Search · four archives
Search · four archives
23 papers · ranked by Valyu relevance
Thimal Kempitiya, Damminda Alahakoon, Evgeny Osipov, Sachin Kahawala + 4 more
'Daswin De Silva' 'Gang Hu' 'Weiguo Zhao' 'Zhenxing Zhang'] We propose a new nature- and neuro-science-inspired algorithm for spatiotemporal learning and prediction based on sequential recall and vector symbolic architecture. A key novelty is the learning of spatial and temporal patterns as decoupled concepts where the…
L. Bonetti, E. Brattico, S.E.P. Bruzzone, G. Donati + 4 more
Pattern recognition is a major scientific topic. Strikingly, while machine learning algorithms are constantly refined, the human brain emerges as an ancestral biological example of such complex procedure. However, how it transforms sequences of single objects into meaningful temporal patterns remains elusive. Using…
Gowtham Atluri, Anuj Karpatne, Vipin Kumar
Large volumes of spatio-temporal data are increasingly collected and studied in diverse domains including, climate science, social sciences, neuroscience, epidemiology, transportation, mobile health, and Earth sciences. Spatio-temporal data differs from relational data for which computational approaches are developed…
Itsaso Rodríguez-Moreno, José María Martínez-Otzeta, Basilio Sierra, Igor Rodriguez + 1 more
'Igor Rodriguez' 'Ekaitz Jauregi'] Video activity recognition, although being an emerging task, has been the subject of important research efforts due to the importance of its everyday applications. Surveillance by video cameras could benefit greatly by advances in this field. In the area of robotics, the tasks of…
Edmund Chong, Monica Moroni, Christopher Wilson, Shy Shoham + 2 more
How does neural activity generate perception? The spatial identities and temporal latencies of activated units correlate with external sensory features, but finding the principal activity subspace that is consequential for perception, remains challenging. We trained mice to recognize synthetic odors constructed from…
Jeiran Choupan, Yaniv Gal, Pamela K. Douglas, Mark S. Cohen + 2 more
The importance of spatiotemporal feature selection in fMRI decoding studies has not been studied exhaustively. Temporal embedding of features allows the incorporation of brain activity dynamics into multivariate pattern classification, and may provide enriched information about stimulus-specific response patterns and…
Tony Lindeberg
A receptive field constitutes a region in the visual field where a visual cell or a visual operator responds to visual stimuli. This paper presents a theory for what types of receptive field profiles can be regarded as natural for an idealized vision system, given a set of structural requirements on the first stages of…
Gisele Helena Barboni Miranda, Jeaneth Machicao, Odemir Martinez Bruno
'Odemir Martinez Bruno'] Network science is an interdisciplinary field which provides an integrative approach for the study of complex systems. In recent years, network modeling has been used for the study of emergent phenomena in many real-world applications. Pattern recognition in networks has been drawing attention…
Ylva Jansson, Tony Lindeberg
This work presents a first evaluation of using spatiotemporal receptive fields from a recently proposed time-causal spatio-temporal scale-space framework as primitives for video analysis. We propose a new family of video descriptors based on regional statistics of spatio-temporal receptive field responses and evaluate…
Kaoutar Mouhcine, Nabila Zrira, Issam Elafi, Ibtissam Benmiloud + 1 more
'Haris Ahmad Khan'] In recent years, dynamic texture classification has become an important task for computer vision. This is a challenging task due to the unknown spatial and temporal nature of dynamic texture. To overcome this challenge, we investigate the potential of deep learning approaches and propose a novel…
Muhammad Yaqoob, Volker Steuber, Borys Wróbel
Most sensory stimuli are temporal in structure. How action potentials encode the information incoming from sensory stimuli remains one of the central research questions in neuroscience. Although there is evidence that the precise timing of spikes represents information in spiking neuronal networks, information…
Rohan Ghosh, Anupam K. Gupta, Siyi Tang, Alcimar Barbosa Soares + 1 more
'Nitish V. Thakor'] Abstract—Unlike conventional frame-based sensors, eventbased visual sensors output information through spikes at a high temporal resolution. By only encoding changes in pixel intensity, they showcase a low-power consuming, low-latency approach to visual information sensing. To use this information…
Brian Ta, Maria E. M. M. Silva, Kelly Bartlett, Umaima Afifa + 43 more
Human vision has a remarkable ability to recognize complex 3D objects such as faces that appear with any size and 3D orientations at any 3D location. If we initially memorize a face only with a normalized size and viewed from directly head on, the direct comparison between the one-sized memory and a new incoming image…
Kumar S. Ray, Sayandip Dutta, Anit Chakraborty
— In this paper, we address the basic problem of recognizing moving objects in video images using SP Theory of Intelligence. The concept of SP Theory of Intelligence which is a framework of artificial intelligence, was first introduced by Gerard J Wolff, where S stands for Simplicity and P stands for Power. Using the…
Núbia Rosa da Silva, Odemir Martinez Bruno
Some mixtures, such as colloids like milk, blood, and gelatin, have homogeneous appearance when viewed with the naked eye, however, to observe them at the nanoscale is possible to understand the heterogeneity of its components. The same phenomenon can occur in pattern recognition in which it is possible to see…
Authors not listed
Localized detection of hydrogen permeation in steel membranes is crucial for practical applications but remains challenging. We present a reflective microscopy (RM) approach combined with machine learning (ML)-driven image analysis to address this issue. Hydrogen permeation in press-hardened steel alters the…
Caominh Le, Samantha Pedersen, Nathaniel Chen, Jonathan Chan + 28 more
Human vision has a remarkable ability to recognize complex 3D objects such as faces that appear at any size and 3D orientations at any 3D location. If we initially memorize a face only with a normalized size upfront at the object center, the direct comparison between the one-sized memory and an incoming new image would…
Sonia Poltoratski, Kendrick Kay, Dawn Finzi, Kalanit Grill-Spector
Spatial processing by receptive fields is a core property of the visual system. However, it is unknown how spatial coding in high-level regions contributes to recognition behavior. As face inversion is thought to disrupt typical ‘holistic’ processing of information in faces, we mapped population receptive fields (pRFs)…
Authors not listed
X-ray diffraction (XRD) is an immediate and powerful characterization technique that provides detailed information on the lattice structure and long-range order in crystalline materials. In recent decades, the quality and quantity of available crystal structure data has exploded, in large part due to the advent of…
Matti Pietikäinen, Guoying Zhao
Texture is an important characteristic for many types of images. In recent years very discriminative and computationally efficient local texture descriptors based on local binary patterns (LBP) have been developed, which has led to significant progress in applying texture methods to different problems and applications.…
Authors not listed
Machine olfaction—the artificial replication of the sense of smell—faces significant challenges due to the absence of large, standardized training datasets. Unlike vision, language, and audio models, which benefit from extensive corpora such as ImageNet, GLUE, and AudioSet, olfaction lacks scaled equivalents and…
Authors not listed
The global drive towards net-zero has accelerated the adoption of carbon fibre reinforced polymers (CFRP) for lightweight structures in various sectors such as aerospace, automotive, energy and biomedical. Mechanical machining of CFRP is often necessary to meet dimensional or assembly-related requirements. However…
Authors not listed
This research presents a novel approach to obstacle detection during navigation using a combination of Convolutional Neural Networks (CNNs) and Long Short-Term Memory (LSTM) networks. The primary objective is to generate accurate image captions that describe the content of images, which is crucial for applications such…