— In this methodological paper, provided with a relevant survey value, an original low-level computer vision (CV) software pipeline, called RGB Image Automatic Mapper (RGBIAM), is presented and discussed. RGBIAM is a lightweight computer program capable of automated near real-time superpixel detection and quality assessment in an uncalibrated monitor-typical red-green-blue (RGB) image, depict...

There are currently five transformation matrices being considered for use in the linear chromatic-adaptation transform in a revision of the CIECAM97s color appearance model. Four of the matrices were developed independently for the purpose of transforming tristimulus values to spectrally sharpened RGB responses. The fifth is the HuntPointer-Estevez transform of CIE tristimulus values to normali...

Visual object tracking technology is one of the key issues in computer vision. In this paper, we propose a visual object tracking algorithm based on cross-modality featuredeep learning using Gaussian-Bernoulli deep Boltzmann machines (DBM) with RGB-D sensors. First, a cross-modality featurelearning network based on aGaussian-Bernoulli DBM is constructed, which can extract cross-modality feature...

Low cost RGB-D sensors have been used extensively in the field of Human Action Recognition. The availability of skeleton joints simplifies the process of feature extraction from depth or RGB frames, and this feature fostered the development of activity recognition algorithms using skeletons as input data. This work evaluates the performance of a skeleton-based algorithm for Human Action Recogni...

This work proposes a robust visual odometry method for structured environments that combines point features with line and plane segments, extracted through an RGB-D camera. Noisy depth maps are processed by a probabilistic depth fusion framework based on Mixtures of Gaussians to denoise and derive the depth uncertainty, which is then propagated throughout the visual odometry pipeline. Probabili...

The purpose of this paper is to investigate if it is possible to use estimation techniques to reduce the difference between predicted and actual RGB values. Images and spectral reflectances of two classes of objects were used: matte, 2D (Munsell chips and Macbeth chart) and natural, 3D objects (faces). In the prediction phase, a simple RGB model was evaluated which takes into account only the s...

Does K-Means reasonably divides the data into k groups is an important question that arises when one works on Image Segmentation? Which color space one should choose and how to ascertain that the k we determine is valid? The purpose of this study was to explore the answers to aforementioned questions. We perform K-Means on a number of 2-cluster, 3cluster and k-cluster color images (k>3) in RGB ...

The colorful appearance of a physical painting is determined by the distribution of paint pigments across the canvas, which we model as a per-pixel mixture of a small number of pigments with multispectral absorption and scattering coefficients. We present an algorithm to efficiently recover this structure from an RGB image, yielding a plausible set of pigments and a low RGB reconstruction error...

The proposed method adopts rule based color model which are defined based on luminance and chrominance content present in an image. YCbCr color space effectively isolates luminance from chrominance compared to other color spaces like RGB and normalized RGB (rgb). The proposed method not only separates fire flame pixels but also isolates high temperature fire centre pixels by taking into account...