Generative Model with Coordinate Metric Learning for Object Recognition Based on 3D Models

Given large amount of real photos for training, Convolutional neural network shows excellent performance on object recognition tasks. However, the process of collecting data is so tedious and the background are also limited which makes it hard to establish a perfect database. In this paper, our generative model trained with synthetic images rendered from 3D models reduces the workload of data collection and limitation of conditions. Our structure is composed of two sub-networks: semantic foreground object reconstruction network based on Bayesian inference and classification network based on multi-triplet cost function for avoiding over-fitting problem on monotone surface and fully utilizing pose information by establishing sphere-like distribution of descriptors in each category which is helpful for recognition on regular photos according to poses, lighting condition, background and category information of rendered images. Firstly, our conjugate structure called generative model with metric learning utilizing additional foreground object channels generated from Bayesian rendering as the joint of two sub-networks. Multi-triplet cost function based on poses for object recognition are used for metric learning which makes it possible training a category classifier purely based on synthetic data. Secondly, we design a coordinate training strategy with the help of adaptive noises acting as corruption on input images to help both sub-networks benefit from each other and avoid inharmonious parameter tuning due to different convergence speed of two sub-networks. Our structure achieves the state of the art accuracy of over 50\% on ShapeNet database with data migration obstacle from synthetic images to real photos. This pipeline makes it applicable to do recognition on real images only based on 3D models.Comment: 14 page

Development of a scale for factors causing delays in infrastructure projects in India

The objective of the paper is to develop a validated scale to measure the factors that cause delays in infrastructure projects. The study employed a standard three phase scale development procedure of Churchill (1979) which was augmented subsequently by Nunnally, Bernstein and Berge (1994) and Prakash and Phadtare (2018). In phase one, 73 factors that cause delays were identified, which were reduced to 45 based on literature review and expert opinions. These 45 factors were subjected to an exploratory factor analysis (EFA) and confirmatory factor analysis (CFA) in phase two and three, respectively, to refine and establish convergent, discriminant and nomological validity of the scale. The study confirms that delays in infrastructure projects happen due to six factors, i.e., Contractor Related Factors (CON); Consultant Related Factors (CS); External Factors (EX); Labour Related Factors (LR); Material Related Factors (MT) and Design Related Factors (DJ). The study is particularly useful for the firms engaged in the development of infrastructure projects globally, as it identifies and ranks the factors that cause delays in a project. However, the study being confirmatory in nature only confirms the grouping of factors causing delays and is also limited by the possibility of sampling error.&nbsp

Estimation of Total Phenols, Flavanols and Extractability of Phenolic Compounds in Grape Seeds Using Vibrational Spectroscopy and Chemometric Tools

Near infrared hyperspectral data were collected for 200 Syrah and Tempranillo grape seed samples. Next, a sample selection was carried out and the phenolic content of these samples was determined. Then, quantitative (modified partial least square regressions) and qualitative (K-means and lineal discriminant analyses) chemometric tools were applied to obtain the best models for predicting the reference parameters. Quantitative models developed for the prediction of total phenolic and flavanolic contents have been successfully developed with standard errors of prediction (SEP) in external validation similar to those previously reported. For these parameters, SEPs were respectively, 11.23 mg g−1 of grape seed, expressed as gallic acid equivalents and 4.85 mg g−1 of grape seed, expressed as catechin equivalents. The application of these models to the whole sample set (selected and non-selected samples) has allowed knowing the distributions of total phenolic and flavanolic contents in this set. Moreover, a discriminant function has been calculated and applied to know the phenolic extractability level of the samples. On average, this discrimination function has allowed a 76.92% of samples correctly classified according their extractability level. In this way, the bases for the control of grape seeds phenolic state from their near infrared spectra have been stablished.España MINECO AGL2017-84793-C2España, Universidad de Sevilla VPPI-II.2, VPPI-II.

Role of Principal Supervision on the Relationship between Students’ Personnel Services and Academic Achievement in Secondary Schools: A Preliminary Report

This study investigates the role principal supervision on the relationship between students’ personnel services and academic achievement in secondary school via mixed-method approaches (quantitative and qualitative). Sample size of 100 students in one of the secondary schools formed the respondents for the quantitative study, while principals (junior and senior section) of the selected school formed the informants for the qualitative study. Questionnaire tagged “Students’ Personnel Services and Supervision Questionnaire (SPSPQ)” was used to elicit data from students while interview protocol tagged “Principal Supervision, Students’ Personnel Services and Academic Achievement (PSA)” was used to collect relevant data from the principals. Data collected through quantitative study was analysed using Statistical Package for Social Sciences (SPSS) and Partial Least Square (PLS software) were used to assess the psychometric properties of the constructs, an Nvivo software (version 10) was used for coding and thematic analysis of the data collected. Quantitative findings revealed that the individual item reliability of the constructs had loadings between .709 and .956 while the composite reliability coefficients of the latent constructs had loadings between .81 and .98. Similarly, the average variance explained of showed high loading (minimum of .68 and maxmum of .78 ) on their respective constructs. In addition, qualitative findings showed that services are provided for students in secondary schools. Conclusively, the preliminary results from the two studies are reliable , therefore it demonstrates the potency for the conduct of the main study on the role of principal supervision on the relationship between students’ personnel services and academic achievement in secondary schools

Unsupervised Adaptive Re-identification in Open World Dynamic Camera Networks

Person re-identification is an open and challenging problem in computer vision. Existing approaches have concentrated on either designing the best feature representation or learning optimal matching metrics in a static setting where the number of cameras are fixed in a network. Most approaches have neglected the dynamic and open world nature of the re-identification problem, where a new camera may be temporarily inserted into an existing system to get additional information. To address such a novel and very practical problem, we propose an unsupervised adaptation scheme for re-identification models in a dynamic camera network. First, we formulate a domain perceptive re-identification method based on geodesic flow kernel that can effectively find the best source camera (already installed) to adapt with a newly introduced target camera, without requiring a very expensive training phase. Second, we introduce a transitive inference algorithm for re-identification that can exploit the information from best source camera to improve the accuracy across other camera pairs in a network of multiple cameras. Extensive experiments on four benchmark datasets demonstrate that the proposed approach significantly outperforms the state-of-the-art unsupervised learning based alternatives whilst being extremely efficient to compute.Comment: CVPR 2017 Spotligh

Semi-Supervised Learning Method for the Augmentation of an Incomplete Image-Based Inventory of Earthquake-Induced Soil Liquefaction Surface Effects

Soil liquefaction often occurs as a secondary hazard during earthquakes and can lead to significant structural and infrastructure damage. Liquefaction is most often documented through field reconnaissance and recorded as point locations. Complete liquefaction inventories across the impacted area are rare but valuable for developing empirical liquefaction prediction models. Remote sensing analysis can be used to rapidly produce the full spatial extent of liquefaction ejecta after an event to inform and supplement field investigations. Visually labeling liquefaction ejecta from remotely sensed imagery is time-consuming and prone to human error and inconsistency. This study uses a partially labeled liquefaction inventory created from visual annotations by experts and proposes a pixel-based approach to detecting unlabeled liquefaction using advanced machine learning and image processing techniques, and to generating an augmented inventory of liquefaction ejecta with high spatial completeness. The proposed methodology is applied to aerial imagery taken from the 2011 Christchurch earthquake and considers the available partial liquefaction labels as high-certainty liquefaction features. This study consists of two specific comparative analyses. (1) To tackle the limited availability of labeled data and their spatial incompleteness, a semi-supervised self-training classification via Linear Discriminant Analysis is presented, and the performance of the semi-supervised learning approach is compared with supervised learning classification. (2) A post-event aerial image with RGB (red-green-blue) channels is used to extract color transformation bands, statistical indices, texture components, and dimensionality reduction outputs, and performances of the classification model with different combinations of selected features from these four groups are compared. Building footprints are also used as the only non-imagery geospatial information to improve classification accuracy by masking out building roofs from the classification process. To prepare the multi-class labeled data, regions of interest (ROIs) were drawn to collect samples of seven land cover and land use classes. The labeled samples of liquefaction were also clustered into two groups (dark and light) using the Fuzzy C-Means clustering algorithm to split the liquefaction pixels into two classes. A comparison of the generated maps with fully and manually labeled liquefaction data showed that the proposed semi-supervised method performs best when selected high-ranked features of the two groups of statistical indices (gradient weight and sum of the band squares) and dimensionality reduction outputs (first and second principal components) are used. It also outperforms supervised learning and can better augment the liquefaction labels across the image in terms of spatial completeness