Automating 3D Dataset Generation with Neural Radiance Fields

3D detection is a critical task to understand spatial characteristics of the environment and is used in a variety of applications including robotics, augmented reality, and image retrieval. Training performant detection models require diverse, precisely annotated, and large scale datasets that involve complex and expensive creation processes. Hence, there are only few public 3D datasets that are additionally limited in their range of classes. In this work, we propose a pipeline for automatic generation of 3D datasets for arbitrary objects. By utilizing the universal 3D representation and rendering capabilities of Radiance Fields, our pipeline generates high quality 3D models for arbitrary objects. These 3D models serve as input for a synthetic dataset generator. Our pipeline is fast, easy to use and has a high degree of automation. Our experiments demonstrate, that 3D pose estimation networks, trained with our generated datasets, archive strong performance in typical application scenarios.

Efficient Region-Based Image Querying

Retrieving images from large and varied repositories using visual contents has been one of major research items, but a challenging task in the image management community. In this paper we present an efficient approach for region-based image classification and retrieval using a fast multi-level neural network model. The advantages of this neural model in image classification and retrieval domain will be highlighted. The proposed approach accomplishes its goal in three main steps. First, with the help of a mean-shift based segmentation algorithm, significant regions of the image are isolated. Secondly, color and texture features of each region are extracted by using color moments and 2D wavelets decomposition technique. Thirdly the multi-level neural classifier is trained in order to classify each region in a given image into one of five predefined categories, i.e., "Sky", "Building", "SandnRock", "Grass" and "Water". Simulation results show that the proposed method is promising in terms of classification and retrieval accuracy results. These results compare favorably with the best published results obtained by other state-of-the-art image retrieval techniques.

On Analysis and Evaluation of Multi-Sensory Cognitive Learning of a Mathematical Topic Using Artificial Neural Networks

This piece of research belongs to the field of educational assessment issue based upon the cognitive multimedia theory. Considering that theory; visual and auditory material should be presented simultaneously to reinforce the retention of a mathematical learned topic, a carefully computer-assisted learning (CAL) module is designed for development of a multimedia tutorial for our suggested mathematical topic. The designed CAL module is a multimedia tutorial computer package with visual and/or auditory material. So, via suggested computer package, Multi-Sensory associative memories and classical conditioning theories are practically applicable at an educational field (a children classroom). It is noticed that comparative practical results obtained are interesting for field application of CAL package with and without associated teacher's voice. Finally, the presented study highly recommends application of a novel teaching trend aiming to improve quality of children mathematical learning performance.