Object Identification Research Articles

Design of Polarization Spectroscopy Integrated Imaging System

To simultaneously acquire the spectral and polarization information of the target and achieve the monitoring and identification of the target object, a polarization spectral integrated imaging system is proposed in this paper. Firstly, the structural principle of the polarization spectral integrated imaging system is introduced. The relationship between the spatial resolution, spectral resolution, and the system’s structural parameters is analyzed. The design of the optical part of the polarization spectral integrated imaging system is completed, along with the tolerance analysis. Secondly, the mechanical structure of the polarization spectral integrated imaging system is designed. Finally, by using a drone to carry the polarization spectral integrated imaging system, a simulation experiment for sea surface oil spill monitoring is conducted, and the hyperspectral and polarization information of the ocean, crude oil, fuel oil, palm oil, diesel, and gasoline are obtained. The polarization and spectral information were integrated. The integration of hyperspectral and polarization data yields remarkable enhancement outcomes, allowing for the clear delineation of previously challenging-to-identify crude oil contamination areas against the marine background in the fused images, characterized by sharper boundaries and improved discriminability. This accomplishment underscores the feasibility of our system for the rapid identification of large-scale oil spill events.

IMMEDIATE OBJECT SPOTTING

Object Spotting has become one of the most essential and challenging tasks in computer vision, particularly in the context of deep learning advancements. This paper reviews the evolution of object detection techniques, highlighting how deep neural networks have transformed the field. We classify the state-of-the-art detection algorithms into three main categories: anchor-based, anchor-free, and transformer-based detectors, each with distinct methodologies for identifying objects in images. This survey includes a comparison of key convolutional neural network (CNN) architectures used for object detection, evaluating their speed-accuracy trade-offs, quality metrics, and training strategies. We discuss the strengths and limitations of these models and offer insights into the future directions of research in object spotting. In parallel, immediate object spotting using OpenCV has gained prominence for its ability to identify and localize objects within live video streams, particularly by leveraging pre-trained models like YOLOv3. This approach processes each video frame in real time, with preprocessing steps such as resizing and normalization to ensure compatibility with the deep learning model. YOLOv3’s efficiency enables it to detect and classify objects quickly, drawing bounding boxes around detected objects and displaying them in real time. This project highlights how combining OpenCV with deep learning models can achieve effective, scalable object spotting while ensuring detection accuracy. The paper also explores the challenges and potential future advancements in improving detection accuracy, robustness, and compatibility with modern hardware systems for diverse applications. Keywords:Immediate object spotting, OpenCV, YOLOv3, Deep learning models, Live video stream, Pre- processing (resizing, normalization), Object identification and localization, Bounding boxes, Detection accuracy, System compatibility

Performance Evaluation of the Convolutional Neural Networks for Object Identification Using RGB and Binary Images

Performance Evaluation of the Convolutional Neural Networks for Object Identification Using RGB and Binary Images

Interleaved Feature Extraction Model: Bridging Multiple Techniques for Enhanced Object Identification

Image mining, an essential process in many industrial image applications, has demonstrated significant utility in fields such as medical diagnostics, agriculture, industrial operations, space research, and education. This process involves extracting both information and image segments, but these tasks are often conducted independently, resulting in different workflows. This paper proposes an approach that integrates feature extraction and object recognition, leading to improved object identification. We introduce a novel method that improves recognition accuracy by increasing the percentage of optimal features. The ORB algorithm, known for its speed, is used in the initial pass, while the SURF algorithm is used as a secondary confirmation step for unrecognized objects. This approach supports the simultaneous processing of many images, which makes it suitable for large-scale applications such as image repositories in social media and expands the scope of research. This refined version maintains the core elements, while making the structure a little more fluid and coherent

Enhancing image quality using super-resolution residual network for small, blurry images

<span lang="EN-US">In the background, when low-resolution images are utilized, image identification tasks are frequently hampered. By employing the residual network super-resolution framework, super-resolution techniques are used to enhance image quality, specifically in the detection and identification of small and blurry objects. Improving resolution, decreasing blur, and enhancing object detail are the main goals of the suggested approach. The novelty of this research resides in its application of the activation exponential linear unit (ELU) to the super-resolution residual network (SR-ResNet) framework, which has been demonstrated to enhance image sharpness. The experimental findings demonstrate a substantial enhancement in the quality of the images, as evidenced by the training data's structural similarity index (SSIM) of 0.9989 and peak signal-to-noise ratio (PSNR) of 91.8455. Furthermore, the validation data demonstrated SSIM 0.9990 and PSNR 92.5520. The results of this study indicate that the implementation of SR-ResNet significantly enhances the capability of the detection system to detect and classify diminutive and opaque entities precisely. The expected and projected enhancement in image quality significantly influences image processing, especially in situations where accuracy and object differentiation are vital.</span>

ProbSevere Version 3: Improved Exploitation of Data Fusion and Machine Learning for Nowcasting Severe Weather

Abstract National Oceanic and Atmospheric Administration (NOAA)/Cooperative Institute for Meteorological Satellite Studies (CIMSS) ProbSevere, or the “probability of severe” models, provide next-hour probabilistic guidance on severe weather in the United States (large hail, strong wind gusts, and tornadoes) using machine learning and meteorological data from remotely sensed platforms and short-term numerical weather models. ProbSevere has been widely used by NOAA’s National Weather Service (NWS) since 2016. ProbSevere version 3 employs multiplatform, multiscale storm object identification and tracking to collect storm features and then uses gradient boosting decision trees to make predictions of convective hazards from the extracted predictors. ProbSevere v3 was trained, validated, and tested on data from 2018 to 2023. Results demonstrate improved performance over ProbSevere v2 for the majority of the United States across all seasons, spanning a wide range of meteorological regimes. The greatest improvements are for the hail, wind, and any severe models, whereas the tornado model demonstrated modest improvement. ProbSevere v3 models were evaluated by NWS forecasters in simulated real-time experiments from 2021 to 2023. The majority of forecasters preferred ProbSevere v3 over v2 due to improved confidence in predictions and greater lead times for the issuance of severe weather warnings. An analysis of predictor importance revealed several radar, lightning, satellite, and numerical model fields that strongly contribute to the model predictions, which is consistent with past research and knowledge of severe thunderstorm forecasting. This reinforces the importance of environmental data fusion for nowcasting severe weather associated with midlatitude convection. Significance Statement This study details the updates made to a U.S.-wide, real-time, probabilistic severe-weather guidance system aimed to improve forecaster confidence and the accuracy of the U.S. National Weather Service severe weather warnings. An evaluation of the system demonstrated improved performance over the operational predecessor, which was driven by radar, ground-based lightning, satellite, and numerical model data sources.

Unsupervised object-based spectral unmixing for subpixel mapping

Subpixel mapping (SPM) addresses the widespread mixed pixel problem in remote sensing images by predicting the spatial distribution of land cover within mixed pixels. However, conventional pixel-based spectral unmixing, a key pre-processing step for SPM, neglects valuable spatial contextual information and struggles with spectral variability, ultimately undermining SPM accuracy. Additionally, while extensively utilized, supervised spectral unmixing is labor-intensive and user-unfriendly. To address these issues, this paper proposes a fully automatic, unsupervised object-based SPM (UO-SPM) model that exploits object-scale information to reduce spectral unmixing errors and subsequently enhance SPM. Given that mixed pixels are typically located at the edges of objects (i.e., the inner part of objects is characterized by pure pixels), segmentation and morphological erosion are employed to identify pure pixels within objects and mixed pixels at the edges. More accurate endmembers are extracted from the identified pure pixels for the secondary spectral unmixing of the remaining mixed pixels. Experimental results on 10 study sites demonstrate that the proposed unsupervised object (UO)-based analysis is an effective model for enhancing both spectral unmixing and SPM. Specifically, the spectral unmixing results of UO show an average increase of 3.65 % and 1.09 % in correlation coefficient (R) compared to Fuzzy-C means (FCM) and linear spectral mixture model (LSMM)-derived coarse proportions, respectively. Moreover, the UO-derived results of four SPM methods (i.e., Hopfield neural network (HNN), Markov random field (MRF), pixel swapping (PSA) and radial basis function interpolation (RBF)) exhibit an average increase of 5.89 % and 3.04 % in overall accuracy (OA) across the four SPM methods and 10 study sites compared to the FCM and LSMM-based results, respectively. Moreover, the proportions of both mixed and pure pixels are more accurately predicted. The advantage of UO-SPM is more evident when the size of land cover objects is larger, benefiting from more accurate identification of objects.

Reconstructing angular light field by learning spatial features from quadrilateral epipolar geometry

Recent research on dense multi-view image reconstruction has attracted considerable attention, due to its enhancement of applications such as 3D reconstruction, de-occlusion, depth sensing, saliency detection, and prominent object identification. This paper introduces a method for reconstructing high-density light field images, addressing the challenge of balancing angular and spatial resolution within the constraints of sensor resolution. We propose a three-stage network architecture for LF reconstruction that processes dense epipolar, spatial, and angular information efficiently. Our network processes epipolar information in the first stage, spatial information in the second stage, and angular information in the third stage. By extracting quadrilateral epipolar features from multiple directions, our model constructs a robust feature hierarchy for accurate reconstruction. We employ weight sharing in the initial stage to enhance feature quality while maintaining a compact model. Experimental results on real-world and synthetic datasets demonstrate that our approach surpasses state-of-the-art methods in both inference time and reconstruction quality.

Locomotion Algorithm for Onroad Autonomous Robots with Real Time Response

Abstract: This research introduces a novel locomotionalgorithm for autonomous on-road robots that are intended to realtime garbage collection from dustbins.The proposed system incorporates a cutting-edge object identification framework called YOLO (You Only Look Once) to recognise roadways, obstructions, and dustbins. The autonomous robot, equipped with a robust hardware platform and a camera, navigates urban environments efficiently, ensuring seamless obstacle avoidance and precise dustbin localization.Our method makes use of real-time processing to facilitate adaptive decision-making and dynamic path planning, which improves the robot's operational effectiveness. The algorithm's efficacy in varioussettings is illustrated by the experimental findings, underscoring its potential for scalable implementation in smart city endeavours. Recent advances in autonomous urban cleaning systems form the basis for this study, which focuses on real-time processing capabilities to enable adaptive decisionmaking and smooth navigation. Experimental validation illustratesthe suggested locomotion algorithm's performance in avariety of urban environments, highlighting its practical application in autonomous garbage management systems. This research aims to address waste management difficulties and contribute to the development of autonomous urban cleaning technology, thereby supporting the realisation of smarter and cleaner cities. By providing a long-term solution to the problems associated with urban waste management, this research advances autonomous urban cleaning systems.

Advancements in Computer Vision: A Comprehensive Survey of Image Processing and Interdisciplinary Applications

Computer vision and image processing are rapidly evolving fields with broad applications across numerous domains, including healthcare, autonomous driving, surveillance, and entertainment. These fields have transformed from simple data recording techniques into sophisticated systems that incorporate digital image processing, pattern recognition, machine learning, and computer graphics. This evolution has prompted interdisciplinary interest, pushed the technology’s boundaries and expanded its practical uses. This paper offers a comprehensive survey of recent advancements in computer vision, focusing on image processing and its applications across various fields. It delves into the theoretical foundations and technologies that make computer vision a valuable tool for interpreting images and videos, extracting relevant information, recognizing patterns, and understanding events. The ability of computer vision to analyze large datasets across multiple application domains makes it instrumental in tasks such as object identification, facial recognition, scene understanding, and even real-time action prediction. This versatility has established computer vision as a key driver of data-driven insights in both scientific and commercial sectors. The study categorizes computer vision into four main areas: image processing, object recognition, machine learning, and computer graphics. Each of these categories is essential to the functionality of modern computer vision systems. Image processing involves techniques for enhancing image quality and extracting important features. Object recognition and machine learning enable the identification of specific elements within images and allow systems to learn from large datasets, enhancing accuracy over time. Computer graphics, on the other hand, aid in visualizing and interpreting processed data. By offering insights into the latest techniques and evaluating their performance, this survey highlights the current state of computer vision while shedding light on future trends. Computer vision’s expanding utility across various fields underscores its critical role in driving interdisciplinary innovation and addressing complex challenges.

An audiovisual cognitive optimization strategy guided by salient object ranking for intelligent visual prothesis systems

Objective.Visual prostheses are effective tools for restoring vision, yet real-world complexities pose ongoing challenges. The progress in AI has led to the emergence of the concept of intelligent visual prosthetics with auditory support, leveraging deep learning to create practical artificial vision perception beyond merely restoring natural sight for the blind.Approach.This study introduces an object-based attention mechanism that simulates human gaze points when observing the external world to descriptions of physical regions. By transforming this mechanism into a ranking problem of salient entity regions, we introduce prior visual attention cues to build a new salient object ranking (SaOR) dataset, and propose a SaOR network aimed at providing depth perception for prosthetic vision. Furthermore, we propose a SaOR-guided image description method to align with human observation patterns, toward providing additional visual information by auditory feedback. Finally, the integration of the two aforementioned algorithms constitutes an audiovisual cognitive optimization strategy for prosthetic vision.Main results.Through conducting psychophysical experiments based on scene description tasks under simulated prosthetic vision, we verify that the SaOR method improves the subjects' performance in terms of object identification and understanding the correlation among objects. Additionally, the cognitive optimization strategy incorporating image description further enhances their prosthetic visual cognition.Significance.This offers valuable technical insights for designing next-generation intelligent visual prostheses and establishes a theoretical groundwork for developing their visual information processing strategies. Code will be made publicly available.

MMYFnet: Multi-Modality YOLO Fusion Network for Object Detection in Remote Sensing Images

Object detection in remote sensing images is crucial for airport management, hazard prevention, traffic monitoring, and more. The precise ability for object localization and identification enables remote sensing imagery to provide early warnings, mitigate risks, and offer strong support for decision-making processes. While traditional deep learning-based object detection techniques have achieved significant results in single-modal environments, their detection capabilities still encounter challenges when confronted with complex environments, such as adverse weather conditions or situations where objects are obscured. To overcome the limitations of existing fusion methods in terms of complexity and insufficient information utilization, we innovatively propose a Cosine Similarity-based Image Feature Fusion (CSIFF) module and integrate it into a dual-branch YOLOv8 network, constructing a lightweight and efficient target detection network called Multi-Modality YOLO Fusion Network (MMYFNet). This network utilizes cosine similarity to divide the original features into common features and specific features, which are then refined and fused through specific modules. Experimental and analytical results show that MMYFNet performs excellently on both the VEDAI and FLIR datasets, achieving mAP values of 80% and 76.8%, respectively. Further validation through parameter sensitivity experiments, ablation studies, and visual analyses confirms the effectiveness of the CSIFF module. MMYFNet achieves high detection accuracy with fewer parameters, and the CSIFF module, as a plug-and-play module, can be integrated into other CNN-based cross-modality network models, providing a new approach for object detection in remote sensing image fusion.

A Literature Review on Object Detection in Autonomous Driving

Object detection technology is a core component of autonomous driving systems, primarily tasked with the real-time identification and localization of objects in the surrounding environment, such as pedestrians, vehicles, and traffic signs. With the advancement of deep learning, various efficient object detection algorithms have emerged in recent years, such as the YOLO series, RetinaNet, and CenterNet. These algorithms have shown significant improvements in both processing speed and accuracy. However, the complexity and variability of autonomous driving environments still pose severe challenges to object detection. This review summarizes the current research status of object detection algorithms in autonomous driving, focusing specifically on the basic processes and characteristics of YOLO, RetinaNet, and CenterNet. It also discusses their improvements and optimization effects in practical applications. Additionally, this paper reveals the advantages and disadvantages of these three algorithms through a comparative analysis of experimental results, providing direction for future research. Finally, the paper discusses the main challenges faced by current object detection methods in the autonomous driving field and potential solutions, aiming to promote further development and application in this area.

Detecting multiple simultaneous and sequential feature changes

The failure to notice changes to objects is called change blindness, and it is often studied with the flicker task. Observers performing this task see two rapidly alternating but slightly different stimulus displays that are usually photos of real-world scenes. In order to detect the change, they must compare objects in the pre-change scene with objects at the same locations in the post-change scene to determine whether they are the same or different. It has been proposed that change blindness can occur when the memory representation of a pre-change object is incomplete and thereby impairs the same/different comparison with the post-change object at the same location. It has also been proposed that even with intact pre-change object memory representations, failure of same/different comparisons for other reasons can cause change blindness. The goal of the current study was to conduct flicker task experiments to examine both proposals. We conducted the current experiments with non-photographic stimuli, varied the degree of feature-based change of colored lines and found that the greater degree of change, the faster the same/different comparisons, and the faster that changes were detected. We also examined the representation integrity account of change blindness by comparing detection times of target objects that underwent a single feature change with those that underwent multiple sequential feature changes. The latter were detected faster, which suggests that multiple identities of these sequentially changing objects were stored in memory and facilitated change detection. In another experiment we found that objects that underwent multiple sequential feature changes were not detected as fast as those that underwent multiple simultaneous feature changes. This is consistent with the representation account of change blindness and suggests that memories of multiple sequentially changing object identities are transient and may become less complete over time. And more generally that multiple simultaneous and multiple sequential feature-based changes to these stimuli can show the extent to which memory is involved when searching for flicker task targets. The results of the current study indicate that both the comparison failure and the representation integrity proposals can account for change blindness.

A semiotic reading of Thomas Mofolo’s Chaka: a structuralist approach

This article explores the impact of various semiotic signs as they appear in the novel Chaka by Thomas Mofolo. The article aims to analyse the signs in the novel and how they contribute to bringing meaning to the novel and the character Chaka, who is a compilation of the signs. Arguing that even though there are numerous cultures in the African context, African cultures have a commonality in the formation of symbolism and the creation of cultural identification of objects, events, people, etc. Therefore, the novel Chaka should be analysed from an African cultural perspective, rather than an individual cultural perspective. Considering the preceding assertion and the analysis of Thomas Mofolo’s novel, Chaka, this article makes use of structuralism as a theoretical framework from a cultural perspective. Signs contribute to the meaning of a novel and interrelatedly portray the character, in this case, Chaka. Structuralism looks at the links among numerous elements in the self-contained, well-organised structure of a text to understand how the text creates meaning. Semiotics promotes structuralism, that is, the ways that varied cultural structures are organised to give meaning.

Online level-2 perspective taking for newly learnt symbols.

Humans demonstrate spontaneous sensitivity to other people's perspectives on object identities in online tasks. Evidence shows that this not only involves representing the mere discrepancy between perspectives, but the content of such perspectives as well (level-2 perspective taking/L2PT). However, this evidence comes from studies using culturally grounded symbols which leaves open the possibility that having extensive, easily accessible background knowledge about an object is necessary for the L2PT effect. Experiment 1 tested this by comparing L2PT across two groups: one performing a verification task on Arabic numbers, and one on newly learnt symbol-label pairs. In both groups, half of the visual stimuli was symmetrical, while half was asymmetrical. In both cases, there was a joint condition: participants performed the task in parallel with a partner, observing stimuli from opposite angles, thus having conflicting interpretations for asymmetric characters. Furthermore, they also performed the verification task individually, while their partner had no visual access to the stimuli. We found an interference effect in both groups. However, while the effect was stable in the number group, it diminished over time in the symbol group. Experiments 2a and 2b demonstrated that the complexity of the recently learnt symbols has an influence on spontaneous L2PT: the same procedure with more complex symbols did not elicit any interference effect. Our results show that online L2PT is not limited to objects that participants have proficiency in identifying. Nevertheless, the L2PT effect seems to diminish when participants have to process increasingly complex novel symbols.

Whole Slide Imaging, Artificial Intelligence, and Machine Learning in Pediatric and Perinatal Pathology: Current Status and Future Directions

The integration of artificial intelligence (AI) into healthcare is becoming increasingly mainstream. Leveraging digital technologies, such as AI and deep learning, impacts researchers, clinicians, and industry due to promising performance and clinical potential. Digital pathology is now a proven technology, enabling generation of high-resolution digital images from glass slides (whole slide images; WSI). WSIs facilitates AI-based image analysis to aid pathologists in diagnostic tasks, improve workflow efficiency, and address workforce shortages. Example applications include tumor segmentation, disease classification, detection, quantitation and grading, rare object identification, and outcome prediction. While advancements have occurred, integration of WSI-AI into clinical laboratories faces challenges, including concerns regarding evidence quality, regulatory adaptations, clinical evaluation, and safety considerations. In pediatric and developmental histopathology, adoption of AI could improve diagnostic efficiency, automate routine tasks, and address specific diagnostic challenges unique to the specialty, such as standardizing placental pathology and developmental autopsy findings, as well as mitigating staffing shortages in the subspeciality. Additionally, AI-based tools have potential to mitigate medicolegal implications by enhancing reproducibility and objectivity in diagnostic evaluations. An overview of recent developments and challenges in applying AI to pediatric and developmental pathology, focusing on machine learning methods applied to WSIs of pediatric pathology specimens is presented.

Polarisation Synthesis Applied to 3D Polarimetric Imaging for Enhanced Buried Object Detection and Identification

Detecting sub-surface objects poses significant challenges, partly due to attenuation of the ground medium and cluttered environments. The acquisition polarisation and antenna orientation can also yield significant variation of detection performance. These challenges can be mitigated by developing more versatile systems and algorithms to enhance detection and identification. In this study, a novel application of a 3D SAR inverse algorithm and polarisation synthesis was applied to ultra-wideband polarimetric data of buried objects. The principle of polarisation synthesis facilitates an adaptable technique which can be used to match the target’s polarisation characteristics, and the application of this revealed hidden structures, enhanced detection, and increased received power when compared to single polarisation results. This study emphasises the significance of polarimetry in ground-penetrating radar (GPR), particularly for target discrimination in high-lift-off applications. The findings offer valuable insights that could drive future research and enhance the performance of these sensing systems.

Retina-Inspired X-Ray Optoelectronic Synapse Using Amorphous Ga2O3 Thin Film.

Machine vision techniques are widely applied for object identification in daily life and industrial production, where images are captured and processed by sensors, memories, and processing units sequentially. Neuromorphic optoelectronic synapses, as a preferable option to promote the efficiency of image recognition, are hotly pursued in non-ionizing radiation range, but rarely in ionizing radiation including X-rays. Here, the study proposes an X-ray optoelectronic synapse using amorphous Ga2O3 (a-Ga2O3) thin film. Boosted by the interfacial VO 2+ defects and its slow neutralization rate, the enhanced electron tunneling process at metal/a-Ga2O3 interface produces remarkable X-ray-induced post-synaptic current, contributing to a sensitivity of 20.5, 64.3, 164.1 µC mGy-1 cm-2 for the 1st, 5th, and 10th excitation periods, respectively. Further, a 64×64 imaging sensor is constructed on a commercial amorphous Si (a-Si) thin film transistor (TFT) array. The image contrast can be apparently improved under a series of X-ray pulses due to an outstanding long-term plasticity of the single pixel, which is beneficial to the subsequent image recognition and classification based on artificial neural network. The merits of large-scale production ability and good compatibility with modern microelectronic techniques belonging to amorphous oxide semiconductors may promote the development of neuro-inspired X-ray imagers and corresponding machine vision systems.

Augmenting musical instruments with digital identities

We explore how augmenting musical instruments with digital identities can enhance their provenance, utility during creative practice, and personal meaning. A literature review reveals the importance of object identities in general and instrument identities in particular, how the identities of things can be digitally augmented, but also that this idea has not been widely applied to musical instruments. A first case study draws on interviews to illuminate the current practice of physically relicing guitars to enhance their identities. A second case study of augmenting a guitar to capture and retell its life story illuminates potential digital identity practices. Reflecting on both case studies, we reconsider musical instruments as product-service systems in which physical instruments come bundled with digital services that forge, perform, and share their identities.