We build a dataset including four kinds of multispectral images with labeled key point correspondence. The dataset includes 7 RGB/NIR image pairs, 4 RGB/Depth image pairs, 3 Flash/no-flash image pairs and 4 different exposure image pairs. In each pair, we uniformly select corner points and label their correspondences. The resolution of the RGB/Depth image pairs is 640 × 480 and all the other im...

the main purpose of this paper is to introduce and study new classes of soft closed sets like soft regular generalized b-closed sets in soft topological spaces (brie y soft rgb-closed set) moreover, soft rg-closed, soft gpr-closed, soft gb-closed, soft gsp-closed, soft g-closed, soft gb-closed, and soft sgb-closed sets in soft topological spaces are introduced in this paper and we investigat...

gait contains important information about the status of the human body and physiological signs. in many medical applications, it isimportant to monitor and accurately analyze the gait of the patient. since walking shows the reproducibility signs in several phases,separating these phases can be used for the gait analysis. in this study, a method based on image processing for extracting phases of...

We present BV photometry of the Sculptor dwarf galaxy to V = 22. These data give evidence for a bimodality in Sculptor’s metallicity distribution based on a discontinuity in the luminosities of horizontal branch (HB) stars and by the presence of two distinct red giant branch (RGB) bumps. A consistent picture of the evolved stars in Sculptor is given by the presence of (1) a metal-poor populatio...

In this paper we propose a multispectral image compression based on lossy to lossless coding, suitable for both spectral and color reproduction. The proposed method divides a multispectral image data into two groups, RGB and residual. The RGB component is extracted from the multispectral image, for example, by using the XYZ Color Matching Functions, a color conversion matrix, and a gamma curve....

This paper studies the problem of RGB-D object recognition. Inspired by the great success of deep convolutional neural networks (DCNN) in AI, researchers have tried to apply it to improve the performance of RGB-D object recognition. However, DCNN always requires a large-scale annotated dataset to supervise its training. Manually labeling such a large RGB-D dataset is expensive and time consumin...

Image labeling tools help to extract objects within images to be used as ground truth for learning and testing in object detection processes. The inputs for such tools are usually RGB images. However with new widely available low-cost sensors like Microsoft Kinect it is possible to use depth images in addition to RGB images. Despite many existing powerful tools for image labeling, there is a ne...