that stress or fear. Equine therapy encourages the individual to be in a calm, relaxed state while caring for the horse. The ability to choose to be calm is a powerful tool in the recovery toolbox.

Finally, the performance of IPPS application to the collaborative robot was tested in a real environment. An object and its scene as well as the task were given; the intelligent perceiving process with the vision system started and the visual process including registration and positioning were finished. The central computer planned the trajectory of the manipulator with improved RRT through interpolation. An operation similar to human motion was predicted, and the manipulator was moved to the predicted pose. At the same time, the RGB images perceived were input to the CNN to plan the grasping points. TCP/IP realized the communication between the cameras, the computer, and the robot hand. The force sensor on the robot hand received the message of grasping, and if it succeeded, the computer would control the manipulator to move to the man-robot interaction position and finish the task. Finally, the manipulator and the robot hand will return to the origin pose. The process of IPPS application in real environment is shown in Figure 16.

Deep Learning Based on CT Angiography in Patient Selection for Endovascular Treatment of Large Vessel Ischemic Stroke

Stroke is a leading cause of long-term disability, and outcome in regaining functionality in areas supplied by anterior circulation large vessel is directly related to timely endovascular therapy (EVT). However, not all patients benefit from rapid intervention. CT perfusion is widely recognized as the selection tool to identify patients who will most likely benefit from reperfusion based on stroke core and penumbra size estimation as well as mismatch quantification. However, it is not routinely performed at many institutions in the United States and around the world. In this proposal, we propose to develop a fully automated artificial intelligence (AI) pipeline that identifies the images/series of interest, detect emergent large vessel occlusion and predicts immediate (e.g. the Thrombolysis in Cerebral Infarction [TICI[ score) and functional (e.g., modified Rankin score [mRS[) outcomes from EVT based on pre-procedure CT angiography. We will establish an end-to-end AI platform that interfaces with the Rhode Island Hospital (RIH) Picture Archiving and Communications Systems for real-time clinical use. The ability to predict immediate outcomes of EVT will affect management because proceduralists will be able to anticipate different reperfusion based on these predictions and adjust their treatment approach accordingly, while prediction of functional outcome assists in patient selection, resulting in improved outcome. We anticipate that the proposed project will further collaboration between the Department of Computer Science and the Department of Diagnostic Imaging, which is crucial in advancing Brown University's position in research on AI, machine learning and computer vision applied to the healthcare system and medical imaging.

PI: Ugur Cetintemel, Professor of Computer Science

Co-PIs: Harrison Bai, Assistant Professor of Diagnostic Imaging; Arko Barman, Assistant Teaching Professor, Rice University

Building a Large Dataset of Articulated 3D Object Models

People spend a large percentage of their lives indoors: in bedrooms, living rooms, offices, kitchens, etc. The demand for virtual versions of these spaces has never been higher, with virtual reality, augmented reality, online furniture retail, computer vision, and robotics applications all requiring high-fidelity virtual environments. To be truly compelling, a virtual interior space must support the same interactions as its real-world counterpart: VR users expect to interact with the scene around them, and interaction with the surrounding environment is crucial for training autonomous robots (e.g. opening doors and cabinets). Most object interactions are characterized by the way the object's parts move or articulate. Unfortunately, it is difficult to create interactive scenes at the scale demanded by the applications above because there do not exist enough articulated 3D object models. Large static object databases exist, but the few existing articulated shape databases are several orders of magnitude smaller.To address this critical need, I propose to create a large dataset of articulated 3D object models: that is, each model in the dataset has a type and a range of motion annotated for each of its movable parts. This dataset will be of the same order of magnitude as the largest existing static shape databases. I will accomplish this goal by aggregating 3D models from existing static shape databases and then annotating them with part articulations. I will conduct the annotation process at scale using crowdsourcing tools (such as Amazon Mechanical Turk) by developing an easy-to-use, web-based annotation interface.

Using machine vision to automate behavioral analysis of C. elegans

Here, we combine our expertise in computer-vision and behavioral genetics to move forward with development of a high-content, computer-vision system for analysis of C. elegans behavior. The automated analysis system will be generally applicable to any small animal moving in a one-dimensional plane, will yield novel insights into behavior in C. elegans ALS models and sleep, and will provide critical preliminary results for NIH applications under review or planned by the co-investigators. The project is based on synergy from very different fields. Dr. Thomas Serre (Brown CLPS) and Dr. Anne Hart (Brown Neuroscience). Dr. Serre is a leading expert in the development of high-content, computer-vision systems. He worked with his student to develop the prototype analysis system used to generate preliminary results herein. Dr. Hart is a leading expert in C. elegans behavior. She worked with her students to obtain C. elegans behavioral data and validate the results of the preliminary computer-vision analysis. Our long-term goals are to 1) develop the versatile open-source, computer-vision behavioral analysis system proposed here 2) optimize the system to allow real-time, accurate scoring of behaviors, without manual intervention or annotation and 3) to establish an online resource/database at Brown University that will allow any researcher to easily reuse and repurpose videos, images and analysis results. This will dramatically raise the stature of Brown University in the field. be457b7860