Bounding overwatch

About: Bounding overwatch is a research topic. Over the lifetime, 966 publications have been published within this topic receiving 15156 citations.

Bounding signal probabilities for testability measurement using conditional syndromes

Abstract: An algorithm for bounding the random pattern testability of individual faults in a circuit is proposed. Auxiliary gates for bounding the testability are constructed, converting the problem into one of determining the signal probability at the output of the auxiliary gate. The results presented are in terms of lower bounds of the testabilities of faults. The bounds generated by the algorithm can be used by designers to identify pseudorandom pattern resistant faults, to enable them to modify the circuit structure to make the faults easy to detect, and, hence, to increase the fault coverage. >

Tracking a minimum bounding rectangle based on extreme value theory

Abstract: In this paper, a novel Bayesian estimator for the minimum bounding axis-aligned rectangle of a point set based on noisy measurements is derived. Each given measurement stems from an unknown point and is corrupted with additive Gaussian noise. Extreme value theory is applied in order to derive a linear measurement equation for the problem. The new estimator is applied to the problem of group target and extended object tracking. Instead of estimating each single group member or point feature explicitly, the basic idea is to track a summarizing shape, namely the minimum bounding rectangle, of the group. Simulation results demonstrate the feasibility of the estimator.

Bounding Boxes Are All We Need: Street View Image Classification via Context Encoding of Detected Buildings

Abstract: Street view images classification aiming at urban land use analysis is difficult because the class labels (e.g., commercial area), are concepts with higher abstract level compared to the ones of general visual tasks (e.g., persons and cars). Therefore, classification models using only visual features often fail to achieve satisfactory performance. In this paper, a novel approach based on a "Detector-Encoder-Classifier" framework is proposed. Instead of using visual features of the whole image directly as common image-level models based on convolutional neural networks (CNNs) do, the proposed framework firstly obtains the bounding boxes of buildings in street view images from a detector. Their contextual information such as the co-occurrence patterns of building classes and their layout are then encoded into metadata by the proposed algorithm "CODING" (Context encOding of Detected buildINGs). Finally, these bounding box metadata are classified by a recurrent neural network (RNN). In addition, we made a dual-labeled dataset named "BEAUTY" (Building dEtection And Urban funcTional-zone portraYing) of 19,070 street view images and 38,857 buildings based on the existing BIC GSV [1]. The dataset can be used not only for street view image classification, but also for multi-class building detection. Experiments on "BEAUTY" show that the proposed approach achieves a 12.65% performance improvement on macro-precision and 12% on macro-recall over image-level CNN based models. Our code and dataset are available at this https URL

Computing CSG-tree boundaries as algebraic expressions

Abstract: Constructive solid geometry (CSG) defines objects as Boolean combinations of primitive solids, and usually stores them in binary trees. A bounding entity is an upper estimate of the extent of a CSG object. One of the problems in CSG is the search for the smallest bounding box of an object with the bounding boxes of the leaves as input. A method is presented that finds the smallest bounding box of the root; only prismatic boxes are considered. The method, called the canonical-form method, introduces an algebra of boxes which is shown to be a lattice. The lattice properties are then used to prove that the algorithm that computes the bounding boxes achieves a better approximation than the others. Some hints on implementation are then presented, and the efficiency of the method is compared with the S-bound method.