We here propose a simple but highly potential algorithm to detect a model object’s position on an... more We here propose a simple but highly potential algorithm to detect a model object’s position on an input image by determining the initially unknown transformational states of the model object, in particular, size and 2D-rotation. In this algorithm, a single feature is extracted around or at the center of the input image through 2D-Gabor wavelet transformation, in order to find not only the most likely relative size and rotation to the model object, but also the most appropriate positional region on the input image for detecting the correct relative transformational states. We also show the reliable function on the face images of different persons, or of different appearance in the same person.
Abstract We address the problem of integrating information about multiple objects and their posit... more Abstract We address the problem of integrating information about multiple objects and their positions on a visual scene. A primate visual system has fewer difficulties in rapidly achieving integration, given even when presented with several objects. Here, we propose a neurally plausible mechanism for simultaneously coordinating the local decision-making process of “what”-and “where”-information for the organization of global multi-object recognition. The mechanism is based on paradigms of binding-by-synchrony and dynamic ...
We introduce visual object detection architecture, making full use of technical merits of so-call... more We introduce visual object detection architecture, making full use of technical merits of so-called multi-scale feature correspondence in the neurally inspired Gabor pyramid. The remarkable property of the multi-scale Gabor feature correspondence is found with scale-space approaches, which an original image Gabor-filtered with the individual frequency levels is approximated to the correspondingly sub-sampled image smoothed with the low-pass filter. The multi-scale feature correspondence is used for effectively reducing computational costs in filtering. In particular, we show that the multi-scale Gabor feature correspondence play an effective role in matching between an input image and the model representation for object detection.
We here address the problem of scale and orientation invariant object recognition, making use of ... more We here address the problem of scale and orientation invariant object recognition, making use of a correspondence-based mechanism, in which the identity of an object represented by sensory signals is determined by matching it to a representation stored in memory. The sensory representation is in general affected by various transformations, notably scale and rotation, thus giving rise to the fundamental problem of invariant object recognition. We focus here on a neurally plausible mechanism that deals simultaneously with identification of the object and detection of the transformation, both types of information being important for visual processing. Our mechanism is based on macrocolumnar units. These evaluate identity- and transformation-specific feature similarities, performing competitive computation on the alternatives of their own subtask, and cooperate to make a coherent global decision for the identity, scale and rotation of the object.
We here propose a simple but highly potential algorithm to detect a model object’s position on an... more We here propose a simple but highly potential algorithm to detect a model object’s position on an input image by determining the initially unknown transformational states of the model object, in particular, size and 2D-rotation. In this algorithm, a single feature is extracted around or at the center of the input image through 2D-Gabor wavelet transformation, in order to find not only the most likely relative size and rotation to the model object, but also the most appropriate positional region on the input image for detecting the correct relative transformational states. We also show the reliable function on the face images of different persons, or of different appearance in the same person.
Abstract We address the problem of integrating information about multiple objects and their posit... more Abstract We address the problem of integrating information about multiple objects and their positions on a visual scene. A primate visual system has fewer difficulties in rapidly achieving integration, given even when presented with several objects. Here, we propose a neurally plausible mechanism for simultaneously coordinating the local decision-making process of “what”-and “where”-information for the organization of global multi-object recognition. The mechanism is based on paradigms of binding-by-synchrony and dynamic ...
We introduce visual object detection architecture, making full use of technical merits of so-call... more We introduce visual object detection architecture, making full use of technical merits of so-called multi-scale feature correspondence in the neurally inspired Gabor pyramid. The remarkable property of the multi-scale Gabor feature correspondence is found with scale-space approaches, which an original image Gabor-filtered with the individual frequency levels is approximated to the correspondingly sub-sampled image smoothed with the low-pass filter. The multi-scale feature correspondence is used for effectively reducing computational costs in filtering. In particular, we show that the multi-scale Gabor feature correspondence play an effective role in matching between an input image and the model representation for object detection.
We here address the problem of scale and orientation invariant object recognition, making use of ... more We here address the problem of scale and orientation invariant object recognition, making use of a correspondence-based mechanism, in which the identity of an object represented by sensory signals is determined by matching it to a representation stored in memory. The sensory representation is in general affected by various transformations, notably scale and rotation, thus giving rise to the fundamental problem of invariant object recognition. We focus here on a neurally plausible mechanism that deals simultaneously with identification of the object and detection of the transformation, both types of information being important for visual processing. Our mechanism is based on macrocolumnar units. These evaluate identity- and transformation-specific feature similarities, performing competitive computation on the alternatives of their own subtask, and cooperate to make a coherent global decision for the identity, scale and rotation of the object.
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Papers by Jenia Jitsev