Visual Texture Research Articles

Soft, crispy, crunchy, sustainable: The role of visual textures in shaping sustainable food preferences

This research evaluates the potential of using visual textural complexity as a subtle yet effective strategy to direct consumer preferences towards more sustainable food choices, addressing the urgent need to reduce greenhouse gas emissions in the food industry.Using a traditional Turkish rice pudding, sütlaç, as a case study, we prepared four variants with varying textural complexities (ranging from simple to complex) by reducing the portion size by half and gradually adding soft, crispy, crunchy, and airy textured food layers onto it. Two parallel sets of variants were formulated to minimize potential bias from differences in composition and visual properties of the layers. One hundred participants individually evaluated paired visual stimuli of these variants under two experimental conditions: non-informative, which included only visuals, and informative, which included visuals with accompanied by sample information.Paired binary analysis revealed consistent preference for variants with higher levels of visual textural complexity, especially those with three layers (soft, crispy and crunchy), regardless of experimental conditions and product formulations. Although sample information affected preference scores, variants with certain levels of textural complexity were still preferred over the traditional sample (p < 0.05). Preferences for sample variants over the control reduced the carbon emission over 31 %. The study links the impact of visual textural complexity on preferences to the Elaboration Likelihood Model (ELM) and demonstrates that this strategy can effectively direct consumer preferences towards more sustainable options. Adopting the insights from this study can assist food producers and marketers in contributing to the broader goal of reducing carbon emissions.

A graphics-based digital twin (GBDT) framework for accurate UAV localization in GPS-denied environments

Autonomous navigation of Unmanned Aerial Vehicles (UAVs) is crucial for effective assessment of large-scale civil infrastructure, as manual UAV control is time consuming and prone to mishaps. Autonomous navigation outdoors typically employs GPS signals to enable accurate localization and reduce long-term drifts. However, in many civil engineering applications, GPS signals are either poor or unavailable due to interference and/or multi-path effects. Current approaches for UAV localization in GPS-denied environments, track geometric features such as corners; however, these natural features can be misrepresented due to the presence of occlusions and/or image processing errors, resulting in significant localization errors that can grow with time. AprilTags can reduce localization errors, but installation on large-scale civil infrastructure can be challenging. Therefore, this paper proposes a framework that leverages the wealth of visual and geometric information encoded in a Graphics-Based Digital Twin (GBDT) of a target infrastructure to provide accurate localization of a UAV. The GBDT is comprised of a computer graphics model that faithfully represents a target structure’s geometry, structural features, and visual textures. The visual details of the GBDT are exploited to design an object recognition algorithm that detects GBDTtags, which are distinctive objects (or a collection of components) on the target structure in advance; GBDTtags provide functionality similar to AprilTags. When the camera attached to a UAV detects one or more GBDTtags, the vertices of the GBDTtags are mapped from the image plane into the GBDT coordinate system, allowing for the UAV to be localized. The framework, termed herein as GBDTpose, is first validated numerically using Blender, Gazebo, and Mavros software-in-the-loop (SITL). Subsequently, field validation is carried out using the Kavita and Lalit Bahl Smart Bridge at the University of Illinois Urbana-Champaign (UIUC). Results show that localization in GPS-denied environments can be achieved with 5-50 cm accuracy without the need for physical markers being placed on the structure.

Determination of the Change of Agricultural Landscapes Based on CORINE Land Cover Agricultural Land Classes Using GIS and Visual Quality Value with the AHP Method: The Case of Tekı̇rdağ Province

Agricultural landscapes contribute positively to the visual texture of the city and change over time. In this context, this study aims to determine the visual landscape quality of agricultural landscapes based on parameters on expert approach the hypothesis; it is important and necessary to determine the visual landscape values based on the change in agricultural landscapes over time. Within the framework of the developed hypothesis, it aims to make an expert-based visual landscape assessment of the agricultural landscapes of Tekirdağ Province, located in the northwestern part of Türkiye, based on nine subclasses created according to the CORINE land cover (CLC) classes, on the basis of 4 main parameters. Using the AHP technique, the priorities of the parameters and agricultural landscape classes and the relationships between basic parameters and visual preferences were determined. In this direction, the questions; What are the priorities of parameters that are effective in determining the visual quality of agricultural landscapes on the basis of expert approach?; What kind of changes have occurred in agricultural landscapes during the years 1990-2000-2006-2012-2018 in 9 subclasses created according to the CLC classes? and; How should the visual landscape quality values of the agricultural landscape subclasses created according to the CLC classes be ranked? The results of the study can be used as a tool in landscape planning and management studies as a factor in strengthening landscape quality.

Towards 360 VR Sickness Mitigation: From Virtual Reality Eye-tracking to Visual Communication.

Most 360 virtual reality (VR) contents have been developed without considering that users could be affected by VR sickness. Accordingly, users' viewing safety has been steadily highlighted as a critical problem in the VR market. In this study, we investigate a novel VR sickness mitigation framework based on human visual characteristics for the rendered VR content. First, we build a large-scale 360 VR content database termed VRSP360 (VR Sickness and Presence 360) dedicated to the analysis of VR sickness and thoroughly conduct eye-tracking experiments to measure human perception. In the experiment, we observe that the users' gaze distribution is highly center-biased when they experience excessive VR sickness. From this observation, we design a foveated filtering framework that limits high-frequency textures in the peripheral view to mitigate VR sickness. Particularly, given the human visual system's (HVS) non-uniform resolution with respect to the fovea, we also adopt the foveation-based filtering method using the trade-off between sickness mitigation and presence conservation, which reduces any loss in perceptual quality despite the filtering. We further demonstrate that our framework can effectively compress visual information by applying foveated compression. In addition, we develop two metrics (visual texture index and perceptual information index) to measure the effective preservation of user-perceived information despite the filtration of peripheral vision textures by our proposed mitigation method. Through rigorous subjective evaluation on both original content and its VR-sickness-mitigated version, we demonstrate that the proposed framework successfully mitigates VR sickness with a reduction rate of ∼19% on the proposed dataset.

Criticism of the Effect of Green Cover Change on Air Quality with i-Tree Canopy (Bursa-Osmangazi Region Sample)

Anything that reflects the ethnicity of a society that has aesthetic and visual textures transferred from past generations to the present day, that is universally accepted, that contains abstract and concrete concepts, and that is intended to be preserved and transferred to future generations is called "cultural heritage". Today's urbanism continues to develop around the settlements of ancient civilisations, which are accepted as cultural heritage. Today, migration from villages to cities has increased and population density in cities has shown a rapid upward trend. The destruction of green areas due to rapid urbanisation has increased visibly and has made smart cities with green areas obligatory. In smart urbanisation, practical methods are sought to quickly identify green areas and determine the benefits provided by these areas. The most widely used software for measuring tree canopy cover is i-Tree Canopy, which allows urban planners to quantify the ecosystem services and benefits of tree communities and forests at multiple scales, including pollution reduction, carbon sequestration and storage, and runoff reduction. Within the scope of the study, the changes on green areas such as urbanisation, industrialisation, increase in transportation networks brought about by population growth in the historical city centre of Bursa and the negative and positive effects of these changes were examined. In the study, the time-dependent changes of green areas in urban heritage and urban planning were determined with GIS (Geographical Information Systems) techniques, and suggestions were developed for existing and new green areas to be planned in the city.

X4D-SceneFormer: Enhanced Scene Understanding on 4D Point Cloud Videos through Cross-Modal Knowledge Transfer

The field of 4D point cloud understanding is rapidly developing with the goal of analyzing dynamic 3D point cloud sequences. However, it remains a challenging task due to the sparsity and lack of texture in point clouds. Moreover, the irregularity of point cloud poses a difficulty in aligning temporal information within video sequences. To address these issues, we propose a novel cross-modal knowledge transfer framework, called X4D-SceneFormer. This framework enhances 4D-Scene understanding by transferring texture priors from RGB sequences using a Transformer architecture with temporal relationship mining. Specifically, the framework is designed with a dual-branch architecture, consisting of an 4D point cloud transformer and a Gradient-aware Image Transformer (GIT). The GIT combines visual texture and temporal correlation features to offer rich semantics and dynamics for better point cloud representation. During training, we employ multiple knowledge transfer techniques, including temporal consistency losses and masked self-attention, to strengthen the knowledge transfer between modalities. This leads to enhanced performance during inference using single-modal 4D point cloud inputs. Extensive experiments demonstrate the superior performance of our framework on various 4D point cloud video understanding tasks, including action recognition, action segmentation and semantic segmentation. The results achieve 1st places, i.e., 85.3% (+7.9%) accuracy and 47.3% (+5.0%) mIoU for 4D action segmentation and semantic segmentation, on the HOI4D challenge, outperforming previous state-of-the-art by a large margin. We release the code at https://github.com/jinglinglingling/X4D.

Trypophobia, skin disease, and the visual discomfort of natural textures

In the last decade, the behavioral sciences have described the phenomenon of trypophobia, which is the discomfort felt by some individuals when viewing images containing clusters of bumps or holes. One evolutionary hypothesis for this phenomenon is that this visual discomfort represents an adaptation which helps organisms avoid skin disease and/or ectoparasites. Even though trypophobic imagery and disease imagery are both examples of visual textures, to date there has been no comparison of the visual discomfort elicited by these two specific kinds of textures within the larger context of the visual comfort elicited by natural texture imagery more generally. In the present study, we administered the Trypophobia Questionnaire (TQ) and recorded the visual comfort ratings elicited by a large set of standard natural texture images, including several trypophobic and skin disease images. In two independent samples, we found that while all observers find skin diseases uncomfortable to view, only those scoring high on the TQ rated trypophobic imagery equally uncomfortable. Comparable effects were observed using both standard ANOVA procedures as well as linear mixed effects modeling. Comparing the ratings of both high-TQ and low-TQ participants to the standard textures, we find remarkably consistent rank-order preferences, with the most unpleasant textures (as rated by both groups) exhibiting qualitative similarities to trypophobic imagery. However, we also find that low-level image statistics which have been previously shown to affect visual comfort are poor predictors of the visual comfort elicited by natural textures, including trypophobic and disease imagery. Our results suggest that a full understanding of the visual comfort elicited by natural textures, including those arising from skin disease, will ultimately depend upon a better understanding of cortical areas specialized for the perception of surface and material properties, and how these visual regions interact with emotional brain areas to evoke appropriate behavioral responses, like disgust.

Active View Planning for Visual SLAM in Outdoor Environments Based on Continuous Information Modeling

The visual simultaneous localization and mapping(vSLAM) is widely used in GPS-denied and open field environments for ground and surface robots. However, due to the frequent perception failures derived from lacking visual texture or the {swing} of robot view direction on rough terrains, the accuracy and robustness of vSLAM are still to be enhanced. The study develops a novel view planning approach of actively perceiving areas with maximal information to address the mentioned problem; a gimbal camera is used as the main sensor. Firstly, a map representation based on feature distribution-weighted Fisher information is proposed to completely and effectively represent environmental information richness. With the map representation, a continuous environmental information model is further established to convert the discrete information space into a continuous one for numerical optimization in real-time. Subsequently, the receding horizon optimization is utilized to obtain the optimal informative viewpoints with simultaneously considering the robotic perception, exploration and motion cost based on the continuous environmental model. Finally, several simulations and outdoor experiments are performed to verify the improvement of localization robustness and accuracy by the proposed approach.

Learning Degradation-Robust Spatiotemporal Frequency-Transformer for Video Super-Resolution.

Video Super-Resolution (VSR) aims to restore high-resolution (HR) videos from low-resolution (LR) videos. Existing VSR techniques usually recover HR frames by extracting pertinent textures from nearby frames with known degradation processes. Despite significant progress, grand challenges remain to effectively extract and transmit high-quality textures from high-degraded low-quality sequences, such as blur, additive noises, and compression artifacts. This work proposes a novel degradation-robust Frequency-Transformer (FTVSR++) for handling low-quality videos that carry out self-attention in a combined space-time-frequency domain. First, video frames are split into patches and each patch is transformed into spectral maps in which each channel represents a frequency band. It permits a fine-grained self-attention on each frequency band so that real visual texture can be distinguished from artifacts. Second, a novel dual frequency attention (DFA) mechanism is proposed to capture the global and local frequency relations, which can handle different complicated degradation processes in real-world scenarios. Third, we explore different self-attention schemes for video processing in the frequency domain and discover that a "divided attention" which conducts joint space-frequency attention before applying temporal-frequency attention, leads to the best video enhancement quality. Extensive experiments on three widely-used VSR datasets show that FTVSR++ outperforms state-of-the-art methods on different low-quality videos with clear visual margins. Code and pre-trained models are available at https://github.com/researchmm/FTVSR.

Detecting visual texture patterns in binary sequences through pattern features.

We measured human ability to detect texture patterns in a signal detection task. Observers viewed sequences of 20 blue or yellow tokens placed horizontally in a row. They attempted to discriminate sequences generated by a random generator ("a fair coin") from sequences produced by a disrupted Markov sequence (DMS) generator. The DMSs were generated in two stages: first a sequence was generated using a Markov chain with probability, pr = 0.9, that a token would be the same color as the token to its left. The Markov sequence was then disrupted by flipping each token from blue to yellow or vice versa with probability, pd-the probability of disruption. Disruption played the role of noise in signal detection terms. We can frame what observers are asked to do as detecting Markov texture patterns disrupted by noise. The experiment included three conditions differing in pd (0.1, 0.2, 0.3). Ninety-two observers participated, each in only one condition. Overall, human observers' sensitivities to texture patterns (d' values) were markedly less than those of an optimal Bayesian observer. We considered the possibility that observers based their judgments not on the entire texture sequence but on specific features of the sequences such as the length of the longest repeating subsequence. We compared human performance with that of multiple optimal Bayesian classifiers based on such features. We identify the single- and multiple-feature models that best match the performance of observers across conditions and develop a pattern feature pool model for the signal detection task considered.

Design and fabrication of 3D-printed patient-specific soft tissue and bone phantoms for CT imaging

The objective of this study is to create patient-specific phantoms for computed tomography (CT) that possess accurate densities and exhibit visually realistic image textures. These qualities are crucial for evaluating CT performance in clinical settings. The study builds upon a previously presented 3D printing method (PixelPrint) by incorporating soft tissue and bone structures. We converted patient DICOM images directly into 3D printer instructions using PixelPrint and utilized calcium-doped filament to increase the Hounsfield unit (HU) range. Density was modeled by controlling printing speed according to volumetric filament ratio to emulate attenuation profiles. We designed micro-CT phantoms to demonstrate the reproducibility, and to determine mapping between filament ratios and HU values on clinical CT systems. Patient phantoms based on clinical cervical spine and knee examinations were manufactured and scanned with a clinical spectral CT scanner. The CT images of the patient-based phantom closely resembled original CT images in visual texture and contrast. Micro-CT analysis revealed minimal variations between prints, with an overall deviation of ± 0.8% in filament line spacing and ± 0.022 mm in line width. Measured differences between patient and phantom were less than 12 HU for soft tissue and 15 HU for bone marrow, and 514 HU for cortical bone. The calcium-doped filament accurately represented bony tissue structures across different X-ray energies in spectral CT (RMSE ranging from ± 3 to ± 28 HU, compared to 400 mg/ml hydroxyapatite). In conclusion, this study demonstrated the possibility of extending 3D-printed patient-based phantoms to soft tissue and bone structures while maintaining accurate organ geometry, image texture, and attenuation profiles.

Texture recognition under scale and illumination variations

ABSTRACT Visual scene recognition is predominantly based on visual textures representing an object's material properties. However, the single material texture varies in scale and illumination angles due to mapping an object's shape. We present a comparative study of the colour histogram, Gabor, opponent Gabor, Local Binary Pattern (LBP), and wide-sense Markovian textural features concerning their sensitivity to simultaneous scale and illumination variations. Due to their application dominance, these textural features are selected from more than 50 published textural features. Markovian features are information preserving, and we demonstrate their superior performance for scale and illumination variable observation conditions over the standard alternative textural features. We bound the scale variation by double size, and illumination variation includes illumination spectra, acquisition devices, and 35 illumination directions spanned above a sample hemisphere. Recognition accuracy is tested on textile patterns from the University of East Anglia and wood veneers from UTIA BTF databases.

Display-Semantic Transformer for Scene Text Recognition.

Linguistic knowledge helps a lot in scene text recognition by providing semantic information to refine the character sequence. The visual model only focuses on the visual texture of characters without actively learning linguistic information, which leads to poor model recognition rates in some noisy (distorted and blurry, etc.) images. In order to address the aforementioned issues, this study builds upon the most recent findings of the Vision Transformer, and our approach (called Display-Semantic Transformer, or DST for short) constructs a masked language model and a semantic visual interaction module. The model can mine deep semantic information from images to assist scene text recognition and improve the robustness of the model. The semantic visual interaction module can better realize the interaction between semantic information and visual features. In this way, the visual features can be enhanced by the semantic information so that the model can achieve a better recognition effect. The experimental results show that our model improves the average recognition accuracy on six benchmark test sets by nearly 2% compared to the baseline. Our model retains the benefits of having a small number of parameters and allows for fast inference speed. Additionally, it attains a more optimal balance between accuracy and speed.

Assessing mechanisms behind crossmodal associations between visual textures and temperature concepts.

In the last decades, there has been a growing interest in crossmodal correspondences, including those involving temperature. However, only a few studies have explicitly examined the underlying mechanisms behind temperature-related correspondences. Here, we investigated the relative roles of an underlying affective mechanism and a semantic path (i.e., regarding the semantic knowledge related to a single common source identity or meaning) in crossmodal associations between visual textures and temperature concepts using an associative learning paradigm. Two online experiments using visual textures previously shown to be associated with low and high thermal effusivity (Experiment 1) and visual textures with no consensual associations with thermal effusivity (Experiment 2) were conducted. Participants completed a speeded categorization task before and after an associative learning task, in which they learned mappings between the visual textures and specific affective or semantic stimuli related to low and high temperatures. Across the two experiments, both the affective and semantic mappings influenced the categorization of visual textures with the hypothesized temperatures, but there was no influence on the reaction times. The effect of learning semantic mappings was larger than that of affective ones in both experiments, suggesting that a semantic path has more weight than an affective mechanism in the formation of the associations studied here. The associations studied here could be modified through associative learning establishing correlations between visual textures and either affective or semantic stimuli. (PsycInfo Database Record (c) 2023 APA, all rights reserved).

Initial neural responses to emotional and neutral stimuli presenting different visual textures

Initial neural responses to emotional and neutral stimuli presenting different visual textures

Taste This Score

Digital scores were introduced in contemporary music some decades ago, as well as extra-musical images and visual signs in music writing. What is not yet explored and seems to be a very fertile field of research is musical writing based on research on transmodality. This article presents an investigation on some of the possibilities of musical and sonic writing through the incorporation of images of food textures. This is part of more extensive work that includes research on sonic relationships with visual textures (not only food) and that also investigates the compositional and performative possibilities of transmodal digital dynamic scores (video scores).

Haralick Texture Features based Syriac(Assyrian) and English or Arabic documents Classification

Script identification is very essential before running an individual OCR system. Automatic language script identification from document images facilitates many important applications such as sorting, transcription of multilingual documents and indexing of large collection of such images, or as a precursor to optical character recognition (OCR), in this paper the characterized are between Syriac and English documents or between Syriac and Arabic documents were the characterized is achieved by extracting Haralick texture Features. it is investigated a texture as a tool for determining the script of document image ,based on the observation that text has a distinct visual texture. Further, K nearest neighbour algorithm is used to classify 300 text blocks into one of the two scripts: Syriac, and English , or Syriac and Arabic based on Haralick texture Features . The script was inserted to the System with different rotation angles between 0º and 135º and the results of recognition were good.

Stretching Deep Architectures: A Deep Learning Method without Back-Propagation Optimization

In recent years, researchers have proposed many deep learning algorithms for data representation learning. However, most deep networks require extensive training data and a lot of training time to obtain good results. In this paper, we propose a novel deep learning method based on stretching deep architectures that are composed of stacked feature learning models. Hence, the method is called “stretching deep architectures” (SDA). In the feedforward propagation of SDA, feature learning models are firstly stacked and learned layer by layer, and then the stretching technique is applied to map the last layer of the features to a high-dimensional space. Since the feature learning models are optimized effectively, and the stretching technique can be easily calculated, the training of SDA is very fast. More importantly, the learning of SDA does not need back-propagation optimization, which is quite different from most of the existing deep learning models. We have tested SDA in visual texture perception, handwritten text recognition, and natural image classification applications. Extensive experiments demonstrate the advantages of SDA over traditional feature learning models and related deep learning models.

TouchRoller: A Rolling Optical Tactile Sensor for Rapid Assessment of Textures for Large Surface Areas.

Tactile sensing is important for robots to perceive the world as it captures the physical surface properties of the object with which it is in contact and is robust to illumination and colour variances. However, due to the limited sensing area and the resistance of their fixed surface when they are applied with relative motions to the object, current tactile sensors have to tap the tactile sensor on the target object a great number of times when assessing a large surface, i.e., pressing, lifting up, and shifting to another region. This process is ineffective and time-consuming. It is also undesirable to drag such sensors as this often damages the sensitive membrane of the sensor or the object. To address these problems, we propose a roller-based optical tactile sensor named TouchRoller, which can roll around its centre axis. It maintains being in contact with the assessed surface throughout the entire motion, allowing for efficient and continuous measurement. Extensive experiments showed that the TouchRoller sensor can cover a textured surface of 8 cm × 11 cm in a short time of 10 s, much more effectively than a flat optical tactile sensor (in 196 s). The reconstructed map of the texture from the collected tactile images has a high Structural Similarity Index (SSIM) of 0.31 on average when compared with the visual texture. In addition, the contacts on the sensor can be localised with a low localisation error, 2.63 mm in the centre regions and 7.66 mm on average. The proposed sensor will enable the fast assessment of large surfaces with high-resolution tactile sensing and the effective collection of tactile images.

Arthroscopic scene segmentation using multispectral reconstructed frames and deep learning

BackgroundKnee arthroscopy is one of the most complex minimally invasive surgeries, and it is routinely performed to treat a range of ailments and injuries to the knee joint. Its complex ergonomic design imposes visualization and navigation constraints, consequently leading to unintended tissue damage and a steep learning curve before surgeons gain proficiency. The lack of robust visual texture and landmark frame features further limits the success of image-guided approaches to knee arthroscopy Feature- and texture-less tissue structures of knee anatomy, lighting conditions, noise, blur, debris, lack of accurate ground-truth label, tissue degeneration, and injury make semantic segmentation an extremely challenging task. To address this complex research problem, this study reported the utility of reconstructed surface reflectance as a viable piece of information that could be used with cutting-edge deep learning technique to achieve highly accurate segmented scenes. MethodsWe proposed an intraoperative, two-tier deep learning method that makes full use of tissue reflectance information present within an RGB frame to segment texture-less images into multiple tissue types from knee arthroscopy video frames. This study included several cadaver knees experiments at the Medical and Engineering Research Facility, located within the Prince Charles Hospital campus, Brisbane Queensland. Data were collected from a total of five cadaver knees, three were males and one from a female. The age range of the donors was 56–93 years. Aging-related tissue degeneration and some anterior cruciate ligament injury were observed in most cadaver knees. An arthroscopic image dataset was created and subsequently labeled by clinical experts. This study also included validation of a prototype stereo arthroscope, along with conventional arthroscope, to attain larger field of view and stereo vision. We reconstructed surface reflectance from camera responses that exhibited distinct spatial features at different wavelengths ranging from 380 to 730 nm in the RGB spectrum. Toward the aim to segment texture-less tissue types, this data was used within a two-stage deep learning model. ResultsThe accuracy of the network was measured using dice coefficient score. The average segmentation accuracy for the tissue-type articular cruciate ligament (ACL) was 0.6625, for the tissue-type bone was 0.84, and for the tissue-type meniscus was 0.565. For the analysis, we excluded extremely poor quality of frames. Here, a frame is considered extremely poor quality when more than 50% of any tissue structures are over- or underexposed due to nonuniform light exposure. Additionally, when only high quality of frames was considered during the training and validation stage, the average bone segmentation accuracy improved to 0.92 and the average ACL segmentation accuracy reached 0.73. These two tissue types, namely, femur bone and ACL, have a high importance in arthroscopy for tissue tracking. Comparatively, the previous work based on RGB data achieved a much lower average accuracy for femur, tibia, ACL, and meniscus of 0.78, 0.50, 0.41, and 0.43 using U-Net and 0.79, 0.50, 0.51, and 0.48 using U-Net++. From this analysis, it is clear that our multispectral method outperforms the previously proposed methods and delivers a much better solution in achieving automatic arthroscopic scene segmentation. ConclusionThe method was based on the deep learning model and requires a reconstructed surface reflectance. It could provide tissue awareness in an intraoperative manner that has a high potential to improve surgical precisions. It could be applied to other minimally invasive surgeries as an online segmentation tool for training, aiding, and guiding the surgeons as well as image-guided surgeries.