Aplikácia kognitivného modelu vizuálnej pozornosti v automatizovanej montáži

Zásobovacie zariadenia a podsystémy v štruktúrach montážnych systémov majú významné postavenie. Technickú zložitosť klasických zásobovacích zariadení a podsystémov je možné eliminovať pružnými programovateľnými automatizovanými zariadeniami. Informácie o spomínanom objekte zabezpečované senzorovými modulmi sa spracovávajú v riadiacom systéme zariadenia resp. na vyššej úrovni riadenia montážneho systému. Spracované informácie sú distribuované ako riadiace informácie výkonným jednotkám a prvkom, ktoré vykonávajú príslušné funkcie. Riadiace systémy programovateľných zásobovacích zariadení a podsystémov plnia viaceré funkcie napr. spracovanie informácií od senzorových jednotiek a modulov, správne vyhodnotenie polohy súčiastky a určenie postupu činnosti výkonných jednotiek a prvkov, distribúcia výkonných inštrukcií pohonovým jednotkám, atď. Programové vybavenie založené na využívaní kognitívneho modelu vizuálnej pozornosti charakterizuje nový prístup k riešeniu uvádzaných problémov. Pri vizuálnom vnímání scény obsahujúcej rôzne objekty a pre potrebu interakcie s určitým cieľovým objektom nachádzajúcim sa v tejto scéne je nutné aby systém upriamil svoju pozornosť na tento (cieľový) objekt. Tento mechanizmus je jedným z principiálnych prvkov videnia a podobne ako mnoho biologicky motivovaných systémov je veľmi výhodne využiteľný v praxi. Navrhovaný model je implementáciou mechanizmu vizuálnej pozornosti vo vytvorenom počítačom simulovanom prostredí.Logistic devices and sub - systems in the structures of assembly systems have significant position. Technical complexity of classical devices and sub - systems can be decreased by using of flexible programmable automated devices. Information's about objects provided by sensor modules are handled in processing system of the device, respective on the higher level of the assembly system. Executed information is distributed like processing information to executive units and elements. Control systems of programmable supply devices and sub - systems take handle of many functions, for example: processing information from sensor devices and modules, right calculating of the bearing of the component, distributing of executive instructions to actuating units, and many others. Software accessories based on the using of cognitive model of visual attention featured a new way of solving former problems. By visual reception the scenes contains miscellaneous objects and for the demand of the interaction with the target object is necessary that the system is need to be focused to this object. This mechanism is one of the pricipally elements of vision, and like many biologically motivated systems is very useful in practice. Designed model is an implementation of the mechanism of visual attention in the computer created simulation environment

Visual pathways from the perspective of cost functions and multi-task deep neural networks

Vision research has been shaped by the seminal insight that we can understand the higher-tier visual cortex from the perspective of multiple functional pathways with different goals. In this paper, we try to give a computational account of the functional organization of this system by reasoning from the perspective of multi-task deep neural networks. Machine learning has shown that tasks become easier to solve when they are decomposed into subtasks with their own cost function. We hypothesize that the visual system optimizes multiple cost functions of unrelated tasks and this causes the emergence of a ventral pathway dedicated to vision for perception, and a dorsal pathway dedicated to vision for action. To evaluate the functional organization in multi-task deep neural networks, we propose a method that measures the contribution of a unit towards each task, applying it to two networks that have been trained on either two related or two unrelated tasks, using an identical stimulus set. Results show that the network trained on the unrelated tasks shows a decreasing degree of feature representation sharing towards higher-tier layers while the network trained on related tasks uniformly shows high degree of sharing. We conjecture that the method we propose can be used to analyze the anatomical and functional organization of the visual system and beyond. We predict that the degree to which tasks are related is a good descriptor of the degree to which they share downstream cortical-units.Comment: 16 pages, 5 figure

Sparse visual models for biologically inspired sensorimotor control

Given the importance of using resources efficiently in the competition for survival, it is reasonable to think that natural evolution has discovered efficient cortical coding strategies for representing natural visual information. Sparse representations have intrinsic advantages in terms of fault-tolerance and low-power consumption potential, and can therefore be attractive for robot sensorimotor control with powerful dispositions for decision-making. Inspired by the mammalian brain and its visual ventral pathway, we present in this paper a hierarchical sparse coding network architecture that extracts visual features for use in sensorimotor control. Testing with natural images demonstrates that this sparse coding facilitates processing and learning in subsequent layers. Previous studies have shown how the responses of complex cells could be sparsely represented by a higher-order neural layer. Here we extend sparse coding in each network layer, showing that detailed modeling of earlier stages in the visual pathway enhances the characteristics of the receptive fields developed in subsequent stages. The yield network is more dynamic with richer and more biologically plausible input and output representation

Finding any Waldo: zero-shot invariant and efficient visual search

Searching for a target object in a cluttered scene constitutes a fundamental challenge in daily vision. Visual search must be selective enough to discriminate the target from distractors, invariant to changes in the appearance of the target, efficient to avoid exhaustive exploration of the image, and must generalize to locate novel target objects with zero-shot training. Previous work has focused on searching for perfect matches of a target after extensive category-specific training. Here we show for the first time that humans can efficiently and invariantly search for natural objects in complex scenes. To gain insight into the mechanisms that guide visual search, we propose a biologically inspired computational model that can locate targets without exhaustive sampling and generalize to novel objects. The model provides an approximation to the mechanisms integrating bottom-up and top-down signals during search in natural scenes.Comment: Number of figures: 6 Number of supplementary figures: 1

Deep Neural Networks Rival the Representation of Primate IT Cortex for Core Visual Object Recognition

The primate visual system achieves remarkable visual object recognition performance even in brief presentations and under changes to object exemplar, geometric transformations, and background variation (a.k.a. core visual object recognition). This remarkable performance is mediated by the representation formed in inferior temporal (IT) cortex. In parallel, recent advances in machine learning have led to ever higher performing models of object recognition using artificial deep neural networks (DNNs). It remains unclear, however, whether the representational performance of DNNs rivals that of the brain. To accurately produce such a comparison, a major difficulty has been a unifying metric that accounts for experimental limitations such as the amount of noise, the number of neural recording sites, and the number trials, and computational limitations such as the complexity of the decoding classifier and the number of classifier training examples. In this work we perform a direct comparison that corrects for these experimental limitations and computational considerations. As part of our methodology, we propose an extension of "kernel analysis" that measures the generalization accuracy as a function of representational complexity. Our evaluations show that, unlike previous bio-inspired models, the latest DNNs rival the representational performance of IT cortex on this visual object recognition task. Furthermore, we show that models that perform well on measures of representational performance also perform well on measures of representational similarity to IT and on measures of predicting individual IT multi-unit responses. Whether these DNNs rely on computational mechanisms similar to the primate visual system is yet to be determined, but, unlike all previous bio-inspired models, that possibility cannot be ruled out merely on representational performance grounds.Comment: 35 pages, 12 figures, extends and expands upon arXiv:1301.353