Implementation Of Computer Vision Algorithms Research Articles

Facial emotion recognition through artificial intelligence

This paper introduces a study employing artificial intelligence (AI) to utilize computer vision algorithms for detecting human emotions in video content during user interactions with diverse visual stimuli. The research aims to unveil the creation of software capable of emotion detection by leveraging AI algorithms and image processing pipelines to identify users' facial expressions. The process involves assessing users through images and facilitating the implementation of computer vision algorithms aligned with psychological theories defining emotions and their recognizable features. The study demonstrates the feasibility of emotion recognition through convolutional neural networks (CNN) and software development and training based on facial expressions. The results highlight successful emotion identification; however, precision improvement necessitates further training for contexts with more diverse images and additional algorithms to distinguish closely related emotional patterns. The discussion and conclusions emphasize the potential of A.I. and computer vision algorithms in emotion detection, providing insights into software development, ongoing training, and the evolving landscape of emotion recognition technology. Further training is necessary for contexts with more diverse images, alongside additional algorithms that can effectively distinguish between facial expressions depicting closely related emotional patterns, enhancing certainty and accuracy.

Towards remote surveillance of marine pests: A comparison between remote operated vehicles and diver surveys

Early detection of marine invasive species is key for mitigating and managing their impacts to marine ecosystems and industries. Human divers are considered the gold standard tool for detecting marine invasive species, especially when dive teams are familiar with the local biodiversity. However, diver operations can be expensive and dangerous, and are not always practical. Remote operated vehicles (ROVs) can potentially overcome these limitations, but it is unclear how sensitive they are compared to trained divers for detecting pests. We assessed the sensitivity and efficiency of ROVs and divers for detecting marine non-indigenous species (NIS), including the potential for automated detection algorithms to reduce post-processing costs of ROV methods. We show that ROVs can detect comparable assemblages of invasive species as divers, but with lower detection rates (0.2 NIS min-1) than divers (0.5 NIS min-1) and covered less seafloor than divers per unit time. While small invertebrates (e.g., skeleton shrimp Caprella mutica) were more easily detected by divers, the invasive goby Acentrogobius pflaumii was only detected by the ROV. We show that implementation of computer vision algorithms can provide accurate identification of larger biofouling organisms and reduce overall survey costs, yet the relative costs of ROV surveys remain almost twice that of diver surveys. We expect that as ROV technologies improve and investment in autonomous and semi-autonomous underwater vehicles increases, much of the current inefficiencies of ROVs will be mitigated, yet practitioners should be aware of limitations in taxonomic resolution and the strengths of specialist diver teams.

Exploring Physics of Ferroelectric Domain Walls in Real Time: Deep Learning Enabled Scanning Probe Microscopy.

The functionality of ferroelastic domain walls in ferroelectric materials is explored in real-time via the in situ implementation of computer vision algorithms in scanning probe microscopy (SPM) experiment. The robust deep convolutional neural network (DCNN) is implemented based on a deep residual learning framework (Res) and holistically nested edge detection (Hed), and ensembled to minimize the out-of-distribution drift effects. The DCNN is implemented for real-time operations on SPM, converting the data stream into the semantically segmented image of domain walls and the corresponding uncertainty. Further the pre-defined experimental workflows perform piezoresponse spectroscopy measurement on thus discovered domain walls, and alternating high- and low-polarization dynamic (out-of-plane) ferroelastic domain walls in a PbTiO3 (PTO) thin film and high polarization dynamic (out-of-plane) at short ferroelastic walls (compared with long ferroelastic walls) in a lead zirconate titanate (PZT) thin film is reported. This work establishes the framework for real-time DCNN analysis of data streams in scanning probe and other microscopies and highlights the role of out-of-distribution effects and strategies to ameliorate them in real time analytics.

Performance Optimization of Object Tracking Algorithms in OpenCV on GPUs

Machine-learning-based computer vision is increasingly versatile and being leveraged by a wide range of smart devices. Due to the limited performance/energy budget of computing units in smart devices, the careful implementation of computer vision algorithms is critical. In this paper, we analyze the performance bottleneck of two well-known computer vision algorithms for object tracking: object detection and optical flow in the Open-source Computer Vision library (OpenCV). Based on our in-depth analysis of their implementation, we found the current implementation fails to utilize Open Computing Language (OpenCL) accelerators (e.g., GPUs). Based on the analysis, we propose several optimization strategies and apply them to the OpenCL implementation of object tracking algorithms. Our evaluation results demonstrate the performance of the object detection is improved by up to 86% and the performance of the optical flow by up to 10%. We believe our optimization strategies can be applied to other computer vision algorithms implemented in OpenCL.

Automatic counting of birds in a bird deterrence field trial.

Decreasing costs in high‐quality digital cameras, image processing, and digital storage allow researchers to generate and store massive amounts of digital imagery. The time needed to manually analyze these images will always be a limiting factor for experimental design and analysis. Implementation of computer vision algorithms for automating the detection and counting of animals reduces the manpower needed to analyze field images.For this paper, we assess the ability of computer vision to detect and count birds in images from a field test that was not designed for computer vision. Using video stills from the field test and Matlab's Computer Vision Toolbox, we designed and evaluated a cascade object detection method employing Haar and Local Binary Pattern feature types.Without editing the images, we found that the Haar feature can have a recall over 0.5 with an Intersection over Union threshold of 0.5. However, using this feature, 86% of the frames without birds had false‐positive bird detections. Reducing the false positives could lead to these detection methods being implemented into a fully automated system for detecting and counting birds.Accurately detecting and counting birds using computer vision will reduce manpower for field experiments, both in experimental design and data analysis. Improvements in automated detection and counting will allow researchers to design extended trials without the added step of optimizing the experimental setup and/or captured images for computer vision.

Canny edge detection and Hough transform for high resolution video streams using Hadoop and Spark

Nowadays, video cameras are increasingly used for surveillance, monitoring, and activity recording. These cameras generate high resolution image and video data at large scale. Processing such large scale video streams to extract useful information with time constraints is challenging. Traditional methods do not offer scalability to process large scale data. In this paper, we propose and evaluate cloud services for high resolution video streams in order to perform line detection using Canny edge detection followed by Hough transform. These algorithms are often used as preprocessing steps for various high level tasks including object, anomaly, and activity recognition. We implement and evaluate both Canny edge detector and Hough transform algorithms in Hadoop and Spark. Our experimental evaluation using Spark shows an excellent scalability and performance compared to Hadoop and standalone implementations for both Canny edge detection and Hough transform. We obtained a speedup of 10.8$$\times$$ and 9.3$$\times$$ for Canny edge detection and Hough transform respectively using Spark. These results demonstrate the effectiveness of parallel implementation of computer vision algorithms to achieve good scalability for real-world applications.

Real Time Implementation of Computer Vision Algorithms on Beagleboard

Real Time Implementation of Computer Vision Algorithms on Beagleboard

Parametric Dense Stereovision Implementation on a System-on Chip (SoC)

This paper proposes a novel hardware implementation of a dense recovery of stereovision 3D measurements. Traditionally 3D stereo systems have imposed the maximum number of stereo correspondences, introducing a large restriction on artificial vision algorithms. The proposed system-on-chip (SoC) provides great performance and efficiency, with a scalable architecture available for many different situations, addressing real time processing of stereo image flow. Using double buffering techniques properly combined with pipelined processing, the use of reconfigurable hardware achieves a parametrisable SoC which gives the designer the opportunity to decide its right dimension and features. The proposed architecture does not need any external memory because the processing is done as image flow arrives. Our SoC provides 3D data directly without the storage of whole stereo images. Our goal is to obtain high processing speed while maintaining the accuracy of 3D data using minimum resources. Configurable parameters may be controlled by later/parallel stages of the vision algorithm executed on an embedded processor. Considering hardware FPGA clock of 100 MHz, image flows up to 50 frames per second (fps) of dense stereo maps of more than 30,000 depth points could be obtained considering 2 Mpix images, with a minimum initial latency. The implementation of computer vision algorithms on reconfigurable hardware, explicitly low level processing, opens up the prospect of its use in autonomous systems, and they can act as a coprocessor to reconstruct 3D images with high density information in real time.