Visual Recognition and Categorization on the Basis of Similarities to Multiple Class Prototypes

To recognize a previously seen object, the visual system must overcome the variability in the object's appearance caused by factors such as illumination and pose. Developments in computer vision suggest that it may be possible to counter the influence of these factors, by learning to interpolate between stored views of the target object, taken under representative combinations of viewing conditions. Daily life situations, however, typically require categorization, rather than recognition, of objects. Due to the open-ended character both of natural kinds and of artificial categories, categorization cannot rely on interpolation between stored examples. Nonetheless, knowledge of several representative members, or prototypes, of each of the categories of interest can still provide the necessary computational substrate for the categorization of new instances. The resulting representational scheme based on similarities to prototypes appears to be computationally viable, and is readily mapped onto the mechanisms of biological vision revealed by recent psychophysical and physiological studies

Registration and Fusion of Multi-Spectral Images Using a Novel Edge Descriptor

In this paper we introduce a fully end-to-end approach for multi-spectral image registration and fusion. Our method for fusion combines images from different spectral channels into a single fused image by different approaches for low and high frequency signals. A prerequisite of fusion is a stage of geometric alignment between the spectral bands, commonly referred to as registration. Unfortunately, common methods for image registration of a single spectral channel do not yield reasonable results on images from different modalities. For that end, we introduce a new algorithm for multi-spectral image registration, based on a novel edge descriptor of feature points. Our method achieves an accurate alignment of a level that allows us to further fuse the images. As our experiments show, we produce a high quality of multi-spectral image registration and fusion under many challenging scenarios

Deep Multi-Spectral Registration Using Invariant Descriptor Learning

In this paper, we introduce a novel deep-learning method to align cross-spectral images. Our approach relies on a learned descriptor which is invariant to different spectra. Multi-modal images of the same scene capture different signals and therefore their registration is challenging and it is not solved by classic approaches. To that end, we developed a feature-based approach that solves the visible (VIS) to Near-Infra-Red (NIR) registration problem. Our algorithm detects corners by Harris and matches them by a patch-metric learned on top of CIFAR-10 network descriptor. As our experiments demonstrate we achieve a high-quality alignment of cross-spectral images with a sub-pixel accuracy. Comparing to other existing methods, our approach is more accurate in the task of VIS to NIR registration

Similarity, Connectionism, and the Problem of Representation in Vision

A representational scheme under which the ranking between represented similarities is isomorphic to the ranking between the corresponding shape similarities can support perfectly correct shape classification, because it preserves the clustering of shapes according to the natural kinds prevailing in the external world. This note discusses the computational requirements of representation that preserves similarity ranks, and points out the relative straightforwardness of its connectionist implementation. 1 Introduction 1.1 Two problems of representation It is possible to distinguish between two problems about mental representation (Cummins, 1989). The first of these, the problem of representations (plural), is basically empirical; two instances of this problem are the search for the representations employed by natural cognitive systems, and the design of the representational substrate for artificial cognitive modules. The second problem is called by Cummins the Problem of Representation..

On Similarity to Prototypes in 3D Object Representation

A representational scheme under which the ranking between represented dissimilarities is isomorphic to the ranking between the corresponding shape dissimilarities can support perfect shape classification, because it preserves the clustering of shapes according to the natural kinds prevailing in the external world. We discuss the computational requirements of rank-preserving representation, and examine its plausibility within a prototype-based framework of shape vision

On Topological Simulations in Developmental Biology

Further study is made of the topological model framework for cell simulations that was introduced by Matel

Visual Recognition and Categorization on the Basis of Similarities to Multiple Class Prototypes

One of the difficulties of object recognition stems from the need to overcome the variability in object appearance caused by factors such as illumination and pose. The influence of these factors can be countered by learning to interpolate between stored views of the target object, taken under representative combinations of viewing conditions. Difficulties of another kind arise in daily life situations that require categorization, rather than recognition, of objects. We show that, although categorization cannot rely on interpolation between stored examples, knowledge of several representative members, or prototypes, of each of the categories of interest can still provide the necessary computational substrate for the categorization of new instances. The resulting representational scheme based on similarities to prototypes is computationally viable, and is readily mapped onto the mechanisms of biological vision revealed by recent psychophysical and physiological studies. 1 Introduction T..

Similarity-based viewspace interpolation and the categorization of 3D objects

Visual objects can be represented by their similarities to a small number of reference shapes or prototypes. This method yields low-dimensional (and therefore computationally tractable) representations, which support both the recognition of familiar shapes and the categorization of novel ones. In this note, we show how such representations can be used in a variety of tasks involving novel objects: viewpoint-invariant recognition, recovery of a canonical view, estimation of pose, and prediction of an arbitrary view. The unifying principle in all these cases is the representation of the view space of the novel object as an interpolation of the view spaces of the reference shapes. Representation by similarities to prototypes To recognize a previously seen object, the visual system must overcome the variability in the object's appearance caused by factors such as illumination and pose. It is possible to counter the influence of these factors, by learning to interpolate between stored view..

Similarity-Based Viewspace Interpolation

Visual objects can be represented by their similarities to a small number of reference shapes or prototypes. This method yields low-dimensional (and therefore computationally tractable) representations, which support both the recognition of familiar shapes and the categorization of novel ones. In this note, we show how such representations can be used in a variety of tasks involving novel objects: viewpoint-invariant recognition, recovery of a canonical view, estimation of pose, and prediction of an arbitrary view. The unifying principle in all these cases is the representation of the view space of the novel object as an interpolation of the view spaces of the reference shapes. Representation by similarities to prototypes To recognize a previously seen object, the visual system must overcome the variability in the object's appearance caused by factors such as illumination and pose. It is possible to counter the influence of these factors, by learning to interpolate between stored view..

A Model of Visual Recognition and Categorization

To recognize a previously seen object, the visual system must overcome the variability in the object's appearance caused by factors such as illumination and pose. Developments in computer vision suggest that it may be possible to counter the influence of these factors, by learning to interpolate between stored views of the target object, taken under representative combinations of viewing conditions. Daily life situations, however, typically require categorization, rather than recognition, of objects. Due to the open-ended character both of natural kinds and of artificial categories, categorization cannot rely on interpolation between stored examples. Nonetheless, knowledge of several representative members, or prototypes, of each of the categories of interest can still provide the necessary computational substrate for the categorization of new instances. The resulting representational scheme based on similarities to prototypes appears to be computationally viable, and is readily mapped..