Set Of Grid Cells Research Articles

A Practice for Object Detection Using YOLO Algorithm

When we look at images or videos, we can easily locate and identify the objects of our interest within moments. Passing on this intelligence to computers is nothing but object detection - locating the object and identifying it. Object Detection has found its application in a wide variety of domains such as video surveillance, image retrieval systems, autonomous driving vehicles and many more. Various algorithms can be used for object detection but we will be focusing on the YoloV3 algorithm. YOLO stands for "You Only Look Once". The YOLO model is very accurate and allows us to detect the objects present in the frame. YOLO follows a completely different approach. Instead of selecting some regions, it applies a neural network to the entire image to predict bounding boxes and their probabilities. YOLO is a single deep convolutional neural network that splits the input image into a set of grid cells, so unlike image classification or face detection, each grid cell in YOLO algorithm will have an associated vector in the output that tells us if an object exists in that grid cell, the class of that object, the predicted bounding box for that object. The model here is progressive so it learns more over time, increasing its prediction accuracy over time. The way the model works is that it makes many predictions in one frame and decides to use the most accurate prediction, thus discarding the other. The predictions are made randomly, so if the model feels like there is an object in the frame which is of a very small pixel it will take that also into consideration. To make it more precise and clearer, the model simply creates bounding boxes around everything in the frame, it would make predictions for each box and pick the one with the most confidence score. All this is done in a small-time frame, thus showing why this specific model is the best to use in a real time situation.

Accounting for multiple testing in the analysis of spatio-temporal environmental data

The statistical analysis of environmental data from remote sensing and Earth system simulations often entails the analysis of gridded spatio-temporal data, with a hypothesis test being performed for each grid cell. When the whole image or a set of grid cells are analyzed for a global effect, the problem of multiple testing arises. When no global effect is present, we expect alpha % of all grid cells to be false positives, and spatially autocorrelated data can give rise to clustered spurious rejections that can be misleading in an analysis of spatial patterns. In this work, we review standard solutions for the multiple testing problem and apply them to spatio-temporal environmental data. These solutions are independent of the test statistic, and any test statistic can be used (e.g., tests for trends or change points in time series). Additionally, we introduce permutation methods and show that they have more statistical power. Real-world data are used to provide examples of the analysis, and the performance of each method is assessed in a simulation study. Unlike other simulation studies, our study compares the statistical power of the presented methods in a comprehensive simulation study. In conclusion, we present several statistically rigorous methods for analyzing spatio-temporal environmental data and controlling the false positives. These methods allow the use of any test statistic in a wide range of applications in environmental sciences and remote sensing.

Searching continuous nearest neighbors in road networks on the air

Recently, people have begun to deal with location-based queries (LBQs) under broadcast environments. To the best of our knowledge, most of the existing broadcast-based LBQ methods are aimed at Euclidean spaces and cannot be readily extended to road networks. This paper takes the first step toward processing Continuous Nearest Neighbor queries in road Networks under wireless Broadcast environments (CN3B). Our method leverages the key properties of Network Voronoi Diagram (NVD). We first present an efficient method to partition the NVD structure of the underlying road networks into a set of grid cells and number the grid cells obtained, based on which we further propose an NVD-based Distributed air Index (NVD-DI) to support CN3B query processing. Finally, we propose an efficient algorithm on the client side to process CN3B queries. Extensive simulation experiments have been conducted to demonstrate the efficiency of our approach. The results show that our proposed method is about 4 and 7.6 times more efficient than a less-sophisticated D-tree air index based method, in access latency and tuning time, respectively.

Adding Layers to Bumped-Body Polyforms with Minimum Perimeter Preserves Minimum Perimeter

In two dimensions, a polyform is a finite set of edge-connected cells on a square, triangular, or hexagonal grid. A layer is the set of grid cells that are vertex-adjacent to the polyform and not part of the polyform. A bumped-body polyform has two parts: a body and a bump. Adding a layer to a bumped-body polyform with minimum perimeter constructs a bumped-body polyform with min perimeter; the triangle case requires additional assumptions. A similar result holds for 3D polyominos with minimum area.

Reconstructing hv-convex polyominoes from orthogonal projections

We address the problem of reconstructing a discrete 2D object, represented by a set of grid cells, from its orthogonal projections. We focus on objects called hv-convex polyominoes, which are connected objects with the property that the cells in each row and column are consecutive. The main result of this paper is a simple, O( mn min( m 2, n 2))-time algorithm for reconstructing hv-convex polyominoes.