Gray Image extraction using Fuzzy Logic

Fuzzy systems concern fundamental methodology to represent and process uncertainty and imprecision in the linguistic information. The fuzzy systems that use fuzzy rules to represent the domain knowledge of the problem are known as Fuzzy Rule Base Systems (FRBS). On the other hand image segmentation and subsequent extraction from a noise-affected background, with the help of various soft computing methods, are relatively new and quite popular due to various reasons. These methods include various Artificial Neural Network (ANN) models (primarily supervised in nature), Genetic Algorithm (GA) based techniques, intensity histogram based methods etc. providing an extraction solution working in unsupervised mode happens to be even more interesting problem. Literature suggests that effort in this respect appears to be quite rudimentary. In the present article, we propose a fuzzy rule guided novel technique that is functional devoid of any external intervention during execution. Experimental results suggest that this approach is an efficient one in comparison to different other techniques extensively addressed in literature. In order to justify the supremacy of performance of our proposed technique in respect of its competitors, we take recourse to effective metrics like Mean Squared Error (MSE), Mean Absolute Error (MAE), Peak Signal to Noise Ratio (PSNR).Comment: 8 pages, 5 figures, Fuzzy Rule Base, Image Extraction, Fuzzy Inference System (FIS), Membership Functions, Membership values,Image coding and Processing, Soft Computing, Computer Vision Accepted and published in IEEE. arXiv admin note: text overlap with arXiv:1206.363

MaestROB: A Robotics Framework for Integrated Orchestration of Low-Level Control and High-Level Reasoning

This paper describes a framework called MaestROB. It is designed to make the robots perform complex tasks with high precision by simple high-level instructions given by natural language or demonstration. To realize this, it handles a hierarchical structure by using the knowledge stored in the forms of ontology and rules for bridging among different levels of instructions. Accordingly, the framework has multiple layers of processing components; perception and actuation control at the low level, symbolic planner and Watson APIs for cognitive capabilities and semantic understanding, and orchestration of these components by a new open source robot middleware called Project Intu at its core. We show how this framework can be used in a complex scenario where multiple actors (human, a communication robot, and an industrial robot) collaborate to perform a common industrial task. Human teaches an assembly task to Pepper (a humanoid robot from SoftBank Robotics) using natural language conversation and demonstration. Our framework helps Pepper perceive the human demonstration and generate a sequence of actions for UR5 (collaborative robot arm from Universal Robots), which ultimately performs the assembly (e.g. insertion) task.Comment: IEEE International Conference on Robotics and Automation (ICRA) 2018. Video: https://www.youtube.com/watch?v=19JsdZi0TW

Applications Of Microspectroscopy, Hyperspectral Chemical Imaging And Fluorescence Microscopy In Chemistry, Biochemistry, Biotechnology, Molecular And Cell Biology

Chemical imaging is a technique for the simultaneous measurement of spectra (chemical information) and images or pictures (spatial information)^1,2^. The technique is most often applied to either solid or gel samples, and has applications in chemistry, biology^3-8^, medicine^9,10^, pharmacy^11^ (see also for example: Chemical Imaging Without Dyeing), food science, Food Physical Chemistry, Biotechnology^12,13^, Agriculture and industry. NIR, IR and Raman chemical imaging is also referred to as hyperspectral, spectroscopic, spectral or multi-spectral imaging (also see micro-spectroscopy). However, other ultra-sensitive and selective, chemical imaging techniques are also in use that involve either UV-visible or fluorescence microspectroscopy

Combining convolutional neural networks and slantlet transform for an effective image retrieval scheme

In the latest years there has been a profound evolution in computer science and technology, which incorporated several fields. Under this evolution, Content Base Image Retrieval (CBIR) is among the image processing field. There are several image retrieval methods that can easily extract feature as a result of the image retrieval methods’ progresses. To the researchers, finding resourceful image retrieval devices has therefore become an extensive area of concern. Image retrieval technique refers to a system used to search and retrieve images from digital images’ huge database. In this paper, the author focuses on recommendation of a fresh method for retrieving image. For multi presentation of image in Convolutional Neural Network (CNN), Convolutional Neural Network - Slanlet Transform (CNN-SLT) model uses Slanlet Transform (SLT). The CBIR system was therefore inspected and the outcomes benchmarked. The results clearly illustrate that generally, the recommended technique outdid the rest with accuracy of 89 percent out of the three datasets that were applied in our experiments. This remarkable performance clearly illustrated that the CNN-SLT method worked well for all three datasets, where the previous phase (CNN) and the successive phase (CNN-SLT) harmoniously worked together