Browse
Submit Data
Databases
API
Help

Dataset Information

39 Views

0 Connections

0 Citations

0 Reanalyses

0 Downloads

Omics score: 0

Contour complexity and contour detection.

ABSTRACT: Itis well-known that "smooth" chains of oriented elements-contours-are more easily detected amid background noise than more undulating (i.e., "less smooth") chains. Here, we develop a Bayesian framework for contour detection and show that it predicts that contour detection performance should decrease with the contour's complexity, quantified as the description length (DL; i.e., the negative logarithm of probability integrated along the contour). We tested this prediction in two experiments in which subjects were asked to detect simple open contours amid pixel noise. In Experiment 1, we demonstrate a consistent decline in performance with increasingly complex contours, as predicted by the Bayesian model. In Experiment 2, we confirmed that this effect is due to integrated complexity along the contour, and does not seem to depend on local stretches of linear structure. The results corroborate the probabilistic model of contours, and show how contour detection can be understood as a special case of a more general process-the identification of organized patterns in the environment.

SUBMITTER: Wilder J

PROVIDER: S-EPMC4429927 | biostudies-literature | 2015

REPOSITORIES: biostudies-literature

ACCESS DATA

Json Xml

Publications

Contour complexity and contour detection.

Wilder John J Feldman Jacob J Singh Manish M

Journal of vision 20150101 6

Itis well-known that "smooth" chains of oriented elements-contours-are more easily detected amid background noise than more undulating (i.e., "less smooth") chains. Here, we develop a Bayesian framework for contour detection and show that it predicts that contour detection performance should decrease with the contour's complexity, quantified as the description length (DL; i.e., the negative logarithm of probability integrated along the contour). We tested this prediction in two experiments in wh ...[more]

PMID: 26024453

Similar Datasets

Contour detection improved by context-adaptive surround suppression.

Project description:Recently, many image processing applications have taken advantage of a psychophysical and neurophysiological mechanism, called "surround suppression" to extract object contour from a natural scene. However, these traditional methods often adopt a single suppression model and a fixed input parameter called "inhibition level", which needs to be manually specified. To overcome these drawbacks, we propose a novel model, called "context-adaptive surround suppression", which can automatically control the effect of surround suppression according to image local contextual features measured by a surface estimator based on a local linear kernel. Moreover, a dynamic suppression method and its stopping mechanism are introduced to avoid manual intervention. The proposed algorithm is demonstrated and validated by a broad range of experimental results.

| S-EPMC5536361 | biostudies-literature

Contour Integration in Dynamic Scenes: Impaired Detection Performance in Extended Presentations.

Project description:Since scenes in nature are highly dynamic, perception requires an on-going and robust integration of local information into global representations. In vision, contour integration (CI) is one of these tasks, and it is performed by our brain in a seemingly effortless manner. Following the rule of good continuation, oriented line segments are linked into contour percepts, thus supporting important visual computations such as the detection of object boundaries. This process has been studied almost exclusively using static stimuli, raising the question of whether the observed robustness and "pop-out" quality of CI carries over to dynamic scenes. We investigate contour detection in dynamic stimuli where targets appear at random times by Gabor elements aligning themselves to form contours. In briefly presented displays (230 ms), a situation comparable to classical paradigms in CI, performance is about 87%. Surprisingly, we find that detection performance decreases to 67% in extended presentations (about 1.9-3.8 s) for the same target stimuli. In order to observe the same reduction with briefly presented stimuli, presentation time has to be drastically decreased to intervals as short as 50 ms. Cueing a specific contour position or shape helps in partially compensating this deterioration, and only in extended presentations combining a location and a shape cue was more efficient than providing a single cue. Our findings challenge the notion of CI as a mainly stimulus-driven process leading to pop-out percepts, indicating that top-down processes play a much larger role in supporting fundamental integration processes in dynamic scenes than previously thought.

| S-EPMC5591827 | biostudies-literature

Dissociable neural correlates of contour completion and contour representation in illusory contour perception.

Project description:Object recognition occurs even when environmental information is incomplete. Illusory contours (ICs), in which a contour is perceived though the contour edges are incomplete, have been extensively studied as an example of such a visual completion phenomenon. Despite the neural activity in response to ICs in visual cortical areas from low (V1 and V2) to high (LOC: the lateral occipital cortex) levels, the details of the neural processing underlying IC perception are largely not clarified. For example, how do the visual areas function in IC perception and how do they interact to archive the coherent contour perception? IC perception involves the process of completing the local discrete contour edges (contour completion) and the process of representing the global completed contour information (contour representation). Here, functional magnetic resonance imaging was used to dissociate contour completion and contour representation by varying each in opposite directions. The results show that the neural activity was stronger to stimuli with more contour completion than to stimuli with more contour representation in V1 and V2, which was the reverse of that in the LOC. When inspecting the neural activity change across the visual pathway, the activation remained high for the stimuli with more contour completion and increased for the stimuli with more contour representation. These results suggest distinct neural correlates of contour completion and contour representation, and the possible collaboration between the two processes during IC perception, indicating a neural connection between the discrete retinal input and the coherent visual percept.

| S-EPMC6870262 | biostudies-literature

Global vision object detection using an improved Gaussian Mixture model based on contour.

Project description:Object detection plays an important role in the field of computer vision. The purpose of object detection is to identify the objects of interest in the image and determine their categories and positions. Object detection has many important applications in various fields. This article addresses the problems of unclear foreground contour in moving object detection and excessive noise points in the global vision, proposing an improved Gaussian mixture model for feature fusion. First, the RGB image was converted into the HSV space, and a mixed Gaussian background model was established. Next, the object area was obtained through background subtraction, residual interference in the foreground was removed using the median filtering method, and morphological processing was performed. Then, an improved Canny algorithm using an automatic threshold from the Otsu method was used to extract the overall object contour. Finally, feature fusion of edge contours and the foreground area was performed to obtain the final object contour. The experimental results show that this method improves the accuracy of the object contour and reduces noise in the object.

| S-EPMC10803047 | biostudies-literature

Reduced crowding and poor contour detection in schizophrenia are consistent with weak surround inhibition.

Project description:BackgroundDetection of visual contours (strings of small oriented elements) is markedly poor in schizophrenia. This has previously been attributed to an inability to group local information across space into a global percept. Here, we show that this failure actually originates from a combination of poor encoding of local orientation and abnormal processing of visual context.MethodsWe measured the ability of observers with schizophrenia to localise contours embedded in backgrounds of differently oriented elements (either randomly oriented, near-parallel or near-perpendicular to the contour). In addition, we measured patients' ability to process local orientation information (i.e., report the orientation of an individual element) for both isolated and crowded elements (i.e., presented with nearby distractors).ResultsWhile patients are poor at detecting contours amongst randomly oriented elements, they are proportionally less disrupted (compared to unaffected controls) when contour and surrounding elements have similar orientations (near-parallel condition). In addition, patients are poor at reporting the orientation of an individual element but, again, are less prone to interference from nearby distractors, a phenomenon known as visual crowding.ConclusionsWe suggest that patients' poor performance at contour perception arises not as a consequence of an "integration deficit" but from a combination of reduced sensitivity to local orientation and abnormalities in contextual processing. We propose that this is a consequence of abnormal gain control, a phenomenon that has been implicated in orientation-selectivity as well as surround suppression.

| S-EPMC3621669 | biostudies-literature

Automated, contour-based tracking and analysis of cell behaviour over long time scales in environments of varying complexity and cell density.

Project description:Understanding single and collective cell motility in model environments is foundational to many current research efforts in biology and bioengineering. To elucidate subtle differences in cell behaviour despite cell-to-cell variability, we introduce an algorithm for tracking large numbers of cells for long time periods and present a set of physics-based metrics that quantify differences in cell trajectories. Our algorithm, termed automated contour-based tracking for in vitro environments (ACTIVE), was designed for adherent cell populations subject to nuclear staining or transfection. ACTIVE is distinct from existing tracking software because it accommodates both variability in image intensity and multi-cell interactions, such as divisions and occlusions. When applied to low-contrast images from live-cell experiments, ACTIVE reduced error in analysing cell occlusion events by as much as 43% compared with a benchmark-tracking program while simultaneously tracking cell divisions and resulting daughter-daughter cell relationships. The large dataset generated by ACTIVE allowed us to develop metrics that capture subtle differences between cell trajectories on different substrates. We present cell motility data for thousands of cells studied at varying densities on shape-memory-polymer-based nanotopographies and identify several quantitative differences, including an unanticipated difference between two 'control' substrates. We expect that ACTIVE will be immediately useful to researchers who require accurate, long-time-scale motility data for many cells.

| S-EPMC4208371 | biostudies-literature

A validated active contour method driven by parabolic arc model for detection and segmentation of mitochondria.

Project description:Recent studies reveal that mitochondria take substantial responsibility in cellular functions that are closely related to aging diseases caused by degeneration of neurons. These studies emphasize that the membrane and crista morphology of a mitochondrion should receive attention in order to investigate the link between mitochondrial function and its physical structure. Electron microscope tomography (EMT) allows analysis of the inner structures of mitochondria by providing highly detailed visual data from large volumes. Computerized segmentation of mitochondria with minimum manual effort is essential to accelerate the study of mitochondrial structure/function relationships. In this work, we improved and extended our previous attempts to detect and segment mitochondria from transmission electron microcopy (TEM) images. A parabolic arc model was utilized to extract membrane structures. Then, curve energy based active contours were employed to obtain roughly outlined candidate mitochondrial regions. Finally, a validation process was applied to obtain the final segmentation data. 3D extension of the algorithm is also presented in this paper. Our method achieved an average F-score performance of 0.84. Average Dice Similarity Coefficient and boundary error were measured as 0.87 and 14nm respectively.

| S-EPMC5308287 | biostudies-literature

Improved Algorithmic Complexity for the 3SEQ Recombination Detection Algorithm.

Project description:Identifying recombinant sequences in an era of large genomic databases is challenging as it requires an efficient algorithm to identify candidate recombinants and parents, as well as appropriate statistical methods to correct for the large number of comparisons performed. In 2007, a computation was introduced for an exact nonparametric mosaicism statistic that gave high-precision P values for putative recombinants. This exact computation meant that multiple-comparisons corrected P values also had high precision, which is crucial when performing millions or billions of tests in large databases. Here, we introduce an improvement to the algorithmic complexity of this computation from O(mn3) to O(mn2), where m and n are the numbers of recombination-informative sites in the candidate recombinant. This new computation allows for recombination analysis to be performed in alignments with thousands of polymorphic sites. Benchmark runs are presented on viral genome sequence alignments, new features are introduced, and applications outside recombination analysis are discussed.

| S-EPMC5850291 | biostudies-literature

Visual extrapolation of contour geometry.

Project description:Computing the shapes of object boundaries from fragmentary image contours poses a formidable problem for the visual system. We investigated the extrapolation of contour shape by human vision. Measurements of extrapolation position and orientation were taken at six distances from the point of occlusion, thereby yielding a detailed representation of the extrapolated contours. Analyses of these measurements revealed that: (i) extrapolation curvature increases linearly with the curvature of the inducing contour, although there is individual bias in the slope; (ii) the precision with which an extrapolated contour is represented is roughly constant, in angular terms, with increasing distance from the point of occlusion; (iii) there is a substantial cost of curvature, in that the overall precision of an extrapolated contour decreases systematically with curvature; (iv) the shapes of visually extrapolated contours are characterized by a nonlinear decrease in curvature, asymptoting to zero; and (v) this decaying pattern of curvature is explained by a Bayesian model in which, with increasing distance from the point of occlusion, the prior tendency to minimize curvature gradually dominates the likelihood tendency to minimize variation in curvature.

| S-EPMC545560 | biostudies-literature

Scotopic Contour Deformation Detection Reveals Early Rod Dysfunction in Age-Related Macular Degeneration With and Without Reticular Pseudodrusen.

Project description:PurposeThe purpose of this study was to investigate scotopic contour deformation detection (sCDD), and its structural determinants, in participants with intermediate age-related macular degeneration (iAMD) with or without reticular pseudodrusen (RPD).MethodsForty-one participants (aged 58-89 years), including 9 with iAMD and RPD, 16 with iAMD only, and 16 controls, underwent functional testing. The sCDD was evaluated with radial frequency arcs presented at 4 loci: ±4 degrees and 8 degrees vertical eccentricity. Scotopic thresholds and dark adaptation (DA) were measured at the same loci. Retinal layers of spectral domain optical coherence tomography (SD-OCT) volume scans were segmented. To establish the concurrent validity of the functional test, we evaluated the fraction of variability in sCDD thresholds explained by SD-OCT data.ResultsThe iAMD group had significantly worse sCDD thresholds compared with controls (8 degrees inferior retina: P = 0.004 and the 4 degrees loci: P < 0.02 for both). Elevated sCDD thresholds were observed in iAMD and RPD eyes at loci with normal scotopic thresholds; the opposite was rarely encountered. Elevated sCDD thresholds were also observed in iAMD eyes with normal DA. Elevated sCDD thresholds were associated with increased age and presence of late AMD in the fellow eye. The optimal machine learning model predicted 16% of variability (cross-validated R2) in sCDD thresholds at 8 degrees.DiscussionA novel scotopic contour deformation task can provide unique information about rod dysfunction in participants with iAMD and RPD not observed with structural and other functional assessments. Rod dysfunction observed with scotopic contour deformation testing was associated with factors linked to risk of AMD progression.

| S-EPMC9234356 | biostudies-literature

OmicsDI is part of the ELIXIR infrastructure

OmicsDI is an Elixir interoperability service. Learn more ›

Tweets

OmicsDI Databases

PRIDE
PeptideAtlas
MassIVE
JPOST Repository
Physiome Model Repository

EGA
EVA
ENA
LINCS
PAXDB
Cell Collective

MetaboLights
Metabolomics Workbench
MetabolomeExpress
GNPS
BioModels
FAIRDOMHub

ArrayExpress
dbGaP
ExpressionAtlas
GEO
NODE

Information

Databases
Help
API
Contact us
Code on GitHub
Terms of use
Submit Data