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Machine Learning
Published in Michael Ljungberg, Handbook of Nuclear Medicine and Molecular Imaging for Physicists, 2022
The output of semantic segmentation, see Example 4, gives a class for each pixel, but the output does not distinguish between different objects of the same class. There are network architectures for object detections: see for example [24, 25], where a bounding box for each object is provided. The output of the architecture is a coarse sampling of the image. At each position the output consists of both classification (which object class it is) as well as information concerning the bounding box at that information. There are also network architectures that provide both a bounding box as well as pixelwise segmentation of that object.
Application-Specific Network-on-Chip Synthesis
Published in Santanu Kundu, Santanu Chattopadhyay, Network-on-Chip, 2018
Santanu Kundu, Santanu Chattopadhyay
The floorplan information generated so far does not include the routers in it. The next stage of the formulation will select router locations in the floorplan with an objective of minimizing the power consumption. Since there can be a large number of potential locations for placement of routers, it is desirable to reduce the number of candidate locations via some means, so that the MILP formulation for router location identification can run faster. To start with, bounding boxes are created for each core placed as a rectangle in the floorplan. A bounding box is a rectangular enclosure of the core rectangle (called a node in subsequent discussion) such that the bounding boxes of two neighboring nodes are touching each other. Figure 9.2 shows such a situation. The bounding box of node 4 extends to the top boundary of node 3 and so on. Once the bounding boxes are designed, a channel intersection graph (Sait and Youssef 1994) will be constructed. The graph has vertices corresponding to two perpendicular boundaries of the floorplan. The edges of the graph correspond to the bounding boxes of the floorplan. Routers can be placed at each vertex of the channel intersection graph. Figure 9.2b shows the possible placement of routers, represented as filled circles in the diagram. Many of the routers are redundant, in the sense that they are placed very close to each other. Hence, in the final layout, it is very much unlikely that both the closely placed routers will be utilized to connect cores. The routers may be removed when: Routers are placed along the perimeter of the layout.Routers are placed less than a specified distance apart.
Object Detection Frameworks and Services in Computer Vision
Published in S. Poonkuntran, Rajesh Kumar Dhanraj, Balamurugan Balusamy, Object Detection with Deep Learning Models, 2023
Sachi Choudhary, Rashmi Sharma, Gargeya Sharma
Although the convolutional sliding window approach is computationally efficient, it still has the issue of not producing the most precise bounding boxes. The bounding box produces a collection of tuples (x, y, w, h), where (x, y) are the coordinates of the center point, w is the box’s width, and h is its height. Therefore, instead of making a square, it forms a rectangle with increased horizontal coverage.
Ship Detection and Classification Using Hybrid Optimization Enabled Deep Learning Approach by Remote Sensing Images
Published in Cybernetics and Systems, 2022
S. Iwin Thanakumar Joseph, N. Shanthini Pandiaraj, Velliangiri Sarveshwaran, M. Mythily
In comparison to other types of annotations, bounding boxes have the advantage of allowing the problem to be geographically constrained (i.e., ideally, the item is exclusive to the bounding box region and completely enclosed in it). Practically, bounding boxes can be specified by two corner coordinates, allowing for quick placement and little data storage. The bounding box is a term used to describe the border coordinates around an image when it comes to digital image processing. They are frequently employed to bind or identify a target, act as a hub for object detection, and produce a collision box for that object. The preprocessed outcome is applied to object detection step to detect significant regions from the preprocessed image. Image segmentation (Meng et al. 2019) is a crucial aspect in many computer vision applications and so far, numerous image segmentation techniques have been developed in the past few years. Though its segmentation process are entirely divergent, they have a general phase of producing object priors and it is the significant phase for identifying ship areas from complex scenes. Interactive segmentation creates object prior by means of user interactions, like scribbles and bounding boxes. The result obtained through bounding box segmentation is specified as
Cyber-attack localisation and tolerant control for microgrid energy management system based on set-membership estimation
Published in International Journal of Systems Science, 2021
Quanwei Qiu, Fuwen Yang, Yong Zhu
In the attack diagnosis stage, a similar method as in the reference (Mousavinejad et al., 2018) is adopted, that is, determining the occurrence of cyber-attack according to the intersection of the prediction ellipsoid and the updated ellipsoid . In order to locate the cyber-attack, the information contained in these two ellipsoids can be further utilised. The bounding box aligned with the axes of the co-ordinate system is adopted here. This kind of bounding box is usually referred to as axis-aligned bounding box (AABB). From the AABBs of the ellipsoids calculated in the attack diagnosis, the boundary limits of the prediction and updated ellipsoids are obtained as and , respectively. Then, the boundary sets can be described as follows:
Tunnel vision optimization method for VR flood scenes based on Gaussian blur
Published in International Journal of Digital Earth, 2021
Lin Fu, Jun Zhu, Weilian Li, Qing Zhu, Bingli Xu, Yakun Xie, Yunhao Zhang, Ya Hu, Jingtao Lu, Pei Dang, Jigang You
To reduce the number of triangles and improve the efficiency of scene rendering, the bounding box detection method is used to simplify the scene objects. Based on the 8 vertices of the ROI, the space plane equations of the six faces are established (in the form given in formula 4, where a, b, and c are the three parameters of the plane equation and X, Y, and Z are the vertex coordinates), and the rectangular bounding box of each scene object is obtained. Then, the relative positions of each bounding box vertex with respect to each plane are determined in accordance with formula 5. The possible relationships are divided into three types: the point may be on the plane (① in formula 5), on the left side of the plane (② in formula 5), or on the right side of the plane (③ in formula 5). Finally, according to the intersection information with the six faces of the frustum, it is determined whether each vertex of each bounding box lies within the frustum. After judging all of the vertices on a bounding box, we can judge the relative position relationship between the corresponding scene object and the ROI: If all vertices of the bounding box are in the ROI, then the scene object must be in the ROI.If only some of the vertices are in the ROI, then the scene object intersects with the ROI and is considered visible.If none of the vertices is in the ROI, then the scene object is likely to be outside the ROI, although there is one exception: the ROI may lie entirely within the bounding box of the scene object, which can be avoided through collision detection.