Unsupervised Panoptic Segmentation

10.32920/17312189 ◽

2021 ◽

Author(s):

Sajeel Aziz

Keyword(s):

Deep Learning ◽

Visual Cortex ◽

Visual System ◽

Unsupervised Clustering ◽

Clustering Methods ◽

Specific Knowledge ◽

Segmentation Approach ◽

Natural Formation ◽

Learning Architectures ◽

The Brain

The contributions of this paper are two-fold. We define unsupervised techniques for the panoptic segmentation of an image. We also define clusters which encapsulate the set of features that define objects of interest inside a scene. The motivation is to provide an approach that mimics natural formation of ideas inside the brain. Fundamentally, the eyes and visual cortex constitute the visual system, which is essential for humans to detect and recognize objects. This can be done even without specific knowledge of the objects. We strongly believe that a supervisory signal should not be required to identify objects in an image. We present an algorithm that replaces the eye and visual cortex with deep learning architectures and unsupervised clustering methods. The proposed methodology may also be used as a one-click panoptic segmentation approach which promises to significantly increase annotation efficiency. We have made the code available privately for review<sup>1</sup>.<div><br></div>

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Blindsight and Unconscious Vision: What They Teach Us about the Human Visual System

The Neuroscientist ◽

10.1177/1073858416673817 ◽

2016 ◽

Vol 23 (5) ◽

pp. 529-541 ◽

Cited By ~ 18

Author(s):

Sara Ajina ◽

Holly Bridge

Keyword(s):

Visual Cortex ◽

Visual System ◽

Primary Visual Cortex ◽

Neural Activity ◽

Human Visual System ◽

Visual Information ◽

Visual Loss ◽

Physiological Conditions ◽

The World ◽

The Brain

Damage to the primary visual cortex removes the major input from the eyes to the brain, causing significant visual loss as patients are unable to perceive the side of the world contralateral to the damage. Some patients, however, retain the ability to detect visual information within this blind region; this is known as blindsight. By studying the visual pathways that underlie this residual vision in patients, we can uncover additional aspects of the human visual system that likely contribute to normal visual function but cannot be revealed under physiological conditions. In this review, we discuss the residual abilities and neural activity that have been described in blindsight and the implications of these findings for understanding the intact system.

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Mammalian Visual System Organization

Oxford Research Encyclopedia of Neuroscience ◽

10.1093/acrefore/9780190264086.013.66 ◽

2017 ◽

Cited By ~ 2

Author(s):

Farran Briggs

Keyword(s):

Visual Cortex ◽

Visual System ◽

Visual Information ◽

Contextual Information ◽

Sensory Information ◽

Visual Image ◽

Brain Structures ◽

Object Representations ◽

System A ◽

The Brain

Many mammals, including humans, rely primarily on vision to sense the environment. While a large proportion of the brain is devoted to vision in highly visual animals, there are not enough neurons in the visual system to support a neuron-per-object look-up table. Instead, visual animals evolved ways to rapidly and dynamically encode an enormous diversity of visual information using minimal numbers of neurons (merely hundreds of millions of neurons and billions of connections!). In the mammalian visual system, a visual image is essentially broken down into simple elements that are reconstructed through a series of processing stages, most of which occur beneath consciousness. Importantly, visual information processing is not simply a serial progression along the hierarchy of visual brain structures (e.g., retina to visual thalamus to primary visual cortex to secondary visual cortex, etc.). Instead, connections within and between visual brain structures exist in all possible directions: feedforward, feedback, and lateral. Additionally, many mammalian visual systems are organized into parallel channels, presumably to enable efficient processing of information about different and important features in the visual environment (e.g., color, motion). The overall operations of the mammalian visual system are to: (1) combine unique groups of feature detectors in order to generate object representations and (2) integrate visual sensory information with cognitive and contextual information from the rest of the brain. Together, these operations enable individuals to perceive, plan, and act within their environment.

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Studies of suppression of epileptic seizures with serotonin-modulating anticonsolidation protein (SMAP)

European Psychiatry ◽

10.1016/s0924-9338(11)72612-8 ◽

2011 ◽

Vol 26 (S2) ◽

pp. 907-907

Author(s):

E.N. Panahova ◽

A.A. Mekhtiev

Keyword(s):

Visual Cortex ◽

Visual System ◽

Inhibitory Activity ◽

Basolateral Amygdala ◽

Epileptic Seizures ◽

Positive Component ◽

Interictal Spikes ◽

Inverse Effect ◽

Evoked Activity ◽

The Brain

Earlier studies revealed that tandem of brain amygdala complex and visual system is related directly to development of a number of the brain pathologies. In the present study the model of amygdala epilepsy in rabbits was formed and on its background the evoked activity in the visual cortex, colliculus superior (CS) and retina was analyzed. Tentative studies showed that penicillin-induced epileptic locus in the basolateral amygdala led to sharp (to 200–300%) enhancement of the evoked potentials (EP) in the cortex and retina, whereas in the CS the responses were suppressed significantly. During seizure fit of V-VI stage, according to Racine scale, in the visual cortex, CS and (that is especially interesting) in the retina the regular interictal spikes were fixed. Intramuscular administration (1 mg/kg) of SMAP, purified earlier from rat brain and being in linear relationship with serotonin, into rabbit led to suppression of seizures 30–40 min later. Administration of polyclonal anti-SMAP antibodies into amygdala (10 μl; 1.5 mg/ml) originally induced clear interictal spikes in the visual cortex, CS and retina, while after 30 min inverse effect was observed: elimination of the initial positive component and strong enhancement of the negative phase in the visual cortex. The latter, probably, indicates to switching on compensatory SMAP synthesis after antibody-mediated SMAP inactivation. So, the results indicate to inhibitory activity of SMAP in cessation of epileptic seizures, while its downregulation may bring to seizure initiation.

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Inhibition of Labyrinthine Nystagmus by Visual Fixation: Effects of Ablation of Visual Cortex and Superior Colliculi

Annals of Otology Rhinology & Laryngology ◽

10.1177/000348947908800321 ◽

1979 ◽

Vol 88 (3) ◽

pp. 419-423

Author(s):

Emil P. Liebman ◽

Joseph U. Toglia

Keyword(s):

Visual Cortex ◽

Visual System ◽

Brain Stem ◽

Light Stimulus ◽

Visual Fixation ◽

Vestibular Nystagmus ◽

Superior Colliculi ◽

Vestibular Testing ◽

Significant Change ◽

The Brain

A study was conducted to destroy two specific areas of the cat's visual system in order to determine if these lesions would affect the visual inhibition of calorically-induced vestibular nystagmus. The occipital visual cortex was removed in eight cats and the superior colliculi were removed bilaterally in nine cats. Postoperative vestibular testing revealed no significant change in the electronystagmography tracings and response to visual fixation. These findings suggest that, in cats, the visual inhibition of labyrinthine nystagmus is not dependent upon the integrity of the visual cortex or superior colliculi. The hypothesis is brought forward that the visual inhibition of the vestibular nystagmus is merely a reflex of the brain stem to light stimulus, mediated via the cerebellum.

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Brain-Inspired Algorithms for Processing of Visual Data

Lecture Notes in Computer Science - Brain-Inspired Computing ◽

10.1007/978-3-030-82427-3_8 ◽

2021 ◽

pp. 105-115

Author(s):

Nicola Strisciuglio ◽

Nicolai Petkov

Keyword(s):

Image Processing ◽

Computer Vision ◽

Visual Cortex ◽

Visual System ◽

Computational Models ◽

Hierarchical Organization ◽

Visual Data ◽

Convolutional Networks ◽

Improved Stability ◽

The Brain

AbstractThe study of the visual system of the brain has attracted the attention and interest of many neuro-scientists, that derived computational models of some types of neuron that compose it. These findings inspired researchers in image processing and computer vision to deploy such models to solve problems of visual data processing.In this paper, we review approaches for image processing and computer vision, the design of which is based on neuro-scientific findings about the functions of some neurons in the visual cortex. Furthermore, we analyze the connection between the hierarchical organization of the visual system of the brain and the structure of Convolutional Networks (ConvNets). We pay particular attention to the mechanisms of inhibition of the responses of some neurons, which provide the visual system with improved stability to changing input stimuli, and discuss their implementation in image processing operators and in ConvNets.

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Deep Learning Approach for Brain Tumor Classification

International Journal for Research in Applied Science and Engineering Technology ◽

10.22214/ijraset.2021.35648 ◽

2021 ◽

Vol 9 (VI) ◽

pp. 3094-3098

Author(s):

Saudagar Punam

Keyword(s):

Deep Learning ◽

Brain Tumor ◽

Tumor Detection ◽

Detection Methods ◽

Brain Images ◽

Abnormal Growth ◽

Images Processing ◽

Learning Architectures ◽

The Brain ◽

Complicated Task

Tumors are complex. There are a lot of variations in sizes and location of tumor. This makes it really hard for complete understanding of tumor. Brain tumour is the abnormal growth of cells inside the brain cranium which limits the functioning of brain. Now a days, medical images processing is a most challenging and developing field. Automated detection of tumor in MRI is extremely crucial because it provides information about abnormal tissues which is important for planning treatment. The conventional method for defect detection in resonance brain images is time consuming. So, automated tumor detection methods are developed because it would save radiologist time and acquire a tested accuracy. The MRI brain tumor detection is complicated task due to complexity and variance of tumors.There are many previously implemented approaches on detecting these kinds of brain tumors. In this paper, we used and implement Convolutional Neural Network (CNN) which is one among the foremost widely used deep learning architectures for classifying a brain tumor into four types. i.e Glioma , Meningioma, Pituitary and No tumour. CNN may be used to effectively locate most cancers cells in brain via MRI. classification.

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Learning Invariant Object and Spatial View Representations in the Brain Using Slow Unsupervised Learning

Frontiers in Computational Neuroscience ◽

10.3389/fncom.2021.686239 ◽

2021 ◽

Vol 15 ◽

Author(s):

Edmund T. Rolls

Keyword(s):

Visual Cortex ◽

Visual System ◽

Superior Temporal Sulcus ◽

Object Motion ◽

Eye Position ◽

Head Direction ◽

Invariant Representations ◽

Spatial View ◽

The Brain ◽

Slow Learning

First, neurophysiological evidence for the learning of invariant representations in the inferior temporal visual cortex is described. This includes object and face representations with invariance for position, size, lighting, view and morphological transforms in the temporal lobe visual cortex; global object motion in the cortex in the superior temporal sulcus; and spatial view representations in the hippocampus that are invariant with respect to eye position, head direction, and place. Second, computational mechanisms that enable the brain to learn these invariant representations are proposed. For the ventral visual system, one key adaptation is the use of information available in the statistics of the environment in slow unsupervised learning to learn transform-invariant representations of objects. This contrasts with deep supervised learning in artificial neural networks, which uses training with thousands of exemplars forced into different categories by neuronal teachers. Similar slow learning principles apply to the learning of global object motion in the dorsal visual system leading to the cortex in the superior temporal sulcus. The learning rule that has been explored in VisNet is an associative rule with a short-term memory trace. The feed-forward architecture has four stages, with convergence from stage to stage. This type of slow learning is implemented in the brain in hierarchically organized competitive neuronal networks with convergence from stage to stage, with only 4-5 stages in the hierarchy. Slow learning is also shown to help the learning of coordinate transforms using gain modulation in the dorsal visual system extending into the parietal cortex and retrosplenial cortex. Representations are learned that are in allocentric spatial view coordinates of locations in the world and that are independent of eye position, head direction, and the place where the individual is located. This enables hippocampal spatial view cells to use idiothetic, self-motion, signals for navigation when the view details are obscured for short periods.

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