Semi-supervised multi-output image manifold regression

2017 ◽  
Author(s):  
Hui Wu ◽  
Scott Spurlock ◽  
Richard Souvenir
Keyword(s):  
Output Image ◽  
Image Manifold

A Two-Color Input, Two-Color Output Image Intensifier Panel

1960 ◽  
Vol 48 (11) ◽  
pp. 1842-1846 ◽  
Author(s):  
F. Nicoll ◽  
Alan Sussman
Keyword(s):  
Image Intensifier ◽  
Output Image

Improved interactive color visualization approach for hyperspectral images

2021 ◽  
pp. 147387162110481
Author(s):  
Haijun Yu ◽  
Shengyang Li
Keyword(s):  
Real Time ◽  
Hyperspectral Images ◽  
High Dimensional ◽  
Interactive Control ◽  
Output Image ◽  
Dr Method ◽  
The Rich ◽  
Low Dimensional ◽  
Color Visualization ◽  
Fusion Coefficient

Hyperspectral images (HSIs) have become increasingly prominent as they can maintain the subtle spectral differences of the imaged objects. Designing approaches and tools for analyzing HSIs presents a unique set of challenges due to their high-dimensional characteristics. An improved color visualization approach is proposed in this article to achieve communication between users and HSIs in the field of remote sensing. Under the real-time interactive control and color visualization, this approach can help users intuitively obtain the rich information hidden in original HSIs. Using the dimensionality reduction (DR) method based on band selection, high-dimensional HSIs are reduced to low-dimensional images. Through drop-down boxes, users can freely specify images that participate in the combination of RGB channels of the output image. Users can then interactively and independently set the fusion coefficient of each image within an interface based on concentric circles. At the same time, the output image will be calculated and visualized in real time, and the information it reflects will also be different. In this approach, channel combination and fusion coefficient setting are two independent processes, which allows users to interact more flexibly according to their needs. Furthermore, this approach is also applicable for interactive visualization of other types of multi-layer data.

scholarly journals Generative Visual Manipulation on the Natural Image Manifold

2016 ◽  
pp. 597-613 ◽  
Author(s):  
Jun-Yan Zhu ◽  
Philipp Krähenbühl ◽  
Eli Shechtman ◽  
Alexei A. Efros
Keyword(s):  
Natural Image ◽  
Image Manifold

Dynamic Building Tracking from UAVs Based on Image Manifold Learning

2013 ◽  
Vol 756-759 ◽  
pp. 4121-4125
Author(s):  
Peng Zhang ◽  
Yuan Yuan Ren
Keyword(s):  
Unmanned Aerial Vehicles ◽  
Visual Tracking ◽  
Target Recognition ◽  
Perceptual Information ◽  
System Control ◽  
Aerial Vehicles ◽  
Robust Feature Extraction ◽  
Image Manifold ◽  
Low Dimensional ◽  
Low Dimensional Features

Fast and accurate visual tracking of ground buildings can provide unmanned aerial vehicles (UAVs) with rich perceptual information, which is very important for target recognition, navigation and system control. However, when an UAV moves fast, both background and buildings in visual scenes change relatively and rapidly. Consequently, there are no constant features for objects' appearance, which poses great challenges for visual tracking of buildings. In this paper, we first build an image manifold of buildings, which can encode the continuous variation of appearance. We then propose an efficient approach to learn this manifold and obtain more robust feature extraction results. By using a simple tracking framework, we successfully apply the extracted low-dimensional features to real-time building tracking. Experimental results demonstrate the effectiveness of the proposed method.

A New Extension of Locality Preserving Projections for Image Feature Extraction

2012 ◽  
Vol 241-244 ◽  
pp. 1715-1718
Author(s):  
Guo Hong Huang
Keyword(s):  
Feature Extraction ◽  
Local Information ◽  
Image Feature ◽  
Two Dimensional ◽  
Two Stage ◽  
Image Feature Extraction ◽  
Locality Preserving ◽  
Image Manifold ◽  
New Framework ◽  
Novel Algorithm

This paper proposes a novel algorithm for image feature extraction, namely, the two-directional two-dimensional locality preserving projection, ((2D)2LPP), which can find an embedding from two directions that not only preserves local information and detect the intrinsic image manifold structure, but also combines the both information between rows and those between columns simultaneously. We also combine the advantages of (2D)2LPP and LDA, and propose a new framework for feature extraction as two-stage: “(2D)2LPP+LDA.” The LDA step is performed to further reduce the dimension of feature matrix in the (2D)2LPP subspace. Experimental results on ORL face databases demonstrate the effectiveness of the proposed methods.

A MODEL OF AN ADAPTIVE RECEPTOR BASED ON THE RETINA

1996 ◽  
Vol 04 (04) ◽  
pp. 503-533
Author(s):  
ERIC BOUSSARD ◽  
KHASHAYAR PAKDAMAN ◽  
GILLES BEDFER ◽  
JEAN-FRANÇOIS VIBERT
Keyword(s):  
Gap Junctions ◽  
Lateral Inhibition ◽  
Dopamine Release ◽  
Ganglion Cells ◽  
Visual Pathway ◽  
Biological Data ◽  
Time Constants ◽  
Output Image ◽  
The Difference ◽  
Local Input

A model of the retina based on biological data is developed to study the processing of images by the retina as a model for an adaptive receptor. The direct visual pathway (photoreceptors, bipolar and ganglion cells) and the horizontal units and their gap-junctions, as well as the dopaminergic interplexiform cells and dopamine release are modeled. A small network of 12 units is first simulated in order to study the behavior of the different components involved, and the role played by lateral inhibition is analyzed. A 64×64 photoreceptors model, containing 16,448 units, is then simulated. The following properties were found for stimuli that remain constant in time: (i) The difference between the time constants of the photoreceptors and the horizontal units lead to both the formation of a transient output image that contains pertinent features of the input, and the formation of a reverse after-image when the stimulus is removed. (ii) Global modifications of lateral inhibition strength allow either contrast rendering or contour enhancement. Local modifications of lateral inhibition strength bring about adaptation to local input characteristics.

A Novel Robot Vision Employing a Silicon Retina

2001 ◽  
Vol 13 (6) ◽  
pp. 614-620 ◽  
Author(s):  
Kazuhiro Shimonomura ◽  
◽  
Seiji Kameda ◽  
Kazuo Ishii ◽  
Tetsuya Yagi ◽  
...  
Keyword(s):  
Real Time ◽  
Integrated Circuit ◽  
Large Scale ◽  
Analog Circuit ◽  
Vision System ◽  
Robot Vision ◽  
Illumination Condition ◽  
Output Image ◽  
Analog Cmos ◽  
Silicon Retina

A Robot vision system was designed using a silicon retina, which has been developed to mimick the parallel circuit structure of the vertebrate retina. The silicon retina used here is an analog CMOS very large-scale integrated circuit, which executes Laplacian-Gaussian like filtering on the image in real time. The processing is robust to change of illumination condition. Analog circuit modules were designed to detect the contour from the output image of the silicon retina and to binarize the output image. The images processed by the silicon retina as well as those by the analog circuit modules are received by the DOS/V-compatible mother-board with NTSC signal, which enables higher level processings using digital image processing techniques. This novel robot vision system can achieve real time and robust processings in natural illumination condition with a compact hardware and a low power consumption.

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