Identification Of Multiple Objects Research Articles

Resources Underlying Visuo-Spatial Working Memory Enable Veridical Large Numerosity Perception.

Humans can quickly approximate how many objects are in a visual image, but no clear consensus has been achieved on the cognitive resources underlying this ability. Previous work has lent support to the notion that mechanisms which explicitly represent the locations of multiple objects in the visual scene within a mental map are critical for both visuo-spatial working memory and enumeration (at least for relatively small numbers of items). Regarding the cognitive underpinnings of large numerosity perception, an issue currently subject to much controversy is why numerosity estimates are often non-veridical (i.e., susceptible to biases from non-numerical quantities). Such biases have been found to be particularly pronounced in individuals with developmental dyscalculia (DD), a learning disability affecting the acquisition of arithmetic skills. Motivated by findings showing that DD individuals are also often impaired in visuo-spatial working memory, we hypothesized that resources supporting this type of working memory, which allow for the simultaneous identification of multiple objects, might also be critical for precise and unbiased perception of larger numerosities. We therefore tested whether loading working memory of healthy adult participants during discrimination of large numerosities would lead to increased interference from non-numerical quantities. Participants performed a numerosity discrimination task on multi-item arrays in which numerical and non-numerical stimulus dimensions varied congruently or incongruently relative to each other, either in isolation or in the context of a concurrent visuo-spatial or verbal working memory task. During performance of the visuo-spatial, but not verbal, working memory task, precision in numerosity discrimination decreased, participants’ choices became strongly biased by item size, and the strength of this bias correlated with measures of arithmetical skills. Moreover, the interference between numerosity and working memory tasks was bidirectional, with number discrimination impacting visuo-spatial (but not verbal) performance. Overall, these results suggest that representing visual numerosity in a way that is unbiased by non-numerical quantities relies on processes which explicitly segregate/identify the locations of multiple objects that are shared with visuo-spatial (but not verbal) working memory. This shared resource may potentially be impaired in DD, explaining the observed co-occurrence of working memory and numerosity discrimination deficits in this clinical population.

Identification of Multiple Objects Using Their Natural Resonant Frequencies

A methodology for identification of multiple objects using their natural resonant frequencies is presented. This methodology is then applied to generate a library of poles of various objects in the frequency domain and compare them to the computed natural poles of unknown objects using the typical transient temporal late time response in the time domain. The Cauchy method is applied directly in the frequency domain to extract the Singularity Expansion Method (SEM) poles, and thus a library of poles of various perfectly conducting objects (PEC) is generated. The Matrix Pencil (MP) method is then applied to the late time response to compute the SEM poles for identification in the time domain. Simulation examples are analyzed to illustrate the potential of this proposed methodology.

Segmentation in structure from motion: modeling and psychophysics

Much work has been done on the question of how the visual system extracts the three-dimensional (3D) structure and motion of an object from two-dimensional (2D) motion information, a problem known as ‘ Structure from Motion’, or SFM. Much less is known, however, about the human ability to recover structure and motion when the optic flow field arises from multiple objects, although observations of this ability date as early as Ullman's well-known two-cylinders stimulus [The interpretation of visual motion (1979)]. In the presence of multiple objects, the SFM problem is further aggravated by the need to solve the segmentation problem, i.e. deciding which motion signal belongs to which object. Here, we present a model for how the human visual system solves the combined SFM and segmentation problems, which we term SSFM, concurrently. The model is based on computation of a simple scalar property of the optic flow field known as def, which was previously shown to be used by human observers in SFM. The def values of many triplets of moving dots are computed, and the identification of multiple objects the image is based on detecting multiple peaks in the histogram of def values. In five experiments, we show that human SSFM performance is consistent with the predictions of the model. We compare the predictions of our model to those of other theoretical approaches, in particular those that use a rigidity hypothesis, and discuss the validity of each approach as a model for human SSFM.

An optical correlator feature extractor neural net system

The three optical information processing techniques of detection, recognition, and identification can and should be combined to achieve the best benefits of each. All methods are required for difficult pattern recognition problems. We consider the identification of multiple objects in the field of view in clutter. A morphological correlator is used to achieve detection. Hierarchical and symbolic pattern recognition correlators can also achieve detection as well as recognition. For very large class probems, feature extractors are required for identification, but first require detection. For difficult multiclass discrimination problems, neural net methods (rather than linear discriminant functions) are needed for identification.