Representation Of Complex Objects Research Articles

The value of 3D printing model combined PCNL in kidney stones: a systematic review and meta-analysis.

With the continuous advancement of medical imaging, 3D printing technology is emerging. This technology allows for the representation of complex objects in a model form. This research aims to delve into the irreplaceable value of percutaneous nephrolithotomy (PCNL) in conjunction with 3D printed models in urinary stone surgery. This forward-looking approach provides doctors with a new perspective, enabling them to plan and execute surgeries with greater precision, ultimately delivering a safer and more efficient treatment experience for patients. We evaluated the literature on PCNL for the kidney stones with the introduction of 3D printing models and conducted a meta-analysis. The assessed parameters included stone clearance rate, operation time, hospital stay, blood loss, puncture accuracy, and the rate of complications. We systematically searched the EMBASE, PubMed, Cochrane Library, SCIE, and Chinese Biomedical Literature Search databases for articles related to PCNL (Percutaneous Nephrolithotomy) with 3D printing models from January 2000 to January 2023. Data were managed and screened using Excel . Meta-analysis was performed for operation time, stone clearance rate, blood loss, puncture accuracy, length of hospital stay, and complications in PCNL combined with 3D printing model for kidney stone treatment. The quality of included articles was assessed using the risk of bias tool by the Cochrane Collaboration. Sensitivity analysis was conducted to assess the reliability of the results. Data were recorded using StataSE 17 software, and publication bias was examined using Egger's linear regression test. We followed the PRISMA (Preferred Reporting Items for Systematic Reviews and Meta-Analyses) guidelines to conduct a systematic search and screening of literature relevant to the use of 3D printed models in the treatment of kidney stones. We conducted an extensive literature search across several major academic databases, including EMBASE, PubMed, Cochrane Library, SCIE, and Chinese Biomedical Literature Search databases, to ensure comprehensive coverage of relevant studies. Following the PRISMA process of screening and analysis, we ultimately included 10 randomized controlled trials with a combined sample of 608 for systematic review. Across these studies, we identified the introduction of 3D printing models prior to surgery for kidney stones resulted in significant advantages for the experimental group compared to the control group in terms of operation time, stone clearance rates, puncture accuracy, hospital stay, blood loss, and the incidence of complications, providing valuable insights for further research and clinical practice.

Идеята за допълнителност в научното познание като стил на мислене

The principle of complementarity, raised by Niels Bohr as a kernel of the Copenhagen interpretation of quantum theory, as well as the principle of equivalence of space geometries, raised by Hans Reichenbach, are critically discussed as epistemological approaches at achieving theoretical representations of complex objects of research, which stay far from the ordinary human experience. Both principles share a congenial style of thinking. I also point to theoretical ways that lead out of the grip of these principles.

EMOVA: Emotion-driven neural volumetric avatar

3D facial reconstruction is essential for metaverse applications. Traditional mesh-based methods have difficulty rendering photorealistic faces and complex objects. Recent advancements in Neural Radiance Fields (NeRFs) have excelled in representing complex objects. However, they struggle with capturing subtle facial differences, particularly around the eyes and mouth, due to their reliance on RGB value comparisons. To address this, we propose an EMOtion-driven Volumetric Avatar (EMOVA) that utilizes emotional stimuli from images and voices to enhance facial precision. Visual emotional features ensure that the reconstructed face aligns with the input emotion, while auditory features enhance facial details from various viewpoints. Through an attention-based fusion of these features, EMOVA accurately captures and reconstructs faces, even in self-occlusion. EMOVA outperforms the state-of-the-art methods by more than 5.93% in terms of LPIPS for face reconstruction.

GeoINR 1.0: an implicit neural network approach to three-dimensional geological modelling

Abstract. Implicit neural representation (INR) networks are emerging as a powerful framework for learning three-dimensional shape representations of complex objects. These networks can be used effectively to model three-dimensional geological structures from scattered point data, sampling geological interfaces, units, and structural orientations. The flexibility and scalability of these networks provide a potential framework for integrating many forms of geological data and knowledge that classical implicit methods cannot easily incorporate. We present an implicit three-dimensional geological modelling approach using an efficient INR network architecture, called GeoINR, consisting of multilayer perceptrons (MLPs). The approach expands on the modelling capabilities of existing methods using these networks by (1) including unconformities into the modelling; (2) introducing constraints on stratigraphic relations and global smoothness, as well as associated loss functions; and (3) improving training dynamics through the geometrical initialization of learnable network variables. These three enhancements enable the modelling of more complex geology, improved data fitting characteristics, and reduction of modelling artifacts in these settings, as compared to an existing INR approach to structural geological modelling. Two diverse case studies also are presented, including a sedimentary basin modelled using well data and a deformed metamorphic setting modelled using outcrop data. Modelling results demonstrate the method's capacity to fit noisy datasets, use outcrop data, represent unconformities, and efficiently model large geographic areas with relatively large datasets, confirming the benefits of the GeoINR approach.

Comparing performance between a deep neural network and monkeys with bilateral removals of visual area TE in categorizing feature-ambiguous stimuli.

In the canonical view of visual processing the neural representation of complex objects emerges as visual information is integrated through a set of convergent, hierarchically organized processing stages, ending in the primate inferior temporal lobe. It seems reasonable to infer that visual perceptual categorization requires the integrity of anterior inferior temporal cortex (area TE). Many deep neural networks (DNNs) are structured to simulate the canonical view of hierarchical processing within the visual system. However, there are some discrepancies between DNNs and the primate brain. Here we evaluated the performance of a simulated hierarchical model of vision in discriminating the same categorization problems presented to monkeys with TE removals. The model was able to simulate the performance of monkeys with TE removals in the categorization task but performed poorly when challenged with visually degraded stimuli. We conclude that further development of the model is required to match the level of visual flexibility present in the monkey visual system.

Constructing continuous curves on point-cloud surfaces with Directed Projection Operator

With the development of 3D acquisition technology, the points based representation of complex objects and environment clouds has been widely applied in tool path generation, robot path planning and other fields. A new challenge is to construct continuous curves on point-cloud. We firstly proposed a new energy function to implement directed projection. Most importantly, we transformed the complex energy optimization relative to directed projection into simple numerical iteration named Directed Projection Operator(DPO). Then, by means of the DPO and its approximating surface, we provided two different approaches to construct G2 continuous curves over the point-cloud surface. The constructed curve can not only be embedded into the point-cloud surface, but also be modified locally. A large number of experimental cases have verified the effectiveness and practicability of the proposed algorithms.

Towards Pointsets Representation Learning via Self-Supervised Learning and Set Augmentation.

Deep metric learning is a supervised learning paradigm to construct a meaningful vector space to represent complex objects. A successful application of deep metric learning to pointsets means that we can avoid expensive retrieval operations on objects such as documents and can significantly facilitate many machine learning and data mining tasks involving pointsets. We propose a self-supervised deep metric learning solution for pointsets. The novelty of our proposed solution lies in a self-supervision mechanism that makes use of a distribution distance for set ranking called the Earth's Mover Distance (EMD) to generate pseudo labels and a pointset augmentation method for supporting the learning solution. Our experimental studies on documents, graphs, and point clouds datasets show that our proposed solutions outperform baselines and state-of-the-art approaches under the unsupervised settings. The learned self-supervised representation can also be used as a pre-trained model, which can boost downstream tasks with a fine-tuning step and outperform state-of-the-art language models.

Synthesis of a comprehensive population code for contextual features in the awake sensory cortex.

How cortical circuits build representations of complex objects is poorly understood. Individual neurons must integrate broadly over space, yet simultaneously obtain sharp tuning to specific global stimulus features. Groups of neurons identifying different global features must then assemble into a population that forms a comprehensive code for these global stimulus properties. Although the logic for how single neurons summate over their spatial inputs has been well explored in anesthetized animals, how large groups of neurons compose a flexible population code of higher-order features in awake animals is not known. To address this question, we probed the integration and population coding of higher-order stimuli in the somatosensory and visual cortices of awake mice using two-photon calcium imaging across cortical layers. We developed a novel tactile stimulator that allowed the precise measurement of spatial summation even in actively whisking mice. Using this system, we found a sparse but comprehensive population code for higher-order tactile features that depends on a heterogeneous and neuron-specific logic of spatial summation beyond the receptive field. Different somatosensory cortical neurons summed specific combinations of sensory inputs supra-linearly, but integrated other inputs sub-linearly, leading to selective responses to higher-order features. Visual cortical populations employed a nearly identical scheme to generate a comprehensive population code for contextual stimuli. These results suggest that a heterogeneous logic of input-specific supra-linear summation may represent a widespread cortical mechanism for the synthesis of sparse higher-order feature codes in neural populations. This may explain how the brain exploits the thalamocortical expansion of dimensionality to encode arbitrary complex features of sensory stimuli.

Experience Transforms Conjunctive Object Representations: Neural Evidence for Unitization After Visual Expertise.

Certain transformations must occur within the brain to allow rapid processing of familiar experiences. Complex objects are thought to become unitized, whereby multifeature conjunctions are retrieved as rapidly as a single feature. Behavioral studies strongly support unitization theory, but a compelling neural mechanism is lacking. Here, we examined how unitization transforms conjunctive representations to become more "feature-like" by recruiting posterior regions of the ventral visual stream (VVS) whose architecture is specialized for processing single features. We used functional magnetic resonance imaging to scan humans before and after visual training with novel objects. We implemented a novel multivoxel pattern analysis to measure a conjunctive code, which represented a conjunction of object features above and beyond the sum of the parts. Importantly, a multivoxel searchlight showed that the strength of conjunctive coding in posterior VVS increased posttraining. Furthermore, multidimensional scaling revealed representational separation at the level of individual features in parallel to the changes at the level of feature conjunctions. Finally, functional connectivity between anterior and posterior VVS was higher for novel objects than for trained objects, consistent with early involvement of anterior VVS in unitizing feature conjunctions in response to novelty. These data demonstrate that the brain implements unitization as a mechanism to refine complex object representations over the course of multiple learning experiences.

Multimodal 3D Shape Reconstruction under Calibration Uncertainty Using Parametric Level Set Methods

We consider the problem of 3D shape reconstruction from multimodal data, given uncertain calibration parameters. Typically, 3D data modalities can come in diverse forms such as sparse point sets, volumetric slices, and 2D photos. To jointly process these data modalities, we exploit a parametric level set method that utilizes ellipsoidal radial basis functions. This method not only allows us to analytically and compactly represent the object; it also confers on us the ability to overcome calibration-related noise that originates from inaccurate acquisition parameters. This essentially implicit regularization leads to a highly robust and scalable reconstruction, surpassing other traditional methods. In our results we first demonstrate the ability of the method to compactly represent complex objects. We then show that our reconstruction method is robust both to a small number of measurements and to noise in the acquisition parameters. Finally, we demonstrate our reconstruction abilities from diverse modalities such as volume slices obtained from liquid displacement (similar to CT scans and X-rays) and visual measurements obtained from shape silhouettes as well as point clouds.

Keyframe extraction using Pearson correlation coefficient and color moments

Keyframe extraction plays a significant role in wide variety of real-time video processing applications such as video summarization, video management and retrieval, etc. A keyframe captures the whole content of its shot and does not contain any redundant information. The keyframe extraction algorithms are facing challenges due to different visual characteristics in videos of different categories. Therefore, a single feature is not enough to capture visual characteristics of a variety of videos. In order to tackle this problem, we propose an approach of keyframe extraction that uses hybridization of features. In the present article, we propose a novel shot detection-based keyframe extraction algorithm based on combination of two features: one is Pearson correlation coefficient (PCC) and other is color moments (CM). The linear transformation invariance property of PCC facilitates the proposed algorithm to work well under varying lighting conditions. On the other hand, the scale and rotation invariance properties of color moments are beneficial for representation of complex objects that may be present in different poses and orientations. These sustained reasons support the combination of these two features, which brings significant benefits for keyframe extraction in the proposed method. The proposed method detects shot boundaries by employing combo feature set (PCC and CM). From each shot, the frame with highest mean and standard deviation is selected as keyframe. Furthermore, another important contribution is that we developed a new dataset by collecting the videos of different categories such as movies, news, serials, animations and personal interviews and made it available online. The proposed method is experimented on three datasets: two publicly available datasets and one dataset developed by us. The performance of the proposed method on these datasets has been evaluated on the basis of different evaluation parameters: figure of merit, detection percentage, accuracy, and missing factor. Principal advantage of proposed work lies in the fact that it is capable to detect both the abrupt and gradual shot transitions. In real-time videos, it is common to have abrupt and small transitions. The experimental results show the superior performance of the proposed method over the other state-of-the-art methods.

SEMANTIC CORE OF DIGITAL PLATFORM

This article gives a generalized description of the integrated complex of declarative and procedural means, which together provide a consistent information (semantic) representation of complex objects at all stages of their life cycle. Knowledge (facts) is represented by a system of ontologies of different levels, and the structure - by taxonomies and classifications. Ontology is defined as a system of three interrelated systems (functional, conceptual and sign), and the system of taxonomies represents classes of objects and processes’ characteristic of the main "coordinates" of activity. Information retrieval, as a component of the semantic core, is considered as a complex self-consistent process of constructing new knowledge, where knowledge is information (texts of found documents) associated with the context of the task and the user's views. Such context is purposefully or indirectly determined by the user by means of predefined semantic structures (taxonomies, ontologies) or by means of dynamically formed components (dictionaries, samples, etc.). This is the essence of the semantic cognitive search, where the system not only implements the selection of documents by traditional retrieval methods, but also forms an image of information needs, which, in turn, will allow the system to synthesize complex, aspect-oriented answers. Automated technologies of linguistic support based on distributivestatistical analysis of both object knowledge and implicit knowledge flows (extracted by the system in the interaction process) and components of conceptual-terminological systems are proposed. Scientific and educational potential can be widely used to create a basic component of information and linguistic support in the early stages. The tools presented in the article are tested in the framework of the developed software xIrbis-ML, designed for semantic search of data in knowledge bases for complex engineering objects.

Decoding and Compression of Channel and Scene Objects for Spatial Audio

Sound fields can be encoded with a fixed number of signals, using microphones or panning functions. The sound field may later be reproduced approximately by decoding the signals to a loudspeaker array. The Stereo and Ambisonic systems provide examples. A framework is presented for addressing general questions about such encodings. The first problem considered is the conversion between encodings. The solution is applied to the decoding of scene encodings to a loudspeaker array. This is generalized to the decoding of subscenes where the resolution is focused in an angular window. Within an object-based audio framework such subscenes are useful for representing complex objects without using all the channels required for a full scene. The second problem considered is the compression of a scene encoding to a smaller encoding, from which the original can be reconstructed. The spatial distribution of compression error can be controlled.

Adaptation sharpens object representations: Evidence from shape discrimination thresholds

Abstract One potential benefit of neural adaptation is the sharpening of neural representations to optimize performance in changing environments. This has been previously observed for low-level features such as color and orientation. Here we test whether adaptation sharpens complex object representations by characterizing the effect of adaptation on behavioral shape discrimination thresholds. We constructed a parametric 3D shape space whose axes correspond to different frequencies and orientations of sinusoidal modulations of a sphere. Two distinct reference shapes (A and B) were selected from this space, along with two clouds of fifty stimuli sampled near each reference by jittering the amplitude and orientation components of the references. Test stimuli were derived from a morph continuum between shapes A and B. In separate blocks, observers were first adapted to either cloud A or B with an initial 1-minute RSVP stream of adaptors, and then performed approximately 100 interleaved match-to-sample trials for the two references. Each trial began with a 5-second top-up adaptation, followed by a test stimulus from the morph continuum flashed for 200 ms at fixation, followed by either reference A or B and the same test item displayed on both sides of fixation until response. Observers indicated which item in the second interval matched in shape to the item in the first interval. Differences between the test and reference stimuli were controlled separately for the two references by interleaved staircases that converged on the 79% discrimination threshold. Results (N=3) indicate significant adaptation for both shape clouds: thresholds for reference A were lower after adaptation to cloud A compared to cloud B (median improvement 27%, range 14%-180%), and vice versa for reference B (median improvement 28%, range 20%-34%). These results offer novel evidence for the sharpening hypothesis in the perception of object shape. Meeting abstract presented at VSS 2015.

Age-Related Deficits in Conjunctive Representation of Complex Objects

Although some evidence is consistent with the notion that distinct cortical systems support memory and perception, mounting evidence supports a representational-hierarchical view of cognition, which posits that distinctions lie in simple feature representations versus more complex conjunctive representations of many stimulus features simultaneously. Thus, typical memory tasks engage different regions from typical perception tasks because they inherently test information on opposing ends of this continuum. Memory deficits are reliably reported with age, but the tasks used to make these conclusions predominantly rely on conjunctive representations. To test the extent to which age-related deficits may be accounted for by perceptual processing, this study investigated discriminations involving conjunctive representations in older adults. Results show that adults aged 50 to 77 are impaired, relative to their younger counterparts, on discriminations requiring feature conjunctions, but not simple feature representations. These findings support recent data showing an agerelated decline in the ability to form conjunctive representations. Furthermore, these data suggest that some ‘mnemonic’ deficits associated with age may in fact be the result of deficits in perception rather than memory.

Age-Related Deficits in Conjunctive Representation of Complex Objects

Age-Related Deficits in Conjunctive Representation of Complex Objects

The temporal evolution of coarse location coding of objects: evidence for feedback.

Objects occupy space. How does the brain represent the spatial location of objects? Retinotopic early visual cortex has precise location information but can only segment simple objects. On the other hand, higher visual areas can resolve complex objects but only have coarse location information. Thus coarse location of complex objects might be represented by either (a) feedback from higher areas to early retinotopic areas or (b) coarse position encoding in higher areas. We tested these alternatives by presenting various kinds of first- (edge-defined) and second-order (texture) objects. We applied multivariate classifiers to the pattern of EEG amplitudes across the scalp at a range of time points to trace the temporal dynamics of coarse location representation. For edge-defined objects, peak classification performance was high and early and thus attributable to the retinotopic layout of early visual cortex. For texture objects, it was low and late. Crucially, despite these differences in peak performance and timing, training a classifier on one object and testing it on others revealed that the topography at peak performance was the same for both first- and second-order objects. That is, the same location information, encoded by early visual areas, was available for both edge-defined and texture objects at different time points. These results indicate that locations of complex objects such as textures, although not represented in the bottom-up sweep, are encoded later by neural patterns resembling the bottom-up ones. We conclude that feedback mechanisms play an important role in coarse location representation of complex objects.

Probing intermediate stages of shape processing

The visual system provides a representation of what and where objects are. This entails parsing the visual scene into distinct objects. Initially, the visual system encodes information locally. While interactions between adjacent cells can explain how local fragments of an objectÂ’s contour are extracted from a scene, more global mechanisms have to be able to integrate information beyond that of neighbouring cells to allow for the representation of extended objects. This talk will examine the nature of intermediate-level computations in the transformation from discrete local sampling to the representation of complex objects. Several paradigms were invoked to study how information concerning the position and orientation of local signals is combined: a shape discrimination task requiring observers to discriminate between contours; a shape coherence task measuring the number of elements required to detect a contour; a shape illusion in which positional and orientational information is combined inappropriately. Results support the notion of mechanisms that integrate information beyond that of neighbouring cells and are optimally tuned to a range of different contour shapes. Global integration is not restricted to central vision: peripheral data show that certain aspects of this process only emerge at intermediate stages. Moreover, intermediate processing appears vulnerable to damage. Diverse clinical populations (migraineurs, pre-term children and children with Cortical Visual Impairment) show specific deficits for these tasks that cannot be accounted for by low-level processes. Taken together, evidence is converging towards the identification of an intermediate level of processing, at which sensitivity to global shape attributes emerges. Meeting abstract presented at VSS 2014

Probing intermediate stages of shape processing.

The visual system provides a representation of what and where objects are. This entails parsing the visual scene into distinct objects. Initially, the visual system encodes information locally. While interactions between adjacent cells can explain how local fragments of an object's contour are extracted from a scene, such computations are ill suited to capture extended objects. This article reviews some of the evidence in favor of intermediate-level computations, tuned to the shape of an object, in the transformation from discrete local sampling to representation of complex objects. Two main paradigms, employed to study how information about the position and orientation of local signals are combined at intermediate levels, are considered here: a shape detection task (measuring the number of signal elements required to detect a shape in noise) and a shape discrimination task (requiring observers to discriminate between shapes). Results support the notion of global mechanisms that integrate information beyond neighboring cells and are optimally tuned to a range of different shapes. These intermediate processing stages appear vulnerable to damage. Diverse clinical conditions (amblyopia, macular disease, migraine, premature birth) show specific deficits for these tasks. Taken together, evidence is converging in favor of intermediate levels of processing, at which sensitivity to the global shape of objects emerges.

Visual complexity exerts opposing effects on object categorization and identification

Visual complexity is a quality of objects that can be understood in terms of information richness: The more complex an object is, the more information it contains. Visual complexity is therefore likely to have a considerable impact on visual object processing. Here we demonstrate that visual complexity does indeed affect visual object processing but that it exerts different effects depending on task requirements. In tasks that require little differentiation among objects (superordinate categorization), reaction times decrease linearly as a function of increasing visual complexity. In tasks that require more differentiation (difficult object decision), complexity exerts the opposite effect. We argue that this dynamic interplay between visual complexity and object individuation can be explained by assuming that complexity affects two operations in visual object recognition differentially: When perceptual representations are formed it exerts a negative effect because representations of complex objects are harder to assemble than representations of less complex objects, but once formed, it exerts a positive effect because complex objects—due to their information richness—activate fewer candidate representations in visual long-term memory that need to be differentiated in the recognition process.