Erratum: Image Size Scalable Full-parallax Coloured Three-dimensional Video by Electronic Holography

With the development of artificial intelligence technologies, it is possible to use computer to read digital medical images. Because Alzheimer’s disease (AD) has the characteristics of high incidence and high disability, it has attracted the attention of many scholars, and its diagnosis and treatment have gradually become a hot topic. In this paper, a multimodal diagnosis method for AD based on three-dimensional shufflenet (3DShuffleNet) and principal component analysis network (PCANet) is proposed. First, the data on structural magnetic resonance imaging (sMRI) and functional magnetic resonance imaging (fMRI) are preprocessed to remove the influence resulting from the differences in image size and shape of different individuals, head movement, noise, and so on. Then, the original two-dimensional (2D) ShuffleNet is developed three-dimensional (3D), which is more suitable for 3D sMRI data to extract the features. In addition, the PCANet network is applied to the brain function connection analysis, and the features on fMRI data are obtained. Next, kernel canonical correlation analysis (KCCA) is used to fuse the features coming from sMRI and fMRI, respectively. Finally, a good classification effect is obtained through the support vector machines (SVM) method classifier, which proves the feasibility and effectiveness of the proposed method.

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Three-Dimensional Image. Size Perception of Binocular Stereoscopic Images and Vergence of the Eye.

The Journal of the Institute of Television Engineers of Japan ◽

10.3169/itej1978.50.1300 ◽

1996 ◽

Vol 50 (9) ◽

pp. 1300-1310

Author(s):

Toshiya Morita ◽

Nobuyuki Hiruma ◽

Tetsuo Mitsuhashi ◽

Toshio Motoki

Keyword(s):

Three Dimensional ◽

Size Perception ◽

Image Size ◽

Stereoscopic Images ◽

Dimensional Image

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Constancy of Height and Speed in Three-Dimensional Information Displays

Proceedings of the Human Factors and Ergonomics Society Annual Meeting ◽

10.1177/1541931213601754 ◽

2017 ◽

Vol 61 (1) ◽

pp. 1562-1566

Author(s):

Douglas J. Gillan

Keyword(s):

Retinal Image ◽

Three Dimensional ◽

Image Size ◽

Physical Size ◽

Depth Cues ◽

Information Displays ◽

Pictorial Cues ◽

Actual Height ◽

Perceived Speed ◽

Emmert’S Law

Pictorial cues to depth create a three-dimensional appearance in two-dimensional displays. With sufficient pictorial depth cues, a given physical size appears to be larger at a greater perceived distance (or the perceived size is constant at different perceived depths, despite changes in the retinal image – size constancy). Two experiments investigated the effects of perceived depth on the relation between the actual height of an object and the perceived height (Experiment 1) and the relation between the actual speed of the object the perceived speed (Experiment 2). Consistent with Emmert’s Law (Perceived Size = Retinal Image Size x Perceived Depth), perceived depth influenced both perceived height and perceived speed. These findings suggest that displays that use pictorial cues to depth could easily result in misperception of the height or speed of objects in the display.

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Oversampling Errors in Multimodal Medical Imaging Are Due to the Gibbs Effect

Mathematics ◽

10.3390/math9121348 ◽

2021 ◽

Vol 9 (12) ◽

pp. 1348

Author(s):

Davide Poggiali ◽

Diego Cecchin ◽

Cristina Campi ◽

Stefano De Marchi

Keyword(s):

Medical Imaging ◽

Medical Images ◽

Interpolation Method ◽

Three Dimensional ◽

Mean Value ◽

Experimental Results ◽

Interpolation Error ◽

Image Size ◽

Experimental Section ◽

Neuroimaging Software

To analyze multimodal three-dimensional medical images, interpolation is required for resampling which—unavoidably—introduces an interpolation error. In this work we describe the interpolation method used for imaging and neuroimaging and we characterize the Gibbs effect occurring when using such methods. In the experimental section we consider three segmented three-dimensional images resampled with three different neuroimaging software tools for comparing undersampling and oversampling strategies and to identify where the oversampling error lies. The experimental results indicate that undersampling to the lowest image size is advantageous in terms of mean value per segment errors and that the oversampling error is larger where the gradient is steeper, showing a Gibbs effect.

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Recent development of 3D focal plane arrays

Electronics and Communications ◽

10.20535/2312-1807.2011.16.4.245522 ◽

2011 ◽

Vol 16 (4) ◽

pp. 122-125

Author(s):

Article Arhiv ◽

G.W. Kamerman

Keyword(s):

Focal Plane ◽

Three Dimensional ◽

Focal Plane Arrays ◽

Frame Rate ◽

Pulse Repetition ◽

Time Of Arrival ◽

Image Size ◽

Frame Size ◽

3D Images ◽

Range Ambiguity

Laser radars have traditionally employed beam scanning and sequential range interrogation in order to generate three-dimensional (3D) images. Pulsed modulation is commonly used in order to measure range. The image size and image rate of 3D or range imaging laser radars are frequently limited by the pulse repetition rate and by scanner efficiency. Increases in the pulse repetition may increase frame size and frame rate, but only at the expense of increased range ambiguity and increased complexity of the scanner and transmitter. Staring arrays that incorporate time of arrival measurements have recently become available. These arrays have the potential of simplifying instrument design while increasing image size and image rate without increasing range ambiguity. In our work, we discuss most sensitive aspects of ladar design using focal plane arrays

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Tensor Tucker Decomposition based Geometry Compression on Three Dimensional LiDAR Point Cloud Image

International Journal of Innovative Technology and Exploring Engineering - Special Issue ◽

10.35940/ijitee.c8551.019320 ◽

2020 ◽

Vol 9 (3) ◽

pp. 1897-1903

Keyword(s):

Point Cloud ◽

Predictive Coding ◽

Three Dimensional ◽

3D Point Cloud ◽

Image Size ◽

Geometry Compression ◽

Memory Time ◽

Efficient Data ◽

Static Images ◽

Value Decomposition

Data Visualization in static images is still dynamically growing and changing with time in recent days. In visualization applications, memory, time and bandwidth are crucial issues when handling the high resolution three dimensional (3D) Light Detection and Ranging (LiDAR) data and they progressively demand efficient data compression strategies. This shortage is strongly motivating us to develop an efficient 3D point cloud image compression methodology. This work introduces an innovative lossless compression algorithm for a 3D point cloud image based on higher-order singular value decomposition (HOSVD) technique. This algorithm starts with the preprocessing method which removes the unreliable 3D points and then it combines the HOSVD together with the normalization, predictive coding followed by Run Length encoding to compress the HOSVD coefficients. This work accomplished lower mean square error (MSE), high (infinitive) Peak signal noise ratio (PSNR) to produce the lossless decompressed 3D point cloud image. The storage size has been reduced to one by fourth of its original 3D LiDAR point cloud image size.

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