A data structure for protein-ligand morphological matching

Pattern recognition techniques can be applied very profitably to proteomics because of the strong linkage of the protein’s molecule morphology and proteins functionalities. In fact, geometric and topological congruence (concavity and convexity correspondences) can be often considered certainly not sufficient but in many cases necessary conditions. In this connection, considering that the “active sites” are always located in one of the biggest concavities (in one of the largest “pockets”) and that the ligand must match this concavity, its effective part must be mainly convex. For this reason, the matching potential can be evaluated through an Extended Gaussian Image (EGI) shape representation. The original EGI, and a few extensions (namely Complex EGI and Enriched Complex EGI) representations and their correspondent concrete data-structures are here discussed. This data structure is then exploited for the implementation and evaluation of the matching stance between the small ligand molecule and a pocket of a protein macromolecule

Protein Gaussian Image (PGI): A protein structural representation based on the spatial attitude of secondary structure

A well-known shape representation usually applied for 3D object recognition is the Extended Gaussian Image (EGI) which maps the histogram of the orientations of the object surface on the unitary sphere. We propose to adopt an analogous “abstract” data-structure named Protein Gaussian Image (PNM) for representing the orientation of the protein secondary structures (e.g. helices or strands) which combines the characteristics of the EGI and the ones of needle maps. The “concrete” data structures is the same as for the EGI, with a hierarchy that starting with a discretization corresponding to the 20 orientations of the icosahedron facets, it is iteratively refined with a factor 4 at each new level (80, 320, 1280, . . . ) up to the maximum precision required. However, in this case to each orientation does not correspond the area of the patches having that orientation but the features of the protein secondary structures having that direction. Among the features we may include the versus (origin versus surface or vice versa), the length of the structure (e.g. the number of amino acids), biochemical properties, and even the sequence of the amino acids (stored as a list). We consider this representation very effective for a preliminary screening when looking in a protein data base for retrieval of a given structural block, or a domain, or even an entire protein. In fact, on this structure it is possible to identify the presence of a given motif, or also sheets (note that parallel or anti-parallel β-sheets are characterized by common or opposite directions of ladders). Herewith some known proteins are described with common typical motifs easily marked in the PGI

Design of automatic vision-based inspection system for solder joint segmentation

Purpose: Computer vision has been widely used in the inspection of electronic components. This paper proposes a computer vision system for the automatic detection, localisation, and segmentation of solder joints on Printed Circuit Boards (PCBs) under different illumination conditions. Design/methodology/approach: An illumination normalization approach is applied to an image, which can effectively and efficiently eliminate the effect of uneven illumination while keeping the properties of the processed image the same as in the corresponding image under normal lighting conditions. Consequently special lighting and instrumental setup can be reduced in order to detect solder joints. These normalised images are insensitive to illumination variations and are used for the subsequent solder joint detection stages. In the segmentation approach, the PCB image is transformed from an RGB color space to a YIQ color space for the effective detection of solder joints from the background. Findings: The segmentation results show that the proposed approach improves the performance significantly for images under varying illumination conditions. Research limitations/implications: This paper proposes a front-end system for the automatic detection, localisation, and segmentation of solder joint defects. Further research is required to complete the full system including the classification of solder joint defects. Practical implications: The methodology presented in this paper can be an effective method to reduce cost and improve quality in production of PCBs in the manufacturing industry. Originality/value: This research proposes the automatic location, identification and segmentation of solder joints under different illumination conditions

Discovery of new TeV supernova remnant shells in the Galactic plane with H.E.S.S

Supernova remnants (SNRs) are prime candidates for efficient particle acceleration up to the knee in the cosmic ray particle spectrum. In this work we present a new method for a systematic search for new TeV-emitting SNR shells in 2864 hours of H.E.S.S. phase I data used for the H.E.S.S. Galactic Plane Survey. This new method, which correctly identifies the known shell morphologies of the TeV SNRs covered by the survey, HESS J1731-347, RX 1713.7-3946, RCW 86, and Vela Junior, reveals also the existence of three new SNR candidates. All three candidates were extensively studied regarding their morphological, spectral, and multi-wavelength (MWL) properties. HESS J1534-571 was associated with the radio SNR candidate G323.7-1.0, and thus is classified as an SNR. HESS J1912+101 and HESS J1614-518, on the other hand, do not have radio or X-ray counterparts that would permit to identify them firmly as SNRs, and therefore they remain SNR candidates, discovered first at TeV energies as such. Further MWL follow up observations are needed to confirm that these newly discovered SNR candidates are indeed SNRs

Maximum Visibility Building Direction for Layered Manufacturing of Jewel Rings

Rapid prototyping is used in jewelry production since the introduction of Stereolithography Apparatus into the market. However, the building orientation is mainly decided by the experience of the operator rather than by any systematic analysis. A theory is therefore needed to analyze the best orientation of jewelry in rapid jewelry production. In this paper, a new orientation methodology is applied to jewel ring models to properly orient them for layer manufacturing. Jewel ring model built is then demonstrated to have better quality and less error. A case study is presented to illustrate the theory.The work described in this paper was supported by a grant from the Hong Kong Polytechnic University (Project No.: G-V617).Mechanical Engineerin

Fully Automatic Registration of 3D Point Clouds

We propose a novel technique for the registration of 3D point clouds which makes very few assumptions: we avoid any manual rough alignment or the use of landmarks, displacement can be arbitrarily large, and the two point sets can have very little overlap. Crude alignment is achieved by estimation of the 3D-rotation from two Extended Gaussian Images even when the data sets inducing them have partial overlap. The technique is based on the correlation of the two EGIs in the Fourier domain and makes use of the spherical and rotational harmonic transforms. For pairs with low overlap which fail a critical verification step, the rotational alignment can be obtained by the alignment of constellation images generated from the EGIs. Rotationally aligned sets are matched by correlation using the Fourier transform of volumetric functions. A fine alignment is acquired in the final step by running Iterative Closest Points with just few iterations

Registration and Recognition in 3D

The simplest Computer Vision algorithm can tell you what color it sees when you point it at an object, but asking that computer what it is looking at is a much harder problem. Camera and LiDAR (Light Detection And Ranging) sensors generally provide streams pixel of values and sophisticated algorithms must be engineered to recognize objects or the environment. There has been significant effort expended by the computer vision community on recognizing objects in color images; however, LiDAR sensors, which sense depth values for pixels instead of color, have been studied less. Recently we have seen a renewed interest in depth data with the democratization provided by consumer depth cameras. Detecting objects in depth data is more challenging in some ways because of the lack of texture and increased complexity of processing unordered point sets. We present three systems that contribute to solving the object recognition problem from the LiDAR perspective. They are: calibration, registration, and object recognition systems. We propose a novel calibration system that works with both line and raster based LiDAR sensors, and calibrates them with respect to image cameras. Our system can be extended to calibrate LiDAR sensors that do not give intensity information. We demonstrate a novel system that produces registrations between different LiDAR scans by transforming the input point cloud into a Constellation Extended Gaussian Image (CEGI) and then uses this CEGI to estimate the rotational alignment of the scans independently. Finally we present a method for object recognition which uses local (Spin Images) and global (CEGI) information to recognize cars in a large urban dataset. We present real world results from these three systems. Compelling experiments show that object recognition systems can gain much information using only 3D geometry. There are many object recognition and navigation algorithms that work on images; the work we propose in this thesis is more complimentary to those image based methods than competitive. This is an important step along the way to more intelligent robots