Search PubMed⌕ Search

SEARCH · Search PubMed

Results for “Pattern Recognition, Visual”

Search indexed PubMed citations on genomics, clinical trials, systematic reviews and public health. Explore titles, authors and supplied subject terms, then open the PubMed record.

Quote a phrase for an exact phrase match. Source license links do not imply unrestricted reuse.

At least 19 recordsLinked to original sources
PubMed1998

Limited translation invariance of human visual pattern recognition.

Visual object recognition is considered to be largely translation invariant. An earlier study (Foster & Kahn, 1985), however, has indicated that recognition of complex novel stimuli is partially specific to location in the visual field: It is significantly easier to determine the identity of two briefly displayed random patterns if both stimuli are presented at the same, rather than at different, locations. In a series of same/different discrimination tasks, we characterize the processes underlying this "displacement effect": Horizontal and vertical translations are equally effective in reducing performance. Making the task more difficult by increasing pattern similarity leads to even higher positional specificity. The displacement effect disappears after rotation or contrast reversal of the patterns, indicating that positional specificity depends on relatively low levels of processing. Control experiments rule out explanations that are independent of visual pattern memory, such as spatial attention, eye movements, or retinal afterimages. Positional specificity of recognition is found only for same trials. Our results demonstrate that position invariance, a widely acknowledged property of the human visual system, is limited to specific experimental conditions. Normalization models involving mental shifts of an early visual representation or of a window of attention cannot easily account for these findings.

Adult↗
PubMed1979

Transformation and relational-structure schemes for visual pattern recognition. Two models tested experimentally with rotated random-dot patterns.

Two models for visual pattern recognition are described; the one based on application of internal compensatory transformations to pattern representations, the other based on encoding of patterns in terms of local features and spatial relations between these local features. These transformations and relational-structure models are each endowed with the same experimentally observed invariance properties, which include independence to pattern translation and pattern jitter, and, depending on the particular versions of the models, independence to pattern reflection and inversion (180 degrees rotation). Each model is tested by comparing the predicted recognition performance with experimentally determined recognition performance using as stimuli random-dot patterns that were variously rotated in the plane. The level of visual recognition of such patterns is known to depend strongly on rotation angle. It is shown that the relational-structure model equipped with an invariance to pattern inversion gives responses which are in close agreement with the experimental data over all pattern rotation angles. In contrast, the transformation model equipped with the same invariances gives poor agreement to the experimental data. Some implications of these results are considered.

Field Dependence-Independence↗
PubMed1994

Context effects in visual pattern recognition by pigeons.

In the experiments described in this paper we examined the effects of contextual stimuli on pigeons' recognition of visual patterns. Experiment 1 showed a context-superiority effect. Specifically, two target forms that were identical except for location in the visual field were not discriminated when presented alone, but the compounds formed when each of these targets was placed between a nearby pair of flanking stimuli were readily discriminated. The size of the context-superiority effect decreased with increasing target-flanker separation. In Experiments 2 and 3 the two targets differed in form rather than spatial location and were readily discriminated in the absence of flankers. Under these circumstances, adding an identical pair of flankers to each target resulted in a context-inferiority effect; this is, the two target-plus-flankers compounds were less readily discriminated than the targets alone. The size of the context-inferiority effect decreased with increasing target-flanker separation. The observed effects of context are predictable from the Heinemann-Chase (1990) model of pattern recognition.

Animals↗
PubMed1978

Size scaling in visual pattern recognition.

Human visual recognition on the basis of shape but regardless of size was investigated by reaction time methods. For successive matching of random figures, reaction time increased linearly with the linear size ratio of stimulus pairs. For single-character classification, reaction time increased with divergence between cued size format and stimulus format such that for character nonrepetitions, the increment in latency was approximately proportional to the logarithm of the linear size ratio of the two formats. However, when reactions to character repetitions were faster than those to nonrepetitions, the repetition reaction time function was similar to that for successive matching of random figures. The results suggested two processes of size scaling: mental-image transformation and perceptual-scale transformation. Image transformation accounted for matching performance based on visual short-term memory, whereas scale transformation accounted for size invariance in recognition based on comparison against visual representations in long-term memory.

Adult↗
PubMed1987

Self-organizing neural network models for visual pattern recognition.

Two neural network models for visual pattern recognition are discussed. The first model, called a "neocognitron", is a hierarchical multilayered network which has only afferent synaptic connections. It can acquire the ability to recognize patterns by "learning-without-a-teacher": the repeated presentation of a set of training patterns is sufficient, and no information about the categories of the patterns is necessary. The cells of the highest stage eventually become "gnostic cells", whose response shows the final result of the pattern-recognition of the network. Pattern recognition is performed on the basis of similarity in shape between patterns, and is not affected by deformation, nor by changes in size, nor by shifts in the position of the stimulus pattern. The second model has not only afferent but also efferent synaptic connections, and is endowed with the function of selective attention. The afferent and the efferent signals interact with each other in the hierarchical network: the efferent signals, that is, the signals for selective attention, have a facilitating effect on the afferent signals, and at the same time, the afferent signals gate efferent signal flow. When a complex figure, consisting of two patterns or more, is presented to the model, it is segmented into individual patterns, and each pattern is recognized separately. Even if one of the patterns to which the models is paying selective attention is affected by noise or defects, the model can "recall" the complete pattern from which the noise has been eliminated and the defects corrected.

Afferent Pathways↗
PubMed1986

A neural network model for selective attention in visual pattern recognition.

A neural network model of the mechanism of selective attention in visual pattern recognition is proposed and simulated on a digital computer. When a complex figure consisting of two patterns or more is presented to the model, it is segmented into individual patterns, and each pattern is recognized separately. Even if one of the patterns to which the model is paying selective attention is affected by noise or defects, the model can recall the complete pattern from which the noise has been eliminated and the defects corrected. It is not necessary for perfect recall that the stimulus pattern should be identical in shape to the training pattern. Even though the pattern is distorted in shape or changed in size, it can be correctly recognized and the missing portions restored. The model consists of a hierarchical neural network which has efferent as well as afferent connections between cells. The afferent and the efferent signals interact with each other in the network: the efferent signals, that is, the signals for selective attention, have a facilitating effect on the afferent ones, and, at the same time, the afferent signals gate efferent signal flow. When some feature in the stimulus is not extracted in the afferent paths, the threshold for detection of that feature is automatically lowered by decreasing the efficiency of inhibition, and the model tries to extract even vague traces of the undetected feature.

Afferent Pathways↗
PubMedDate not supplied

[A cross correlation analysis of the electrical activity of different regions of the cerebral cortex during visual pattern recognition in children].

Characteristics of spatial-temporal organization of brain biopotentials were examined in one to 2.5 year old children during recognition of visual images. Crosscorrelation EEG analysis of frontal, motor, inferior parietal, temporal and occipital cortical zones has shown that recognition of familiar visual objects is accompanied by an increase in spatial synchronization of biopotentials, especially in the inferior parietal zones of both hemispheres and occipital centres of the left hemisphere. There is a considerable increase in the number of highly synchronous synphasic oscillations at the 4--5 per sec frequency with an intensified periodicity of processes. Recognition of unfamiliar objects does not produce a similar effect. Temporal organization of biopotentials of the associative (frontal and inferior parietal) and projection visual areas in the course of recognition of images depends on the existence of a notion of the whole object in the child's memory.

Brain Mapping↗
PubMed1986

On functional concepts for the explanation of visual pattern recognition.

An overview is given covering approximately 30 years of research in the field of functional concepts for the explanation of visual pattern recognition processes. Due to its important role, the Perceptron approach is briefly sketched. This concept, representing to a great extent the present opinion about pattern recognition in the field of visual sciences, is investigated with respect to its invariance properties and its structure. Fundamental facts about the nature of human perception (Gestalt-aspect) and about the processing structure of the visual system are used as qualification criteria. Alternatively, a recent concept that explains the high degree of invariance in visual perception by the evaluation of inner pattern relations is introduced and investigated with respect to its biological plausibility. It is also compared with the Perceptron concept.

Computers↗
PubMed1993

Visual pattern recognition in Drosophila involves retinotopic matching.

Honeybees remember the shapes of flowers and are guided by visual landmarks on their foraging trips. How insects recognize visual patterns is poorly understood. Experiments suggest that they try to match retinotopically the incoming visual pattern with a previously stored memory image. But bees can be conditioned to individual pattern parameters such as orientation of contours, colour or size. These and other results are difficult to reconcile with simple template matching. In such investigations, freely moving animals are observed; their behaviour and visual input, therefore, are not well known. Mostly, processing strategies are inferred from stimulus design. We have studied visual pattern recognition with tethered flies (Drosophila melanogaster) in a flight simulator and report here that flies store visual images at, or together with, fixed retinal positions and can retrieve them from there only. Position invariance, an acknowledged property of human pattern recognition, may not exist as a primary mechanism in insects.

Animals↗
PubMed1987

Visual pattern recognition in humans. I. Evidence for adaptive filtering.

We have investigated how observers learn to classify compound Gabor signals as a function of their differentiating frequency components. Performance appears to be consistent with decision processes based upon the least squares minimum distance classifier (LSMDC) operating over a cartesian feature space consisting of the real (even) and imaginary (odd) components of the signals. The LSMDC model assumes observers form prototype signals, or adaptive filters, for each signal class in the learning phase, and classify as a function of their degree of match to each prototype. The underlying matching process can be modelled in terms of cross-correlation between prototype images and the input sample.

Form Perception↗
PubMed1998

Diagnostic subgrouping of depressed patients by principal component analysis and visualized pattern recognition.

A data-analytical method is described for identifying behavioral and biological variables in psychiatric patients with predictive value in defining clinical subgroups. The procedure, based on principal component analysis (PCA) and graphical analysis, was applied in a group of 28 depressed patients. The 28 depressed patients of unipolar type were observed for up to 15 years for re-evaluation of the diagnoses at the start of the study. Platelet monoamine oxidase activity, post-dexamethasone serum cortisol and serum melatonin predicted two main clinical subgroups as well as a smaller subgroup of bipolar patients. The selection procedure revealed which of several variables were predictive of subgroups that were not possible to identify by univariate methods. The three biological variables may thus be useful in further assessment of clinical subgroups of unipolar depressed patients studied by other research groups.

Adult↗
PubMed2005

Effects of identical context on visual pattern recognition by pigeons.

The effects of identical context on pattern recognition by pigeons for outline drawings of faces were investigated by training pigeons to identify (Experiment 1) and categorize (Experiment 2) these stimuli according to the orientation of the mouth-an upright U shape representing a smiling mouth or an inverted U shape representing a sad mouth. These target stimuli were presented alone (Pair 1), with three dots in a triangular orientation to represent a nose and eyes (Pair 2), and with the face pattern surrounded by an oval (Pair 3). In Experiment 1, the pigeons trained with Pair 1 were most accurate, those trained with Pair 2 were less so, and those trained with Pair 3 failed to acquire the discrimination within eighty 100-trial sessions. The same ordering was found in Experiment 2 for pigeons trained on the three pairs simultaneously. The authors suggest that a contrasting finding in humans, the face superiority effect, might be due to a gain in discriminability resulting from recognition of the pattern as a face. An exemplar model of information processing that excludes linguistic coding accounts for the present results.

Animals↗