Visual Research Articles

A Computer Vision System for the Assessment of Ice Cream Melting Behavior

A Computer Vision System for the Assessment of Ice Cream Melting Behavior

LGGL-03 TREATMENT OUTCOMES ON OPTIC GLIOMA: A CASE STUDY

Abstract 11 year old male presented in December 2021 with complaints of headache and visual impairment. On examination [he had] multiple freckles in the right axilla and cafe au lait lesions all over his body with largest being 3cm in the inguinal region. Pretreatment MRI showed minimally enhancing mass seen in the optic chiasm extending anteriorly into the distal optic nerves bilaterally and the optic tract posteriorly with extensive vasogenic edema along the optic pathway into the lateral thalamus and extending into the bilateral lentiform nuclei. H-MR spectroscopy of the mass demonstrated a choline peak. Findings in keeping with optic pathway glioma. The baseline fundoscopy showed a normal anterior segment, slightly pale disc on the posterior segment. The patient was commenced on weekly Vinblastine via chemo port. At 24 weeks of treatment patient was assessed MRI and fundoscopy. MRI showed a mild interval decrease in size of lobulated mass lesion involving the optic chiasma, bilateral optic nerves up to intraorbital segments and optic tracts. The lesion also involves the infundibular stalk and tuber cinereum. No interval change in hyperintense T2/FLAIR signal with no enhancement seen at the lateral geniculate bodies, hippocampi, bilateral lentiform nuclei and lateral thalami. He completed 72 weeks of Vinblastine with good clinical Response. NF 1 can cause tumors to form and optic nerve gliomas that may occur in about 15 percent of children with this disease in about 2/3 of the time the tumors stop growing and disappears however there are some tumors that may require treatment.

Minimal exposure durations reveal visual processing priorities for different stimulus attributes

Human vision can detect a single photon, but the minimal exposure required to extract meaning from stimulation remains unknown. This requirement cannot be characterised by stimulus energy, because the system is differentially sensitive to attributes defined by configuration rather than physical amplitude. Determining minimal exposure durations required for processing various stimulus attributes can thus reveal the system’s priorities. Using a tachistoscope enabling arbitrarily brief displays, we establish minimal durations for processing human faces, a stimulus category whose perception is associated with several well-characterised behavioural and neural markers. Neural and psychophysical measures show a sequence of distinct minimal exposures for stimulation detection, object-level detection, face-specific processing, and emotion-specific processing. Resolving ongoing debates, face orientation affects minimal exposure but emotional expression does not. Awareness emerges with detection, showing no evidence of subliminal perception. These findings inform theories of visual processing and awareness, elucidating the information to which the visual system is attuned.

Connectomic reconstruction predicts visual features used for navigation

Many animals use visual information to navigate1–4, but how such information is encoded and integrated by the navigation system remains incompletely understood. In Drosophila melanogaster, EPG neurons in the central complex compute the heading direction5 by integrating visual input from ER neurons6–12, which are part of the anterior visual pathway (AVP)10,13–16. Here we densely reconstruct all neurons in the AVP using electron-microscopy data17. The AVP comprises four neuropils, sequentially linked by three major classes of neurons: MeTu neurons10,14,15, which connect the medulla in the optic lobe to the small unit of the anterior optic tubercle (AOTUsu) in the central brain; TuBu neurons9,16, which connect the AOTUsu to the bulb neuropil; and ER neurons6–12, which connect the bulb to the EPG neurons. On the basis of morphologies, connectivity between neural classes and the locations of synapses, we identify distinct information channels that originate from four types of MeTu neurons, and we further divide these into ten subtypes according to the presynaptic connections in the medulla and the postsynaptic connections in the AOTUsu. Using the connectivity of the entire AVP and the dendritic fields of the MeTu neurons in the optic lobes, we infer potential visual features and the visual area from which any ER neuron receives input. We confirm some of these predictions physiologically. These results provide a strong foundation for understanding how distinct sensory features can be extracted and transformed across multiple processing stages to construct higher-order cognitive representations.

Unveiling Visual Physiology and Steady-State Evoked Potentials using Low-Cost and Transferable Electroencephalography for Evaluating Neuronal Activation

Purpose: The ability to see and process images depends on the function of the eyes and the processing of visual information by neurons in the cerebral cortex, something that could be measured through electroencephalography (EEG). Although the EEG is used to evaluate visual pathways in children and demyelination diseases, the limited utilization of brain recording techniques in other applications like therapy is primarily due to budget constraints. The goal of this paper is to demonstrate results from studying brain aspects of vision, utilizing measurements based on oscillatory activity analysis, low-cost, portable equipment, and a processing pipeline relying on Python’s open-source libraries. These studies involve healthy subjects who wear glasses to assess changes in visual perception. Methods: First, electroencephalographic signals were recorded while the subjects observed a visually standardized stimulus. The signals were processed and filtered to reduce artifacts, and the power spectral density (PSD) was calculated to observe the presence of steady-state visual potentials (VEP) to confirm the capture of neuronal activation to the visual stimulus. Results: It was possible to establish a difference between subjects wearing and not wearing their glasses, allowing validation that the information acquired with the transferable equipment is adequate for the analysis of neuronal activity related to visual processing, opening the possibility to be used in future studies in therapy. Conclusion: This study contributes to the development of cost-effective and portable EEG solutions for visual system analysis. It demonstrates the potential for applying transferable EEG devices in clinical settings and highlights the importance of tailored visual stimuli for reliable neural activation.

Development of Higher-Level Vision: A Network Perspective.

Most studies on the development of the visual system have focused on the mechanisms shaping early visual stages up to the level of primary visual cortex (V1). Much less is known about the development of the stages after V1 that handle the higher visual functions fundamental to everyday life. The standard model for the maturation of these areas is that it occurs sequentially, according to the positions of areas in the adult hierarchy. Yet, the existing literature reviewed here paints a different picture, one in which the adult configuration emerges through a sequence of unique network configurations that are not mere partial versions of the adult hierarchy. In addition to studying higher visual development per se to fill major gaps in knowledge, it will be crucial to adopt a network-level perspective in future investigations to unravel normal developmental mechanisms, identify vulnerabilities to developmental disorders, and eventually devise treatments for these disorders.

Unique properties of clinical manifestation and magnetic resonance imaging for differential diagnosis of optic nerve hemangioblastoma.

To investigate the unique properties of clinical manifestation and radiological imaging for differential diagnosis of optic nerve hemangioblastoma (ONH) from adult optic nerve glioma (ONG) prior to surgical resection. ONH and adult ONG patients were recruited from 2012 to 2022. A total of seven ONH patients (8 eyes) and 23 adult ONG patients (24 eyes) were assessed. The median onset age of ONH patients was older than that of adult ONG patients (p = 0.007). There were 42.9% (3/7) of ONH patients closely associated with the diagnosis of Von Hippel-Lindau (VHL) syndrome. Notably in Magnetic Resonance Imaging (MRI), the retrobulbar hemangioblastomas in all ONH patients were primarily intraorbital (100%, 8/8), canalicular (87.5%, 7/8), and anterior intracranial (12.5%, 1/8), especially concentrated in the orbital apex, with little posterior optic pathway involvement. Nearly all affected parts of optic nerve in the ONH group (75.0%, 6/8) presented with circumscribed cystic-solid components, characterized by cystic lesions (peritumoral oedema) anteriorly and solid tumours posteriorly, with significant heterogeneous enhancement. Adult ONG lesions prior to extend from the anterior portion to the whole length of the optic nerve, with relatively innocent enlargement in the postcontrast study. Optic nerve tumours in those with older ages at diagnosis (>30 years) or those diagnosed with VHL syndrome are more likely to be indicative of ONH. In the absence of associated VHL syndrome, a distinguishing MRI feature is the concentration of tumours in the orbital apex, characterized by circumscribed cystic-solid components and heterogeneous enhancement of the solid portion.

PDOC-01 THERAPEUTIC DIFFICULTY IN THE MANAGEMENT OF A RECURRENT CRANIOPHARYNGIOMA IN A CHILD: REVIEW OF THE LITERATURE AND CASE REPORT

Abstract BACKGROUND Craniopharyngiomas are rare, benign epithelial tumors of embryonic origin in children that develop along the craniopharyngeal duct from Rathke’s pouch. The optimal treatment algorithm remains challenging. We report a child case of recurrent craniopharyngioma with therapeutic challenges. CASE PRESENTATION 12-year-old patient, followed for a multi-operated type 2 craniopharyngioma (December 2017, November 2018, and April 2022), stroke on left sylvian stenosis with right hemiplegia in 2020 during proton therapy (52 Gray), thrombolysed with good recovery after rehabilitation, sequellar panhypopituitarism. He was seen in consultation for visual field deterioration. Clinical examination showed a conscious, coherent, obese patient with a deteriorating visual field. Ophthalmological examination showed visual acuity 10/10 with slight temporal visual field amputation on the left and monocular blindness on the right due to probable optic atrophy sequelae of the craniopharyngioma. Magnetic resonance imaging revealed progression of the residual multicystic lesion in the sellar and left temporal regions, entering the sylvian valley and severely compressing the optic pathways. The tumor was removed under general anesthetic in a supine position, with release of the optic nerve, the carotid artery, and its division, and the left 3rd cranial nerve. The postoperative course is straightforward. CONCLUSION Morbidity and the assessment of sequelae are now at the forefront of the management of craniopharyngiomas. The current therapeutic approach should be reconsidered, in terms of the indications for surgery and the surgical protocol. This patient’s case is a typical example of the therapeutic difficulties involved in the management of craniopharyngiomas.

TCDM: Transformational Complexity Based Distortion Metric for Perceptual Point Cloud Quality Assessment.

The goal of objective point cloud quality assessment (PCQA) research is to develop quantitative metrics that measure point cloud quality in a perceptually consistent manner. Merging the research of cognitive science and intuition of the human visual system (HVS), in this article, we evaluate the point cloud quality by measuring the complexity of transforming the distorted point cloud back to its reference, which in practice can be approximated by the code length of one point cloud when the other is given. For this purpose, we first make space segmentation for the reference and distorted point clouds based on a 3D Voronoi diagram to obtain a series of local patch pairs. Next, inspired by the predictive coding theory, we utilize a space-aware vector autoregressive (SA-VAR) model to encode the geometry and color channels of each reference patch with and without the distorted patch, respectively. Assuming that the residual errors follow the multi-variate Gaussian distributions, the self-complexity of the reference and transformational complexity between the reference and distorted samples are computed using covariance matrices. Additionally, the prediction terms generated by SA-VAR are introduced as one auxiliary feature to promote the final quality prediction. The effectiveness of the proposed transformational complexity based distortion metric (TCDM) is evaluated through extensive experiments conducted on five public point cloud quality assessment databases. The results demonstrate that TCDM achieves state-of-the-art (SOTA) performance, and further analysis confirms its robustness in various scenarios.

A Task-Guided, Implicitly-Searched and Meta-Initialized Deep Model for Image Fusion.

Image fusion plays a key role in a variety of multi-sensor-based vision systems, especially for enhancing visual quality and/or extracting aggregated features for perception. However, most existing methods just consider image fusion as an individual task, thus ignoring its underlying relationship with these downstream vision problems. Furthermore, designing proper fusion architectures often requires huge engineering labor. It also lacks mechanisms to improve the flexibility and generalization ability of current fusion approaches. To mitigate these issues, we establish a Task-guided, Implicit-searched and Meta-initialized (TIM) deep model to address the image fusion problem in a challenging real-world scenario. Specifically, we first propose a constrained strategy to incorporate information from downstream tasks to guide the unsupervised learning process of image fusion. Within this framework, we then design an implicit search scheme to automatically discover compact architectures for our fusion model with high efficiency. In addition, a pretext meta initialization technique is introduced to leverage divergence fusion data to support fast adaptation for different kinds of image fusion tasks. Qualitative and quantitative experimental results on different categories of image fusion problems and related downstream tasks (e.g., visual enhancement and semantic understanding) substantiate the flexibility and effectiveness of our TIM.

Deep Learning Neural Network-assisted Badminton Movement Recognition and Physical Fitness Training Optimization

This work aims to solve the problem of low accuracy in recognizing the trajectory of badminton movement. This work focuses on the visual system in badminton robots and conducts side detection and tracking of flying badminton in two-dimensional image plane video streams. Then, the cropped video images are input into a convolutional neural network frame by frame. By adding an attention mechanism, it helps identify the badminton movement trajectory. Finally, to address the detection challenge of flying badminton as a small target in video streams, the deep learning one-stage detection network, Tiny YOLOv2, is improved from both the loss function and network structure perspectives. Moreover, it is combined with the Unscented Kalman Filter algorithm to predict the trajectory of badminton movement. Simulation results show that the improved algorithm performs excellently in tracking and predicting badminton trajectories compared with the existing algorithms. The average accuracy of the proposed method for tracking badminton trajectories is 91.40%, and the recall rate is 84.60%. The average precision, recall, and frame rate of the measured trajectories in four simple and complex scenarios of badminton flight video streams are 96.7%, 95.7%, and 29.2 frames/second, respectively. They are all superior to other classic algorithms. It is evident that the proposed method can provide powerful support for badminton trajectory recognition and help improve the accuracy of badminton movement recognition.

RD-VIO: Robust Visual-Inertial Odometry for Mobile Augmented Reality in Dynamic Environments.

It is typically challenging for visual or visual-inertial odometry systems to handle the problems of dynamic scenes and pure rotation. In this work, we design a novel visual-inertial odometry (VIO) system called RD-VIO to handle both of these two problems. First, we propose an IMU-PARSAC algorithm which can robustly detect and match keypoints in a two-stage process. In the first state, landmarks are matched with new keypoints using visual and IMU measurements. We collect statistical information from the matching and then guide the intra-keypoint matching in the second stage. Second, to handle the problem of pure rotation, we detect the motion type and adapt the deferred-triangulation technique during the data-association process. We make the pure-rotational frames into the special subframes. When solving the visual-inertial bundle adjustment, they provide additional constraints to the pure-rotational motion. We evaluate the proposed VIO system on public datasets and online comparison. Experiments show the proposed RD-VIO has obvious advantages over other methods in dynamic environments.

Brain Functional Alterations in Patients With Benign Paroxysmal Positional Vertigo Demonstrate the Visual-Vestibular Interaction and Integration.

This study aimed to analyze the features of resting-state functional magnetic resonance imaging (rs-fMRI) and clinical relevance in patients with benign paroxysmal positional vertigo (BPPV) that have undergone repositioning maneuvers. A total of 38 patients with BPPV who have received repositioning maneuvers and 38 matched healthy controls (HCs) were enrolled in the present study from March 2018 to August 2021. Imaging analysis software was employed for functional image preprocessing and indicator calculation, mainly including the amplitude of low-frequency fluctuation (ALFF), fractional ALFF (fALFF), percent amplitude of fluctuation (PerAF), and seed-based functional connectivity (FC). Statistical analysis of the various functional indicators in patients with BPPV and HCs was also conducted, and correlation analysis with clinical data was performed. Patients with BPPV displayed decrease in ALFF, fALFF, and PerAF values, mainly in the bilateral occipital lobes in comparison with HCs. Additionally, their ALFF and fALFF values in the proximal vermis region of the cerebellum increased relative to HCs. The PerAF values in the bilateral paracentral lobules, the right supplementary motor area (SMA), and the left precuneus decreased in patients with BPPV and were negatively correlated with dizziness visual analog scale (VAS) scores 1 week after repositioning (W1). In addition, in the left fusiform gyrus and lingual gyrus, the PerAF values show a negative correlation with dizziness handicap inventory (DHI) scores at initial visit (W0). Seed-based FC analysis using the seeds from differential clusters of fALFF, ALFF, and PerAF showed reductions between the left precuneus and bilateral occipital lobe, the left precuneus and left paracentral lobule, and within the occipital lobes among patients with BPPV. The spontaneous activity of certain brain regions in the bilateral occipital and frontoparietal lobes of patients with BPPV was reduced, whereas the activity in the cerebellar vermis was increased. Additionally, there were reductions in FC between the precuneus and occipital cortex or paracentral lobule, as well as within the occipital cortex. The functional alterations in these brain regions may be associated with the inhibitory interaction and functional integration of visual, vestibular, and sensorimotor systems. The functional alterations observed in the visual cortex and precuneus may represent adaptive responses associated with residual dizziness.

Positive serial dependence in ratings of food images for appeal and calories.

Food is fundamental to survival, and our brains are highly attuned to rapidly process food stimuli. Neural signals show that foods can be discriminated as edible or inedible as early as 85ms after stimulus onset,1 distinguished as processed or unprocessed beginning at 130ms,2 and as high or low density from 165ms.3 Recent evidence revealed specialized processing of food stimuli in the ventral visual pathway,4,5,6 an area that underlies perception of faces and other important objects. For many visual objects, perception can be biased toward recent perceptual history (known as serial dependence7,8). We examined serial dependence for food in two large samples (n>300) who rated sequences of food images for either "appeal" or "calories." Ratings for calories were highly correlated between participants and were similar for males and females. Appeal ratings varied considerably between participants, consistent with the idiosyncratic nature of food preferences, and tended to be higher for males than females. High-calorie ratings were associated with high appeal, especially in males. Importantly, response biases showed clear positive serial dependences: higher stimulus values in the previous trials led to positive biases, and vice versa. The effects were similar for males and females and for calories and appeal ratings and were remarkably consistent across participants. These findings square with recently found food selectively in the visual temporal cortex, reveal a new mechanism influencing food decision-making, and suggest a new sensory-level component that could complement cognitive strategies in diet intervention.

ID: 332409 Shape Analysis and Tractography of the Visual Pathway in Temporal Lobe Epilepsy

ID: 332409 Shape Analysis and Tractography of the Visual Pathway in Temporal Lobe Epilepsy

Structural And Functional Integrity Of The Visual System Following A Collision-sport Season

Structural And Functional Integrity Of The Visual System Following A Collision-sport Season

SNN using color-opponent and attention mechanisms for object recognition

The current spiking neural network (SNN) relies on spike-timing-dependent plasticity (STDP) primarily for shape learning in object recognition tasks, overlooking the equally critical aspect of color information. To address this gap, our study introduces an unsupervised variant of STDP that incorporates principles from color-opponency mechanisms (COM) and classical receptive fields (CRF) found in the biological visual system, facilitating the integration of color information during parameter updates within the SNN architecture. Our approach initially preprocesses images into two distinct feature maps: one for shape and another for color. Then, signals derived from COM and intensity concurrently drive the STDP process, thereby updating parameters associated with both color and shape feature maps. Furthermore, we propose a channel-wise attention mechanism to enhance differentiation among objects sharing similar shapes or colors. Specifically, this mechanism utilizes convolution to generate an output spike-wave, identifying a winner based on earliest spike timing and maximal potential. The winning kernel computes attention, which is then applied via convolution to each input image feature map, generating post-feature maps. A STDP-like normalization rule compares firing times between pre- and post-feature maps, dynamically adjusting channel weights to optimize object recognition during the training phase.We assessed the proposed algorithm using SNN with both single-layer and multi-layer architectures across three datasets. Experimental findings highlight its efficacy and superiority in complex object recognition tasks compared to state-of-the-art (SOTA) algorithms. Notably, our approach achieved a significant 20% performance improvement over the SOTA on the Caltech-101 dataset. Moreover, the algorithm is well-suited for hardware implementation and energy efficiency, leveraging a winner-selection mechanism based on the earliest spike time.

A Study on Elements of Machine Vision and Machine Learning in Industrial Vision Systems

The article presents an analysis of issues regarding the current state and prospects of using machine learning elements in industrial applications of vision systems. The analysis is preceded by a short introduction to the subject of machine vision as well as a presentation of the results of research on industrial 3D vision systems, leading to conclusions that affect the prospects for further development of vision systems with elements of machine learning.

Visual Perceptual Learning of Form–Motion Integration: Exploring the Involved Mechanisms with Transfer Effects and the Equivalent Noise Approach

Background: Visual perceptual learning plays a crucial role in shaping our understanding of how the human brain integrates visual cues to construct coherent perceptual experiences. The visual system is continually challenged to integrate a multitude of visual cues, including form and motion, to create a unified representation of the surrounding visual scene. This process involves both the processing of local signals and their integration into a coherent global percept. Over the past several decades, researchers have explored the mechanisms underlying this integration, focusing on concepts such as internal noise and sampling efficiency, which pertain to local and global processing, respectively. Objectives and Methods: In this study, we investigated the influence of visual perceptual learning on non-directional motion processing using dynamic Glass patterns (GPs) and modified Random-Dot Kinematograms (mRDKs). We also explored the mechanisms of learning transfer to different stimuli and tasks. Specifically, we aimed to assess whether visual perceptual learning based on illusory directional motion, triggered by form and motion cues (dynamic GPs), transfers to stimuli that elicit comparable illusory motion, such as mRDKs. Additionally, we examined whether training on form and motion coherence thresholds improves internal noise filtering and sampling efficiency. Results: Our results revealed significant learning effects on the trained task, enhancing the perception of dynamic GPs. Furthermore, there was a substantial learning transfer to the non-trained stimulus (mRDKs) and partial transfer to a different task. The data also showed differences in coherence thresholds between dynamic GPs and mRDKs, with GPs showing lower coherence thresholds than mRDKs. Finally, an interaction between visual stimulus type and session for sampling efficiency revealed that the effect of training session on participants’ performance varied depending on the type of visual stimulus, with dynamic GPs being influenced differently than mRDKs. Conclusion: These findings highlight the complexity of perceptual learning and suggest that the transfer of learning effects may be influenced by the specific characteristics of both the training stimuli and tasks, providing valuable insights for future research in visual processing.

Analysis of Potential Collisions of Elements of Therapeutic Accelerator on the Basis of the System of Virtual Visualization of Radiation Therapy

Analysis of Potential Collisions of Elements of Therapeutic Accelerator on the Basis of the System of Virtual Visualization of Radiation Therapy