Support vector machine (SVM) and HMAX model are two powerful recent techniques. SVMs are classifiers which have demonstrated high generalization capabilities in many different tasks, including the object recognition problem. HMAX is a feature extraction method and this method is motivated by a quantitative model of visual cortex. In this paper we combine these two techniques for the palmprint v...

To improve robustness in object recognition, many artificial visual systems imitate the way in which the human visual cortex encodes object information as a hierarchical set of features. These systems are usually evaluated in terms of their ability to accurately categorize well-defined, unambiguous objects and scenes. In the real world, however, not all objects and scenes are presented clearly,...

The human visual system is assumed to transform low level visual features to object and scene representations via features of intermediate complexity. How the brain computationally represents intermediate features is still unclear. To further elucidate this, we compared the biologically plausible HMAX model and Bag of Words (BoW) model from computer vision. Both these computational models use v...

OBJECTIVE To test the hypothesis that motoneuron excitability in stroke subjects is influenced by peripheral sensory input through passive exercise to the hemiplegic upper extremity. DESIGN Case-control prospective study. SETTING Physical medicine and rehabilitation inpatient and outpatient clinic at a tertiary Veterans Affairs medical center. PARTICIPANTS Nineteen hemiplegic adult subjec...

Previous biological models of object recognition in cortex have been evaluated using idealized scenes and have hard-coded features, such as the HMAX model by Riesenhuber and Poggio [10]. Because HMAX uses the same set of features for all object classes, it does not perform well in the task of detecting a target object in clutter. This thesis presents a new model that integrates learning of obje...

This paper proposes an end-to-end, scale invariant, visual object recognition system, composed of computational components that mimic the cortex in the brain. The system uses a two stage process. The first stage is a filter that extracts scale invariant features from the visual field. The second stage uses inference based spacio-temporal analysis of these features to identify objects in the vis...

The electric field induced by repetitive peripheral magnetic stimulation (RPMS) is able to activate muscles artificially due to the stimulation of deep intramuscular motor axons. RPMS applied to the muscle induces proprioceptive input to the central nervous system in different ways. Firstly, the indirect activation of mechanoreceptors and secondly, direct activation of afferent nerve fibers. Th...

About ten years ago, HMAX was proposed as a simple and biologically feasible model for object recognition, based on how the visual cortex processes information. However, the model does not encompass sparse firing, which is a hallmark of neurons at all stages of the visual pathway. The current paper presents an improved model, called sparse HMAX, which integrates sparse firing. This model is abl...

Object recognition is one of the most important tasks of the visual cortex. Even though it has been closely studied in the field of computer vision and neuroscience, the underlying processes in the visual cortex are not completely understood. A model that lately has gained attention is the HMAX model, which describes a feedforward hierarchical structure. This model shows a degree of scale and t...