Distinctive and efficient description of image features is an essential task for image registration in photogrammetry and remote sensing. The majority of existing Descriptors estimate a dominant orientation parameter for rotation invariant image matching. The dominant orientation assignment is an error-prone process, and it decreases the capability of the Descriptors. In this paper, a novel feature Descriptor based on the local Binary pattern operator named RILBP (Rotation Invariant Local Binary Pattern) is proposed that is inherently rotation invariant. To compute the RILBP Descriptor, the pixels in the given image region are divided into several sub-regions based on distance and intensity order constraints. Then, a local Binary pattern histogram is generated for each sub-region based on a rotation invariant coordinate system. To increase the Descriptor robustness against geometric distortions, a special weighting process based on a combined ring and Gaussian functions is applied. The proposed RILBP Descriptor was successfully applied for matching of various remote sensing images as: SPOT 5, ETM+, Sentinel 2, IKONOS, IRS P6 and ZY3 sensors, and the results demonstrate its capability compared to common feature Descriptors such as CS-LBP, SIFT, LSS, and MROGH. Compared to the standard CS-LBP Descriptor, the RILBP Descriptor indicates an average performance improvement of about 25%, 10% and 30%, in terms of Recall, Precision and number of correct matches, respectively.

Local Binary Pattern (LBP) is one of popular texture images Descriptors. It is used to extraction features of texture images for classification.  However, in this paper a new version of LBP is proposed that is not used for this purpose.  The proposed Descriptor is used for image retrieval. In other words it can be compare to Scale Invariant Features Transform (SIFT). In term of speed, the proposed method outperforms the SIFT. In addition, in this paper it is used for Infrared images retrieval. These images are low quality and low contrast and if they are used without any pre processing and enhancement, SIFT Descriptor versions cannot extract good features of them. The proposed method can provide better result with higher speed. The proposed method have been compared to center symmetric LBP (CS-LBP) and SIFT by using both infrared images and standard image data-sets.

Recently, one-two Descriptor has been defined and it has been shown that it is a good predictor of the heat capacity at P constant (CP) and of the total surface area (TSA). In this paper, we analyze its generalizations by replacing the value 2 by arbitrary positive value α . We show that these analyses may be on interest, because even good predictions of CP and TSA can be slightly improved. Furthermore, it can be expected that this more general Descriptor can find a wider range of application than the original one. The extremal values of trees have been found for all values of α .

This paper presents a novel Texture-Edge Descriptor, TED, for background modeling and pedestrian detection in video sequences which models texture and edge information of each image block simultaneously. Each block is modeled as a group of adaptive TED histograms that are calculated for pixels of the block over a rectangular neighborhood. TED is an 8-bit Binary code which is independent of the neighborhood size. Experimental results over real-world sequences from PETS database clearly show that TED outperforms LBP.

Singular systems have been studied extensively during the last two decades due to their many practical applications. Such systems possess numerous properties not shared by the well-known state variable systems. This paper considers the linear tracking problem for the continuous-time singular systems. The Hamilton-Jacobi theory is used in order to compute the optimal control and associated trajectory. Two methods are presented for solving these trajectories. The first method uses the concept of the Drazin inverse, and the second involves the derivation and solution of a Riccati equation. Similar to the linear regulator problem, necessary and sufficient conditions for existence and uniqueness of a solution are stated.

The Bertz indices, derived by counting the number of connecting edges of line graphs of a molecule were used in deriving the QSPR models for the physicochemical properties of alkanes. The inability of these indices to identify the hetero centre in a chemical compound restricted their applications to hydrocarbons only. In the present work, a novel molecular Descriptor has been derived from the weighted line graph of the molecular structure and applied in correlating the physicochemical properties of alkane isomers with these Descriptors. A weight is tagged at the vertex of the line graph, which consequently modifies the weight of the edge. These Descriptors were found to classify the alkane isomers and served well in deriving the QSPR models for various physicochemical properties. The mathematical calculations include the quantitative treatment on the role of substituents (alkyl) in governing the properties under study of the alkane isomers. Further, the use of weighted line graph in the enumeration of the topological index opens up a new vista on application to heteroatomic systems.

Targets and objects registration and tracking in a sequence of images play an important role in various areas. One of the methods in image registration is feature-based algorithm which is accomplished in two steps. The first step includes finding features of sensed and reference images. In this step, a scale space is used to reduce the sensitivity of detected features to the scale changes. Afterward, we attribute feature points that obtained in the first step, descriptions using brightness value around the feature points. In this paper, a new algorithm is proposed based on Binary Robust Invariant Scalable Keypoints (BRISK) and Scale Invariant Feature Transform (SIFT) algorithms. The proposed algorithm uses the directional pattern to describe the edges which are around the keypoints. This pattern is perpendicular to the direction of keypoints which shows the direction of the edge and provides more useful information regarding brightness around the feature point to make Descriptor vector. Furthermore, in the proposed algorithm, the output vector consists of multilevel values instead of Binary values which means further useful information is involved in the Descriptor vector. Also, levels of output vectors can be adjusted using a single parameter so that the processor with low computing ability can tune the output to a Binary vector. Experimental results show that the proposed algorithm is more robust than the BRISK algorithm and the efficiency of the algorithm is about the same as BRISK algorithm.

In this paper, we present a new efficient method based on local Binary pattern Descriptor, for face recognition. Because, the calculations in Local Binary pattern are done between two pixels values, so, small changes in the Binary pattern affect its performance. In this paper, a new local average Binary pattern Descriptor is presented based on cellular learning automata and evolutionary computation (CLA-EC). In the proposed method, first, the LABP operator are used to extract uniform local Binary patterns from face images; it should be noted that, in LABP operator to obtain more robust feature representation, many sample points has been used. Then, the best subset of patterns found by CLA-EC methods, and the histogram of these patterns is obtained. Finally, support vector machine is used for classification. The results of experiment on FERET data base show the advantage of the proposed algorithm compared to other algorithms.