Gaze-contingent flicker pupil perimetry detects scotomas in patients with cerebral visual impairments or glaucoma

The pupillary light reflex is weaker for stimuli presented inside as compared to outside absolute scotomas. Pupillograph perimetry could thus be an objective measure of impaired visual processing. However, the diagnostic accuracy in detecting scotomas has remained unclear. We quantitatively investigated the accuracy of a novel form of pupil perimetry. The new perimetry method, termed gaze-contingent flicker pupil perimetry, consists of the repetitive on, and off flickering of a bright disk (2 hz; 320 cd/m; 4° diameter) on a gray background (160 cd/m) for 4 seconds per stimulus location. The disk evokes continuous pupil oscillations at the same rate as its flicker frequency, and the oscillatory power of the pupil reflects visual sensitivity. We monocularly presented the disk at a total of 80 locations in the central visual field (max. 15°). The location of the flickering disk moved along with gaze to reduce confounds of eye movements (gaze-contingent paradigm). The test lasted ~5 min per eye and was performed on 7 patients with cerebral visual impairment (CVI), 8 patients with primary open angle glaucoma (age >45), and 14 healthy, age/gender-matched controls. For all patients, pupil oscillation power (FFT based response amplitude to flicker) was significantly weaker when the flickering disk was presented in the impaired as compared to the intact visual field (CVI: 12%, AUC = 0.73; glaucoma: 9%, AUC = 0.63). Differences in power values between impaired and intact visual fields of patients were larger than differences in power values at corresponding locations in the visual fields of the healthy control group (CVI: AUC = 0.95; glaucoma: AUC = 0.87). Pupil sensitivity maps highlighted large field scotomas and indicated the type of visual field defect (VFD) as initially diagnosed with standard automated perimetry (SAP) fairly accurately in CVI patients but less accurately in glaucoma patients. We provide the first quantitative and objective evidence of flicker pupil perimetry's potential in detecting CVI-and glaucoma-induced VFDs. Gaze-contingent flicker pupil perimetry is a useful form of objective perimetry and results suggest it can be used to assess large VFDs with young CVI patients whom are unable to perform SAP

Generalized Completed Local Binary Patterns for Time-Efficient Steel Surface Defect Classification

© 2018 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted ncomponent of this work in other works.Efficient defect classification is one of the most important preconditions to achieve online quality inspection for hot-rolled strip steels. It is extremely challenging owing to various defect appearances, large intraclass variation, ambiguous interclass distance, and unstable gray values. In this paper, a generalized completed local binary patterns (GCLBP) framework is proposed. Two variants of improved completed local binary patterns (ICLBP) and improved completed noise-invariant local-structure patterns (ICNLP) under the GCLBP framework are developed for steel surface defect classification. Different from conventional local binary patterns variants, descriptive information hidden in nonuniform patterns is innovatively excavated for the better defect representation. This paper focuses on the following aspects. First, a lightweight searching algorithm is established for exploiting the dominant nonuniform patterns (DNUPs). Second, a hybrid pattern code mapping mechanism is proposed to encode all the uniform patterns and DNUPs. Third, feature extraction is carried out under the GCLBP framework. Finally, histogram matching is efficiently accomplished by simple nearest-neighbor classifier. The classification accuracy and time efficiency are verified on a widely recognized texture database (Outex) and a real-world steel surface defect database [Northeastern University (NEU)]. The experimental results promise that the proposed method can be widely applied in online automatic optical inspection instruments for hot-rolled strip steel.Peer reviewe

Miniature mobile sensor platforms for condition monitoring of structures

In this paper, a wireless, multisensor inspection system for nondestructive evaluation (NDE) of materials is described. The sensor configuration enables two inspection modes-magnetic (flux leakage and eddy current) and noncontact ultrasound. Each is designed to function in a complementary manner, maximizing the potential for detection of both surface and internal defects. Particular emphasis is placed on the generic architecture of a novel, intelligent sensor platform, and its positioning on the structure under test. The sensor units are capable of wireless communication with a remote host computer, which controls manipulation and data interpretation. Results are presented in the form of automatic scans with different NDE sensors in a series of experiments on thin plate structures. To highlight the advantage of utilizing multiple inspection modalities, data fusion approaches are employed to combine data collected by complementary sensor systems. Fusion of data is shown to demonstrate the potential for improved inspection reliability

CINFormer: Transformer network with multi-stage CNN feature injection for surface defect segmentation

Surface defect inspection is of great importance for industrial manufacture and production. Though defect inspection methods based on deep learning have made significant progress, there are still some challenges for these methods, such as indistinguishable weak defects and defect-like interference in the background. To address these issues, we propose a transformer network with multi-stage CNN (Convolutional Neural Network) feature injection for surface defect segmentation, which is a UNet-like structure named CINFormer. CINFormer presents a simple yet effective feature integration mechanism that injects the multi-level CNN features of the input image into different stages of the transformer network in the encoder. This can maintain the merit of CNN capturing detailed features and that of transformer depressing noises in the background, which facilitates accurate defect detection. In addition, CINFormer presents a Top-K self-attention module to focus on tokens with more important information about the defects, so as to further reduce the impact of the redundant background. Extensive experiments conducted on the surface defect datasets DAGM 2007, Magnetic tile, and NEU show that the proposed CINFormer achieves state-of-the-art performance in defect detection

No-Service Rail Surface Defect Segmentation via Normalized Attention and Dual-scale Interaction

No-service rail surface defect (NRSD) segmentation is an essential way for perceiving the quality of no-service rails. However, due to the complex and diverse outlines and low-contrast textures of no-service rails, existing natural image segmentation methods cannot achieve promising performance in NRSD images, especially in some unique and challenging NRSD scenes. To this end, in this paper, we propose a novel segmentation network for NRSDs based on Normalized Attention and Dual-scale Interaction, named NaDiNet. Specifically, NaDiNet follows the enhancement-interaction paradigm. The Normalized Channel-wise Self-Attention Module (NAM) and the Dual-scale Interaction Block (DIB) are two key components of NaDiNet. NAM is a specific extension of the channel-wise self-attention mechanism (CAM) to enhance features extracted from low-contrast NRSD images. The softmax layer in CAM will produce very small correlation coefficients which are not conducive to low-contrast feature enhancement. Instead, in NAM, we directly calculate the normalized correlation coefficient between channels to enlarge the feature differentiation. DIB is specifically designed for the feature interaction of the enhanced features. It has two interaction branches with dual scales, one for fine-grained clues and the other for coarse-grained clues. With both branches working together, DIB can perceive defect regions of different granularities. With these modules working together, our NaDiNet can generate accurate segmentation map. Extensive experiments on the public NRSD-MN dataset with man-made and natural NRSDs demonstrate that our proposed NaDiNet with various backbones (i.e., VGG, ResNet, and DenseNet) consistently outperforms 10 state-of-the-art methods. The code and results of our method are available at https://github.com/monxxcn/NaDiNet.Comment: 10 pages, 6 figures, Accepted by IEEE Transactions on Instrumentation and Measurement 202