Meaningful Objects Research Articles

Mental synthesis and creative thinking in typically developed Italian

The study concerns the relationships between mental synthesis and creative thinking in typically developed Italian children. Creative Mental Synthesis task was applied to analyze the mental synthesis of visualized forms (Finke, Ward, & Smith, 1992) by means of rotation, dimension, superimposition, and inclusion. It consisted of a paper-pencil protocol with three stimuli, a capital letter V, a square, and a circle: each child was instructed to imagine combining the stimuli to make meaningful objects and was allowed to imagine the stimuli in any size and to combine them in anyway, without altering or modifying the structural shapes. Test of Creative Thinking (Williams, 1994) was used to measure fluency, flexibility, elaboration, originality, and verbal production; it was made up of a protocol with 12 frames, containing incomplete graphic stimuli shown to children who were asked to draw a picture. Results: the more the children were able in mental synthesis and, mainly, in inclusion and superimposition of visual forms, the more they better performed in elaboration, flexibility, and originality. Future research could deepen the role of mental imagery in development of creativity through curricula focused on strategies for strengthening the processes related to mental imagery.
 Keywords: Mental imagery; creative thinking; typical development;

Unsupervised Deep Joint Segmentation of Multitemporal High-Resolution Images

High/very-high-resolution (HR/VHR) multitemporal images are important in remote sensing to monitor the dynamics of the Earth's surface. Unsupervised object-based image analysis provides an effective solution to analyze such images. Image semantic segmentation assigns pixel labels from meaningful object groups and has been extensively studied in the context of single-image analysis, however not explored for multitemporal one. In this article, we propose to extend supervised semantic segmentation to the unsupervised joint semantic segmentation of multitemporal images. We propose a novel method that processes multitemporal images by separately feeding to a deep network comprising of trainable convolutional layers. The training process does not involve any external label, and segmentation labels are obtained from the argmax classification of the final layer. A novel loss function is used to detect object segments from individual images as well as establish a correspondence between distinct multitemporal segments. Multitemporal semantic labels and weights of the trainable layers are jointly optimized in iterations. We tested the method on three different HR/VHR data sets from Munich, Paris, and Trento, which shows the method to be effective. We further extended the proposed joint segmentation method for change detection (CD) and tested on a VHR multisensor data set from Trento.

A relay Mackey—Glass model with two delays

We study the dynamics of the generalized Mackey—Glass equation with two delays using the large-parameter method. After a special exponential replacement of variables, a singularly perturbed equation is obtained, for which we construct a meaningful limit object, a differential—difference relay equation with two delays. We prove that the relay equation has a simple periodic solution with one interval on the period where the solution is positive. To illustrate the obtained result, we numerically analyze the original singularly perturbed equation, for which we find a solution near the periodic solution of the limit relay equation.

Variational Mode Decomposition-Based Multilevel Threshold Selection Scheme for Color Image Segmentation

Image segmentation is a method of subdividing an image into numerous meaningful regions or objects, which makes the image more informative and easy to analyze. Thresholding-based approaches are extensively employed for segmenting the image due to their low computational cost and are easy implementation. However, histogram-based thresholding schemes suffer from high variation that leads to abnormalities and sharp specifics. In this paper, we propose a technique for multilevel color image segmentation through variational mode decomposition (VMD) and Kapur’s entropy. Initially, the VMD is employed in order to decompose the histogram into corresponding submodes of analysis and attributes extraction, which leads to the removal of the unfavorable effects. Then, Kapur’s entropy is incorporated in order to generate accurate and optimal thresholds for segmentation. For the performance evaluation of the presented VMD–Kapur algorithm, various qualitative metrics have been used such as probability rand index, mean-square error, peak signal-to-noise ratio, variation of information, structural similarity index, dice error, feature similarity index, entropy, Jaccard/Tanimoto error, and normalized absolute error. The experimental results show that the proposed technique produces best-segmented images compared to Kapur’s, Tsallis, Masi, and fuzzy entropies.

Identifying occupation-based needs and services for individuals experiencing homelessness using interviews and photovoice.

Homelessness can impact society and individuals in many ways. The purpose of this project was to examine the current occupation-based needs and services among persons experiencing homelessness (PEH) in a major city. Researchers conducted semi-structured interviews with eight PEH residing in a homeless shelter. Three questions were asked of each participant: What daily personal activities have been most impacted since undergoing homelessness? Which services have been most helpful to you in overcoming homelessness? What other services might you find helpful to individuals experiencing housing insecurity? Researchers also gathered qualitative data utilizing a Photovoice component in which twelve PEH had the opportunity to photograph one object and provide a quote associated with the meaningful object. Under-addressed areas of occupation, services received, and services requested were identified. Findings suggest that areas of occupation most impacted by homelessness include instrumental activities of daily living (IADLs), activities of daily living (ADLs), leisure, social participation, and rest and sleep. Services received included case management and often met basic self-care needs of PEH. Services requested included group therapy, restructuring of the shelter to support individualization, and staff training. Occupational therapy practitioners have the skill set to assess needed services among PEH. Meaningful objects captured by the clients via Photovoice may relate to past, present and future satisfaction despite being homeless.

Text-to-image via mask anchor points

Text-to-image is a process of generating an image from the input text. It has a variety of applications in art generation, computer-aided design, and data synthesis. In this paper, we propose a new framework which leverages mask anchor points to incorporate two major steps in the image synthesis. In the first step, the mask image is generated from the input text and the mask dataset. In the second step, the mask image is fed into the state-of-the-art mask-to-image generator. Note that the mask image captures the semantic information and the location relationship via the anchor points. We also developed a user-friendly interface which helps parse the input text into the meaningful semantic objects. As a result, our framework is able to produce clear, reasonable, and more realistic images. The experiments on the most challenging COCO-stuff dataset illustrate the superiority of our proposed approach over the previous state of the arts.

Using object‐based image analysis with multi‐temporal aerial imagery and LiDAR to detect change in temperate intertidal habitats

Abstract Intertidal habitat maps are needed at both fine and coarse scales to monitor change and inform conservation and management, but current methods of field survey and expert interpretation of aerial imagery can be time‐consuming and subjective. Object‐based image analysis (OBIA) of remote sensing data is increasingly employed for producing habitat or land cover maps. Users create automated workflows to segment imagery, creating ecologically meaningful objects, which are then classified based on their spectral or geometric properties, relationships to other objects and contextual data. This study evaluates the change‐detection capability of OBIA in the intertidal environment by developing and comparing two OBIA methods for quantifying change in extent and distribution of habitats from freely available multi‐temporal aerial imagery and LiDAR data. Despite considerable variability in the data, pre‐ and post‐classification change detection methods had sufficient accuracy (mean overall accuracy from 70.5 to 82.6%) to monitor deviation from a background level of natural environmental fluctuation. This insight into spatial and temporal patterns of natural cyclical change and their detectability by OBIA could inform use of remote sensing for regular, rapid coastal assessment, providing an alert system to direct survey resources to areas of ecologically relevant change.

Why are places so special? Uncovering how our brain reacts to meaningful places

People are drawn towards personally meaningful places. Seeing or remembering those places improves mood and supports wellbeing. But existing evidence relies on self-reports and comparisons with unpleasant places. Using brain imaging techniques, we examined reactions towards images of personally meaningful places, meaningful objects, neutral places and objects, and pre-validated (IAPS) images, among 19 volunteers (10 female) between 19 and 53 years old. A whole brain analysis showed that meaningful places and IAPS images elicited the largest response in the amygdala, associated with the processing of emotion. Similarly high activity was found for the medial prefrontal cortex (mPFC), associated with self-referential processing, emotional appraisal, and memory processing. This was not found for meaningful objects or neutral places. The parahippocampal place area (PPA) showed enhanced activity only to personally meaningful places. Personally meaningful places clearly evoke distinctive neurological responses supporting the importance of this holistic and complex concept for human wellbeing and urban planning.

Segmentation of 3D Point Cloud Data Based on Supervoxel Technique

Abstract Segmentation is a significant challenge posed to the computer vision applications dealing with the 3D point cloud data. In this paper, the bottom-up 3D point cloud supervoxel technique is proposed for segmenting both outdoor and indoor scenes. Using this method, the dense point cloud data is represented in a compact manner and providing better computational efficiency. Initially, voxels and supervoxel are generated in the geometric space using octree data structure and voxel-based cloud connectivity algorithm (VCCS). Then, a bottom-up approach constraint planar cut (CPC) of greedy cuts through a local connectivity is established between supervoxels which is used to create realistic and meaningful objects (segments). The experiments have been performed on the datasets acquired over a part of the urban area around Vaihingen, Germany for the outdoor scene and RGB-D kitchen dataset for the indoor scene. It is demonstrated that our proposed method can achieve good results, especially for strict partitioning scenes. The quantitative comparison between various 3D point cloud segmentation methods is obtained and the proposed method confirms its efficiency and effectiveness.

Color-Based Forest Cover Type Image Segmentation using K-Means Clustering Approach

In order to understand forest composition, classifying forest cover type can help research regarding forest resilience, carbon sequestration, and climate change concerns. The purposes of this study were to develop and implement some image processing functions based on the histogram of forest cover type color image, and to classify forest cover type using its color feature sets of image pixels. Color-based image segmentation that is based on the color feature of image pixels assumes that homogeneous colors in the image correspond to separate clusters and hence meaningful objects in the image. The Image Processing Toolbox of MATLAB R2019a was used to convert the original forest cover type image to the enhance contrast image, including histogram of enhance contrast image. Furthermore, It was also used to analyze color-based forest cover type image segmentation using the enhance contrast image for this study. Using K-Means clustering analysis, a three-cluster solution was developed, labeled as Hardwoods (Yellow Color) Cover Type, Hardwoods (Gray Color) Cover Type, and Loblolly Pines Cover Type. There was a significant difference among three different forest cover type clusters in terms of histograms and L*a*b* color space features visually.

Image Segmentation with Partial Convexity Shape Prior Using Discrete Conformality Structures

Image segmentation aims to partition an image into meaningful regions and extract important objects therein. In real applications, the given images may contain multiple overlapping objects with noisy background, creating great challenges to the segmentation task. In these cases, prior information of the target object is essential for an accurate and meaningful segmentation result. In this paper, we present a new convexity shape prior segmentation framework to guarantee the segmented region to be fully or partially convex according to the user's preference. The basic idea is to incorporate a registration-based segmentation model with a specially designed convexity constraint. The convexity constraint is based on the discrete conformality structures of the image mesh. To solve the segmentation model, we propose an iterative scheme, which smoothly deforms a template object to trace the boundary of the target object. A projection is carried out to enforce the convexity constraint. The target object is then captured by a (fully or partially) convex region. Convexity is the only prior information needed for a (fully) convex shape, whereas the location of partial convexity is needed for a partially convex shape. Experiments have been carried out on both synthetic and real images and the results demonstrate the effectiveness of our proposed framework.

Framework and operationalisation challenges for quantitative comparative research in higher education

AbstractThe increasing availability of data on higher education systems, institutions and their members creates new opportunities for comparative research adopting a quantitative approach. The value of future studies crucially depends on the capability to recognise and address some major methodological challenges existing in quantitative comparative research in higher education. The higher education context presents in fact specific features that can hinder comparisons, and political and social processes occurred in recent decades further enhanced complexity. This article aims to discuss key challenges that are currently met in quantitative comparative research in higher education while developing the conceptual framework and in research operationalisation, to discuss possible solutions and the value of configurational and multilevel analytical approaches in identifying meaningful objects of comparisons, to take into account the complexity of the higher education context and in identifying causal relationships.

Perception and Performance on a Virtual Reality Cognitive Stimulation for Use in the Intensive Care Unit: A Non-randomized Trial in Critically Ill Patients.

Background: Newly acquired long-term cognitive impairments are common among survivors of critical illness. They have been linked to the stressful situation that patients experience in the intensive care unit (ICU). In this paper we use virtual reality (VR) technology to comfort critically ill patients and reduce stress during their ICU stay. We investigate the acceptance, comfort, recollection, and visual perception of VR stimulation and how it affects physiological parameters.Methods: A VR head-mounted display was used to present immersive nature scenes to 33 critically ill cardiac surgery patients [mean age 63 years (range 32–83)]. Data was collected with an eye tracker fitted inside the VR head-mounted display to measure eye movements (250 Hz) and sensors to record physiological parameters (240 Hz). Patients received VR stimulation (for 5 min.) prior to ICU admission, during ICU stay, and 3 months after discharge. Acceptance, recollection and comfort were assessed with validated questionnaires.Results: The number of gazed meaningful objects per minute was significantly lower during the ICU session compared to pre- and follow-up sessions, whereas mean duration of fixation on meaningful moving objects did not differ between the sessions. While respiratory rate decreased significantly during VR stimulation, heart rate and blood pressure remained constant. Post-ICU rating of VR acceptance during ICU stay was moderate to high and discomfort low. Recollection of VR was high [28/33 patients (84.8%)], while recollection of ICU stay was low [10/33 patients (30.3%)].Conclusion: Eye movements indicate that patients were able to perceive and process cognitive stimulation during their ICU stay. VR was recalled better than the rest of the ICU stay and well accepted. Decreased respiratory rate during stimulation indicate a relaxing effect of VR.

Cortical sensitivity to natural scene structure.

Natural scenes are inherently structured, with meaningful objects appearing in predictable locations. Human vision is tuned to this structure: When scene structure is purposefully jumbled, perception is strongly impaired. Here, we tested how such perceptual effects are reflected in neural sensitivity to scene structure. During separate fMRI and EEG experiments, participants passively viewed scenes whose spatial structure (i.e., the position of scene parts) and categorical structure (i.e., the content of scene parts) could be intact or jumbled. Using multivariate decoding, we show that spatial (but not categorical) scene structure profoundly impacts on cortical processing: Scene‐selective responses in occipital and parahippocampal cortices (fMRI) and after 255 ms (EEG) accurately differentiated between spatially intact and jumbled scenes. Importantly, this differentiation was more pronounced for upright than for inverted scenes, indicating genuine sensitivity to spatial structure rather than sensitivity to low‐level attributes. Our findings suggest that visual scene analysis is tightly linked to the spatial structure of our natural environments. This link between cortical processing and scene structure may be crucial for rapidly parsing naturalistic visual inputs.

The relationship between the intrinsic Čech and persistence distortion distances for metric graphs

Metric graphs are meaningful objects for modeling complex structures that arise in many real-world applications, such as road networks, river systems, earthquake faults, blood vessels, and filamentary structures in galaxies. To study metric graphs in the context of comparison, we are interested in determining the relative discriminative capabilities of two topology-based distances between a pair of arbitrary finite metric graphs: the persistence distortion distance and the intrinsic Cech distance. We explicitly show how to compute the intrinsic Cech distance between two metric graphs based solely on knowledge of the shortest systems of loops for the graphs. Our main theorem establishes an inequality between the intrinsic Cech and persistence distortion distances in the case when one of the graphs is a bouquet graph and the other is arbitrary. The relationship also holds when both graphs are constructed via wedge sums of cycles and edges.

Developing knowledge work skills in a university course

ABSTRACTA blended university course whose design is inspired by the Trialogical Learning Approach is presented. The structure, activities, and content of the course are described in detail and followed by an explorative case-study conducted on data collected during the 2017–2018 edition of the course. The general aim was to understand the impact of the course on students’ perceptions of their acquisition of knowledge work skills and on their overall appreciation of the course. One hundred and nine psychology students voluntarily participated in a course titled ‘Experimental Pedagogy’ held at Sapienza, University of Rome (IT). The data collection was informed by the trialogical design principles that inspired the course and defined the knowledge work skills to be observed, i.e. collaboration, continuous improvement, and digital skills. Two anonymous self-report questionnaires were administered. Data was analysed using a mixed-methods (qualitative and quantitative) approach. Results indicated that the course was perceived to effectively promote the majority of the intended skills. Moreover, students appreciated the possibility to concretely work on a project-based learning activity that allowed them to create a shared and meaningful object and reflect their actual learning as intended by the theoretical approach.

The structure of illusory conjunctions reveals hierarchical binding of multipart objects.

The world around us is filled with complex objects, full of color, motion, shape, and texture, and these features seem to be represented separately in the early visual system. Anne Treisman pointed out that binding these separate features together into coherent conscious percepts is a serious challenge, and she argued that selective attention plays a critical role in this process. Treisman also showed that, consistent with this view, outside the focus of attention we suffer from illusory conjunctions: misperceived pairings of features into objects. Here we used Treisman's logic to study the structure of pre-attentive representations of multipart, multicolor objects, by exploring the patterns of illusory conjunctions that arise outside the focus of attention. We found consistent evidence of some pre-attentive binding of colors to their parts, and weaker evidence of binding multiple colors of the same object. The extent to which such hierarchical binding occurs seems to depend on the geometric structure of multipart objects: Objects whose parts are easier to separate seem to exhibit greater pre-attentive binding. Together, these results suggest that representations outside the focus of attention are not entirely a "shapeless bundles of features," but preserve some meaningful object structure.

Interactive Hierarchical Object Proposals

Object proposal algorithms have been demonstrated to be very successful in accelerating object detection process. High object localization quality and detection recall can be obtained using thousands of proposals. However, the performance with a small number of proposals is still unsatisfactory. This paper demonstrates that the performance of a few proposals can be significantly improved with the minimal human interaction—a single touch point. To this end, we first generate hierarchical superpixels using an efficient tree-organized structure as our initial object proposals, and then select only a few proposals from them by learning an effective Convolutional neural network for objectness ranking. We explore and design an architecture to integrate human interaction with the global information of the whole image for objectness scoring, which is able to significantly improve the performance with a minimum number of object proposals. Extensive experiments show the proposed method outperforms all the state-of-the-art methods for locating the meaningful object with the touch point constraint. Furthermore, the proposed method is extended for video. By combining with the novel interactive motion segmentation cue for generating hierarchical superpixels, the performance on a single proposal is satisfactory and can be used in the interactive vision systems, such as selecting the input of a real-time tracking system.

On the interaction of core and emergent common ground in Internet memes

AbstractInternet memes are meaningful objects of diverse shapes that spread across networks of mediated participation (term fromMilner 2012: 10). The distribution and reception of memes bears aspects of communicative interaction, because memes establish usage conventions. This paper will be concerned with the pragmatics of Internet memes. Given that flexibility, novelty and originality are driving forces in meme culture, the question arises how traditional pragmatic notions likerecipient designandcommon groundcan be said to apply for the interaction with memes. Kecskes’ (2008,2010,2012,2014;Kecskes and Zhang 2009) distinction between core common ground and emergent common ground will be discussed and put to use for an explanation of the complex interactive dynamics of Internet communication. This modern form of communication oscillates between reference to shared cultural contents and the establishment and perpetuation of conventions on the one hand, and the pursuit of originality on the other hand. This paper will demonstrate how memes can vary with respect to the degree to which they require core common ground or the generation of emergent common ground for their proper usage. The scale presented as a result of the discussion represents a continuum of the prevalence of semantics versus pragmatics involved in the usage and interpretation of memes.

An investigation of geographic object based image analysis (GEOBIA) for human settlement detection in South Africa

Abstract. The changes to the landscape are constantly occurring both naturally and human induced. One of such changes is the human settlement expansion. The ability to map human settlements is vital for variety of studies including urban development planning and management. For this study human settlement detection is essential for topographic map update. The newly identified human settlements also serves as change detection area indicator for further update of other topographic features that are represented on the topographic map (such as roads etc.). The semi-automated human settlement detection has been conducted through geographic object based image analysis (GEOBIA) method using 2012 SPOT 5 imagery in the KwaZulu-Natal Province of South Africa. GEOBIA is relative new development of image processing and analysis of remote sensed imagery. It involves partitioning an imagery into discrete entities or segments from which meaningful image object, based on the spatial and spectral attributes can be generated. Through a multiresolution segmentation model implemented by the eCognition software, image segmentation was attained. This entailed evaluation of different segmentation parameter in order obtain suitable objects of interest. The following step was to determine appropriate variables obtained from image segmentation to classify the image. These include: layer values, geometry, position, texture, hierarchy and thematic attributes. The layer value option entails spectral statistics such as mean value and mean brightness for image reflectance bands together with capability of further applying band ratio combinations. Under the texture and geometry option also several alternatives are applicable (such as length/width under extend and asymmetry under shape properties). Under the level co-occurrence matrix (GLCM) the study explored the contrast textual measurements developed by Haralick et al., (1973). Other assessed variables are mean brightness, density, length/width and band ratios. The last step for GEOBIA was to determine suitable variables for the rule-set based classification. This resulted to 70.7% overall accuracy.These results were further compared to the existing South African global human settlement layer (SA_GHSL) for the same study area which also used the same year 2012 SPOT 5 imagery. The SA_GHSL had an overall accuracy of 60%. The GEOBIA presents an opportunity to apply semi-automated method to target areas of new settlement development more efficiently and with consistent repeatable manner. Thus assisting topographic update analyst to be drawn to more areas of new settlement development at an enhanced efficient rate. However the spectral variability of roof tops proved to be the most challenging obstacle towards of both the semi-automated settlement detection methods.