Horizon Line Detection: Edge-less and Edge-based Methods

Planetary rover localization is a challenging problem due to unavailability ofconventional localization cues e.g. GPS, architectural landmarks etc. Hori-zon line (boundary segmenting sky and non-sky regions) nds its applicationsfor smooth navigation of UAVs/MAVs, visual geo-localization of mountain-ous images, port security and ship detection and has proven to be a promisingvisual cue for outdoor robot/vehicle localization.Prominent methods for horizon line detection are based on faulty as-sumptions and rely on mere edge detection which is inherently a non-stableapproach due to parameter choices. We investigate the use of supervisedmachine learning for horizon line detection. Specically we propose two dif-ferent machine learning based methods; one relying on edge detection andclassication while other solely based on classication. Given a query image;an edge or classication map is rst built and converted into a multi-stagegraph problem. Dynamic programming is then used to nd a shortest pathwhich conforms to the detected horizon line in the given image. For the rstmethod we provide a detailed quantitative analysis for various texture fea-tures (SIFT, LBP, HOG and their combinations) used to train an SupportVector Machine (SVM) classier and dierent choices (binary edges, classi-ed edge score, gradient score and their combinations) for the nodal costsfor Dynamic Programming. For the second method we investigate the use ofdense classication maps for horizon line detection. We use Support VectorMachines (SVMs) and Convolutional Neural Networks (CNNs) as our classi-er choices and use raw intensity patches as features. Dynamic Programmingis then applied on the resultant dense classier score image to nd the hori-zon line. Both proposed formulations are compared with a prominent edgebased method on three dierent data sets: City (Reno) Skyline, Basalt Hillsand Web data sets and outperform the previous method by a high margin

Machine Learning based Mountainous Skyline Detection and Visual Geo-Localization

With the ubiquitous availability of geo-tagged imagery and increased computational power, geo-localization has captured a lot of attention from researchers in computer vision and image retrieval communities. Significant progress has been made in urban environments with stable man-made structures and geo-referenced street imagery of frequently visited tourist attractions. However, geo-localization of natural/mountain scenes is more challenging due to changed vegetations, lighting, seasonal changes and lack of geo-tagged imagery. Conventional approaches for mountain/natural geo-localization mostly rely on mountain peaks and valley information, visible skylines and ridges etc. Skyline (boundary segmenting sky and non-sky regions) has been established to be a robust natural feature for mountainous images, which can be matched with the synthetic skylines generated from publicly available terrain maps such as Digital Elevation Models (DEMs). Skyline or visible horizon finds further applications in various other contexts e.g. smooth navigation of Unmanned Aerial Vehicles (UAVs)/Micro Aerial Vehicles (MAVs), port security, ship detection and outdoor robot/vehicle localization.\parProminent methods for skyline/horizon detection are based on non-realistic assumptions and rely on mere edge detection and/or linear line fitting using Hough transform. We investigate the use of supervised machine learning for skyline detection. Specifically we propose two novel machine learning based methods, one relying on edge detection and classification while other solely based on classification. Given a query image, an edge or classification map is first built and converted into a multi-stage graph problem. Dynamic programming is then used to find a shortest path which conforms to the detected skyline in the given image. For the first method, we provide a detailed quantitative analysis for various texture features (Scale Invariant Feature Transform (SIFT), Local Binary Patterns (LBP), Histogram of Oriented Gradients (HOG) and their combinations) used to train a Support Vector Machine (SVM) classifier and different choices (binary edges, classified edge score, gradient score and their combinations) for the nodal costs for Dynamic Programming (DP). For the second method, we investigate the use of dense classification maps for horizon line detection. We use Support Vector Machines (SVMs) and Convolutional Neural Networks (CNNs) as our classifier choices and use normalized intensity patches as features. Both proposed formulations are compared with a prominent edge based method on two different data sets.\par We propose a fusion strategy which boosts the performance of the edge-less approach using edge information. The fusion approach, which has been tested on an additional challenging data set, outperforms each of the two methods alone. Further, we demonstrate the capability of our formulations to detect absence of horizon boundary and detection of partial horizon lines. This could be of great value in applications where a confidence measure of the detection is necessary e.g. localization of planetary rovers/robots. In an extended work, we compare our edge-less skyline detection approach against deep learning networks recently proposed for semantic segmentation on an additional data set. Specifically, we compare our proposed fusion formulation with Fully Convolutional Network (FCN), SegNet and another classical supervised learning based method.\par We further propose a visual geo-localization pipeline based on evolutionary computing; where Particle Swarm Optimization (PSO) is adopted to find/refine an orientation estimate by minimizing the cost function based on horizon-ness probability of pixels. The dense classification score image resulting from our edge-less/fusion approach is used as a fitness measure to guide the particles toward best solution where the rendered horizon from DEM perfectly aligns with the actual horizon from the image without even requiring its explicit detection. The effectiveness of the proposed geo-localization pipeline is evaluated on a decent sized data set

A comprehensive study on the Bayesian modelling of extreme rainfall: A case study from Pakistan

AbstractIn this paper, the modelling of extreme rainfall is carried out in Pakistan by analysing annual daily maximum rainfall data via frequentist and Bayesian approaches. In frequentist settings, the parameters and return levels of the best fitted probabilistic model (i.e., generalized extreme value) are estimated using maximum likelihood and linear moments method. On the other side, under the Bayesian framework, the parameters and return levels are calculated both for noninformative and informative priors. This task is completed with the help of the Markov Chain Monte Carlo method using the Metropolis‐Hasting algorithm. This study also highlights a procedure to build an informative prior through historical records of the underlying processes from other nearby weather stations. The findings attained from the Bayesian paradigm demonstrate that the posterior inference could be affected by the choice of past knowledge used for the construction of informative priors. Additionally, the best method for the modelling of extreme rainfall over the country is decided with the support of assessment measures. In general, the Bayesian paradigm linked with the informative priors offers an adequate estimations scheme in terms of accuracy as compared to frequentist methods, accounting for ambiguity in parameters and return levels. Hence, these findings are very helpful in adopting accurate flood protection measures and designing infrastructures over the country

Modelling of discrete extremes through extended versions of discrete generalized Pareto distribution

The statistical modelling of integer-valued extremes such as large avalanche counts has received less attention than their continuous counterparts in the extreme value theory (EVT) literature. One approach to moving from continuous to discrete extremes is to model threshold exceedances of integer random variables by the discrete version of the generalized Pareto distribution. Still, the optimal threshold selection that defines exceedances remains a problematic issue. Moreover, within a regression framework, the treatment of the many data points (those below the chosen threshold) is either ignored or decoupled from extremes. Considering these issues, we extend the idea of using a smooth transition between the two tails (lower and upper) to force large and small discrete extreme values to comply with EVT. In the case of zero inflation, we also develop models with an additional parameter. To incorporate covariates, we extend the Generalized Additive Models (GAM) framework to discrete extreme responses. In the GAM forms, the parameters of our proposed models are quantified as a function of covariates. The maximum likelihood estimation procedure is implemented for estimation purposes. With the advantage of bypassing the threshold selection step, our findings indicate that the proposed models are more flexible and robust than competing models (i.e. discrete generalized Pareto distribution and Poisson distribution).Comment: 32 pages including supplementary materials, 11 figures including supplementary materials figures, 8 Tables including supplementary materials figure

Food Security in Pakistan and Need for Public Policy Adjustments

Sustainable food and nutrition security solutions demand integration and alignment in public policies, particularly in the post-COVID-19 scenarios. The introduction of integrated public policies to address the food and nutrition needs in Pakistan is an immediate requirement. This study has applied the Foster, Greer and Thorbecke (FGT) index to estimate food and nutrition security dimensions through primary and secondary data. This analysis reveals that food utilization and sustainability have destabilized and deteriorated in Pakistan in recent years. It shows that non-farmers are more food insecure (8 percent) than farmers (4 percent) and this ratio has increased from 2008 to 2018. Food insecurity in terms of food availability and food accessibility has decreased. A holistic approach in public policies toward food security is the clarion call of the time. Therefore, the paper recommends that more focus should be given to knowledge transmission about dietary diversity, provision of quality education, and health facilities in the formulation as well as execution of food security policies

Comparison of Semantic Segmentation Approaches for Horizon/Sky Line Detection

Horizon or skyline detection plays a vital role towards mountainous visual geo-localization, however most of the recently proposed visual geo-localization approaches rely on \textbf{user-in-the-loop} skyline detection methods. Detecting such a segmenting boundary fully autonomously would definitely be a step forward for these localization approaches. This paper provides a quantitative comparison of four such methods for autonomous horizon/sky line detection on an extensive data set. Specifically, we provide the comparison between four recently proposed segmentation methods; one explicitly targeting the problem of horizon detection\cite{Ahmad15}, second focused on visual geo-localization but relying on accurate detection of skyline \cite{Saurer16} and other two proposed for general semantic segmentation -- Fully Convolutional Networks (FCN) \cite{Long15} and SegNet\cite{Badrinarayanan15}. Each of the first two methods is trained on a common training set \cite{Baatz12} comprised of about 200 images while models for the third and fourth method are fine tuned for sky segmentation problem through transfer learning using the same data set. Each of the method is tested on an extensive test set (about 3K images) covering various challenging geographical, weather, illumination and seasonal conditions. We report average accuracy and average absolute pixel error for each of the presented formulation.Comment: Proceedings of the International Joint Conference on Neural Networks (IJCNN) (oral presentation), IEEE Computational Intelligence Society, 201

In vitro storage of synthetic seeds: Effect of different storage conditions and intervals on their conversion ability

In vitro derived shoots of olive cv. Moraiolo were employed in synthetic seeds preparation by alginate encapsulation, and then stored in artificial endosperm solution at cold (4°C) and room storage (21 ± 2°C) conditions in interaction with different storage intervals of 0, 15, 30, 45 and 60 days to evaluate the comparative regrowth and conversion capacity of synthetic seeds. Cold stored synthetic seeds were superior in terms of their regrowth capacity than that of room stored ones for all the growth parameters studied. A promising degree of interaction was observed between 4°C and 45 days of storage interval for regrowth percentage as well as for shoot and root development. Moreover, an ascending trend was recorded in conversion potential with an increase in storage intervals up to 45 days (S3) whereas there was a declining trend after that up to 60 days (S4). Moreover plantlets regenerated from synthetic seeds, with 4 - 6 fully expanded leaves and well developed root system were successfully acclimatized under ex vitro conditions. The protocol can be used for germplasm exchange of woody trees and preparation of synthetic seed.Keyword: Synthetic, seed, olive, encapsulation, storage, conversionAfrican Journal of Biotechnology Vol. 9(35), pp. 5712-5721, 30 August, 201

Exploring key physiological attributes of grapevine cultivars under the influence of seasonal environmental variability

Seasonal climatic variability is a key challenge in many grape-growing regions across the globe, affecting phenology, growth, physiological responses, and yield at harvest. Unfavourable climatic conditions impair the plant's physiological processes, such as chlorophyll accumulation, gas exchange and photosynthesis in grapevine leaves. It is critical to unlock the complex physiological behaviour of grapevine cultivars at key phenological stages and under varying environmental conditions. The present study was designed to evaluate the key physiological processes, such as gas exchange, chlorophyll contents and water use efficiency (WUE), of four table grape cultivars at key growth stages under varying environmental conditions of the Pothwar region, in a factorial experimental set up (Location× Year × Cultivar × Phenological stage). The physiological responses of the table grape cultivars were recorded at the 5-leaf stage, full bloom, berry set, veraison and harvest during two consecutive vintages (2019 and 2020) in two locations (Islamabad and Chakwal). The results show that the mean photosynthetic activity in colder Islamabad was 30.7 % higher than in Chakwal, and the transpiration rate and WUE were 10.4 % and 28.6 % higher. Similarly, 12 % higher photosynthetic activity, with 13 % more WUE, was observed in the colder vintage of 2020 compared to that of 2019. The vine physiological activity also varied among cultivars; for example, cv. Sugraone was found to have 12 % more chlorophyll and 30 % higher photosynthetic activity than cv. Kings Ruby. Similarly, higher photosynthetic activity and transpiration rates were recorded at the berry set stage, while WUE peaked near blooming. The biplot analysis for the first two principal components also showed cv. Sugraone to be a highly responsive and physiologically efficient cultivar. The findings of the present research will help to better assess the effect of seasonal variability on vine physiological performance and to identify genotypes with higher photosynthetic potential and WUE. It could also assist in devising vineyard management strategies to better adapt to varying environmental conditions

Comparison of Early vs. Late Tracheostomy in Subdural Hematoma Operated at GCS Six or Below

Objectives:To compare the outcomes of early tracheostomy vs. late tracheostomy in post-operative patients after acute subdural hematoma at receiving GCS (Glasgow comma scale) of six or below. Method:  A quasi observational study was conducted on 30 patients with acute subdural hematoma after RTA (road traffic accident) and were operated in The Department of Neurosurgery Unit 2, Punjab Institute of Neurosciences, LGH, Lahore. The age range was 20 – 65 years. All patients were operated upon within 12 hours of RTA. Results:  In Group A, 12 (40%) patients, decompressive craniectomy with the evacuation of acute subdural hematoma and early tracheostomy were performed. In Group B, 8 (26%) patients’ craniotomy and evacuation of acute subdural hematoma were done along with early tracheostomy. In 6(20%) patients, decompressive craniectomy and evacuation were done and their tracheostomies were done at the 10th post-operative day. In 4 (13.33%) patients’ craniotomy and evacuation of hematoma done and their tracheostomies were also done at 10th post-operative day. In Group A, on 5th postoperative day GCS of 16 (53.33%) patients with early tracheostomies and fewer comorbidities improved, they were extubated, while 2 (6.67%) patients did not improve and 2 (6.67%) patients died. In Group B, in 30 patients with late tracheostomies, only 4 (13.33%) patients were improved. On 10th post-op day, GCS of 4 (13.33%) patients improved, GCS of 3 (10%) patients not improved and 3 (10%) patients died. Conclusion:  Early tracheostomy in patients with acute subdural hematoma yields good results as compared to late tracheostomy