3D Image Reconstruction Research Articles

Image Analysis Artificial Intelligence Technologies for Plant Phenotyping: Current State of the Art

Modern agriculture is characterized by the use of smart technology and precision agriculture to monitor crops in real time. The technologies enhance total yields by identifying requirements based on environmental conditions. Plant phenotyping is used in solving problems of basic science and allows scientists to characterize crops and select the best genotypes for breeding, hence eliminating manual and laborious methods. Additionally, plant phenotyping is useful in solving problems such as identifying subtle differences or complex quantitative trait locus (QTL) mapping which are impossible to solve using conventional methods. This review article examines the latest developments in image analysis for plant phenotyping using AI, 2D, and 3D image reconstruction techniques by limiting literature from 2020. The article collects data from 84 current studies and showcases novel applications of plant phenotyping in image analysis using various technologies. AI algorithms are showcased in predicting issues expected during the growth cycles of lettuce plants, predicting yields of soybeans in different climates and growth conditions, and identifying high-yielding genotypes to improve yields. The use of high throughput analysis techniques also facilitates monitoring crop canopies for different genotypes, root phenotyping, and late-time harvesting of crops and weeds. The high throughput image analysis methods are also combined with AI to guide phenotyping applications, leading to higher accuracy than cases that consider either method. Finally, 3D reconstruction and a combination with AI are showcased to undertake different operations in applications involving automated robotic harvesting. Future research directions are showcased where the uptake of smartphone-based AI phenotyping and the use of time series and ML methods are recommended.

Effective and efficient coded aperture cone-beam computed tomography via generative adversarial U-Net

Coded aperture cone-beam computed tomography (CBCT) represents a crucial method for acquiring high-fidelity three-dimensional (3D) tomographic images while reducing radiation exposure. However, projections are non-uniformly and discontinuously sampled with the coded apertures placed in front of the x-ray source, leading to very small reconstruction scale and time-intensive iterations. In this study, an alternative approach to reconstruct coded aperture CBCT based on generative adversarial U-net is proposed to effectively and efficiently reconstruct large scale 3D CBCT images. Our method entails predicting complete and uniform projections from incomplete and non-uniform coded projections, enabling the requirement of continuity for the use of analytical algorithms in 3D image reconstruction. This novel technique effectively mitigates the traditional trade-off between image fidelity and computational complexity inherent in conventional coded aperture CBCT reconstruction methods. Our experimental results, conducted using clinical datasets comprising CBCT images from 102 patients at Nanjing Medical University, demonstrate that high-quality CBCT images with voxel dimensions of 400 × 400 × 400 can be reconstructed within 35 s, even when 95% of projections are blocked, yielding images with PSNR values exceeding 25dB and SSIM values surpassing 0.85.

Reduction Accelerated Adaptive Step‐Size FISTA Based Smooth‐Lasso Regularization for Fluorescence Molecular Tomography Reconstruction

ABSTRACTIn this paper, a reduced accelerated adaptive fast iterative shrinkage threshold algorithm based on Smooth‐Lasso regularization (SL‐RAFISTA‐BB) is proposed for fluorescence molecular tomography (FMT) 3D reconstruction. This method uses the Smooth‐Lasso regularization to fuse the group sparse prior information which can balance the relationship between the sparsity and smoothness of the solution, simplifying the process of calculation. In particular, the convergence speed of the FISTA is improved by introducing a reduction strategy and Barzilai‐Borwein variable step size factor, and constructing a continuation strategy to reduce computing costs and the number of iterations. The experimental results show that the proposed algorithm not only accelerates the convergence speed of the iterative algorithm, but also improves the positioning accuracy of the tumor target, alleviates the over‐sparse or over‐smooth phenomenon of the reconstructed target, and clearly outlines the boundary information of the tumor target. We hope that this method can promote the development of optical molecular tomography.

Sparse-view neutron CT 3D image reconstruction algorithm based on split Bregman method

Sparse-view neutron CT 3D image reconstruction algorithm based on split Bregman method

Spreading anomaly semantic segmentation and 3D reconstruction of binder jet additive manufacturing powder bed images

Variability in the inherently dynamic nature of additive manufacturing introduces imperfections that hinder the commercialization of new materials. Binder jetting produces ceramic and metallic parts, but low green densities and spreading anomalies reduce the predictability and processability of resulting geometries. In situ feedback presents a method for robust evaluation of spreading anomalies, reducing the number of required builds to refine processing parameters in a multivariate space. In this study, we report layer-wise powder bed semantic segmentation for the first time with a visually light ceramic powder, alumina, or Al2O3, leveraging an image analysis software to rapidly segment optical images acquired during the additive manufacturing process. Using preexisting image analysis tools allowed for rapid analysis of 316 stainless steel and alumina powders with small data sets by providing an accessible framework for implementing neural networks. Models trained on five build layers for each material to classify base powder, parts, streaking, short spreading, and bumps from recoater friction with testing categorical accuracies greater than 90%. Lower model performance accompanied the more subtle spreading features present in the white alumina compared to the darker steel. Applications of models to new builds demonstrated repeatability with the resulting models, and trends in classified pixels reflected corrections made to processing parameters. Through the development of robust analysis techniques and feedback for new materials, parameters can be corrected as builds progress.

Three-dimensional ghost imaging via the Scheimpflug detection

For previous three-dimensional ghost imaging, the acquisition of absolute distance information is mainly based on the principle of time-of-flight, which usually needs lots of measurements and a large detection/modulation bandwidth product. Here we present a technique called three-dimensional ghost imaging via the Scheimpflug detection (3D-GISD), which exploits the principle of a similar binocular stereoscopic vision for distance information acquisition and can dramatically reduce the measurements required for high-quality 3D image reconstruction. The experimental results demonstrate that high-quality 3D-GISD can be still obtained even if the target exceeds the depth of field of Scheimpflug imaging system and less than 500 measurements are adopted for an image with 128×128 pixels. What’s more, the ranging accuracy of 0.2 mm can be achieved by 3D-GISD at about 1.1 m detection distance for a real scenario. Factors influencing the accuracy of distance measurement for 3D-GISD are also discussed.

From irregular to regular eutectic growth in the Al-Al3Ni system: In situ observations during directional solidification

We investigate the irregular eutectic growth dynamics of the Al-Al3Ni alloy, in which one of the solid phases (Al3Ni) grows faceted from the liquid. Leveraging in situ optical microscopy and synchrotron transmission x-ray microscopy, we address the question of the degree of coupling between Al and Al3Ni at the growth front and that of the shape of the microstructures left behind in the bulk solid during directional solidification. Real-time optical observations bring evidence for a morphological transition from a eutectic-grain dependent, irregular eutectic growth at low solidification velocity V (typically 1μms−1), to a weakly anisotropic, regular growth at higher V (reaching 10μms−1). Unprecedented x-ray nano-imaging of the solid–liquid interface, and 3D characterization of the growth patterns, were made possible by a new DS setup at Brookhaven National Laboratory’s NSLS-II. At low V, the leading tips of partly faceted Al3Ni crystals are observed to grow not far ahead of the Al growth front. Correlating in situ images and postmortem 3D tomographic reconstructions reveals that the presence of faceted and non-faceted regions of Al3Ni crystals in the solid is a direct consequence of coupling and decoupling during DS, respectively. Upon increasing V, the lead distance of Al3Ni vanishes, and the shape of Al3Ni ceases to be governed by faceted growth. These observations cast light onto the basic mechanisms (faceted growth, diffusive coupling, and the dynamics of trijunctions) governing a faceted to rod-like transition upon increasing V in the Al3Ni system, with broad implications to a large class of irregular eutectics.

Electron tomography visualization of HIV-1 virions trapped by fusion inhibitors to host cells in infected tissues.

HIV-1 delivers its genetic material to infect a cell after fusion of the viral and host cell membranes, which takes place after the viral envelope (Env) binds host receptor and co-receptor proteins. Binding of host receptor CD4 to Env results in conformational changes that allow interaction with a host co-receptor (CCR5 or CXCR4). Further conformational rearrangements result in an elongated pre-hairpin intermediate structure in which Env is anchored to the viral membrane by its transmembrane region and to the host cell membrane by its fusion peptide. Although budding virions can be readily imaged by electron tomography (ET) of HIV-1-infected tissues and cultured cells, virions that are fusing (attached to host cells via pre-hairpin intermediates) are not normally visualized, perhaps because the process of membrane fusion is too fast to capture by EM. To image virions during fusion, we used fusion inhibitors to prevent downstream conformational changes in Env that lead to membrane fusion, thereby trapping HIV-1 virions linked to target cells by prehairpin intermediates. ET of HIV-1 pseudovirions bound to CD4+/CCR5+ TZM-bl cells revealed presumptive pre-hairpin intermediates as 2-4 narrow spokes linking a virion to the cell surface. To extend these results to a more physiological setting, we used ET to image tissues and organs derived from humanized bone marrow, liver, thymus (BLT) mice infected with HIV-1 and then treated with CPT31, a high-affinity D-peptide fusion inhibitor linked to cholesterol. Trapped HIV-1 virions were found in all tissues studied (small intestine, mesenteric lymph nodes, spleen, and bone marrow), and spokes representing pre-hairpin intermediates linking trapped virions to cell surfaces were similar in structure and number to those seen in the previous pseudovirus and cultured cell ET study.

2D and 3D analysis improvements with machine learning for muography applications

Transmission muography is a non-invasive and non-destructive imaging method which allows to estimate the integrated density of a volume in a given direction (also referred as opacity). It is used in multiple societal applications like archaeology, nuclear safety or geoscience. It relies on the reconstruction of muon tracks that crossed the studied volume compared to the corresponding open sky expectation. The portable experimental setups developed by CEA/Irfu group operates four Micromegas gaseous detectors, HV and DAQ modules, and an embedded computer allowing remote control.Used Micromegas detectors have a multiplexed readout to optimize the DAQ system, while keeping good spatial resolution. However, the natural muon flux is relatively low, so muography images could have high levels of statistical noise, which would be propagated to the reconstructed 3D images. For this reason, we propose three new methods, using machine learning, which increase significantly the quality of 2D images and 3D tomographies.We developed a new demultiplexing method for the Micromegas. It showed its efficiency both for 1D and 2D multiplexed detectors, in a hodoscopic tracker and a Time Projection Chamber (TPC). In the TPC, this method can embed a particle identification between muon-hits and electron-hits.We demonstrated how neural networks could denoise muography images. In the context of the scanning of a nuclear reactor, we used data augmentation, modeling few hundreds fake nuclear reactors. Muographies from different points of view were simulated to train a denoising neural network.These new methods significantly improved the muography images. Nonetheless, the used 3D tomography algorithm still has some limitations. We proved that it was possible to build a 3D post-process neural network, which was trained to compensate the reconstruction algorithm’s limitations.

Protein Corona-Directed Cellular Recognition and Uptake of Polyethylene Nanoplastics by Macrophages.

The widespread use of plastic products in daily life has raised concerns about the health hazards associated with nanoplastics (NPs). When exposed, NPs are likely to infiltrate the bloodstream, interact with plasma proteins, and trigger macrophage recognition and clearance. In this study, we focused on establishing a correlation between the unique protein coronal signatures of high-density (HDPE) and low-density (LDPE) polyethylene (PE) NPs with their ultimate impact on macrophage recognition and cytotoxicity. We observed that low-density and high-density lipoprotein receptors (LDLR and SR-B1), facilitated by apolipoproteins, played an essential role in PE-NP recognition. Consequently, PE-NPs activated the caspase-3/GSDME pathway and ultimately led to pyroptosis. Advanced imaging techniques, including label-free scattered light confocal imaging and cryo-soft X-ray transmission microscopy with 3D-tomographic reconstruction (nano-CT), provided powerful insights into visualizing NPs-cell interactions. These findings underscore the potential risks of NPs to macrophages and introduce analytical methods for studying the behavior of NPs in biological systems.

Research on Polarization Modulation of Electro-Optical Crystals for 3D Imaging Reconstruction.

A method for enhancing the resolution of 3D imaging reconstruction by employing the polarization modulation of electro-optical crystals is proposed. This technique utilizes two polarizers oriented perpendicular to each other along with an electro-optical modulation crystal to achieve high repetition frequency and narrow pulse width gating. By varying the modulation time series of the electro-optical crystal, three-dimensional gray images of the laser at different distances are acquired, and the three-dimensional information of the target is reconstructed using the range energy recovery algorithm. This 3D imaging system can be implemented with large area detectors, independent of the an Intensified Charge-Coupled Device (ICCD) manufacturing process, resulting in improved lateral resolution. Experimental results demonstrate that when imaging a target at the distance of 20 m, the lateral resolution within the region of interest is 2560 × 2160, with a root mean square error of 3.2 cm.

AI-Powered 3D Image Reconstruction for Analyzing Lithium Ion Battery Microstructures at the Nanoscale

A clean energy future will rely heavily on lithium-ion batteries to shift away from carbon-based energy sources. These devices are closed off from the outside world and operate under highly non-ambient conditions (e.g., liquid electrolytes, air-free environments). Their performance is largely driven by the 3D microstructures of the internal components and their evolutions. For example, breached layers in batteries due to lithium dendrite growth can lead to catastrophic failures and safety concerns. As such, imaging the internal microstructures and processes is important for observing and measuring device construction details and understanding how defects are distributed and change performance during operation. In particular, 3D imaging allows for analysis of features and parameters that can only be fully understood in 3D, like porosity and tortuosity, and can also provide realistic microstructural input for 3D modelling and simulation approaches.Within this scope, X-ray microscopy provides a unique method to image electrochemical devices because of the non-destructive nature of the technique. Modern x-ray microscopes enable 3D imaging even at the nanoscale, bringing capabilities once reserved for the synchrotron into the laboratory. Advances in tomographic image reconstruction have allowed researchers to increasingly maximize the impact of x-ray microscopy data through higher image quality and enabling faster data acquisition schemes. Recently, artificial intelligence (AI) has been incorporated into these algorithms, dramatically increasing the performance and capability of the technique. Here, we apply multiple novel reconstruction technologies that leverage AI to dramatically improve the performance of laboratory-based nanoscale x-ray microscopes, showing examples of improvements in imaging lithium-ion battery electrode materials.We show how AI-powered tomographic reconstruction can reveal new levels of detail in lithium-ion battery materials and enable up to 10 times faster data collection across the micrometer and nanometer length-scales. These capabilities shift the paradigm in volume analysis for electrochemical devices and give researchers unprecedented insight into the detailed microstructures that drive device performance, enabling new levels of understanding and helping to power the drive to a clean energy future.

Assessing Electronics with Advanced 3D X-ray Imaging Techniques, Nanoscale Tomography, and Deep Learning

This paper presents advanced workflows that combine 3D X-ray microscopy (XRM), nanoscale tomography, and deep learning (DL) to generate a detailed visualization of the interior of electronic devices and assemblies to enable the study of internal components for failure analysis (FA). Newly developed techniques, such as the integration of DL-based algorithms for 3D image reconstruction to improve scan quality through increased contrast and denoising, are also discussed in this article. In addition, a DL-based tool called DeepScout is presented. DeepScout uses 3D XRM scans in targeted regions of interest as training data for upscaling high-resolution in a low-resolution dataset, of a wider field of view, using a neural network model. Ultimately, these workflows can be run independently or complementary to other multiscale correlative microscopy evaluations, e.g., electron microscopy, and they will provide valuable insights into the inner workings of electronic packages and integrated circuits at multiple length scales, from macroscopic features on electronic devices (i.e., hundreds of mm) to microscopic details in electronic components (in the tens of nm). Understanding advanced electronic systems through X-ray imaging and machine learning—perhaps complemented with some additional correlative microscopy investigations—can speed development time, increase cost efficiency, and simplify FA and quality inspection of printed circuit boards (PCBs) and electronic devices assembled with new emerging technologies.

Lattice Boltzmann Model Investigation of Simultaneous Imbibition/Evaporation in PEMFC Cathodic Catalyst Layer Using Reconstructed 3D Image Data from Transmission Electron Microscopy

Proton Exchange Membrane Fuel Cells (PEMFCs) are widely used and significant attention is directed towards optimizing of the cathode catalyst layer. The critical focus revolves around refining the multi-phase boundary of the electrically conducting carbon support, the ion-conducting ionomer, the catalyst particles as well as the pore space that can be either occupied by oxygen (feedstock) or water (product). Challenges related to mass transport limitations of oxygen towards these multiple-phase boundaries, also denoted as active sites, persist, especially when liquid water increasingly saturates the pore space (Fig. 1). Furthermore, the evaporated water additionally hinders oxygen transport in the gas phase1,2. This study focuses on the investigation of pore-scale transport mechanisms inside of the cathodic catalyst layer, taking into account the counter-current diffusion of oxygen and water vapor in the gas phase as well as the generation of liquid water that increasingly saturates the void space. For this purpose, a multi-phase and multi-component pore scale Lattice Boltzmann model (LBM), coupled to two-phase reaction, was implemented. The LBM is tailored based on previous works3,4, including the localized generation of water at constant current densities, the imbibition of the pore structure with water counter-currently to the diffusion of oxygen, and the simultaneous evaporation of water. An actual 3D cathode catalyst layer from a commercial membrane electrode assembly, reconstructed from transmission electron microscopy (TEM) image data, was employed. It was imaged using a source voltage of 200kV and a current of 2 nA. The scanned sample had an approximate total volume of 500 × 500 × 100 nm3. From this, a 3D domain of 138 × 138 × 58 nm3 was reconstructed for this study. A 2D cross-section of the reconstructed image is exemplarily shown in Fig. 1. The in-silico study of water and oxygen transport through the catalyst layer elaborates the relationship between the rates of water invasion and evaporation in dependence of current density and catalyst layer structure. The critical conditions for water flooding are evaluated for different process conditions. Fig. 1 Schematic representation of the cathode catalyst layer: The ionomer is represented in white; the carbon for electron connectivity is represented in gray; pore space for oxygen and water transport in yellow; the black circles represent the active sites, i.e., Pt-particles located at the multi-phase boundary of ionomer, carbon and oxygen-filled pores; the red circles represent the inactive sites, that have no connection to ion and/or oxygen transport pathways. References A. Suzuki et al., Int. J. Hydrogen Energy, 36, 2221–2229 (2011) https://www.sciencedirect.com/science/article/pii/S0360319910022913.M. El Hannach, M. Prat, and J. Pauchet, Int. J. Hydrogen Energy, 37, 18996–19006 (2012) https://www.sciencedirect.com/science/article/pii/S0360319912022069.S. Bhaskaran et al., Dry. Technol., 40, 735–747 (2022) https://doi.org/10.1080/07373937.2021.1898417.S. Bhaskaran et al., Int. J. Hydrogen Energy, 47, 31551–31565 (2022) https://www.sciencedirect.com/science/article/pii/S0360319922030932. Figure 1

Optics-free reconstruction of 2D images via DNA barcode proximity graphs.

Spatial genomic technologies include imaging- and sequencing-based methods ( 1-3 ). An emerging subcategory of sequencing-based methods relies on a surface coated with coordinate-associated DNA barcodes, which are leveraged to tag endogenous nucleic acids or cells in an overlaid tissue section ( 4-7 ). However, the physical registration of DNA barcodes to spatial coordinates is challenging, necessitating either high density printing of coordinate-specific oligonucleotides or in situ sequencing/probing of randomly deposited, oligonucleotide-bearing beads. As a consequence, the surface areas available to sequencing-based spatial genomic methods are constrained by the time, labor, cost, and instrumentation required to either print, synthesize or decode a coordinate-tagged surface. To address this challenge, we developed SCOPE (Spatial reConstruction via Oligonucleotide Proximity Encoding), an optics-free, DNA microscopy ( 8 ) inspired method. With SCOPE, the relative positions of randomly deposited beads on a 2D surface are inferred from the ex situ sequencing of chimeric molecules formed from diffusing "sender" and tethered "receiver" oligonucleotides. As a first proof-of-concept, we apply SCOPE to reconstruct an asymmetric "swoosh" shape resembling the Nike logo (16.75 × 9.25 mm). Next, we use a microarray printer to encode a "color" version of the Snellen eye chart for visual acuity (17.18 × 40.97 mm), and apply SCOPE to achieve optics-free reconstruction of individual letters. Although these are early demonstrations of the concept and much work remains to be done, we envision that the optics-free, sequencing-based quantitation of the molecular proximities of DNA barcodes will enable spatial genomics in constant experimental time, across fields of view and at resolutions that are determined by sequencing depth, bead size, and diffusion kinetics, rather than the limitations of optical instruments or microarray printers.

The arrangements of the microvasculature and surrounding glial cells are linked to blood-brain barrier formation in the cerebral cortex.

The blood-brain barrier (BBB) blocks harmful substances from entering the brain and dictates the central nervous system (CNS)-specific pharmacokinetics. Recent studies have shown that perivascular astrocytes and microglia also control BBB functions, however, information about the formation of BBB glial architecture remains scarce. We investigated the time course of the formation of BBB glial architecture in the rat brain cerebral cortex using Evans blue (EB) and tissue fixable biotin (Sulfo-NHS Biotin). The extent of the leakage into the brain parenchyma showed that the BBB was not formed at postnatal Day 4 (P4). The BBB gradually strengthened and reached a plateau at P15. We then investigated the changes in the configurations of blood vessels, astrocytes, and microglia with age by 3D image reconstruction of the immunohistochemical data. The endfeet of astrocytes covered the blood vessels, and the coverage rate rapidly increased after birth and reached a plateau at P15. Interestingly, microglia were also in contact with the capillaries, and the coverage rate was highest at P15 and stabilized at P30. It was also clarified that the microglial morphology changed from the amoeboid type to the ramified type, while the areas of the respective contact sites became smaller during P4 and P15. These results suggest that the perivascular glial architecture formation of the rat BBB occurs from P4 to P15 because the paracellular transport and the arrangements of perivascular glial cells at P15 are totally the same as those of P30. In addition, the contact style of perivascular microglia dramatically changed during P4-P15.

Three-dimensional quantitative characterization of defects in inconel 625 superalloy based on deep learning image identification

Three-dimensional (3D) quantitative characterization of defects in superalloys is an important way to promote the ability of material design and service life prediction. In this work, 3D spatial distribution of defects for Inconel 625 superalloy manufactured by laser additive manufacturing (LAM) is carried out deep learning (DL) image identification technology and 3D image reconstruction. Firstly, computer tomography (CT) technology was used to obtain continuous slice images of sample. The U-net DL algorithm was applied to intelligently identify material defects in the continuous slices. On this basis, quantitative identification and analysis of spatial defect positions and typical sizes is achieved by using 3D reconstruction software. Compared with traditional threshold segmentation (TS) techniques, the defect recognition rate has significantly improved from 61.90 ​% to 95.00 ​%. This work provides a promising characterization method for efficient characterizing alloy defects and damage especially during material performance evaluation.

High diffraction efficiency complex amplitude modulation technique by using antiwave functions.

Complex amplitude modulation (CAM) is a single-step technique that codes the amplitude and phase of a diffracted optical field into a real function. Loading this function onto a phase-only spatial light modulator enables the reconstruction of 3D images. However, the obtained images have poor brightness because of the low diffraction efficiency. In this work, we show a novel solution, to the best of our knowledge, that overcomes this limitation. This is done by employing antiwave complex exponential functions, which modify the spectrum of the diffracted field. Proper configuration of these functions allows transferring energy across the diffraction spectrum to the informative diffraction order, which significantly improves the brightness of the reconstructed image. Simulations and experiments prove the efficiency of the proposed method.

A Super-Resolution and 3D Reconstruction Method Based on OmDF Endoscopic Images.

In the field of endoscopic imaging, challenges such as low resolution, complex textures, and blurred edges often degrade the quality of 3D reconstructed models. To address these issues, this study introduces an innovative endoscopic image super-resolution and 3D reconstruction technique named Omni-Directional Focus and Scale Resolution (OmDF-SR). This method integrates an Omnidirectional Self-Attention (OSA) mechanism, an Omnidirectional Scale Aggregation Group (OSAG), a Dual-stream Adaptive Focus Mechanism (DAFM), and a Dynamic Edge Adjustment Framework (DEAF) to enhance the accuracy and efficiency of super-resolution processing. Additionally, it employs Structure from Motion (SfM) and Multi-View Stereo (MVS) technologies to achieve high-precision medical 3D models. Experimental results indicate significant improvements in image processing with a PSNR of 38.2902 dB and an SSIM of 0.9746 at a magnification factor of ×2, and a PSNR of 32.1723 dB and an SSIM of 0.9489 at ×4. Furthermore, the method excels in reconstructing detailed 3D models, enhancing point cloud density, mesh quality, and texture mapping richness, thus providing substantial support for clinical diagnosis and surgical planning.

Computer Vision Algorithm Design in Image Processing Based on Projective Geometry

Image processing with computer vision, particularly in the realm of projective geometry, offers remarkable potential for various applications. Through the lens of projective geometry, images can be transformed, augmented, and reconstructed with precision, facilitating tasks such as image rectification, 3D reconstruction, and object tracking. Landmark estimation in computer vision is a vital task with broad applications across various domains. This process involves identifying key points or landmarks within images, enabling tasks such as facial recognition, object tracking, and gesture recognition. This paper, proposed a novel approach for landmark estimation in computer vision using Projective Geometry Landmark Estimation (PGLM). The proposed model aims to estimate the landmark features by a projective geometry model. With the estimation of the geometry features landmarks related to the facial, object, and medical images are computed. The PGLM model uses the point features for the location of the landmark features. In order to compare PGLM's performance to that of more conventional classification methods like Random Forest, K-Nearest Neighbors (KNN), and Support Vector Machine (SVM), simulation analysis is carried out. From what we can see, PGLM routinely beats these alternatives when we compare their accuracy, precision, recall, and F1 score. The findings stated the effectiveness of PGLM as a promising approach for landmark estimation in image processing tasks, paving the way for further advancements in this domain.