Object Recognition Research Articles

착용형 증강현실 기반 체험형 콘텐츠 연구

This paper proposes AR stone tower content, an experiential content based on wearable augmented reality (AR). Although wearable augmented reality is gaining attention, the acceptance of the technology is still focused on specialized applications such as industrial sites. On the other hand, the proposed AR stone tower content is based on the material of 'stone tower' so that general users can relate to it and easily participate in it, and it is organized to utilize space in a moving environment and find and stack stones based on natural hand gestures. The proposed AR stone tower content was implemented in the HoloLens 2 environment and evaluated by general users through a pilot exhibition in a small art museum. The evaluation results showed that the overall satisfaction with the content averaged 3.85, and the content appropriateness for the stone tower material was very high at 4.15. In particular, users were highly satisfied with content comprehension and sound, but somewhat less satisfied with object recognition, body adaptation, and object control. The above user evaluations confirm the resonance and positive response to the material, but also highlight the difficulties of the average user in experiencing and interacting with the wearable AR environment.

Mitochondrial antioxidant elamipretide improves learning and memory impairment induced by chronic sleep deprivation in mice.

The inflammation and synaptic dysfunction induced by mitochondrial dysfunction play essential roles in the learning and memory impairment associated with sleep dysfunction. Elamipretide (SS-31), a novel mitochondrion-targeted antioxidant, was proven to improve mitochondrial dysfunction, the inflammatory response, synaptic dysfunction, and cognitive impairment in models of cerebral ischemia, sepsis, and type 2 diabetes. However, the potential for SS-31 to improve the cognitive impairment induced by chronic sleep deprivation (CSD) and its underlying mechanisms is unknown. Adult c57BL/6J mice were subjected to CSD for 21 days using an activity wheel accompanied by daily intraperitoneal injection of SS-31 (5mg/kg). The novel object recognition and Morris water maze test were used to evaluate hippocampus-dependent cognitive function. Western blotting and reverse transcription-quantitative polymerase chain reaction assays were used to determine the effects of CSD and SS-31 on markers of mitochondria, inflammation response, and synaptic function. Enzyme-linked immunosorbent assays were used to examine the levels of proinflammatory cytokines. SS-31 could improve the cognitive impairment induced by CSD. In particular, SS-31 treatment restored the CSD-induced decrease in sirtuin 1 (SIRT1) and peroxisome proliferator-activated receptor γ coactivator alpha levels and the increase in levels nuclear factor kappa-B and inflammatory cytokines, including interleukin (IL)-1β, IL-6, and tumor necrosis factor-alpha. Furthermore, SS-31 significantly increased the levels of brain-derived neurotrophic factor, postsynaptic density protein-95, and synaptophysin in CSD mice. Taken together, these results suggest that SS-31 could improve CSD-induced mitochondrial biogenesis dysfunction, inflammatory response, synaptic dysfunction, and cognitive impairment by increasing SIRT1 expression levels.

Efficient Helmet and Number Plate Detection using YOLOv3: A Road Safety Perspective

Abstract: Object recognition is an important aspect of machine learning and CV tasks, in addition to many real-world uses, including traffic welfare. In this work, we provide a YOLOv3-based trained model for number plate and helmet recognition in photos. Pre-processing the data improved its quality and diversity. We gathered a dataset of annotated photos of helmets and licence plates. We trained our model with a multi-phase program for learning rates using the dataset, then we assessed its efficacy on an independent test collection set. Additionally, we examined its advantages along with disadvantages of the methodology we used also spoken about potential developments that can be done in coming times to raise the model's precision and effectiveness. Our study advances the development of realistic, efficient, along systems for detecting objects that increase traffic welfare.

Insights into Image Understanding: Segmentation Methods for Object Recognition and Scene Classification

Image understanding plays a pivotal role in various computer vision tasks, such as extraction of essential features from images, object detection, and segmentation. At a higher level of granularity, both semantic and instance segmentation are necessary for fully grasping a scene. In recent times, the concept of panoptic segmentation has emerged as a field of study that unifies semantic and instance segmentation. This article sheds light on the pivotal role of panoptic segmentation as a visualization tool for understanding scene components, including object detection, categorization, and precise localization of scene elements. Advancements in achieving panoptic segmentation and suggested improvements to the predicted outputs through a top-down approach are discussed. Furthermore, datasets relevant to both scene recognition and panoptic segmentation are explored to facilitate a comparative analysis. Finally, the article outlines certain promising directions in image recognition and analysis by underlining the ongoing evolution in image understanding methodologies.

인류세 시대, 우리는 무엇을 먹고 싶은가?

This study explores Ruth Ozeki's My Year of Meats through the lens of Okashi aesthetics in the tradition of Japanese literature, crystallizing the psycho power of eating and identifying the intersection between what we want to eat and what we want to be eco-friendly in the Anthropocene era. The Okashi aesthetics, which pursue objective reality recognition, capture the duality of phenomena, and seek proactive social change, let Jane and Akiko recognize the oppressive psycho power, critically engage with these ambivalence, and channel their desires towards catalyzing social transformation in the novel. This research contributes to alternative dietary practices in the Anthropocene by examining the convergence of our culinary preferences with the imperative of environmental sustainability.

Visual imagery deficits in posterior cortical atrophy

ABSTRACT Visual imagery has a close overlapping relationship with visual perception. Posterior cortical atrophy (PCA) is a neurodegenerative syndrome marked by early impairments in visuospatial processing and visual object recognition. We asked whether PCA would therefore also be marked by deficits in visual imagery, tested using objective forced-choice questionnaires, and whether imagery deficits would be selective for certain properties. We recruited four patients with PCA and a patient with integrative visual agnosia due to bilateral occipitotemporal strokes for comparison. We administered a test battery probing imagery for object shape, size, colour lightness, hue, upper-case letters, lower-case letters, word shape, letter construction, and faces. All subjects showed significant impairments in visual imagery, with imagery for lower-case letters most likely to be spared. We conclude that PCA subjects can show severe deficits in visual imagery. Further work is needed to establish how frequently this occurs and how early it can be found.

Adaptive Scale and Correlative Attention PointPillars: An Efficient Real-Time 3D Point Cloud Object Detection Algorithm

Recognizing 3D objects from point clouds is a crucial technology for autonomous vehicles. Nevertheless, LiDAR (Light Detection and Ranging) point clouds are generally sparse, and they provide limited contextual information, resulting in unsatisfactory recognition performance for distant or small objects. Consequently, this article proposes an object recognition algorithm named Adaptive Scale and Correlative Attention PointPillars (ASCA-PointPillars) to address this problem. Firstly, an innovative adaptive scale pillars (ASP) encoding method is proposed, which encodes point clouds using pillars of varying sizes. Secondly, ASCA-PointPillars introduces a feature enhancement mechanism called correlative point attention (CPA) to enhance the feature associations within each pillar. Additionally, a data augmentation algorithm called random sampling data augmentation (RS-Aug) is proposed to solve the class imbalance problem. The experimental results on the KITTI 3D object dataset demonstrate that the proposed ASCA-PointPillars algorithm significantly boosts the recognition performance and RS-Aug effectively enhances the training effects on an imbalanced dataset.

Live Object Recognition Using YOLO

Abstract: Live object recognition refers to the real-time process of identifying and categorizing objects within a given visual input, such as images. This technology utilizes computer vision techniques and advanced algorithms to detect objects, determine their dimension, area and weight and often classify them into defined categories. Our system proposesR-CNN and YOLO to determine the dimensions of the objects in real time. YOLO takes a different approach by treating object detection as a single regression problem. A single neural network is trained to directly predict bounding boxes and class probabilities for multiple objects in an image. The input image is divided into a grid, and each grid cell is responsible for predicting the objects whose center fall within that cell. Live object recognition finds applications in various fields, including autonomous vehicles, surveillance systems, robotics,augmented reality, and more. By providing instantaneous and accurate insights into the surrounding environment, this technology contributes to enhanced decision-making,interaction, and automation across numerous domains. The objects which can be recognized are solid objects which we use daily such as electronic items, stationery items, culinary items and many more. Our system “Live Object Recognition using YOLO” is aimed to detect and determine the objects and dimensions in real time.

Weapon Detection Using FRCNN

Abstract: Due to the increase in crime in remote and congested places, security is a top priority in all fields. Computer vision is having numerous uses for abnormal objects recognition and monitoring to deal with an array of scenarios. It's necessary for video surveillance systems to be employed as a way to fulfil the growing need and the protection of individual's financial assets, safety, and security, and to enhance having the capacity to detect and assess unusual events, is necessary for intelligence monitoring.This enables the utilization of FRCNN machine learning that autonomously identify weapons. Two different types of datasets will be utilized in the provided implementation. A pair of datasets can be downloaded, one with labelled photos and the second with a combination of manually labelled photos. In almost all of the aforementioned scenarios, algorithms gather precise data; nevertheless, how well speed and precision coincide could impact how algorithms are used in real-life instances

GAMNet: A deep learning approach for precise gesture identification

Deep learning has gained popularity across several industries, including object recognition and classification. In the case of Convolutional Neural Networks (CNN), the first layers extract the most noticeable elements, such as shape and margin. As the model progresses, it learns to extract more complex features such as texture and color; conversely, skeleton features encompass significant locations (joints) that do not naturally align with the grid-like architecture intended for these networks. This study emphasizes the importance of structural features in enhancing the performance of deep learning models. It introduces the Gesture Analysis Module Network (GAMNet), which computes abstract structural values within the architecture for feature extraction, prioritization, and classification. These values go through a rigorous evaluation process along with the cutting-edge deep learning model, CNN, and result in intermediate representations, leading to better performance in gesture analysis. An automated dance gesture identification system can address the challenges of recognizing hand movements in unpredictable lighting, varied backgrounds, noise, and changing camera angles. Despite these challenges, GAMNet performed remarkably well, surpassing renowned models like VGGNet, ResNet, EfficientNet, and CNN, achieving a classification accuracy of 96.80%, even in challenging image circumstances. This paper highlights how GAMNet can revolutionize the world of classical Indian dance, opening up new opportunities for research and development in this field.

Interaction of high-fat diet and brain trauma alters adipose tissue macrophages and brain microglia associated with exacerbated cognitive dysfunction

Obesity increases the morbidity and mortality of traumatic brain injury (TBI). Detailed analyses of transcriptomic changes in the brain and adipose tissue were performed to elucidate the interactive effects between high-fat diet-induced obesity (DIO) and TBI. Adult male mice were fed a high-fat diet (HFD) for 12 weeks prior to experimental TBI and continuing after injury. High-throughput transcriptomic analysis using Nanostring panels of the total visceral adipose tissue (VAT) and cellular components in the brain, followed by unsupervised clustering, principal component analysis, and IPA pathway analysis were used to determine shifts in gene expression patterns and molecular pathway activity. Cellular populations in the cortex and hippocampus, as well as in VAT, during the chronic phase after combined TBI-HFD showed amplification of central and peripheral microglia/macrophage responses, including superadditive changes in selected gene expression signatures and pathways. Furthermore, combined TBI and HFD caused additive dysfunction in Y-Maze, Novel Object Recognition (NOR), and Morris water maze (MWM) cognitive function tests. These novel data suggest that HFD-induced obesity and TBI can independently prime and support the development of altered states in brain microglia and VAT, including the disease-associated microglia/macrophage (DAM) phenotype observed in neurodegenerative disorders. The interaction between HFD and TBI promotes a shift toward chronic reactive microglia/macrophage transcriptomic signatures and associated pro-inflammatory disease-altered states that may, in part, underlie the exacerbation of cognitive deficits. Thus, targeting of HFD-induced reactive cellular phenotypes, including in peripheral adipose tissue immune cell populations, may serve to reduce microglial maladaptive states after TBI, attenuating post-traumatic neurodegeneration and neurological dysfunction.

The impact of Nrf2 knockout on the neuroprotective effects of dexmedetomidine in a mice model of cognitive impairment

The nuclear factor erythroid 2-related factor 2 (Nrf2) signalling pathway represents a crucial intrinsic protective system against oxidative stress and inflammation and plays a significant role in various neurological disorders. However, the effect of Nrf2 signalling on the regulation of cognitive impairment remains unknown. Dexmedetomidine (DEX) has neuroprotective effects and can ameliorate lipopolysaccharide (LPS)-induced cognitive dysfunction. Our objective was to observe whether Nrf2 knockout influences the efficacy of DEX in improving cognitive impairment and to attempt to understand its underlying mechanisms. An LPS-induced cognitive dysfunction model in wild-type and Nrf2 knockout mice (Institute of Cancer Research background; male; 8–12 weeks) was used to observe the impact of DEX on cognitive dysfunction. LPS was intraperitoneally injected, followed by novel object recognition and morris water maze experiments 24 h later. Hippocampal tissues were collected for histopathological and molecular analyses. Our research findings suggest that DEX enhances the expression of NQO1, HO-1, PSD95, and SYP proteins in hippocampal tissue, inhibits microglial proliferation, reduces pro-inflammatory cytokines IL-1β and TNF-ɑ, increases anti-inflammatory cytokine IL-10, and improves dendritic spine density, thereby alleviating cognitive dysfunction induced by LPS. However, the knockout of the Nrf2 gene negated the aforementioned effects of DEX. In conclusion, DEX alleviates cognitive deficits induced by LPS through mechanisms of anti-oxidative stress and anti-inflammation, as well as by increasing synaptic protein expression and dendritic spine density. However, the knockout of the Nrf2 gene reversed the effects of DEX. The Nrf2 signaling pathway plays a crucial role in the mitigation of LPS-induced cognitive impairment by DEX.

A Survey on Theories and Application for Self Driving Cars on Deep Learning Methods

In today’s, Self-driving cars are one of the hot research topics in technology, which has a great impact on social and economic development. Self-driving cars, powered by deep learning algorithms, represent a groundbreaking fusion of artificial intelligence and transportation technology. Deep learning is also one of the current major areas in artificial intelligence research. It has been widely applied in natural language understanding, image processing, etc. In recent years, more and more deep learning methods have been introduced to the solution in the field of self-driving cars and have achieved excellent results. This survey provides information related to self-driving cars and summarizes the application of deep learning methods in the field of self-driving cars. Then the main problems in self-driving cars and their solutions are analyzed based on deep learning methods, such as lane detection, scene recognition and navigation etc. Also briefly described are some representative approaches to self-driving cars using deep learning methods. Finally, future challenges in applications of deep learning for self-driving cars are described.

Object Recognition Application for Education using Transfer Learning

In today's technology-driven world, fostering a love of learning and nurturing curiosity in young minds is essential. This project introduces an object recognition application designed to enrich young learners' educational experiences. The app focuses on identifying fruits, vegetables, and flowers using the EfficientNetB0 model, known for its proficiency in image recognition. By leveraging transfer learning from the pre-trained ImageNet dataset, the application achieves high accuracy in recognizing these objects. Beyond identification, the app integrates interactive games such as guessing, matching, and memory games with identical object cards, providing engaging ways to develop vocabulary and cognitive skills. This research explores fine-tuning EfficientNetB0 for targeted tasks and designs an intuitive interface to support positive learning experiences, potentially impacting children's educational journeys. Key Words: Transfer Learning, education, games, application

Hemispheric asymmetries in hippocampal volume related to memory in left and right temporal variants of frontotemporal degeneration.

In addition to Alzheimer's disease (AD), the hippocampus is now known to be affected in variants of frontotemporal degeneration (FTD). In semantic variant primary progressive aphasia (svPPA), characterized by language impairments, hippocampal atrophy is greater in the left hemisphere. Nonverbal impairments (e.g., visual object recognition) are prominent in the right temporal variant of FTD (rtvFTD), and hippocampal atrophy may be greater in the right hemisphere. In this study we examined the hypothesis that leftward hippocampal asymmetry (predicted in svPPA) would be associated with selective verbal memory impairments (with relative preservation of visual memory), while rightward asymmetry (predicted in rtvFTD) would be associated with the opposite pattern (greater visual memory impairment). In contrast, we predicted that controls and individuals in the amnestic mild cognitive impairment stage of AD (aMCI), both of whom were expected to show symmetrical hippocampal volumes, would show roughly equivalent scores in verbal and visual memory. Participants completed delayed recall tests with words and geometric shapes, and hippocampal volumes were assessed with MRI. The aMCI sample showed symmetrical hippocampal atrophy, and similar degree of verbal and visual memory impairment. The svPPA sample showed greater left hippocampal atrophy and verbal memory impairment, while rtvFTD showed greater right hippocampal atrophy and visual memory impairment. Greater asymmetry in hippocampal volumes was associated with larger differences between verbal and visual memory in the FTD samples. Unlike AD, asymmetry is a core feature of brain-memory relationships in temporal variants of FTD.

Topographic organization across foveal visual areas in macaques.

While the fovea on the retina covers only a small region of the visual field, a significant portion of the visual cortex is dedicated to processing information from the fovea being a critical center for object recognition, motion control, and visually guided attention. Despite its importance, prior functional imaging studies in awake monkeys often focused on the parafoveal visual field, potentially leading to inaccuracies in understanding the brain structure underlying function. In this study, our aim is to unveil the neuronal connectivity and topography in the foveal visual cortex in comparison to the parafoveal visual cortex. Using four different types of retrograde tracers, we selectively injected them into the striate cortex (V1) or V4, encompassing the regions between the fovea and parafovea. V1 and V4 exhibited intense mutual connectivity in the foveal visual field, in contrast to the parafoveal visual field, possibly due to the absence of V3 in the foveal visual field. While previous live brain imaging studies failed to reveal retinotopy in the foveal visual fields, our results indicate that the foveal visual fields have continuous topographic connectivity across V1 through V4, as well as the parafoveal visual fields. Although a simple extension of the retinotopic isoeccentricity maps from V1 to V4 has been suggested from previous fMRI studies, our study demonstrated that V3 and V4 possess gradually smaller topographic maps compared to V1 and V2. Feedback projections to foveal V1 primarily originate from the infragranular layers of foveal V2 and V4, while feedforward projections to foveal V4 arise from both supragranular and infragranular layers of foveal V1 and V2, consistent with previous findings in the parafoveal visual fields. This study provides valuable insights into the connectivity of the foveal visual cortex, which was ambiguous in previous imaging studies.

Image and Video Segmentation Using Yolo-Nas and Segment Anything Model (Sam): Machine Learning

In this article, we evaluate the effectiveness of computer vision and machine learning, which are revolutionizing photography and video studies, ushering in an unprecedented era of creativity and informed consent for study. Despite many advances in the field, object recognition and classification have become technologies with many applications, from advanced analytics to enhancing driving safety for augmented reality. This research uses two advanced models, YOLO-NAS (You Just Look at Neural Architecture Search) and Segment One (SAM), to analyze the evolution of image and video segmentation. In real-time search of products and components, YOLO-NAS and SAM models have established a good relationship and are expected to become a unified system for image and video segmentation in digital transformation. Key Words: YOLO-NAS, Convolutional Neural Networks (Cnns), Object Recognition, Computer Vision, Spatial Attention Module (SAM), Instance Video Segmentation, And Image Segmentation.

Piroxicam reduced the intensity of epileptic seizures in a kindling seizure model

ABSTRACT Introduction: The close relationship between inflammatory processes and epileptic seizures is already known, although the exact pathophysiological mechanism is unclear. In this study, the anticonvulsant capacity of piroxicam, an anti-inflammatory drug, was evaluated. A rat pentylenetetrazole kindling model was used. Methods: Male Wistar rats, 8–9 weeks old, received piroxicam (0.15 and 0.30 mg/kg), diazepam (2 mg/kg) or saline for 14 days, and PTZ, on alternate days. Intraperitoneal was chosen as the route of administration. The intensity of epileptic seizures was assessed using a modified Racine scale. The open field test and object recognition analysis were performed at the beginning of the study to ensure the safety of the drugs used. At the end of the protocol, the animals were euthanized to measure the levels of inflammatory (TNF-a and IL-6) and anti-inflammatory (IL-10) cytokines in the cortex, hippocampus, and serum. Results:There were no changes in the open field test and object recognition analysis. Piroxicam was found to decrease Racine scale scores at both concentrations. The reported values for IL-6 levels remained steady in all structures, whereas the TNF-alpha level in the cortex was higher in animals treated with piroxicam than in the saline and diazepam subjects. Finally, animals treated with the anti-inflammatory drug presented reduced IL-10 levels in the cortex and hippocampus. onclusions: Using inflammation as a guiding principle, the anticonvulsant effect of PIRO could be associated with the hippocampal circuits, since this structure showed no increase in inflammatory cytokines.

Improving Oriented Object Detection by Scene Classification and Task-Aligned Focal Loss

Oriented object detection (OOD) can precisely detect objects with arbitrary direction in remote sensing images (RSIs). Up to now, the two-stage OOD methods have attracted more attention because of their high detection accuracy. However, the two-stage methods only rely on the features of each proposal for object recognition, which leads to the misclassification problem because of the intra-class diversity, inter-class similarity and clutter backgrounds in RSIs. To address the above problem, an OOD model combining scene classification is proposed. Considering the fact that each foreground object has a strong contextual relationship with the scene of the RSI, a scene classification branch is added to the baseline OOD model, and the scene classification result of input RSI is used to exclude the impossible categories. To focus on the hard instances and enhance the consistency between classification and regression, a task-aligned focal loss (TFL) which combines the classification difficulty with the regression loss is proposed, and TFL assigns lager weights to the hard instances and optimizes the classification and regression branches simultaneously. The ablation study proves the effectiveness of scene classification branch, TFL and their combination. The comparisons with 15 and 14 OOD methods on the DOTA and DIOR-R datasets validate the superiority of our method.

Manipulating Camera Gimbal Positioning by Deep Deterministic Policy Gradient Reinforcement Learning for Drone Object Detection

The object recognition technology of unmanned aerial vehicles (UAVs) equipped with “You Only Look Once” (YOLO) has been validated in actual flights. However, here, the challenge lies in efficiently utilizing camera gimbal control technology to swiftly capture images of YOLO-identified target objects in aerial search missions. Enhancing the UAV’s energy efficiency and search effectiveness is imperative. This study aims to establish a simulation environment by employing the Unity simulation software for target tracking by controlling the gimbal. This approach involves the development of deep deterministic policy-gradient (DDPG) reinforcement-learning techniques to train the gimbal in executing effective tracking actions. The outcomes of the simulations indicate that when actions are appropriately rewarded or penalized in the form of scores, the reward value can be consistently converged within the range of 19–35. This convergence implies that a successful strategy leads to consistently high rewards. Consequently, a refined set of training procedures is devised, enabling the gimbal to accurately track the target. Moreover, this strategy minimizes unnecessary tracking actions, thus enhancing tracking efficiency. Numerous benefits arise from training in a simulated environment. For instance, the training in this simulated environment is facilitated through a dataset composed of actual flight photographs. Furthermore, offline operations can be conducted at any given time without any constraint of time and space. Thus, this approach effectively enables the training and enhancement of the gimbal’s action strategies. The findings of this study demonstrate that a coherent set of action strategies can be proficiently cultivated by employing DDPG reinforcement learning. Furthermore, these strategies empower the UAV’s gimbal to rapidly and precisely track designated targets. Therefore, this approach provides both convenience and opportunities to gather more flight-scenario training data in the future. This gathering of data will lead to immediate training opportunities and help improve the system’s energy consumption.