• Journal of Internet Computing and Services
• /
• v.16 no.3
• /
• pp.21-32
• /
• 2015

Since spatio-temporal local features for video representation have become an important issue of modeless bottom-up approaches in action recognition, various methods for feature extraction and description have been proposed in many papers. In particular, BoF(bag of features) has been promised coherent recognition results. The most important part for BoF is how to represent dynamic information of actions in videos. Most of existing BoF methods consider the video as a spatio-temporal volume and describe neighboring 3D interest points as complex volumetric patches. To simplify these complex 3D methods, this paper proposes a novel method that builds BoF representation as a way to learn 2D interest points directly from video data. The basic idea of proposed method is to gather feature points not only from 2D xy spatial planes of traditional frames, but from the 2D time axis called spatio-temporal frame as well. Such spatial-temporal features are able to capture dynamic information from the action videos and are well-suited to recognize human actions without need of 3D extensions for the feature descriptors. The spatio-temporal BoF approach using SIFT and SURF feature descriptors obtains good recognition rates on a well-known actions recognition dataset. Compared with more sophisticated scheme of 3D based HoG/HoF descriptors, proposed method is easier to compute and simpler to understand.

• Journal of the Korean Society of Surveying, Geodesy, Photogrammetry and Cartography
• /
• v.30 no.6_2
• /
• pp.643-651
• /
• 2012

Object recognition belongs to high-level processing that is one of the difficult and challenging tasks in computer vision. Digital photogrammetry based on the computer vision paradigm has begun to emerge in the middle of 1980s. However, the ultimate goal of digital photogrammetry - intelligent and autonomous processing of surface reconstruction - is not achieved yet. Object recognition requires a robust shape description about objects. However, most of the shape descriptors aim to apply 2D space for image data. Therefore, such descriptors have to be extended to deal with 3D data such as LiDAR(Light Detection and Ranging) data obtained from ALS(Airborne Laser Scanner) system. This paper introduces extension of chain code to 3D object space with hierarchical approach for segmenting point cloud data. The experiment demonstrates effectiveness and robustness of the proposed method for shape description and point cloud data segmentation. Geometric characteristics of various roof types are well described that will be eventually base for the object modeling. Segmentation accuracy of the simulated data was evaluated by measuring coordinates of the corners on the segmented patch boundaries. The overall RMSE(Root Mean Square Error) is equivalent to the average distance between points, i.e., GSD(Ground Sampling Distance).

• KSII Transactions on Internet and Information Systems (TIIS)
• /
• v.10 no.8
• /
• pp.3806-3825
• /
• 2016

This paper brings out a neoteric frame of reference for visual semantic based 3d video search and retrieval applications. Newfangled 3D retrieval application spotlight on shape analysis like object matching, classification and retrieval not only sticking up entirely with video retrieval. In this ambit, we delve into 3D-CBVR (Content Based Video Retrieval) concept for the first time. For this purpose we intent to hitch on BOVW and Mapreduce in 3D framework. Here, we tried to coalesce shape, color and texture for feature extraction. For this purpose, we have used combination of geometric & topological features for shape and 3D co-occurrence matrix for color and texture. After thriving extraction of local descriptors, TB-PCT (Threshold Based- Predictive Clustering Tree) algorithm is used to generate visual codebook. Further, matching is performed using soft weighting scheme with L₂ distance function. As a final step, retrieved results are ranked according to the Index value and produce results .In order to handle prodigious amount of data and Efficacious retrieval, we have incorporated HDFS in our Intellection. Using 3D video dataset, we fiture the performance of our proposed system which can pan out that the proposed work gives meticulous result and also reduce the time intricacy.

• Molecular & Cellular Toxicology
• /
• v.5 no.1
• /
• pp.14-22
• /
• 2009

In vitro cytotoxicity of 23 alkyl phenols (APs) on human cervical cancer cell lines (HeLa) was determined using the lactate dehydrogenase (LDH) cytotoxicity assay. Two different sets of descriptors were used to construct the calibration model based on Genetic Algorithm-Multiple Linear Regression (GA-MLR) based on the experimental data. A statistically robust Structure-Activity Relationships (QSAR) model was achieved ($R^2$=95.05%, $Q^2_{LOO}$=91.23%, F=72.02 and SE= 0.046) using three Dragon descriptors based on Me (0D-Constitutional descriptor), BELp8 (2D-Burden eigenvalue descriptor) and HATS8p (3D-GETAWAY descriptor). However, external validation could not fully prove its validity of the selected QSAR in characterization of the cytotoxicity of APs towards HeLa cells. Nevertheless, the cytotoxicity profiles showed a finding that 4-n-octylphenol (4-NOP), 4-tert-octyl-phenol (4-TOP), 4-n-nonylphenol (4-NNP) had a more potent cytotoxic effect than other APs tested, inferring that increased length and molecular bulkiness of the substituent had important influence on the LDH cytotoxicity.

• The Korean Journal of Pesticide Science
• /
• v.6 no.4
• /
• pp.231-243
• /
• 2002

Starting with linear free energy relationships (LFER), drug design to mimic of the activated complexes at transition state, and hydrolysis mechanisms to control the potency and residual properties of pesticides were introduced and summarized for the necessity. In order to understand the searching or development of new agrochemicals by two dimensional quantitative structure-activity relationship (2D QSAR) methodology, a series of the various descriptors, steric constants, electronic constants including quantum pharmacological parameters and hydrophobic constants were classified and discussed for results of the several studied cases. In addition, the processes of development of new agrochemicals by QSAR techniques were introduced simply.

• Proceedings of the IEEK Conference
• /
• 2002.07c
• /
• pp.1531-1534
• /
• 2002

Shape description and its corresponding matching algorithm is one of the main concerns in MPEG-7. In this paper, a new method is proposed for shape registration of 2D objects for MPEG-7 Shapes are recognized using the Hu statistical moments in frequency domain. The Hu moments are moment-based descriptors of planar shapes, which are invariant under general translation, rotational, scaling, and reflection transformation. The image is transformed into frequency domain using Fourier Transform. Annular and radial wedge distributions fur the power spectra are extracted. Different statistical features (Hu moments) are found f3r the power spectrum of each selected transformed individual feature. The Euclidean distance of the extracted moment descriptors of the features are found with respect to the shapes in the database. The minimum Euclidean distance is the candidate for the matched shape. The simulation results are performed on the test shapes of MPEG-7.

• Journal of the Korean Chemical Society
• /
• v.60 no.4
• /
• pp.225-234
• /
• 2016

The activity of 34 sulfonamide derivatives has been estimated by means of multiple linear regression (MLR), artificial neural network (ANN), simulated annealing (SA) and genetic algorithm (GA) techniques. These models were also utilized to select the most efficient subsets of descriptors in a cross-validation procedure for non-linear -log (IC50) prediction. The results obtained using GA-ANN were compared with MLR-MLR, MLR-ANN, SA-ANN and GA-ANN approaches. A high predictive ability was observed for the MLR-MLR, MLR-ANN, SA-ANN and MLR-GA models, with root mean sum square errors (RMSE) of 0.3958, 0.1006, 0.0359, 0.0326 and 0.0282 in gas phase and 0.2871, 0.0475, 0.0268, 0.0376 and 0.0097 in solvent, respectively (N=34). The results obtained using the GA-ANN method indicated that the activity of derivatives of sulfonamides depends on different parameters including DP03, BID, AAC, RDF035v, JGI9, TIE, R7e+, BELM6 descriptors in gas phase and Mor 32u, ESpm03d, RDF070v, ATS8m, MATS2e and R4p, L1u and R3m in solvent. In conclusion, the comparison of the quality of the ANN with different MLR models showed that ANN has a better predictive ability.

• Journal of applied mathematics & informatics
• /
• v.34 no.3_4
• /
• pp.319-330
• /
• 2016

The hyper-Zagreb index HM(G) of a simple graph G is defined as the sum of the terms (d_u+d_v)² over all edges uv of G, where d_u denotes the degree of the vertex u of G. In this paper, we present several upper and lower bounds on the hyper-Zagreb index in terms of some molecular structural parameters and relate this index to various well-known molecular descriptors.

• Bulletin of the Korean Chemical Society
• /
• v.30 no.1
• /
• pp.67-76
• /
• 2009

The development of a variety of methods like AM1, PM3, PM5 and DFT now allows the calculation of atomic and molecular properties with high precision as well as the treatment of large molecules with predictive power. In this paper, these methods have been used to calculate a number of quantum chemical descriptors (like Klopman atomic softness in terms of $E_n^{\ddag}\;and\;E_m^{\ddag}$, chemical hardness, global softness, electronegativity, chemical potential, electrophilicity index, heat of formation, total energy etc.) for 75 alkanes to predict their boiling point values. The 3D modeling, geometry optimization and semiempirical & DFT calculations of all the alkanes have been made with the help of CAChe software. The calculated quantum chemical descriptors have been correlated with observed boiling point by using multiple linear regression (MLR) analysis. The predicted values of boiling point are very close to the observed values. The values of correlation coefficient ($r^2$) and cross validation coefficient ($r_{cv}^2$) also indicates the generated QSPR models are valuable and the comparison of all the methods indicate that the DFT method is most reliable while the addition of Klopman atomic softness $E_n^{\ddag}$ in DFT method improves the result and provides best correlation.

• Asian Pacific Journal of Cancer Prevention
• /
• v.17 no.8
• /
• pp.3997-4001
• /
• 2016

Background: Cancer is a worldwide health problem and pain is among the most common and unpleasant effects affecting well-being of cancer patients. Accurate description of pain can help physicians to improve its management. Many English tools have been developed to assess pain. Onkly a limited number of these are applied in Arab countries. Our aim was to assess the quality, the nature and the severity of pain using the short McGill Pain Questionnaire (SF-MPQ) on cancer patients in the National Institute of Oncology (NIO) in Rabat, Morocco. Materials and Methods: The tool used is the SF-MPQ inspired from the Arabic version of the MPQ. The subjects were cancer patients (N=182) attending the NIO, from 24th October 2015 to 8th January 2016, aging ${\geq}18$ years old, experiencing pain and coming to have or to update their pain medication. Results: The rate of participation was 96.3%. Eight patients had difficulties to express their pain using descriptors, but could use the Visual Analogue Scale (VAS) and the body diagram. The most frequent sensory descriptors were 'Throbbing', 'Shooting', 'Hot-Burning'. The most used affective descriptor was 'Tiring-Exhausting'. The mean VAS was 6.6 (2.4). The mean score of all items was 11.9 (7.8). The patients were suffering from severe pain. The internal consistency of the form was s acceptable. Conclusions: The findings indicate that most of the patients attending the pain center of the NIO could use the descriptors of the SF-MPQ to describe their pain. They indicate the usefulness of the SF-MPQ to assess the nature and the severity of pain in cancer patients. This tool should be tested in other Moroccan and Arabic contexts associated with other tools in clinical trials.