Object Detection Using Deep Learning – With the explosion in the size and sophistication of modern 3D images, most of the tasks associated with object detection have to focus on image segmentation. In this work, we propose a method to exploit the 3D geometry and shape data to detect objects from natural images in a supervised and natural environment. This provides a framework for automatically segmenting objects in large images. The segmentation is performed using a deep convolutional convolutional neural network (CNN) and a 3D convolutional neural network (CNN-DNN). Our approach performs fine-tuning and visualizations with the goal of understanding objects in a large-scale scenario. We show that our CNN-DNN approach can easily generate object classes with more than 20% spatial precision, surpassing state-of-the-art approaches on a benchmark dataset.
In this paper, a general framework for detecting, segmenting, and quantifying image segmentation has been presented. This framework combines several approaches and applies them to various image segmentation systems. The main idea of the proposed framework is twofold. First, an evaluation on image segmentation systems with different performance measures to select the best segmentation feature is established. Second, an evaluation on the performance of different types of feature selection metrics is established. The results show that we learn from the evaluation and develop a network with the highest performance by a significant margin for this paper. The evaluation using different metrics were obtained to improve performance of each metric. This performance evaluation shows that the proposed framework outperforms the other metrics in terms of accuracy and speed.
Annotation weight assignment in semantic classifiers via cross-entropy model
Avalon: A Taxonomy of Different Classes of Approximate Inference and Inference in Pareto Frontals
Object Detection Using Deep Learning
Computational Models from Structural and Hierarchical Data
Robust Particle Filter based Image Enhancement with Particle GibbsIn this paper, a general framework for detecting, segmenting, and quantifying image segmentation has been presented. This framework combines several approaches and applies them to various image segmentation systems. The main idea of the proposed framework is twofold. First, an evaluation on image segmentation systems with different performance measures to select the best segmentation feature is established. Second, an evaluation on the performance of different types of feature selection metrics is established. The results show that we learn from the evaluation and develop a network with the highest performance by a significant margin for this paper. The evaluation using different metrics were obtained to improve performance of each metric. This performance evaluation shows that the proposed framework outperforms the other metrics in terms of accuracy and speed.