In spite of the huge economic importance of Aframomum melegueta in the herbal and pharmaceutical industries, its production is limited by lack of planting materials (propagules). The plant also lacks scientific descriptors, which has often led to misidentification with adverse health implications. We therefore aimed at developing a descriptor list to facilitate the identification of A. meleguet...

(Received April 21, 2010) ABSTRACT The full combinatorial search methodology has been used with a set of molecular connectivity indices plus five experimental parameters and the molar mass to extract the best descriptors for twelve properties of a set of organic solvents. The performance of the full combinatorial methodology is compared with the performance of the greedy search methodology obt...

This work presents study and experimentation for object recognition when isolated objects are under discussion. The circumstances of similarity transformations, presence of noise, and occlusion have been included as the part of the study. For simplicity, instead of objects, outlines of the objects have been used for the whole process of the recognition. Fourier Descriptors have been used as fea...

In this paper we compare the performance of interest point descriptors. Many different descriptors have been proposed in the literature. However, it is unclear which descriptors are more appropriate and how their performance depends on the interest point detector. The descriptors should be distinctive and at the same time robust to changes in viewing conditions as well as to errors of the point...

Trajectory-pooled Deep-learning Descriptors have been the state-of-the-art feature descriptors for human action recognition in video on many datasets. This paper improves their performance by applying the proposed eigen-evolution pooling to each trajectory, encoding the temporal evolution of deep learning features computed along the trajectory. This leads to Eigen-Evolution Trajectory (EET) des...

In the literature of pattern recognition and computer vision, local descriptors have been widely used in applications such as shape matching and object recognition. Numerous descriptors have been proposed and evaluated, but little work is reported in the area of medical image, especially ultrasonic images. In this paper, we assess the performance of different local descriptors to detect specifi...

Despite the progress made in the area of local image descriptors in recent years, virtually no literature is available on the use of more recent descriptors for the problem of 3D face recognition, such as BRIEF, ORB, BRISK or FREAK, which are binary in nature and, therefore, tend to be faster to compute and match, while requiring significantly less memory for storage than, for example, SIFT or ...

We propose a reinforcement learning approach for an adaptive selection and application of 3D point cloud feature descriptors for the purpose of 3D object classification. The result of the learning process is an autonomously learned strategy of selection of descriptors with the property that the successive application of these descriptors to a 3D point cloud yields high classification rates amon...

Analyzing protein binding sites provides detailed insights into the biological processes proteins are involved in, e.g., into drug-target interactions, and so is of crucial importance in drug discovery. Herein, we present novel alignment-independent binding site descriptors based on DrugScore potential fields. The potential fields are transformed to a set of information-rich descriptors using a...

We introduce several series of novel ZE-isomerism descriptors derived directly from two-dimensional molecular topology. These descriptors make use of a quantity named ZE-isomerism correction, which is added to the vertex degrees of atoms connected by double bonds in Z and E configurations. This approach is similar to the one described previously for topological chirality descriptors (Golbraikh,...