In robotic applications and especially 3D map generation of indoor environments، analyzing RGB-D images have become a key problem. The mapping problem is one of the most important problems in creating autonomous mobile robots. Autonomous mobile robots are used in mine excavation، rescue missions in collapsed buildings and even planets’ exploration. Furthermore، indoor mapping is beneficial in finding and rescuing missions. With recent advances، mobile robots are used in hazardous missions such as radioactive areas or collapsing buildings. Having the environment’ s map beforehand can boost efficiency and effectiveness of the mission. In order to digitize the environment، several 3D scans are needed. However، these scans should be merged according to a global coordination system to create a correct، consistent model. This process is called image registration. If the robot with 3D scanner is able to accurately localize itself، the registration can be done directly by robots pose. However، due to imprecise robot sensors، self-localization is error prone. Therefore، the geometric structure of overlapping 3D scans is considered. In order to registering various points sets، Iterative Closest Point (ICP) algorithm is used. ICP is the most common approach to align point clouds in two consecutive image frames. This algorithm uses a point to point approach. RGB and depth images which are captured by Kinect are used in this study. In order to reducing data points and performing faster 3D map creation، depth images are converted to point clouds and then segmentation is done according to image planes. For this purpose RGB images are segmented by region growing segmentation algorithm. In this algorithm، the image was initially over segmented. This algorithm uses stack data structure and Euclidean distance in Lab color space to segment the image. Euclidean distance in Lab color space describes the resemblance of two colors to each other. In this algorithm، the aim is to label each pixel to a segment. To this end، each unlabeled pixels Euclidean distance to its neighboring mean color is checked to be within a threshold. For over-segmentation، if the distance satisfies the smaller threshold، the more pixels will be merged to the segment. Afterwards a plane was fit to each segment. After segmentation، each segment should be represented by a plane. Eventually، the segments were merged based on the product of normal vectors and plane fitting error criteria. After segmentation، planes were fit to the new segments again. A given number of points were generated on the plane. ICP algorithm was executed on these points and transfer and rotation matrices were obtained. Generating points on the plane results in fewer points. Therefore، the points were reduced and algorithms performance was increased. The results show that the proposed method increases the speed up to 55 and 91 percent in consecutive and non-consecutive frames on average، respectively.

Spectral decomposition of time series has a significant role in seismic data processing and interpretations. Since the earth acts as a low-pass filter, it changes the frequency content of the passing seismic waves. Conventional methods of representing signals in a time domain and frequency domain cannot show the time information and the frequency information simultaneously. Time-frequency transforms an upgraded spectral decomposition to a new step and can show time and frequency information simultaneously.Time-frequency transforms generate a high volume of spectral components, which contain useful information about the reservoir and can be decomposed into single frequency volumes. These single frequency volumes can overload the limited space of a computer hard disk and are not easy for an interpreter to investigate them individually; therefore, it is important to use methods to decrease the volume without losing information. The frequency slices are thus separated from these volumes and used for an interpretation.In this study, three different methods were used to represent a buried channel. In the first method, the numbers of the single frequency slices were investigated, variations of the frequency amplitudes in the slices were observed, and an expert interpreter could obtain some information about the channel content and lateral variation. Since different frequencies contain different types of information (low frequencies are sensible to channel content and high frequencies are sensible to channel boundaries), none of the slices were able to show all information simultaneously. In the next two methods using a color stacking method, the RGB plots were constructed which, due to the different frequency content, resulted in more information than the frequency slice representation method.An RGB image, sometimes referred to as a true color image, is an image that defines red, green, and blue color components for each individual pixel and has an intensity between 0 and 1. In this study, RGB plots were constructed in two different manners, RGB plots based on conventional RGB plot methods and RGB plots using basis functions. In the conventional method, three different frequency slices were mapped against the red, green and blue components. Although this method obviates some drawbacks of the single frequency plots, it uses only three slices and practically ignores a big part of information. Using basis functions and defining windows, the interpreter was able to introduce some frequency intervals and plot them against the primary components and use the total bandwidth or its major part. Three simple raised cosine functions having different frequency centers and different periods were chosen. The image quality strongly depended on these two parameters. Longer window widths will introduce longer frequency widths into every primary component and resulted in smoother color combinations for images and very short periods had the same results as the conventional RGB plot method. Different centers showed different details. Low frequency centers showed channel content properties, and high frequency centers showed channel boundaries and fine branches.In this study, the spectral decomposition was first performed on land seismic data from an oil field in Iran using a short time Fourier (STFT) transform and an S transform. Then three demonstration methods were applied for channel detection. Finally it was shown that how RGB color stacking method represented buried channels in more precise images and how a basis function based RGB represents better results than the conventional RGB method.

Wetlands are one of the most prominent ecosystems in the world. These vital and diverse habitats are among life-giving systems that have no alternative. However, none of the world's ecosystems have experienced human-centered injuries and damages as much as wetlands. One of the main threats to Gilan wetlands are human factors such as urban, domestic and industrial wastewater, overfishing and converting wetland marginal lands into agricultural lands. In this study, RGB images were used to assess the water quality parameters of Anzali wetland (Beheshti Island Station) and the related data were compared to the values obtained from the TSS measurement. Based on the obtained data, the intensity of red color (R) in the macroscopic images (with the naked eye) from the wetland can be an environmental indicator to measure TSS concentration. The results of RGB analysis for red color with a correlation coefficient of 0. 8513, for green color (G) with a correlation coefficient of 0. 832 and for blue color (B) with a correlation coefficient of 0. 663 were obtained. Finally, a correlation coefficient (R2=0/8035) between the decrease of RGB values and the increase of TSS concentration was obtained. Other parameters such as pH and Secchi Depth test were also measured in this study.

Introduction: Color is the first quality attribute of food evaluated by consumers, and is therefore an important quality component of food which influences consumer’s choice and preferences (Maguire, 1994). Color measurement of food products has been used as an indirect measure of other quality attributes such as flavor and contents of pigments because it is simpler, faster and correlates well with other physicochemical properties. Therefore, rapid and objective measurement of food color is required in quality control for the commercial grading of products (Trusell et al., 2005). Among different color spaces, L*a*b* color space is the most practical system used for measuring of color in food due to the uniform distribution of colors in this system as well its high similarity to human perception of color. …

Detailed information on forest combination is required for many environmental, monitoring, and forest protection purposes. The link between ecology and remote sensing provides valuable information for the study of forest trees to facilitate the study of ecosystem performance and to measure the spatial distribution of vegetation. In recent years, the use of modern remote sensing methods and techniques based on UAVs have been used for regular updating of forest inventory. In this research, different data sources including multi-spectral and RGB images with very high spatial resolution, were used for tree species recognition in plain forests of Noor City located in Mazandaran province. Also, taking images was performed in the growing season to prepare a time series of UAV-RGB images for investigating the effect of tree crown phonological changes on classification accuracy.  Following orthomosaic generation, RGB (NGB, NRB) and multi-spectral (NDVI, CIgreen) indices were calculated and the random forest classification method was used for forest species classification. Based on single-time images, late April images provided the highest overall accuracy (75%). However, the results of the time series obtained from RGB images showed an increase in accuracy of up to 86%. Species identification based on multispectral images obtained from the Sequoia sensor also provided 85% accuracy. The results showed that the single-time image at the appropriate time using a UAV-RGB, compared to taking a time series and using a UAV equipped with multispectral sensors, has acceptable and less expensive results for tree recognition in the study area.

Face detection is an important part of many computer vision systems and has several applications in areas, such as face tracking, visual surveillance, video conferencing, face recognition, intelligent human-computer interfaces and content-based information retrieval. For use of face detection in this applications, need a fast and precise face detection algorithm. But Detection speed of traditional face detection method based on Ada Boost algorithm is slow since an exhaustive search in image. Over the past few years, the availability of color images with corresponding depth data has increased due to the popularity of low-cost RGB-Depth cameras, notably Kinect. The complementary nature of the depth and visual information provided by the Kinect sensor opens up new opportunities to solve fundamental problems in face detection with intelligently constraining search over the image. In this paper, utilize additional depth data to reduce the computational cost of face detection. Leveraging the additional depth images from a Kinect camera, and use of Recurring in nature idea, we are able to accelerate the Viola-Jones face detector by 270%.