We examine the use of fusing data from multiple data sources for use within object recognition systems. We then continue, to illustrate the system that we have created for our own object recognition needs. The data fusion model that we use is embedded within an object recognition system that analyses simulated FLIR and LADAR data to recognise and track aircraft. The data fusion is based upon th...

The present study examined why perirhinal cortex lesions in rats impair the spontaneous ability to select novel objects in preference to familiar objects, when both classes of object are presented simultaneously. The study began by repeating this standard finding, using a test of delayed object recognition memory. As expected, the perirhinal cortex lesions reduced the difference in exploration ...

Visual object recognition involves the identification of images of 3-D objects seen from arbitrary viewpoints. We suggest an approach to object recognition in which a view is represented as a collection of points given by their location in the image. An object is modeled by a set of 2-D views together with the correspondence between the views. We show that any novel view of the object can be ex...

There has recently been an increase in interest in the effects of visual interference on memory processing, with the aim of elucidating the role of the perirhinal cortex (PRC) in recognition memory. One view argues that the PRC processes highly complex conjunctions of object features, and recent evidence from rodents suggests that these representations may be vital for buffering against the eff...

The success of skeletal model in object recognition from segmented images motivates the development of a skeletal model for top-down object recognition and segmentation. We propose a novel skeleton-based generative shape model which is suitable for efficient search using dynamic programming (DP). We have devised an exclusion principle enabling DP to discover multiple instances of an object cate...

This paper presents training of novel hybrid network based on three deep convolutional neural network architectures applied to object recognition, based on the depth information supplied for a RGBD camera. For this case, the depth information allows to set the dataset of training images of each network, its architecture and its characteristics, generating a dynamic recognition application by va...

Two of the most formidable skills that characterize human beings are language and our prowess in visual object recognition. They may also be developmentally intertwined. Two experiments, a large sample cross-sectional study and a smaller sample 6-month longitudinal study of 18- to 24-month-olds, tested a hypothesized developmental link between changes in visual object representation and noun le...