Evaluation of Robust Feature Descriptors for Texture Classification

Texture is an important characteristic in real and synthetic scenes. Texture analysis plays a critical role in inspecting surfaces and provides important techniques in a variety of applications. Although several descriptors have been presented to extract texture features, the development of object recognition is still a difficult task due to the complex aspects of texture. Recently, many robust and scaling-invariant image features such as SIFT, SURF and ORB have been successfully used in image retrieval and object recognition. In this paper, we have tried to compare the performance for texture classification using these feature descriptors with k-means clustering. Different classifiers including K-NN, Naive Bayes, Back Propagation Neural Network , Decision Tree and Kstar were applied in three texture image sets - UIUCTex, KTH-TIPS and Brodatz, respectively. Experimental results reveal SIFTS as the best average accuracy rate holder in UIUCTex, KTH-TIPS and SURF is advantaged in Brodatz texture set. BP neuro network works best in the test set classification among all used classifiers

Evaluation of Robust Feature Descriptors for Texture Classification

Texture is an important characteristic in real and synthetic scenes. Texture analysis plays a critical role in inspecting surfaces and provides important techniques in a variety of applications. Although several descriptors have been presented to extract texture features, the development of object recognition is still a difficult task due to the complex aspects of texture. Recently, many robust and scaling-invariant image features such as SIFT, SURF and ORB have been successfully used in image retrieval and object recognition. In this paper, we have tried to compare the performance for texture classification using these feature descriptors with k-means clustering. Different classifiers including K-NN, Naive Bayes, Back Propagation Neural Network , Decision Tree and Kstar were applied in three texture image sets - UIUCTex, KTH-TIPS and Brodatz, respectively. Experimental results reveal SIFTS as the best average accuracy rate holder in UIUCTex, KTH-TIPS and SURF is advantaged in Brodatz texture set. BP neuro network works best in the test set classification among all used classifiers

Comparisons of the GlideScope and Macintosh Laryngoscope in Tracheal Intubation by Medical Students on Fresh Human Cadavers

AbstractObjectiveThe GlideScope Video Laryngoscope (GS) is an intubating device that provides equal or better glottic views than conventional laryngoscopes, but correct tube placement is more time-consuming, even when performed by experienced operators. The aim of this study was to investigate the use of the GS compared with the more conventional Macintosh laryngoscope in easy and difficult tracheal intubation when performed by inexperienced medical students on fresh human cadaversPatients and MethodsForty-one medical students were assigned to perform tracheal intubation using the direct Macintosh laryngoscope (DL) and the GS. Each student was given four attempts, with a maximum of 180 seconds for each attempt, to successfully intubate the trachea with a 6.5-mm tracheal tube in each of two scenarios, one with an easy airway and the other with a difficult airway cadaver.ResultsThe total time of intubation for the easy airway cadaver was significantly longer in the GS group (61.4 ± 4.8 seconds vs. 40.6 ± 5.3 seconds; p < 0.001) despite the modified Cormack-Lehane scores showing no difference between the two groups. In the difficult airway cadaver, total time of intubation was significant shorter in the GS group (64.3 ± 6.5 seconds vs. 98.7 ± 10.2 seconds; p < 0.001)ConclusionMost inexperienced operators found the GS to be more time-consuming for tracheal intubation than DL in the easy airway cadaver. However, an obvious advantage was demonstrated when the GS was used for the difficult airway

A Distributed Data-Parallel PyTorch Implementation of the Distributed Shampoo Optimizer for Training Neural Networks At-Scale

Shampoo is an online and stochastic optimization algorithm belonging to the AdaGrad family of methods for training neural networks. It constructs a block-diagonal preconditioner where each block consists of a coarse Kronecker product approximation to full-matrix AdaGrad for each parameter of the neural network. In this work, we provide a complete description of the algorithm as well as the performance optimizations that our implementation leverages to train deep networks at-scale in PyTorch. Our implementation enables fast multi-GPU distributed data-parallel training by distributing the memory and computation associated with blocks of each parameter via PyTorch's DTensor data structure and performing an AllGather primitive on the computed search directions at each iteration. This major performance enhancement enables us to achieve at most a 10% performance reduction in per-step wall-clock time compared against standard diagonal-scaling-based adaptive gradient methods. We validate our implementation by performing an ablation study on training ImageNet ResNet50, demonstrating Shampoo's superiority over standard training recipes with minimal hyperparameter tuning.Comment: 38 pages, 8 figures, 5 table

Urolithiasis Is a Risk Factor for Uroseptic Shock and Acute Kidney Injury in Patients With Urinary Tract Infection.

Urinary tract infection (UTI) is a common complication in patients with urolithiasis. This study aimed to compare clinical manifestations and treatment outcomes among UTI patients with or without urolithiasis. It also focused on identifying relationships among urolithiasis, uroseptic shock, and acute kidney injury (AKI). This retrospective study enrolled hospitalized UTI patients who underwent imaging in an acute care setting from January 2006 to March 2015. Of 662 participants enrolled, 113 (17.1%) had urolithiasis, 107 (16.2%) developed uroseptic shock, and 184 (27.8%) developed AKI. A multivariate logistic regression analysis showed that in UTI patients, urolithiasis is associated with an increased risk of uroseptic shock (OR 1.80, 95% CI: 1.08-3.02, P = 0.025), AKI (OR 1.95, 95% CI: 1.22-3.12, P = 0.005), and bacteremia (OR 1.68, 95% CI: 1.08-2.64, P = 0.022). Urolithiasis is common in UTI patients and is associated with an increased risk of uroseptic shock and AKI