Search results for: imageNet large scale visual recognition challenge (ILSVRC)

Authors: Ali Ganoun, Wesam Algablawi, Wasim BenAnaif

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Video based traffic surveillance based on License Plate Recognition (LPR) system is an essential part for any intelligent traffic management system. The LPR system utilizes computer vision and pattern recognition technologies to obtain traffic and road information by detecting and recognizing vehicles based on their license plates. Generally, the video based LPR system is a challenging area of research due to the variety of environmental conditions. The LPR systems used in a wide range of commercial applications such as collision warning systems, finding stolen cars, controlling access to car parks and automatic congestion charge systems. This paper presents an automatic LPR system of Libyan license plate. The performance of the proposed system is evaluated with three video sequences.

Keywords: license plate recognition, localization, segmentation, recognition

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Authors: Lai Junzhe, Wang Lihao, Burra Venkata Durga Kumar

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In recent years, due to today's global technological advances, big data and artificial intelligence technologies have been widely used in various industries and fields, playing an important role in reducing costs and increasing efficiency. Among them, artificial intelligence has derived another branch in its own continuous progress and the continuous development of computer personnel, namely distributed artificial intelligence computing systems. Distributed AI is a method for solving complex learning, decision-making, and planning problems, characterized by the ability to take advantage of large-scale computation and the spatial distribution of resources, and accordingly, it can handle problems with large data sets. Nowadays, distributed AI is widely used in military, medical, and human daily life and brings great convenience and efficient operation to life. In this paper, we will discuss three areas of distributed AI computing systems in vision processing, blockchain, and smart home to introduce the performance of distributed systems and the role of AI in distributed systems.

Keywords: distributed system, artificial intelligence, blockchain, IoT, visual information processing, smart home

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Authors: Delowar Hossain, Genci Capi

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This paper concerns with the problem of deep learning parameters tuning using a genetic algorithm (GA) in order to improve the performance of deep learning (DL) method. We present a GA based DL method for robot object recognition and grasping. GA is used to optimize the DL parameters in learning procedure in term of the fitness function that is good enough. After finishing the evolution process, we receive the optimal number of DL parameters. To evaluate the performance of our method, we consider the object recognition and robot grasping tasks. Experimental results show that our method is efficient for robot object recognition and grasping.

Keywords: deep learning, genetic algorithm, object recognition, robot grasping

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Authors: Fatima Akhmedova, Simon Liao

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One of the most critical decision points in the design of a face recognition system is the choice of an appropriate face representation. Effective feature descriptors are expected to convey sufficient, invariant and non-redundant facial information. In this work, we propose a set of Hahn moments as a new approach for feature description. Hahn moments have been widely used in image analysis due to their invariance, non-redundancy and the ability to extract features either globally and locally. To assess the applicability of Hahn moments to Face Recognition we conduct two experiments on the Olivetti Research Laboratory (ORL) database and University of Notre-Dame (UND) X1 biometric collection. Fusion of the global features along with the features from local facial regions are used as an input for the conventional k-NN classifier. The method reaches an accuracy of 93% of correctly recognized subjects for the ORL database and 94% for the UND database.

Keywords: face recognition, Hahn moments, recognition-by-parts, time-lapse

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Authors: Mohammad Saleh Vahdatpour, Yanqing Zhang

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Understanding the neural mechanisms underlying motion detection in the human visual system has long been a fascinating challenge in neuroscience and artificial intelligence. This paper presents a spiking neural network model inspired by the processing of motion information in the primate visual system, particularly focusing on the Middle Temporal (MT) area. In our study, we propose a multi-layer spiking neural network model to perform motion detection tasks, leveraging the idea that synaptic delays in neuronal communication are pivotal in motion perception. Synaptic delay, determined by factors like axon length and myelin insulation, affects the temporal order of input spikes, thereby encoding motion direction and speed. Overall, our spiking neural network model demonstrates the feasibility of capturing motion detection principles observed in the primate visual system. The combination of synaptic delays, learning mechanisms, and shared weights and delays in SMD provides a promising framework for motion perception in artificial systems, with potential applications in computer vision and robotics.

Keywords: neural network, motion detection, signature detection, convolutional neural network

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Authors: Jiawei Dai, Xinrong Li, Yulong Sun, Yunxiao Hao

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Recently, participatory approaches have become popular in a variety of fields, including social work, community, and population health, as important research tools for researchers to understand and immerse communities and conceptualize social phenomena. The participatory visual research methods promote the diversification and depth of the exploration process and communication forms to support the feasibility and practicality of the scheme, which helps to further inspire designers and avoid blind spots caused by the solidification of single thinking. This paper focuses on how to develop visual toolkits for participatory methods to assist and shape crowd participation and trigger idea generation in community issues. This project helps to verify the value of participatory visual tools in shaping participation and arousing expression, which provides support for gaining community diversity insights and community problem-solving. In addition, a visual toolbox was developed based on an actual case in a community for field testing, and further discussion was carried out after the data results were analyzed.

Keywords: participatory design, community service, visual toolbox, visual metaphor

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Authors: Xingyu Pan, Laure Tougne

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For recognizing coins, the graved release date is important information to identify precisely its monetary type. However, reading characters in coins meets much more obstacles than traditional character recognition tasks in the other fields, such as reading scanned documents or license plates. To address this challenging issue in a numismatic context, we propose a training-free approach dedicated to detection and recognition of the release date of the coin. In the first step, the date zone is detected by comparing histogram features; in the second step, a topology-based algorithm is introduced to recognize coin numbers with various font types represented by binary gradient map. Our method obtained a recognition rate of 92% on synthetic data and of 44% on real noised data.

Keywords: coin, detection, character recognition, topology

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Authors: Ana Ferreira, Ines Costa, Patricia Polónia, Josué Pereira, Olinda Faria, Pedro Alberto Silva

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Introduction: Optical coherence tomography (OCT) and visual field tools are pivotal in evaluating neurological deficits and predicting potential visual improvement following surgical decompression in neurosurgical patients. Despite their clinical significance, a comprehensive understanding of their utility in this context is lacking in the literature. This study aims to elucidate the applications of OCT and visual field assessment, delineating distinct patterns of visual deficit presentations within the studied cohort. Methods: This retrospective analysis considered all adult patients who underwent a single surgery for pituitary adenoma or anterior skull base meningioma with optic nerve involvement, coupled with neuro-ophthalmology evaluation, between July 2020 and January 2023. A minimum follow-up period of 6 months was deemed essential. Results: A total of 24 patients, with a median age of 61, were included in the analysis. Three primary patterns emerged: 1) Low visual field involvement with compromised OCT, 2) High visual field involvement with relatively unaffected OCT, and 3) Significant compromise observed in both OCT and visual fields. Conclusion: This study delineates various findings in OCT and visual field assessments with illustrative examples. Based on the current findings, a prospective cohort will be systematically collected to further investigate and validate these patterns and their prognostic significance, enhancing our understanding of the utility of OCT and visual fields in neurosurgical patients.

Keywords: OCT, neurosurgery, visual field, optic nerve

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Authors: Guillaume Morin-Duponchelle, Ahmed Nait Chabane, Benoit Zerr, Pierre Schoesetters

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Industrial inspection can be achieved through robotic systems, allowing visual and chemical servoing. A popular scheme for visual servo-controlled robotic is the image-based servoing sys-tems. In this paper, an approach of visual and chemical servoing of a hexapod robot using a visual and chemical Jacobian matrix are proposed. The basic idea behind the visual Jacobian matrix is modeling the differential relationship between the camera system and the robotic control system to detect and track accurately points of interest in confined environments. This approach allows the robot to easily detect and navigates to the QR code or seeks a gas source localization using surge cast algorithm. To track the QR code target, a visual servoing based on Jacobian matrix is used. For chemical servoing, three gas sensors are embedded on the hexapod. A Jacobian matrix applied to the gas concentration measurements allows estimating the direction of the main gas source. The effectiveness of the proposed scheme is first demonstrated on simulation. Finally, a hexapod prototype is designed and built and the experimental validation of the approach is presented and discussed.

Keywords: chemical servoing, hexapod robot, Jacobian matrix, visual servoing, navigation

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Authors: Rujia Chen, Akbar Ghobakhlou, Ajit Narayanan

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This study investigates the task of identifying musical instruments in polyphonic compositions using Convolutional Neural Networks (CNNs) from spectrogram inputs, focusing on binary classification. The model showed promising results, with an accuracy of 97% on solo instrument recognition. When applied to polyphonic combinations of 1 to 10 instruments, the overall accuracy was 64%, reflecting the increasing challenge with larger ensembles. These findings contribute to the field of Music Information Retrieval (MIR) by highlighting the potential and limitations of current approaches in handling complex musical arrangements. Future work aims to include a broader range of musical sounds, including electronic and synthetic sounds, to improve the model's robustness and applicability in real-time MIR systems.

Keywords: binary classifier, CNN, spectrogram, instrument

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Authors: Khodijah Salimah

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Game Space Program is the program design and development game for therapy the autistic child who had problems with sensory processing and integration. This program is the basic for game space to expand treatment therapy in many areas to help autistic's ability to think through visual perception. This problem can be treated with sensory experience and integration with visual experience to learn how to think and how to learn with visual perception. This perception can be accommodated through an understanding of visual thinking received from sensory exist in game space as virtual healthcare facilities are adjusted based on the sensory needs of children with autism. This paper aims to analyze the potential of virtual visual thinking for treatment autism with the game space program.

Keywords: autism, game space program, sensory, virtual healthcare facilities, visual perception

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Authors: Guoliang Lu, Changhou Lu, Xueyong Li

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In action recognition, previous studies have demonstrated the effectiveness of using multiple features to improve the recognition performance. We focus on two practical issues: i) most studies use a direct way of concatenating/accumulating multi features to evaluate the similarity between two actions. This way could be too strong since each kind of feature can include different dimensions, quantities, etc; ii) in many studies, the employed classification methods lack of a flexible and effective mechanism to add new feature(s) into classification. In this paper, we explore an unified scheme based on recently-proposed multi-dimensional dynamic time warping (MD-DTW). Experiments demonstrated the scheme's effectiveness of combining multi-feature and the flexibility of adding new feature(s) to increase the recognition performance. In addition, the explored scheme also provides us an open architecture for using new advanced classification methods in the future to enhance action recognition.

Keywords: action recognition, multi features, dynamic time warping, feature combination

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Authors: Khenfer-Koummich Fatma, Hendel Fatiha, Mesbahi Larbi

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this paper presents an approach based on Hidden Markov Models (HMM: Hidden Markov Model) using HTK tools. The goal is to create a man-machine interface with a voice recognition system that allows the operator to tele-operate a mentor robot to execute specific tasks as rotate, raise, close, etc. This system should take into account different levels of environmental noise. This approach has been applied to isolated words representing the robot commands spoken in two languages: French and Arabic. The recognition rate obtained is the same in both speeches, Arabic and French in the neutral words. However, there is a slight difference in favor of the Arabic speech when Gaussian white noise is added with a Signal to Noise Ratio (SNR) equal to 30 db, the Arabic speech recognition rate is 69% and 80% for French speech recognition rate. This can be explained by the ability of phonetic context of each speech when the noise is added.

Keywords: voice command, HMM, TIMIT, noise, HTK, Arabic, speech recognition

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Authors: F. M. Pisano, M. Ciminello

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Aerospace, mechanical, and civil engineering infrastructures can take advantages from damage detection and identification strategies in terms of maintenance cost reduction and operational life improvements, as well for safety scopes. The challenge is to detect so called “barely visible impact damage” (BVID), due to low/medium energy impacts, that can progressively compromise the structure integrity. The occurrence of any local change in material properties, that can degrade the structure performance, is to be monitored using so called Structural Health Monitoring (SHM) systems, in charge of comparing the structure states before and after damage occurs. SHM seeks for any "anomalous" response collected by means of sensor networks and then analyzed using appropriate algorithms. Independently of the specific analysis approach adopted for structural damage detection and localization, textual reports, tables and graphs describing possible outlier coordinates and damage severity are usually provided as artifacts to be elaborated for information extraction about the current health conditions of the structure under investigation. Visual Analytics can support the processing of monitored measurements offering data navigation and exploration tools leveraging the native human capabilities of understanding images faster than texts and tables. Herein, a SHM system enrichment by integration of a Visual Analytics component is investigated. Analytical dashboards have been created by combining worksheets, so that a useful Visual Analytics tool is provided to structural analysts for exploring the structure health conditions examined by a Principal Component Analysis based algorithm.

Keywords: interactive dashboards, optical fibers, structural health monitoring, visual analytics

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Authors: Redouane Tlemsani, Abdelkader Benyettou

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Work is in on line Arabic character recognition and the principal motivation is to study the Arab manuscript with on line technology. This system is a Markovian system, which one can see as like a Dynamic Bayesian Network (DBN). One of the major interests of these systems resides in the complete models training (topology and parameters) starting from training data. Our approach is based on the dynamic Bayesian Networks formalism. The DBNs theory is a Bayesians networks generalization to the dynamic processes. Among our objective, amounts finding better parameters, which represent the links (dependences) between dynamic network variables. In applications in pattern recognition, one will carry out the fixing of the structure, which obliges us to admit some strong assumptions (for example independence between some variables). Our application will relate to the Arabic isolated characters on line recognition using our laboratory database: NOUN. A neural tester proposed for DBN external optimization. The DBN scores and DBN mixed are respectively 70.24% and 62.50%, which lets predict their further development; other approaches taking account time were considered and implemented until obtaining a significant recognition rate 94.79%.

Keywords: Arabic on line character recognition, dynamic Bayesian network, pattern recognition, computer vision

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Authors: Dan Ohev Zion, Alon Cohen

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In recent years, the presence of short-range millimeter-wave radar in civil application has increased significantly. Autonomous driving, security, 3D imaging and high data rate communication systems are a few examples. The next challenge is the integration inside small form-factor devices, such as smartphones (e.g. gesture recognition). The main challenge is implementation of a truly low-power, low-complexity high-resolution radar. The most popular approach is the Frequency Modulated Continuous Wave (FMCW) radar, with an analog multiplication front-end. In this paper, we present an approach for adaptive estimation and correction of impairments of such front-end, specifically implemented using the Six-Port Device (SPD) as the multiplier element. The proposed algorithm was simulated and implemented on a 60 GHz radar lab prototype.

Keywords: radar, FMCW Radar, IQ mismatch, six port

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Authors: Prasad Kalagura, B. Veeresh kumar

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We constructed an interface system that would allow a similarly paralyzed user to interact with a computer with almost full functional capability. A real-time tracking algorithm is implemented based on adaptive skin detection and motion analysis. The clicking of the mouse is activated by the user's eye blinking through a sensor. The keyboard function is implemented by voice recognition kit.

Keywords: embedded ARM7 processor, mouse pointer control, voice recognition

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Authors: Aishrith Rao

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Human activity recognition is an important aspect of video analytics, and many approaches have been recommended to enable action recognition. In this approach, the model is used to identify the action of the multiple people in the frame and classify them accordingly. A few approaches use RNNs and 3D CNNs, which are computationally expensive and cannot be trained with the small datasets which are currently available. Multi-person action recognition has been performed in order to understand the positions and action of people present in the video frame. The size of the video frame can be adjusted as a hyper-parameter depending on the hardware resources available. OpenPose has been used to calculate pose estimate using CNN to produce heap-maps, one of which provides skeleton features, which are basically joint features. The features are then extracted, and a classification algorithm can be applied to classify the action.

Keywords: human activity recognition, computer vision, pose estimates, convolutional neural networks

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Authors: Salim Ouchtati, Jean Sequeira, Mouldi Bedda

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In this work we present an off line system for the recognition of the Arabic handwritten words of the Algerian departments. The study is based mainly on the evaluation of neural network performances, trained with the gradient back propagation algorithm. The used parameters to form the input vector of the neural network are extracted on the binary images of the handwritten word by several methods: the parameters of distribution, the moments centered of the different projections and the Barr features. It should be noted that these methods are applied on segments gotten after the division of the binary image of the word in six segments. The classification is achieved by a multi layers perceptron. Detailed experiments are carried and satisfactory recognition results are reported.

Keywords: handwritten word recognition, neural networks, image processing, pattern recognition, features extraction

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Authors: Gabrielle Peck, Ryan Hayes

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The use of a phi meter allows for definition of equivalence ratio during a fire test. Previous phi meter designs have used expensive catalysts and had restricted portability due to the large furnace and requirement for pure oxygen. The new design of the phi meter did not require the use of a catalyst. The furnace design was based on the existing micro-scale combustion calorimetry (MCC) furnace and operating conditions based on the secondary oxidizer furnace used in the steady state tube furnace (SSTF). Preliminary tests were conducted to study the effects of varying furnace temperatures on combustion efficiency. The SSTF was chosen to validate the phi meter measurements as it can both pre-set and independently quantify the equivalence ratio during a test. The data were in agreement with the data obtained on the SSTF. It was also validated by a comparison of CO2 yields obtained from the SSTF oxidizer and those obtained by the phi meter. The phi meter designed and constructed in this work was proven to work effectively on a bench-scale. The phi meter was then used to measure the equivalence ratio on a series of large-scale ISO 9705 tests for numerous fire conditions. The materials used were a range of non-homogenous materials such as polyurethane. The measurements corresponded accurately to the data collected, showing the novel design can be used from bench to large-scale tests to measure equivalence ratio. This cheaper, more portable, safer and easier to use phi meter design will enable more widespread use and the ability to quantify fire conditions of tests, allowing for better understanding of flammability and smoke toxicity.

Keywords: phi meter, smoke toxicity, fire condition, ISO9705, novel equipment

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Authors: Shen Jian, Hu Jie, Zhu Guo Niu, Peng Ying Hong

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There are two issues exited in the traditional visual prosthesis: lacking systematic method and the low level of humanization. To tackcle those obstacles, a visual prosthesis design method based on biologically inspired design is proposed. Firstly, a constrained FBS knowledge cell model is applied to construct the functional model of visual prosthesis in biological field. Then the clustering results of engineering domain are ob-tained with the use of the cross-domain knowledge cell clustering algorithm. Finally, a prototype system is designed to support the bio-logically inspired design where the conflict is digested by TRIZ and other tools, and the validity of the method is verified by the solution scheme

Keywords: knowledge-based engineering, visual prosthesis, biologically inspired design, biomedical engineering

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Authors: Candy Chieh Lee

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Objective: The purpose of this study is to investigate the relationship between visual perception and social function in patients with schizophrenia. The specific aims are: 1) To explore performances in visual perception and social function in patients with schizophrenia 2) to examine the correlation between visual perceptual skills and social function in patients with schizophrenia The long-term goal is to be able to provide the most adequate intervention program for promoting patients’ visual perceptual skills and social function, as well as compensatory techniques. Background: Perceptual deficits in schizophrenia have been well documented in the visual system. Clinically, a considerable portion (up to 60%) of schizophrenia patients report distorted visual experiences such as visual perception of motion, color, size, and facial expression. Visual perception is required for the successful performance of most activities of daily living, such as dressing, making a cup of tea, driving a car and reading. On the other hand, patients with schizophrenia usually exhibit psychotic symptoms such as auditory hallucination and delusions which tend to alter their perception of reality and affect their quality of interpersonal relationship and limit their participation in various social situations. Social function plays an important role in the prognosis of patients with schizophrenia; lower social functioning skills can lead to poorer prognosis. Investigations on the relationship between social functioning and perceptual ability in patients with schizophrenia are relatively new but important as the results could provide information for effective intervention on visual perception and social functioning in patients with schizophrenia. Methods: We recruited 50 participants with schizophrenia in the mental health hospital (Taipei City Hospital, Songde branch, Taipei, Taiwan) acute ward. Participants who have signed consent forms, diagnosis of schizophrenia and having no organic vision deficits were included. Participants were administered the test of visual-perceptual skills (non-motor), third edition (TVPS-3) and the personal and social performance scale (PSP) for assessing visual perceptual skill and social function. The assessments will take about 70-90 minutes to complete. Data Analysis: The IBM SPSS 21.0 will be used to perform the statistical analysis. First, descriptive statistics will be performed to describe the characteristics and performance of the participants. Lastly, Pearson correlation will be computed to examine the correlation between PSP and TVPS-3 scores. Results: Significant differences were found between the means of participants’ TVPS-3 raw scores of each subtest with the age equivalent raw score provided by the TVPS-3 manual. Significant correlations were found between all 7 subtests of TVPS-3 and PSP total score. Conclusions: The results showed that patients with schizophrenia do exhibit visual perceptual deficits and is correlated social functions. Understanding these facts of patients with schizophrenia can assist health care professionals in designing and implementing adequate rehabilitative treatment according to patients’ needs.

Keywords: occupational therapy, social function, schizophrenia, visual perception

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Authors: Kasidis Chomrat, Nopasit Chakpitak, Anukul Tamprasirt, Annop Thananchana

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Internet of Things devices or IoT and Edge Computing has become one of the biggest things happening in innovations and one of the most discussed of the potential to improve and disrupt traditional business and industry alike. With rises of new hang cliff challenges like COVID-19 pandemic that posed a danger to workforce and business process of the system. Along with drastically changing landscape in business that left ruined aftermath of global COVID-19 pandemic, looming with the threat of global energy crisis, global warming, more heating global politic that posed a threat to become new Cold War. How emerging technology like edge computing and usage of specialized design visual processing units will be great opportunities for business. The literature reviewed on how the internet of things and disruptive wave will affect business, which explains is how all these new events is an effect on the current business and how would the business need to be adapting to change in the market and world, and example test benchmarking for consumer marketed of newer devices like the internet of things devices equipped with new edge computing devices will be increase efficiency and reducing posing a risk from a current and looming crisis. Throughout the whole paper, we will explain the technologies that lead the present technologies and the current situation why these technologies will be innovations that change the traditional practice through brief introductions to the technologies such as cloud computing, edge computing, Internet of Things and how it will be leading into future.

Keywords: internet of things, edge computing, machine learning, pattern recognition, image classification

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Authors: Hasanah Md. Amin

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The existence of cyberspace and its growing contents is real and overwhelming. Visual as one of the properties of cyber contents is increasingly becoming more significant and popular among creator and user. The visual and aesthetic of the content is consistent with many similarities. Aesthetic, although universal, has slight differences across the world. Aesthetic power could impress, influence, and cause bias among the users. The content creator who knows how to manipulate this visuals and aesthetic expression can dominate the scenario and the user who is ‘expressive literate’ will gain much from the scenes. User who understands aesthetic will be rewarded with competence, confidence, and certainly, a personality enhanced experience in carrying out a task when participating in this chaotic but promising cyberworld. The aim of this article is to gain knowledge from related literature and research regarding User-Generated Content (UGC), which focuses on aesthetic expression by the Net generation. The objective of this preliminary study is to analyze the aesthetic expression linked to visual from the participatory cultural viewpoint looking for meaning, value, patterns, and characteristics.

Keywords: visual overloaded, user-generated content, net generation, visual arts

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Authors: Shuo Liu, Dan Liu

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In the field of named entity recognition, most of the research has been conducted around simple entities. However, for nested named entities, which still contain entities within entities, it has been difficult to identify them accurately due to their boundary ambiguity. In this paper, a hierarchical recognition model is constructed based on the grammatical structure and semantic features of Chinese text for boundary calculation based on lexical features. The analysis is carried out at different levels in terms of granularity, semantics, and lexicality, respectively, avoiding repetitive work to reduce computational effort and using the semantic features of words to calculate the boundaries of entities to improve the accuracy of the recognition work. The results of the experiments carried out on web-based microblogging data show that the model achieves an accuracy of 86.33% and an F1 value of 89.27% in recognizing nested named entities, making up for the shortcomings of some previous recognition models and improving the efficiency of recognition of nested named entities.

Keywords: coarse-grained, nested named entity, Chinese natural language processing, word embedding, T-SNE dimensionality reduction algorithm

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Authors: Pablo De Macedo Silveira Vallejos

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This article aims to observe autobiographical and visual narrative practices among users on Instagram. In this way, the work proposes to reflect on how image resources are used to develop edited representations of the self in that social network. The research aims to explore the uses of editing and the degrees of fictionalization present on Instagram.

Keywords: autobiography, visual narratives, representation, fiction, social media

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Authors: Avantika Verma

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In industrialized nations, corneal lacerations are one of the most common reason for hospitalization. This study was designed to study visual and clinical outcome in patients presenting with full thickness corneal lacerations in Indian population and to ascertain the impact of various preoperative and operative factors influencing prognosis after repair of corneal lacerations. Males in third decade with injuries at work with metallic objects were common. Lens damage, hyphema, vitreous hemorrhage, retinal detachment and endophthalmitis were seen. All the patients underwent primary repair within first 24 hours of presentation. At 3 months, 74.3% had a good visual outcome. About 5.7% of patients had no perception of light.In conclusion, various demographic and preoperative factors like age, time of presentation, vision at presentation, length of corneal wound, involvement of visual axis, associated ocular features like hyphaema, lenticular changes, vitreous haemorrhage and retinal detachment are significant prognostic indicators for final visual outcome.

Keywords: corneal laceration, corneal wound repair, injury, visual outcome

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Authors: Ofelia Loani Elvir Lazo, Roya Yumul, David Chernobylsky, Omar Durra

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Background: It is crucial to detect the patient’s existing anxiety to assist patients in a perioperative setting which is to be caused by the fear associated with surgical and anesthetic complications. However, the current gold standard for assessing patient anxiety, the STAI, is problematic to use in the preoperative setting, given the duration and concentration required to complete the 40-item questionnaire. Our primary aim in the study is to investigate the correlation of the Yumul Visual Facial Anxiety Scale (VFAS) and Numerical Verbal Rating Scale (NVRS) to State-Trait Anxiety Inventory (STAI) to determine the optimal anxiety scale to use in the perioperative setting. Methods: A clinical study of patients undergoing various surgeries was conducted utilizing each of the preoperative anxiety scales. Inclusion criteria included patients undergoing elective surgeries, while exclusion criteria included patients with anesthesia contraindications, inability to comprehend instructions, impaired judgement, substance abuse history, and those pregnant or lactating. 293 patients were analyzed in terms of demographics, anxiety scale survey results, and anesthesia data via Spearman Coefficients, Chi-Squared Analysis, and Fischer’s exact test utilized for comparative analysis. Results: Statistical analysis showed that VFAS had a higher correlation to STAI than NVRS (rs=0.66, p<0.0001 vs. rs=0.64, p<0.0001). The combined VFAS-Categorization Scores showed the highest correlation with the gold standard (rs=0.72, p<0.0001). Subgroup analysis showed similar results. STAI evaluation time (247.7 ± 54.81 sec) far exceeds VFAS (7.29 ± 1.61 sec), NVRS (7.23 ± 1.60 sec), and Categorization scales (7.29 ± 1.99 sec). Patients preferred VFAS (54.4%), Categorization (11.6%), and NVRS (8.8%). Anesthesiologists preferred VFAS (63.9%), NVRS (22.1%), and Categorization Scales (14.0%). Of note, the top five causes of preoperative anxiety were determined to be waiting (56.5%), pain (42.5%), family concerns (40.5%), no information about surgery (40.1%), or anesthesia (31.6%). Conclusıons: Both VFAS and Categorization tests also take significantly less time than STAI, which is critical in the preoperative setting. Combined VFAS-Categorization Score (VCS) demonstrates the highest correlation to the gold standard, STAI. Among both patients and anesthesiologists, VFAS was the most preferred scale. This forms the basis of the Yumul Faces Anxiety Scale, designed for quick quantization and assessment in the preoperative setting while maintaining a high correlation to the golden standard. Additional studies using the formulated Yumul Faces Anxiety Scale are merited.

Keywords: numerical verbal anxiety scale, preoperative anxiety, state-trait anxiety inventory, visual facial anxiety scale

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Authors: Ksheeraj Sai Vepuri, Nada Attar

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We as humans use words with accompanying visual and facial cues to communicate effectively. Classifying facial emotion using computer vision methodologies has been an active research area in the computer vision field. In this paper, we propose a simple method for facial expression recognition that enhances accuracy. We tested our method on the FER-2013 dataset that contains static images. Instead of using Histogram equalization to preprocess the dataset, we used Unsharp Mask to emphasize texture and details and sharpened the edges. We also used ImageDataGenerator from Keras library for data augmentation. Then we used Convolutional Neural Networks (CNN) model to classify the images into 7 different facial expressions, yielding an accuracy of 69.46% on the test set. Our results show that using image preprocessing such as the sharpening technique for a CNN model can improve the performance, even when the CNN model is relatively simple.

Keywords: facial expression recognittion, image preprocessing, deep learning, CNN

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Authors: Flavio Cordeiro Fontanella, Asla Medeiros e Sá, Moacyr Alvim Horta Barbosa da Silva

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In the present work, we analyse the performance of football results forecasting models. In order to do so, we have performed the data collection from eight different forecasting models during the 2021 Brazilian football season. First, we guide the analysis through visual representations of the data, designed to highlight the most prominent features and enhance the interpretation of differences and similarities between the models. We propose using a 2-simplex triangle to investigate visual patterns from the results forecasting models. Next, we compute the expected points for every team playing in the championship and compare them to the final league standings, revealing interesting contrasts between actual to expected performances. Then, we evaluate forecasts’ accuracy using the Ranked Probability Score (RPS); models comparison accounts for tiny scale differences that may become consistent in time. Finally, we observe that the Wisdom of Crowds principle can be appropriately applied in the context, driving into a discussion of results forecasts usage in practice. This paper’s primary goal is to encourage football forecasts’ performance discussion. We hope to accomplish it by presenting appropriate criteria and easy-to-understand visual representations that can point out the relevant factors of the subject.

Keywords: accuracy evaluation, Brazilian championship, football results forecasts, forecasting models, visual analysis

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