Social and Behavioral Sciences Commons^™

A Quantitative Validation Of Multi-Modal Image Fusion And Segmentation For Object Detection And Tracking, Nicholas Lahaye, Michael J. Garay, Brian D. Bue, Hesham El-Askary, Erik Linstead

Mathematics, Physics, and Computer Science Faculty Articles and Research

In previous works, we have shown the efficacy of using Deep Belief Networks, paired with clustering, to identify distinct classes of objects within remotely sensed data via cluster analysis and qualitative analysis of the output data in comparison with reference data. In this paper, we quantitatively validate the methodology against datasets currently being generated and used within the remote sensing community, as well as show the capabilities and benefits of the data fusion methodologies used. The experiments run take the output of our unsupervised fusion and segmentation methodology and map them to various labeled datasets at different levels of global …

A Brief Bibliometric Survey On Night Vision Bot Using Dynamic Ir And Object Detection, Devesh Abhyankar Mr., Gurumoorty Suresh Mr., Hrithik Sambhaji Karjule Mr., Parth Bhardwaj Mr., Harish Muleva Mr., Anurag Mahajan Dr.

Library Philosophy and Practice (e-journal)

This study aims to analyse the work done in the field of Night Vision Robots using IR and Object Detection from 2011 to 2021, using the bibliometric methods. This paper presents a Scopus database review on "Night Vision Bot using Dynamic IR and Object Detection". The necessity for doing this bibliometric survey is that to know how the technology in the field of mobile robotics and night vision, as well as to object detection, has evolved over the years. This paper shows the importance of Night Vision Robot from the year 2011 and continued up to 2021 April. The database …

Bibliometric Review On Image Based Plant Phenotyping, Shrikrishna Ulhas Kolhar, Jayant Jagtap

Library Philosophy and Practice (e-journal)

Plant phenotyping is a quantitative description of structural, physiological and temporal traits of plants resulting from interaction of plant genotypes with the environment. A rapid development is in progress in the field of image-based plant phenotyping. Plant phenotyping has wide range of applications in plant breeding research, plant growth prediction, biotic and abiotic stress analysis, crop management and early disease detection. The main motive is to provide detailed bibliometric review in order to know the available literature and current research trends in the area of plant phenotyping using plant images. The bibliometric analysis is primarily based on Scopus, web of …

Atmospheric Turbulence Distortion In Video: Restoration Utilizing Sparse Analysis, Benjamin J. Sanda

Dissertations

The removal of atmospheric turbulence (AT) distortion in long range imaging is one of the most challenging areas of research in imaging processing with an immediate need for solutions in several applications such as in military and transportation systems. AT exacerbates distortion due to non-linear geometric blur and scintillations in long-distance images and videos, severely reducing image quality and information interpretation. AT negatively impacts both human and computer vision systems, compromising visibility essential for accurate object identification and tracking.

In this dissertation, a novel sparse analysis framework is developed to address efficient AT blur and scintillation removal in video. Operating …

Design Of A Monocular Multi-Spectral Skin Detection, Melanin Estimation, And False-Alarm Suppression System, Keith R. Peskosky

Theses and Dissertations

A real-time skin detection, false-alarm reduction, and melanin estimation system is designed targeting search and rescue (SAR) with application to special operations for manhunting and human measurement and signatures intelligence. A mathematical model of the system is developed and used to determine how the physical system performs under illumination, target-to-sensor distance, and target-type scenarios. This aspect is important to the SAR community to gain an understanding of the deployability in different operating environments. A multi-spectral approach is developed and consists of two short-wave infrared cameras and two visible cameras. Through an optical chain of lenses, custom designed and fabricated dichroic …

Seasonal Adaptation Of Vegetation Color In Satellite Images, Srinivas Jakkula, Vamsi K.R. Mantena, Ramu Pedada, Yuzhong Shen, Jiang Li, Hamid R. Arabnia (Ed.)

Electrical & Computer Engineering Faculty Publications

Remote sensing techniques like NDVI (Normal Difference vegetative Index) when applied to phenological variations in aerial images, ascertained the seasonal rise and decline of photosynthetic activity in different seasons, resulting in different color tones of vegetation. The rise and fall of NDVI values decide the biological response, either the green up or brown down [1]. Vegetation in green up period appears with more vegetative vigor and during brown down period it has a dry appearance. This paper proposes a novel method that identifies vegetative patterns in satellite images and then alters vegetation color to simulate seasonal changes based on training …

Vegetation Identification Based On Satellite Imagery, Vamsi K.R. Mantena, Ramu Pedada, Srinivas Jakkula, Yuzhong Shen, Jiang Li, Hamid R. Arabnia (Ed.)

Electrical & Computer Engineering Faculty Publications

Automatic vegetation identification plays an important role in many applications including remote sensing and high performance flight simulations. This paper presents a method to automatically identify vegetation based upon satellite imagery. First, we utilize the ISODATA algorithm to cluster pixels in the images where the number of clusters is determined by the algorithm. We then apply morphological operations to the clustered images to smooth the boundaries between clusters and to fill holes inside clusters. After that, we compute six features for each cluster. These six features then go through a feature selection algorithm and three of them are determined to …