Mahmoudwiggins7183

This survey provides a snapshot of over 20 years of literature in iAR, useful to understand established practices to orientate in iAR interface design and to present future research directions.Local image feature matching lies in the heart of many computer vision applications. Achieving high matching accuracy is challenging when significant geometric difference exists between the source and target images. The traditional matching pipeline addresses the geometric difference by introducing the concept of support region. Around each feature point, the support region defines a neighboring area characterized by estimated attributes like scale, orientation, affine shape, etc. To correctly assign support region is not an easy job, especially when each feature is processed individually. In this paper, we propose to estimate the relative affine transformation for every pair of to-be-compared features. This "tailored" measurement of geometric difference is more precise and helps improve the matching accuracy. Our pipeline can be incorporated into most existing 2D local image feature detectors and descriptors. We comprehensively evaluate its performance with various experiments on a diversified selection of benchmark datasets. The results show that the majority of tested detectors/descriptors gain additional matching accuracy with proposed pipeline.Crowd counting is a challenging problem due to the diverse crowd distribution and background interference. In this paper, we propose a new approach for head size estimation to reduce the impact of different crowd scale and background noise. Different from just using local information of distance between human heads, the global information of the people distribution in the whole image is also under consideration. We obey the order of far- to near-region (small to large) to spread head size, and ensure that the propagation is uninterrupted by inserting dummy head points. The estimated head size is further exploited, such as dividing the crowd into parts of different densities and generating a high-fidelity head mask. On the other hand, we design three different head mask usage mechanisms and the corresponding head masks to analyze where and which mask could lead to better background filtering1. Based on the learned masks, two competitive models are proposed which can perform robust crowd estimation against background noise and diverse crowd scale. We evaluate the proposed method on three public crowd counting datasets of ShanghaiTech [2], UCFQNRF [3] and UCFCC_50 [4]. Experimental results demonstrate that the proposed algorithm performs favorably against the state-of-the-art crowd counting approaches.A ternary solid solution of lead-free Na1/2Bi1/2TiO3-BaTiO3 and BiGaO3 (NBT-BT-BG) was prepared using conventional, solid-state synthesis. Compositions were prepared near the morphotropic phase boundary (MPB) of ( 1- x )NBT- x BT, located near x = 0.04 -0.09 , and then systematically substituted with 2-5 mol% BG to investigate the effect of the compositional change on the accompanying properties. Dielectric, ferroelectric (FE), and piezoelectric properties were analyzed and compared for all prepared compositions. The FE to ergodic (ER) relaxor transition temperature ( [Formula see text]) and the reversible electric field-induced relaxor to FE transition were investigated to determine their effects on the strain response. It was found that the MPB composition of 0.93NBT-0.07BT required the least amount of the tertiary phase, 3 mol% BG, to reach a disordered, ER state while also requiring the largest electric fields to induce an FE phase compared with similarly substituted NBT- x BT samples. This led to a maximum unipolar strain of 0.53% (d33* = 866 pm/V) for the 0.93NBT-0.07BT-0.04BG composition. The largest strains for each system occurred in compositions that were in the ER region at room temperature. These results demonstrate that the addition of BG most effectively destabilizes the long-range dipole order near the MPB composition of NBT-BT, which results in an enhanced electric field-induced strain.This article presents a row-column (RC) capacitive micromachined ultrasonic transducer (CMUT) array fabricated using anodic bonding on a borosilicate glass substrate. This is shown to reduce the bottom electrode-to-substrate capacitive coupling. Docetaxel cost This subsequently improves the relative response of the elements when top or bottom electrodes are used as the "signal" (active) electrode. This results in a more uniform performance for the two cases. Measured capacitance and resonant frequency, pulse-echo signal amplitude, and frequency response are presented to support this. Biasing configurations with varying ac and dc arrangements are applied and subsequently explored. Setting the net dc bias voltage across an off element to zero is found to be most effective to minimize spurious transmission. To achieve this, a custom switching circuit was designed and implemented. This circuit was also used to obtain orthogonal B-mode cross-sectional images of a rotationally asymmetric target.Albeit great success has been achieved in image defocus blur detection,there are still several unsolved challenges,e.g.,interference of background clutter,scale sensitivity and missing of boundary details.To deal with these issues,we propose a deep neural network which recurrently fuses and refines multi-scale deep features (DeFusionNet) for defocus blur detection.We first fuse features from different layers of FCN as shallow features and semantic features,respectively.Then,the fused shallow features are propagated to deep layers for refining the details of detected defocus blur regions,and the fused semantic features are propagated to shallow layers to assist in better locating blur regions.The fusion and refinement are performed recurrently.In order to narrow the gap between different feature levels,we embed a feature adaptation module before feature propagating to exploit complementary information and reduce contradictory response of different layers.Since different feature channels are with different extents of discrimination blur detection,we design a channel attention module to select discriminative features for feature refinement.Finally,the output of each layer at last recurrent step are fused to obtain the final result.We collect a new dataset consists of various challenging images and their pixel-wise annotations for promoting further study.Experiments on commonly used and our newly collected datasets are conducted to demonstrate efficacy and efficiency of DeFusionNet.