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1. Application of Image Processing and Big Data Science for Flood Label Detection

   Pally, R; Samadi, S. (April 2021, European Geosciences Union)

   null (Ed.)

   Due to the importance of object detection in video analysis and image annotation, it is widely utilized in a number of computer vision tasks such as face recognition, autonomous vehicles, activity recognition, tracking objects and identity verification. Object detection does not only involve classification and identification of objects within images, but also involves localizing and tracing the objects by creating bounding boxes around the objects and labelling them with their respective prediction scores. Here, we leverage and discuss how connected vision systems can be used to embed cameras, image processing, Edge Artificial Intelligence (AI), and data connectivity capabilities for flood label detection. We favored the engineering definition of label detection that a label is a sequence of discrete measurable observations obtained using a capturing device such as web cameras, smart phone, etc. We built a Big Data service of around 1000 images (image annotation service) including the image geolocation information from various flooding events in the Carolinas (USA) with a total of eight different object categories. Our developed platform has several smart AI tools and task configurations that can detect objects’ edges or contours which can be manually adjusted with a threshold setting so as to best segment the image. The tool has the ability to train the dataset and predict the labels for large scale datasets which can be used as an object detector to drastically reduce the amount of time spent per object particularly for real-time image-based flood forecasting. This research is funded by the US National Science Foundation (NSF). 

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2. Image classification and training with severe data loss

   https://doi.org/10.1117/12.2633172  

   Marquard, Dillon; Wright, Kyle; Marcia, Roummel F. (October 2022, SPIE Optical Engineering + Applications, 2022)

   Zelinski, Michael E.; Taha, Tarek M.; Howe, Jonathan (Ed.)

   Image classification forms an important class of problems in machine learning and is widely used in many realworld applications, such as medicine, ecology, astronomy, and defense. Convolutional neural networks (CNNs) are machine learning techniques designed for inputs with grid structures, e.g., images, whose features are spatially correlated. As such, CNNs have been demonstrated to be highly effective approaches for many image classification problems and have consistently outperformed other approaches in many image classification and object detection competitions. A particular challenge involved in using machine learning for classifying images is measurement data loss in the form of missing pixels, which occurs in settings where scene occlusions are present or where the photodetectors in the imaging system are partially damaged. In such cases, the performance of CNN models tends to deteriorate or becomes unreliable even when the perturbations to the input image are small. In this work, we investigate techniques for improving the performance of CNN models for image classification with missing data. In particular, we explore training on a variety of data alterations that mimic data loss for producing more robust classifiers. By optimizing the categorical cross-entropy loss function, we demonstrate through numerical experiments on the MNIST dataset that training with these synthetic alterations can enhance the classification accuracy of our CNN models. 

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3. A No-Reference Image Quality Model for Object Detection on Embedded Cameras

   https://doi.org/10.4018/IJMDEM.2019010102  

   Kong, Lingchao; Ikusan, Ademola; Dai, Rui; Zhu, Jingyi; Ros, Dara (January 2019, International Journal of Multimedia Data Engineering and Management)

   Automatic video analysis tools are an indispensable component in imaging applications. Object detection, the first and the most important step for automatic video analysis, is implemented in many embedded cameras. The accuracy of object detection relies on the quality of images that are processed. This paper proposes a new image quality model for predicting the performance of object detection on embedded cameras. A video data set is constructed that considers different factors for quality degradation in the imaging process, such as reduced resolution, noise, and blur. The performances of commonly used low-complexity object detection algorithms are obtained for the data set. A no-reference regression model based on a bagging ensemble of regression trees is built to predict the accuracy of object detection using observable features in an image. Experimental results show that the proposed model provides more accurate predictions of image quality for object detection than commonly known image quality measures. 

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4. Smart City Traffic Intersection: Impact of Video Quality and Scene Complexity on Precision and Inference

   https://doi.org/10.1109/HPCC-DSS-SmartCity-DependSys53884.2021.00226  

   Duan, Zhuoxu; Yang, Zhengye; Samoilenko, Richard; Oza, Dwiref Snehal; Jagadeesan, Ashvin; Sun, Mingfei; Ye, Hongzhe; Xiong, Zihao; Zussman, Gil; Kostic, Zoran (May 2022, in Proc. 19th IEEE Int. Conf. on Smart City, 2021)

   Traffic intersections are prime locations for deployment of infrastructure sensors and edge computing nodes to realize the vision of a smart city. It is expected that the needs of a smart city, in regards to traffic and pedestrian traffic systems monitored by cameras/video, can be met by using stateof-the-art artificial-intelligence (AI) based object detectors and trackers. A critical component in designing an effective real-time object detection/tracking pipeline is the understanding of how object density, i.e., the number of objects in a scene, and imageresolution and frame rate influence the performance metrics. This study explores the accuracy and speed metrics with the goal of supporting pipelines that meet the precision and latency needs of a real-time environment. We examine the impact of varying image-resolution, frame rate and object-density on the object detection performance metrics. The experiments on the COSMOS testbed dataset show that varying the frame width from 416 pixels to 832 pixels, and cropping the images to a square resolution, result in the increase in average precision for all object classes. Decreasing the frame rate from 15 fps to 5 fps preserves more than 90% of the highest F1 score achieved for all object classes. The results inform the choice of video preprocessing stages, modifications to established AI-based object detection/tracking methods, and suggest optimal hyper-parameter values. Index Terms—Object Detection, Smart City, Video Resolution, Deep Learning Models. 

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5. Distributed and consistent multi-image feature matching via QuickMatch

   https://doi.org/10.1177/0278364920917465  

   Serlin, Zachary; Yang, Guang; Sookraj, Brandon; Belta, Calin; Tron, Roberto (June 2020, The International Journal of Robotics Research)

   In this work, we consider the multi-image object matching problem in distributed networks of robots. Multi-image feature matching is a keystone of many applications, including Simultaneous Localization and Mapping, homography, object detection, and Structure from Motion. We first review the QuickMatch algorithm for multi-image feature matching. We then present NetMatch, an algorithm for distributing sets of features across computational units (agents) that largely preserves feature match quality and minimizes communication between agents (avoiding, in particular, the need to flood all data to all agents). Finally, we present an experimental application of both QuickMatch and NetMatch on an object matching test with low-quality images. The QuickMatch and NetMatch algorithms are compared with other standard matching algorithms in terms of preservation of match consistency. Our experiments show that QuickMatch and Netmatch can scale to larger numbers of images and features, and match more accurately than standard techniques. 

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