K. Palaniappan

University of Missouri | Mizzou · Department of Computer Science and IT

In this paper, we present EC-WAMI, the first successful application of neuromorphic event cameras (ECs) for Wide-Area Motion Imagery (WAMI) and Remote Sensing (RS), showcasing their potential for advancing Structure-from-Motion (SfM) and 3D reconstruction across diverse imaging scenarios. ECs, which detect asynchronous pixel-level brightness change...

Manual trial-and-error methods are employed for image parameter selection decisions in the processes that control cyber-enabled scientific instruments. Particularly in materials manufacturing use cases, where image analytics can be iterative, time-consuming and prone to errors, there is a need to enhance existing processes by using agents featuring...

Combining images from multiple cameras using computational imaging techniques is a common way in remote sensing to achieve a larger field-of-view (FOV) and wider scene coverage using manned and unmanned aerial vehicles (UAVs). This chapter proposes an analytical approach, called Light-Field Dynamic Homography (LDH), to create a large virtual focal...

Semantic point cloud segmentation is a critical task in 3D computer vision, offering valuable contextual information for navigation, cartography, landmarks, object recognition, and building modeling. We developed GLSNet++, an innovative deep learning architecture for robust context-dependent 3D point cloud segmentation. GLSNet++ uniquely combines d...

Model-based deep learning has the potential to significantly reduce the size of deep architectures while matching the competitive performance of much deeper and wider architectures. We demonstrate the advantage of combining model-based handcrafted features with learned features for AI-enabled computational pathology. Digital histopathology with who...

This research focuses on real-time surveillance systems as a means for tackling the issue of non-compliance with helmet regulations, a practice that considerably amplifies the risk for motorcycle drivers or riders. Despite the well-established advantages of helmet usage, achieving widespread compliance remains challenging due to diverse contributin...

Discriminating salient moving objects against complex, cluttered backgrounds, with occlusions and challenging environmental conditions like weather and illumination, is essential for stateful scene perception in autonomous systems. We propose a novel deep architecture, named DeepFTSG, for robust moving object detection that incorporates single and...

This research focuses on real-time surveillance systems as a means for tackling the issue of non-compliance with helmet regulations, a practice that considerably amplifies the risk for motorcycle drivers or riders. Despite the well-established advantages of helmet usage, achieving widespread compliance remains challenging due to diverse contributin...

We present a pipeline for predicting mechanical properties of vertically-oriented carbon nanotube (CNT) forest images using a deep learning model for artificial intelligence (AI)-based materials discovery. Our approach incorporates an innovative data augmentation technique that involves the use of multi-layer synthetic (MLS) or quasi-2.5D images wh...

The Cell Tracking Challenge is an ongoing benchmarking initiative that has become a reference in cell segmentation and tracking algorithm development. Here, we present a significant number of improvements introduced in the challenge since our 2017 report. These include the creation of a new segmentation-only benchmark, the enrichment of the dataset...

Electron microscopy images of carbon nanotube (CNT) forests are difficult to segment due to the long and thin nature of the CNTs; density of the CNT forests resulting in CNTs touching, crossing, and occluding each other; and low signal-to-noise ratio electron microscopy imagery. In addition, due to image complexity, it is not feasible to prepare tr...

Automatic diagnosis of eye diseases from retinal fundus images is quite challenging. Common public datasets include images of subjects with multiple diseases with uneven distribution of labels. Rare diseases are especially challenging due to their under-representation in such datasets. In this paper, we propose a training pipeline for the multi-lab...

Change impact analysis evaluates the changes that are made in the software and finds the ripple effects, in other words, finds the affected software components. Code changes and bug fixes can have a high impact on code quality by introducing new vulnerabilities or increasing their severity. A recent high-visibility example of this is the code chang...

The vocal folds (VFs) are a pair of muscles in the larynx that play a critical role in breathing, swallowing, and speaking. VF function can be adversely affected by various medical conditions including head or neck injuries, stroke, tumor, and neurological disorders. In this paper, we propose a deep learning system for automated detection of laryng...

Understanding and controlling the self-assembly of vertically oriented carbon nanotube (CNT) forests is essential for realizing their potential in myriad applications. The governing process–structure–property mechanisms are poorly understood, and the processing parameter space is far too vast to exhaustively explore experimentally. We overcome thes...

While the physical properties of carbon nanotubes (CNTs) are often superior to conventional engineering materials, their widespread adoption into many applications is limited by scaling the properties of individual CNTs to macroscale CNT assemblies known as CNT forests. The self-assembly mechanics of CNT forests that determine their morphology and...

Malaria is a major health threat caused by Plasmodium parasites that infect the red blood cells. Two predominant types of Plasmodium parasites are Plasmodium vivax (P. vivax) and Plasmodium falciparum (P. falciparum). Diagnosis of malaria typically involves visual microscopy examination of blood smears for malaria parasites. This is a tedious, erro...

Key points Microvascular network architecture defines coupling of fluid and protein exchange. Network arrangements markedly reduce capillary hydrostatic pressures and resting fluid movement at the same time as increasing the capacity for change The presence of vascular remodelling or angiogenesis puts constraints of network behaviour The sites of f...

Accurate segmentation and tracking of cells in microscopy image sequences is extremely beneficial in clinical diagnostic applications and biomedical research. A continuing challenge is the segmentation of dense touching cells and deforming cells with indistinct boundaries, in low signal-to-noise-ratio images. In this paper, we present a dual-stream...

Detection, segmentation, and quantification of microvascular structures are the main steps towards studying microvascular remodeling. Combined with appropriate staining, confocal microscopy imaging enables exploration of the full 3D anatomical characteristics of microvascular systems. Segmentation of confocal microscopy images is a challenging task...

Analysis of morphometric features of nuclei plays an important role in understanding disease progression and predict efficacy of treatment. First step towards this goal requires segmentation of individual nuclei within the imaged tissue. Accurate nuclei instance segmentation is one of the most challenging tasks in computational pathology due to bro...

This work presents a 3D-enabled method to register aerial image sequences. Our approach is based on a novel Bootstrapped Structure-from-Motion (BSfM)Bootstrapped Structure-from-Motion (BSfM) followed by analytical homography reprojection or georegistration. BSfMBootstrapped Structure-from-Motion (BSfM) is a fast and robust method to recover the 3D...

Precise positioning of neurons resulting from cell division and migration during development is critical for normal brain function. Disruption of neuronal migration can cause a myriad of neurological disorders. To investigate the functional consequences of defective neuronal positioning on circuit function, we studied a zebrafish frizzled3a (fzd3a)...

Multi-Unmanned Aerial Vehicle (UAV) systems with high-resolution cameras have been found useful for operations such as smart city and disaster management. These systems feature Flying Ad-Hoc Networks (FANETs) that connect the computation edge with UAVs and a Ground Control Station (GCS) through air-to-ground wireless network links. Leveraging the e...

The reconstruction of bacterial and archaeal genomes from shotgun metagenomes has enabled insights into the ecology and evolution of environmental and host-associated microbiomes. Here we applied this approach to >10,000 metagenomes collected from diverse habitats covering all of Earth’s continents and oceans, including metagenomes from human and a...

A Correction to this paper has been published: https://doi.org/10.1038/s41587-021-00898-4.

3D city-scale point cloud stitching is a critical component for large data collection, environment change detection, in which massive amounts of 3D data are captured under different times and conditions. This paper proposes a novel point cloud stitching approach, that automatically and accurately stitches multiple city-scale point clouds, which onl...

Characterizing the spatial relationship between the blood vessels and lymphatic vascular structures, in the mice dura mater tissue, is useful for modeling fluid flows and changes in dynamics in various disease processes. We propose a new deep learning-based approach to fuse a set of multi-channel single-focus microscopy images within each volumetri...

In this paper we introduce a novel end-to-end framework for generation of large, aerial, city-scale, realistic synthetic image sequences with associated accurate and precise camera metadata. The two main purposes for this data are (i) to enable objective, quantitative evaluation of computer vision algorithms and methods such as feature detection, d...

Ubiquitous low cost multi-rotor and fixed wing drones or unmanned aerial vehicles (UAVs) have accelerated the need for reliable, robust, and scalable Structure-from-Motion (SfM) and Multi-View Stereo (MVS) pipelines suitable for a variety of flightpath trajectories especially in degraded environments. Feature tracking being a core part of SfM and M...

Background Light microscopy is often used for malaria diagnosis in the field. However, it is time-consuming and quality of the results depends heavily on the skill of microscopists. Automating malaria light microscopy is a promising solution, but it still remains a challenge and an active area of research. Current tools are often expensive and invo...

Viruses are integral components of all ecosystems and microbiomes on Earth. Through pervasive infections of their cellular hosts, viruses can reshape microbial community structure and drive global nutrient cycling. Over the past decade, viral sequences identified from genomes and metagenomes have provided an unprecedented view of viral genome diver...

An amendment to this paper has been published and can be accessed via a link at the top of the paper.

Computer-assisted algorithms have become a mainstay of biomedical applications to improve accuracy and reproducibility of repetitive tasks like manual segmentation and annotation. We propose a novel pipeline for red blood cell detection and counting in thin blood smear microscopy images, named RBCNet, using a dual deep learning architecture. RBCNet...

The Human Activity Recognition (HAR) is a pattern recognition task that learns to identify human physical activities recorded by different sensor modalities. The application areas include human behavior analysis, ambient assistive living, surveillance-based security, gesture recognition, and context-aware computing. The HAR remains challenging as t...

In biomedical imaging using video microscopy, understanding large tissue structures at cellular and finer resolution poses many image acquisition challenges including limited field-of-view and tissue dynamics during imaging. Automated mosaicing or stitching of live tissue video microscopy enables the visualization and analysis of subtle morphologic...

RTip is a tool to quantify plant root growth velocity using high resolution microscopy image sequences at sub-pixel accuracy. The fully automated RTip tracker is designed for high-throughput analysis of plant phenotyping experiments with episodic perturbations. RTip is able to auto-skip past these manual intervention perturbation activity, i.e. whe...

Premise: Aerial imagery from small unmanned aerial vehicle systems is a promising approach for high-throughput phenotyping and precision agriculture. A key requirement for both applications is to create a field-scale mosaic of the aerial imagery sequence so that the same features are in registration, a very challenging problem for crop imagery. M...

Cyber foraging has been shown to be especially effective for augmenting low-power Internet-of-Thing (IoT) devices by offloading video processing tasks to nearby edge/cloud computing servers. Factors such as dynamic network conditions, concurrent user access, and limited resource availability, cause offloading decisions that negatively impact overal...