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1. Novel Image Processing Method for Detecting Strep Throat (Streptococcal Pharyngitis) Using Smartphone

   https://doi.org/10.3390/s19153307  

   Askarian, Behnam; Yoo, Seung-Chul; Chong, Jo Woon (August 2019, Sensors)

   In this paper, we propose a novel strep throat detection method using a smartphone with an add-on gadget. Our smartphone-based strep throat detection method is based on the use of camera and flashlight embedded in a smartphone. The proposed algorithm acquires throat image using a smartphone with a gadget, processes the acquired images using color transformation and color correction algorithms, and finally classifies streptococcal pharyngitis (or strep) throat from healthy throat using machine learning techniques. Our developed gadget was designed to minimize the reflection of light entering the camera sensor. The scope of this paper is confined to binary classification between strep and healthy throats. Specifically, we adopted k-fold validation technique for classification, which finds the best decision boundary from training and validation sets and applies the acquired best decision boundary to the test sets. Experimental results show that our proposed detection method detects strep throats with 93.75% accuracy, 88% specificity, and 87.5% sensitivity on average. 

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2. Multi-level Scene Modeling and Matching for Smartphone-Based Indoor Localization

   https://doi.org/10.1109/ISMAR-Adjunct.2019.00-22  

   Chen, Lidong; Zou, Yin; Chang, Yaohua; Liu, Jinyun; Lin, Benjamin; Zhu, Zhigang (October 2019, Proceedings - International Symposium on Mixed and Augmented Reality, ISMAR)

   Accurate indoor positioning has attracted a lot of attention for a variety of indoor location-based applications, with the rapid development of mobile devices and their onboard sensors. A hybrid indoor localization method is proposed based on single off-the-shelf smartphone, which takes advantage of its various onboard sensors, including camera, gyroscope and accelerometer. The proposed approach integrates three components: visual-inertial odometry (VIO), point-based area mapping, and plane-based area mapping. A simplified RANSAC strategy is employed in plane matching for the sake of processing time. Since Apple's augmented reality platform ARKit has many powerful high-level APIs on world tracking, plane detection and 3D modeling, a practical smartphone app for indoor localization is developed on an iPhone that can run ARKit. Experimental results demonstrate that our plane-based method can achieve an accuracy of about 0.3 meter, which is based on a much more lightweight model, but achieves more accurate results than the point-based model by directly using ARKit's area mapping. The size of the plane-based model is less than 2KB for a closed-loop corridor area of about 45m*15m, comparing to about 10MB of the point-based model. 

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3. Active Laser-Camera Scanning for High-Precision Fruit Localization in Robotic Harvesting: System Design and Calibration

   https://doi.org/10.3390/horticulturae10010040  

   Zhang, Kaixiang; Chu, Pengyu; Lammers, Kyle; Li, Zhaojian; Lu, Renfu (January 2024, Horticulturae)

   Robust and effective fruit detection and localization is essential for robotic harvesting systems. While extensive research efforts have been devoted to improving fruit detection, less emphasis has been placed on the fruit localization aspect, which is a crucial yet challenging task due to limited depth accuracy from existing sensor measurements in the natural orchard environment with variable lighting conditions and foliage/branch occlusions. In this paper, we present the system design and calibration of an Active LAser-Camera Scanner (ALACS), a novel perception module for robust and high-precision fruit localization. The hardware of the ALACS mainly consists of a red line laser, an RGB camera, and a linear motion slide, which are seamlessly integrated into an active scanning scheme where a dynamic-targeting laser-triangulation principle is employed. A high-fidelity extrinsic model is developed to pair the laser illumination and the RGB camera, enabling precise depth computation when the target is captured by both sensors. A random sample consensus-based robust calibration scheme is then designed to calibrate the model parameters based on collected data. Comprehensive evaluations are conducted to validate the system model and calibration scheme. The results show that the proposed calibration method can detect and remove data outliers to achieve robust parameter computation, and the calibrated ALACS system is able to achieve high-precision localization with the maximum depth measurement error being less than 4 mm at distance ranging from 0.6 to 1.2 m. 

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4. Lifting with Sunflowers

   Lovett, Shachar; Meka, Raghu; Mertz, Ian; Pitassi, Toniann; Zhang, Jiapeng (January 2022, Leibniz international proceedings in informatics)

   Query-to-communication lifting theorems translate lower bounds on query complexity to lower bounds for the corresponding communication model. In this paper, we give a simplified proof of deterministic lifting (in both the tree-like and dag-like settings). Our proof uses elementary counting together with a novel connection to the sunflower lemma. In addition to a simplified proof, our approach opens up a new avenue of attack towards proving lifting theorems with improved gadget size - one of the main challenges in the area. Focusing on one of the most widely used gadgets - the index gadget - existing lifting techniques are known to require at least a quadratic gadget size. Our new approach combined with robust sunflower lemmas allows us to reduce the gadget size to near linear. We conjecture that it can be further improved to polylogarithmic, similar to the known bounds for the corresponding robust sunflower lemmas. 

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5. Blink detection for off-angle iris images using deep learning

   https://doi.org/10.1117/12.2662248  

   Palta, Hasan; Omoteso, Timi; Karakaya, Mahmut (June 2023, Pattern Recognition and Tracking XXXIV)

   Alam, Mohammad S.; Asari, Vijayan K. (Ed.)

   Iris recognition is one of the well-known areas of biometric research. However, in real-world scenarios, subjects may not always provide fully open eyes, which can negatively impact the performance of existing systems. Therefore, the detection of blinking eyes in iris images is crucial to ensure reliable biometric data. In this paper, we propose a deep learning-based method using a convolutional neural network to classify blinking eyes in off-angle iris images into four different categories: fully-blinked, half-blinked, half-opened, and fully-opened. The dataset used in our experiments includes 6500 images of 113 subjects and contains images of a mixture of both frontal and off-angle views of the eyes from -50 to 50 in gaze angle. We train and test our approach using both frontal and off-angle images and achieve high classification performance for both types of images. Compared to training the network with only frontal images, our approach shows significantly better performance when tested on off-angle images. These findings suggest that training the model with a more diverse set of off-angle images can improve its performance for off-angle blink detection, which is crucial for real-world applications where the iris images are often captured at different angles. Overall, the deep learning-based blink detection method can be used as a standalone algorithm or integrated into existing standoff biometrics frameworks to improve their accuracy and reliability, particularly in scenarios where subjects may blink. 

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