Precise measurement of position and attitude based on convolutional neural network and visual correspondence relationship

Accurate measurement of position and attitude information is particularly important. Traditional measurement methods generally require high-precision measurement equipment for analysis, leading to high costs and limited applicability. Vision-based measurement schemes need to solve complex visual relationships. With the extensive development of neural networks in related fields, it has become possible to apply them to the object position and attitude. In this paper, we propose an object pose measurement scheme based on convolutional neural network and we have successfully implemented end-toend position and attitude detection. Furthermore, to effectively expand the measurement range and reduce the number of training samples, we demonstrated the independence of objects in each dimension and proposed subadded training programs. At the same time, we generated generating image encoder to guarantee the detection performance of the training model in practical applications

A blind stereoscopic image quality evaluator with segmented stacked autoencoders considering the whole visual perception route

Most of the current blind stereoscopic image quality assessment (SIQA) algorithms cannot show reliable accuracy. One reason is that they do not have the deep architectures and the other reason is that they are designed on the relatively weak biological basis, compared with findings on human visual system (HVS). In this paper, we propose a Deep Edge and COlor Signal INtegrity Evaluator (DECOSINE) based on the whole visual perception route from eyes to the frontal lobe, and especially focus on edge and color signal processing in retinal ganglion cells (RGC) and lateral geniculate nucleus (LGN). Furthermore, to model the complex and deep structure of the visual cortex, Segmented Stacked Auto-encoder (S-SAE) is used, which has not utilized for SIQA before. The utilization of the S-SAE complements weakness of deep learning-based SIQA metrics that require a very long training time. Experiments are conducted on popular SIQA databases, and the superiority of DECOSINE in terms of prediction accuracy and monotonicity is proved. The experimental results show that our model about the whole visual perception route and utilization of S-SAE are effective for SIQA

No-reference quality assessment of stereoscopic videos with inter-frame cross on a content-rich database

© 1991-2012 IEEE. With the wide application of stereoscopic video technology, the quality of stereoscopic video has attracted people's attention. Objective stereoscopic video quality assessment (SVQA) is highly challenging, but essential, particularly the no-reference (NR) SVQA method, where reference information is not needed and a large number of samples are required for training and testing sets. However, as far as we know, there are only a few samples in the established stereo video database, which is unsuitable for NR quality assessment and seriously hampers the development of NR-SVQA method. For these difficulties that we encountered, we carry out a comprehensive subjective evaluation of stereoscopic video quality in our newly established TJU-SVQA databases that contain various contents, mixed resolution coding and symmetrically/asymmetrically distorted stereoscopic videos. Furthermore, we propose a new inter-frame cross map to predict the objective quality scores. We compare and analyze the performance of several state-of-the-art 2D and 3D quality evaluation methods on our new databases. The experimental results on our established databases and a public database demonstrate that the proposed method can robustly predict the quality of stereoscopic videos

No reference quality assessment for screen content images using stacked auto-encoders in pictorial and textual regions

Recently, the visual quality evaluation of screen content images (SCIs) has become an important and timely emerging research theme. This paper presents an effective and novel blind quality evaluation metric for SCIs by using stacked auto-encoders (SAE) based on pictorial and textual regions. Since the SCI consists of not only the pictorial area but also the textual area, the human visual system (HVS) is not equally sensitive to their different distortion types. Firstly, the textual and pictorial regions can be obtained by dividing an input SCI via a SCI segmentation metric. Next, we extract quality-aware features from the textual region and pictorial region, respectively. Then, two different SAEs are trained via an unsupervised approach for quality-aware features which are extracted from these two regions. After the training procedure of the SAEs, the quality-aware features can evolve into more discriminative and meaningful features. Subsequently, the evolved features and their corresponding subjective scores are input into two regressors for training. Each regressor can obtain one output predictive score. Finally, the final perceptual quality score of a test SCI is computed by these two predicted scores via a weighted model. Experimental results on two public SCI-oriented databases have revealed that the proposed scheme can compare favorably with the existing blind image quality assessment metrics

Additional file 1: of Swept-source optical coherence tomography imaging of macular retinal and choroidal structures in healthy eyes

STROBE Statement—Checklist of items that should be included in reports of transversal study. (DOC 88 kb

AS-OCT measurement.

<p>AS-OCT image showing the measurements of scleral spur (SS), ACD, ACW, LV, AOD750, IT750, and iris curvature.</p

Uni- and multivariate linear regression for associated baseline factors of iris thickness, iris area and choroidal thickness.

<p>* β/P value: regression coefficient and P values of the independent variables in the univariate linear regression model;</p><p>† β/P value: regression coefficient and P values of the independent variables in the multiple linear regression model. Insignificant variables were not present in multivariate regressions;</p><p>In regression models, female was coded as 1 and male as 2 for gender;</p><p>95% CI: 95% confidence interval.</p><p>Uni- and multivariate linear regression for associated baseline factors of iris thickness, iris area and choroidal thickness.</p

Uni- and multivariate linear regression analysis of the association between choroidal thickness and iris parameters.

<p>*Adjusted for age, gender, AL, ACW, PD</p><p>Uni- and multivariate linear regression analysis of the association between choroidal thickness and iris parameters.</p

SS-OCT measurement.

<p>SS-OCT image showing the measurements of choroidal thickness. (A) Choroidal thickness map of the 6×6 mm area centered on the fovea was created. The mean choroidal thickness was obtained for each sector. (B) Automatic placement of the chorioscleral border made by the automatic built-in software in one of the B-scan images of the 3D data set. (C) Choroidal topographic map of the 6×6 mm area.</p

Clinical characteristics of the study subjects.

<p>* P Value: Significance of differences between female and male: 2-samples independent t-test.</p><p>† r/P value: Pearson correlation between ASOCT/SSOCT parameters with age in all subjects.</p><p>Data are expressed as the mean (SD)</p><p>IOP = intraocular pressure; AL = axial length; SE = Spherical equivalent; D = diopter; ACD = anterior chamber depth; ACW = anterior chamber width; ACA = anterior chamber area; ACV = anterior chamber volume; PD = pupil diameter; AOD750 = angle opening distance at 750 μm from the scleral spur; TISA750 = trabecular–iris space area at 750 μm from the scleral spur; ARA = anterior chamber area; IT750 = anterior chamber volume; IAREA = iris area; ICURV = iris curvature; LV = lens vault; SD = standard deviation</p><p>Clinical characteristics of the study subjects.</p