This paper presents a scattering-based technique for object singulation in a cluttered environment. An analytical model-based control scattering approach is necessary for controlled object singulation. Controlled scattering implies achieving the desired distances between objects after collision. However, current analytical approaches are limited due to insufficient information of the physical environment properties, such as the coefficient of restitution, coefficient of friction, and masses of objects. In this paper, this limitation is overcome by introducing a technique to learn these parameters from unlabeled videos. For the analytical model, an impulse-based approach is used. A virtual world simulator is designed based on a dynamic model and the estimated physical properties of all objects in the environment. Experiments are performed in a virtual world until the targeted scattering pattern is achieved. The targeted scattering pattern implies that all objects are singulated. Finally, the desired input from the virtual world is fed to the robot manipulator to perform real-world scattering.
Multiple-license plate recognition is gaining popularity in the Intelligent Transport System (ITS) applications for security monitoring and surveillance. Advancements in acquisition devices have increased the availability of high definition (HD) images, which can capture images of multiple vehicles. Since license plate (LP) occupies a relatively small portion of an image, therefore, detection of LP in an image is considered a challenging task. Moreover, the overall performance deteriorates when the aforementioned factor combines with varying illumination conditions, such as night, dusk, and rainy. As it is difficult to locate a small object in an entire image, this paper proposes a two-step approach for plate localization in challenging conditions. In the first step, the Faster-Region-based Convolutional Neural Network algorithm (Faster R-CNN) is used to detect all the vehicles in an image, which results in scaled information to locate plates. In the second step, morphological operations are employed to reduce non-plate regions. Meanwhile, geometric properties are used to localize plates in the HSI color space. This approach increases accuracy and reduces processing time. For character recognition, the look-up table (LUT) classifier using adaptive boosting with modified census transform (MCT) as a feature extractor is used. Both proposed plate detection and character recognition methods have significantly outperformed conventional approaches in terms of precision and recall for multiple plate recognition.
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