Object Perception Research Articles

Collaborative Perception-The Missing Piece in Realizing Fully Autonomous Driving.

Environment perception plays a crucial role in enabling collaborative driving automation, which is considered to be the ground-breaking solution to tackling the safety, mobility, and sustainability challenges of contemporary transportation systems. Despite the fact that computer vision for object perception is undergoing an extraordinary evolution, single-vehicle systems' constrained receptive fields and inherent physical occlusion make it difficult for state-of-the-art perception techniques to cope with complex real-world traffic settings. Collaborative perception (CP) based on various geographically separated perception nodes was developed to break the perception bottleneck for driving automation. CP leverages vehicle-to-vehicle and vehicle-to-infrastructure communication to enable vehicles and infrastructure to combine and share information to comprehend the surrounding environment beyond the line of sight and field of view to enhance perception accuracy, lower latency, and remove perception blind spots. In this article, we highlight the need for an evolved version of the collaborative perception that should address the challenges hindering the realization of level 5 AD use cases by comprehensively studying the transition from classical perception to collaborative perception. In particular, we discuss and review perception creation at two different levels: vehicle and infrastructure. Furthermore, we also study the communication technologies and three different collaborative perception message-sharing models, their comparison analyzing the trade-off between the accuracy of the transmitted data and the communication bandwidth used for data transmission, and the challenges therein. Finally, we discuss a range of crucial challenges and future directions of collaborative perception that need to be addressed before a higher level of autonomy hits the roads.

Narrative re/defining of the cultural identity of Bosnia and Herzegovina in the literature of the Bosnia and Herzegovina diaspora

Starting from the hypothesis that spatial-cultural displacement necessarily conditions the deconstruction of cultural identity, the work, based on the theoretical assumptions of imagology and intercultural hermeneutics, considers (inter)cultural aspects of the narrative de/construction of the cultural identity of Bosnia and Herzegovina in the literature of the Bosnian diaspora. Finding examples in the novels "The Book of My Lives" and "My Parents: An Introduction" by Aleksandar Hemon, the novel "Nowhere, out of nowhere" by Bekim Sejranović and the collection of stories "Well, to start over" by Cecilija Toskić, the work tries to point out the spatial-cultural displacement and intercultural processes not only as factors of crosscultural adaptation, acculturation and assimilation, but also as factors that enable objective perception and narrative representation of Bosnian cultural identity. The paper concludes that the experience of spatialcultural displacement, represented in the above-mentioned works, results in the narrative construction of hybrid identities squeezed between two (or more) cultures, and that precisely this position of (non)belonging will enable a narrative re/definition of the cultural identity of Bosnia and Herzegovina.

How race influences perceptions of objectivity and hiring preferences

Objectivity norms can act as a source of mistrust of marginalized voices within organizations. In this paper, we study White evaluators' perceptions of Black applicants' objectivity and hireability in a field where objectivity is considered imperative: journalism. We predicted that Black journalists will be viewed as less objective and as having more ingroup bias regarding racial issues coverage compared to White journalists. Importantly, we expected these patterns to emerge in opposition to hiring judgments that would, overall, favor Black journalists over White journalists for roles reporting on racial issues due to perceptions of their racial expertise. Meta-analyses of three samples (N = 1725) found that White perceivers rated Black journalists as less objective and more biased, yet more racially expert and hireable, than White journalists. In follow up correlational analysis we found consistent evidence that perceptions of racial expertise positively impact hiring judgments for Black journalists even as perceptions of objectivity suppress hiring preferences. Overall, these studies illuminate the costs of racial marginalization in primarily-White workplaces, even when there are apparent hiring advantages, and demonstrate potential barriers to inclusion and accurate racial issues coverage.

Breathable Metal-Organic Framework Enhanced Humidity-Responsive Nanofiber Actuator with Autonomous Triboelectric Perceptivity.

Autonomous object manipulation and perception with environmental factor-triggered and self-powered actuation is one of the most attractive directions for developing next-generation soft robotics with a smart human-machine-environment interface. Humidity, as a sustainable energy source ubiquitous in the surrounding environment, can be used for triggering smart grippers. In this work, it is proposed that by contacts between the gripper and objects upon humidity-induced actuation, real-time distinguishable triboelectric signals can be generated to realize the humidity-driven object manipulation and identification. Herein, a thermo-modified electrospun polyvinylpyrrolidone/poly(acrylic acid)/MIL-88A (T-PPM) nanofibrous film with micro-to-nano cross-scale porosity is developed, and a bilayer humidity-responsive actuator (T-HRA) was designed, mimicking the tamariskoid spikemoss to enhance the humidity-driven actuation. The breathing effect of MIL-88A and hierarchical porous structure of the T-PPM facilitate moisture diffusion and offer huge actuation (2.41 cm-1) with a fast response (0.084 cm-1 s-1). For autonomous object manipulation perception, T-PPM was verified as a tribo-positive material located between paper and silk. Accordingly, the T-HRA was demonstrated as a smart soft gripper that generates a different electric signal upon contact with objects of different material. This work proposes a concept of soft robots that are interactive with the environment for both autonomous object manipulation and information acquisition.

Cyber Mobility Mirror: A Deep Learning-Based Real-World Object Perception Platform Using Roadside LiDAR

Object perception plays a fundamental role in Cooperative Driving Automation (CDA) which is regarded as a revolutionary promoter for next-generation transportation systems. However, the vehicle-based perception may suffer from the limited sensing range and occlusion as well as low penetration rates in connectivity. In this paper, we propose <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">Cyber Mobility Mirror</i> ( <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">CMM</i> ), a next-generation real-world object perception system for 3D object detection, tracking, localization, and reconstruction, to explore the potential of roadside sensors for enabling CDA in the real world. The CMM system consists of six main components: i) the data pre-processor to retrieve and preprocess the raw data; ii) the roadside 3D object detector to generate 3D detection results; iii) the multi-object tracker to identify detected objects; iv) the global locator to generate geo-localization information; v) the mobile-edge-cloud-based communicator to transmit perception information to equipped vehicles, and vi) the onboard advisor to reconstruct and display the real-time traffic conditions. An automatic perception evaluation approach is proposed to support the assessment of data-driven models without human-labeling requirements and a CMM field-operational system is deployed at a real-world intersection to assess the performance of the CMM. Results from field tests demonstrate that our CMM prototype system can achieve 96.99% precision and 83.62% recall for detection and 73.55% ID-recall for tracking. High-fidelity real-time traffic conditions (at the object level) can be geo-localized with a root-mean-square error (RMSE) of <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$0.69m$</tex-math> </inline-formula> and <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$0.33m$</tex-math> </inline-formula> for lateral and longitudinal direction, respectively, and displayed on the GUI of the equipped vehicle with a frequency of <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$3-4 Hz$</tex-math> </inline-formula> .

Substandard vocabulary in the sublanguage of the coal mining industry

The article is devoted to the multidimensional characterization of the substandard vocabulary of the sublanguage of the coal mining industry. The functional-semiotic and structural heterogeneity of sublanguage slang has been established on the concrete material of the modern miner's sublanguage. The stratification categories of substandard vocabulary (professionalisms, interprofessionalisms, quasi-professionalisms, professional jargonisms), lexico-thematic groups, functions are described in detail. Special attention is paid to the characteristics of the ways and sources of the formation of special vocabulary of the lower case of the miners sublanguage slang, the main ways of forming the analyzed vocabulary are described in detail: the creation of a formally new unit and various semantic changes of words. It is concluded that the emergence and functioning of a developed system of substandard vocabulary in the miners' sublanguage is associated with the action of two main trends: the tendency to nominativeness, due to the need to overcome lacunarity and linguistic redundancy, as well as the tendency to portability, imagery and expressiveness. The thesis is put forward and supported by linguistic material that the special vocabulary of the sublanguage slang of the coal mining industry, considered in the socio-cultural aspect, represents the linguistic embodiment of the world of a special professional subculture, demonstrates the peculiarities of perception of objects of the outside world, ways of interacting with them, cultural stereotypes, values, motives and attitudes common to representatives of this profession.

Dynamic Rigid Bodies Mining and Motion Estimation Based on Monocular Camera.

Dynamic object perception is an important yet challenging direction in the field of robot navigation. Without any prior knowledge about motion and objects, a novel dynamic rigid bodies mining and motion estimation method based on monocular camera is proposed in this article. Different from the existing works based on sampling that associate feature points to motion hypotheses according to the reprojection errors, our work endeavors to find the intrinsic relevance among motion hypotheses. To represent this relevance, the concept of the probabilistic field on the Lie group Sim(3) manifold is introduced, which is established using random sampling. It provides a computable way for the regions on the manifold where rigid bodies possibly appear. The probability of a motion hypothesis falling on a region is expressed by its confidence. The regions with large confidences in the probabilistic field are selected as potential rigid bodies, whose corresponding feature points are further sampled for pose calculation. As a result, the randomness of sampling is reduced and the inliers for possible rigid bodies are enhanced, which guarantees the accuracy of motion estimation. On this basis, the tracking of rigid bodies is achieved. The proposed method distinguishes the feature points of dynamic objects with 3-D motion from those in the static background, thus enabling simultaneous localization and mapping (SLAM) to be initialized in dynamic environments. The experimental results on the KITTI, Hopkins 155, and MTPV62 datasets demonstrate the effectiveness. Comparison experiments indicate that our method outperforms the other methods in sensitivity of dynamic objects perception.

Serial dependence in visual perception: A meta-analysis and review.

Positive sequential dependencies are phenomena in which actions, perception, decisions, and memory of features or objects are systematically biased toward visual experiences from the recent past. Among many labels, serial dependencies have been referred to as priming, sequential dependencies, sequential effects, or serial effects. Despite extensive research on the topic, the field still lacks an operational definition of what counts as serial dependence. In this meta-analysis, we review the vast literature on serial dependence and quantitatively assess its key diagnostic characteristics across several different domains of visual perception. The meta-analyses fully characterize serial dependence in orientation, face, and numerosity perception. They show that serial dependence is defined by four main kinds of tuning: serial dependence decays with time (temporal-tuning), it depends on relative spatial location (spatial-tuning), it occurs only between similar features and objects (feature-tuning), and it is modulated by attention (attentional-tuning). We also review studies of serial dependence that report single observer data, highlighting the importance of individual differences in serial dependence. Finally, we discuss a range of outstanding questions and novel research avenues that are prompted by the meta-analyses. Together, the meta-analyses provide a full characterization of serial dependence as an operationally defined family of visual phenomena, and they outline several of the key diagnostic criteria for serial dependence that should serve as guideposts for future research.

Rethinking how children individuate objects: spatial indexicals in early development

The current understanding of cognitive development rests on the premise that infants can individuate objects early on. However, the so-called object-first account faces severe difficulties explaining extant empirical findings in object individuation tasks while alternative, more parsimonious explanations are available. In this paper, we assume that children start as feature-thinkers without being able to individuate objects and show how this ability can be learned by thinkers who do not already implicitly possess the notion of an object. Based on Tugendhat's ideas on the relation between singular terms and object reference, we argue that spatial indexicals comprise the fundamental means of object individuation and describe how feature thinkers might acquire the complex substitutional system of spatial indexicals. In closing, two accounts of object cognition that do not rely on symbolic capacities, namely Pylyshyn's FINST indexes and Burge's perceptual objectivity, are critically discussed.

Signs of deliberative applications in the ancient Arabic linguistic lessondièctics as a model

We recently spotted a relationship problem of Arab linguistic heritage to linguistics. This heritage which is still standing up to the latest linguistic theories; The underlying origins and disciplines of the theory of Arabic grammar established by the sculptor in the study and analysis of the linguistic phenomenon. One of the most important features of this theory is what is common in the textbooks of the grammar. So our research is betting on extrapolating the various scientific norms on which the ancient Arabic linguistic lesson was based. and to counteract this with the various theories and scientific principles defined by contemporary Western linguistics in their various synthetic, semantic and deliberative dimensions, Focused on " the deictic expressions " as a witness in our dissertation, Finally, let us present an objective perception of our linguistic heritage that is abundant away from all provisions of value or subjectivity.

Towards History Itself: On the Production of Marx’s Concept of Perceptual Consciousness

Towards history itself is the basic spirit of Marx’s philosophy of history. The process of Marx’s restoration of human history is both a process of continual sublation and transcendence of traditional philosophy and a process of continual return to the realistic sensual life. The work of historical restoration that Marx accomplished created the core of its philosophy of history, perceptual consciousness. Perceptual consciousness is not simply equivalent to the relevant concepts of Feuerbach’s philosophy, but always corresponds to human perceptual activity and forms an organic unity with it. It overcomes the grasp of human conceptualisation and absoluteness and has a certain phenomenological character with a strong orientation towards history itself. Marx’s spiritual effort to restore history has roughly gone through the following three steps:Firstly, the reduction from self-consciousness to perceptual object. Secondly, the reduction from perceptual object to perceptual practice. Thirdly, the comprehensive development of perceptual consciousness. Through the generation of the concept of perceptual consciousness, Marx blazed a trail of reality to a state of historical clarity and completed the clarification of the original meaning of history.

Triangle-Mesh-Rasterization-Projection (TMRP): An Algorithm to Project a Point Cloud onto a Consistent, Dense and Accurate 2D Raster Image.

The projection of a point cloud onto a 2D camera image is relevant in the case of various image analysis and enhancement tasks, e.g., (i) in multimodal image processing for data fusion, (ii) in robotic applications and in scene analysis, and (iii) for deep neural networks to generate real datasets with ground truth. The challenges of the current single-shot projection methods, such as simple state-of-the-art projection, conventional, polygon, and deep learning-based upsampling methods or closed source SDK functions of low-cost depth cameras, have been identified. We developed a new way to project point clouds onto a dense, accurate 2D raster image, called Triangle-Mesh-Rasterization-Projection (TMRP). The only gaps that the 2D image still contains with our method are valid gaps that result from the physical limits of the capturing cameras. Dense accuracy is achieved by simultaneously using the 2D neighborhood information (rx,ry) of the 3D coordinates in addition to the points P(X,Y,V). In this way, a fast triangulation interpolation can be performed. The interpolation weights are determined using sub-triangles. Compared to single-shot methods, our algorithm is able to solve the following challenges. This means that: (1) no false gaps or false neighborhoods are generated, (2) the density is XYZ independent, and (3) ambiguities are eliminated. Our TMRP method is also open source, freely available on GitHub, and can be applied to almost any sensor or modality. We also demonstrate the usefulness of our method with four use cases by using the KITTI-2012 dataset or sensors with different modalities. Our goal is to improve recognition tasks and processing optimization in the perception of transparent objects for robotic manufacturing processes.

Aging and temporal integration in the visual perception of object shape

It has been known for more than 160 years that highly occluded objects that would normally be visually unrecognizable can be successfully identified when they move. This anorthoscopic perception relies on the visual system’s ability to integrate information over time to complete the perception of an entire object’s shape. In this experiment, 16 younger and older adults (mean ages were 20.5 and 74.6 years, respectively) were familiarized with the (unoccluded) shapes of five naturally-shaped objects (bell peppers, Capsicum annuum) until they could be easily identified (i.e., with accuracies of at least 90 percent correct). All observers then viewed the stimulus objects anorthoscopically as they moved behind narrow slits; only small object fragments could be seen at any given time, because the objects were almost totally occluded from view. Even though the object identification performance for all observers was equivalent when whole object shapes were visible, a large age-related deficit in object identification emerged during anorthoscopic viewing such that the younger adults’ identification performance was 45.4 percent higher than that of the older adults. This first ever study of aging and anorthoscopic perception demonstrates that there is an age-related deficit in performing the temporal integration needed for successful object recognition.

Multisensory perception constrains the formation of object categories: a review of evidence from sensory-driven and predictive processes on categorical decisions.

Although object categorization is a fundamental cognitive ability, it is also a complex process going beyond the perception and organization of sensory stimulation. Here we review existing evidence about how the human brain acquires and organizes multisensory inputs into object representations that may lead to conceptual knowledge in memory. We first focus on evidence for two processes on object perception, multisensory integration of redundant information (e.g. seeing and feeling a shape) and crossmodal, statistical learning of complementary information (e.g. the 'moo' sound of a cow and its visual shape). For both processes, the importance attributed to each sensory input in constructing a multisensory representation of an object depends on the working range of the specific sensory modality, the relative reliability or distinctiveness of the encoded information and top-down predictions. Moreover, apart from sensory-driven influences on perception, the acquisition of featural information across modalities can affect semantic memory and, in turn, influence category decisions. In sum, we argue that both multisensory processes independently constrain the formation of object categories across the lifespan, possibly through early and late integration mechanisms, respectively, to allow us to efficiently achieve the everyday, but remarkable, ability of recognizing objects. This article is part of the theme issue 'Decision and control processes in multisensory perception'.

TwP: Two-stage projection framework with manifold constraint for image reconstruction

As an extensively studied task in computer vision, image reconstruction aims to reconstruct original data from acquired measurements. It is a highly ill-posedness inverse problem since a countably infinite number of feasible solutions exist. To achieve high-quality reconstruction performance, we propose a two-stage projection (TwP) framework built upon the projection technique and manifold constraint. We first exploit the generalized non-convex alternating projection method for image reconstruction in the pre-reconstruction stage, that incorporates the image manifold constraint implicitly imposed by deep denoiser. It provides a unified method to solve linear/non-linear inverse problems in a plug-and-play manner and deliver a good estimation. In the post-processing stage, a feed-forward network fine-tuned for a specific task is utilized to reproject the pre-reconstruction results onto the low-dimensional image manifold, which enables the outcomes to be in or closest to the manifold. Extensive experiments across various image reconstruction tasks, such as single-photon imaging, image compressive sensing, and image deblurring, demonstrate that the proposed TwP delivers promising results against some existing state-of-the-art algorithms in terms of objective and visual perceptions.

Similarity Binds (and Bends) the Perception of Objects

Similarity Binds (and Bends) the Perception of Objects

Characterizing and decoding visual percepts of real objects in a blind individual using fMRI

Characterizing and decoding visual percepts of real objects in a blind individual using fMRI

Size perception of 3D objects in general poses

Size perception of 3D objects in general poses

Towards reliable object representation via sparse directional patches and spatial center cues

In the process of image understanding, the human visual system (HVS) performs multiscale analysis on various objects. HVS primarily focuses on marginally conspicuous image patches located within or around distinct objects rather than scanning the image pixels point by point. Inspired by the HVS mechanism, in this paper, we aimed to describe and exploit multiscale decomposition-based patch detection models for automatic visual feature representation and object localization in images. Our investigation into mimicking and modeling the HVS to capture conspicuous sparse patches and their spatial distribution clues makes a profound contribution to the automatic comprehension and characterization of images by machines. This study demonstrates that the sparse patch-based visual representation with spatial center cues is intrinsically tolerant to object positioning and understanding beyond object variations in spatial position, multiresolution, and chrominance, which has significant implications for many vision-based automatic object grabbing and perception applications, such as robotics, human‒machine interaction, and unmanned aerial vehicles (UAVs).

The functional unit of neural circuits and its relations to eventual sentience of artificial intelligence systems

The authors discuss a functional unit of neuronal circuits, crucial in both brain structure and artificial intelligence (AI) systems. These units recognize objects denoted by single words or verbal phrases and are responsible for object perception, memorization of image patterns, and retrieval of those patterns as imagination. Functional neuronal units, activated by the working memory system, contribute to problem-solving. The paper highlights the authors' achievements in developing a theory for these functional units, known as 'the equimerec - units', which combine 'the threshold logic unit' and 'the feedback control loop'. The authors emphasize the importance of this theory, especially in the context of highly advanced language-based AI systems. Additionally, the authors note that the functional units' essence relies on backpropagation connections, causing impulse circulation in closed circuits, ultimately leading to the emergence of an electromagnetic field. This phenomenon explains the long-known existence of the human brain's endogenous electromagnetic field. The authors suggest that a similar field likely arises in AI systems. In light of Joe McFadden's "Conscious Electromagnetic Information Field Theory (cemi)", the authors argue that the potential emergence of self-awareness in AI systems deserves due attention.