Visual Distinctness Research Articles

Evidence for morph-specific substrate choice in a green-brown polymorphic grasshopper.

Orthopteran insects are characterized by high variability in body coloration, in particular featuring a widespread green-brown color polymorphism. The mechanisms that contribute to the maintenance of this apparently balanced polymorphism are not yet understood. To investigate whether morph-dependent microhabitat choice might contribute to the continued coexistence of multiple morphs, we studied substrate choice in the meadow grasshopper Pseudochorthippus parallelus. The meadow grasshopper occurs in multiple discrete, genetically determined color morphs that range from uniform brown to uniform green. We tested whether three common morphs preferentially choose differently colored backgrounds in an experimental arena. We found that a preference for green backgrounds was most pronounced in uniform green morphs. If differential choices improve morph-specific performance in natural habitats via crypsis and/or thermoregulatory benefits, they could help to equalize fitness differences among color morphs and potentially produce frequency-dependent microhabitat competition, though difference appear too small to serve as the only explanation. We also measured the reflectance of the grasshoppers and backgrounds and used visual modeling to quantify the detectability of the different morphs to a range of potential predators. Multiple potential predators, including birds and spiders, are predicted to distinguish between morphs chromatically, while other species, possibly including grasshoppers themselves, will perceive only differences in brightness. Our study provides the first evidence that morph-specific microhabitat choice might be relevant to the maintenance of the green-brown polymorphisms in grasshoppers and shows that visual distinctness of color morphs varies between perceivers.

Difference expansion based reversible data embedding and edge detection

Difference Expansion (DE) based techniques are evolving in the last decade. The DE based techniques aims at reversibly embedding the data into cover content. Most of the DE based reversible embedding techniques aims to reduce/avoid the amount of auxiliary information to be embedded into the cover images and then state that the auxiliary information generated in the process has no applications rather to keep track of the overflow and/or underflow problems that may arise during the reversible embedding. Our objective is to address the above issues as well as address other related issues as stated below. In this article, we present multiple contributions based on difference expansion technique. At first, we propose a DE based reversible scheme to reduce the amount of auxiliary information to be embedded into the image, to increase the length of the watermark, using the pixel pair information of the changeable differences. The application which emphasizes the need for preserving the auxiliary information, generated by layer-2 DE embedding, is identified. A simple mechanism is suggested to increase the visual quality when the data is embedded using layer-2 embedding. Next, we propose an edge detection scheme using the DE technique. We used PSNR and SSIM visual quality assessment metrics and embedding capacity measure to assess the effectiveness of the proposed data embedding algorithms. The metrics Precision and Recall are used to assess the DE based edge detection scheme. The experimental tests have been conducted on two different datasets having complex visual distinctness features. The analytical and experimental results demonstrate that the proposed work significantly improved state of the art.

Effects of pictorial cues on reaching depend on the distinctiveness of target objects.

There is an ongoing debate under what conditions learned object sizes influence visuomotor control under preserved stereovision. Using meaningful objects (matchboxes of locally well-known brands in the UK) a previous study has nicely shown that the recognition of these objects influences action programming by means of reach amplitude and grasp pre-shaping even under binocular vision. Using the same paradigm, we demonstrated that short-term learning of colour-size associations was not sufficient to induce any visuomotor effects under binocular viewing conditions. Now we used the same matchboxes, for which the familiarity effect was shown in the UK, with German participants who have never seen these objects before. We addressed the question whether simply a high degree of distinctness, or whether instead actual prior familiarity of these objects, are required to affect motor computations. We found that under monocular and binocular viewing conditions the learned size and location influenced the amplitude of the reaching component significantly. In contrast, the maximum grip aperture remained unaffected for binocular vision. We conclude that visual distinctness is sufficient to form reliable associations in short-term learning to influence reaching even for preserved stereovision. Grasp pre-shaping instead seems to be less susceptible to such perceptual effects.

Effect of Manufacturing Method and Aging Environment on Painted Automotive Carbon Fibre Composite Surfaces

In this paper, the effect of various aging environments on the painted surface finish of unidirectional carbon fibre composite laminates, manufactured by autoclave and a novel out-of-autoclave technique was investigated. Laminates were exposed to water immersion, 95 % relative humidity and cyclic environments for 552 h and the surface finish was evaluated using visual and wave-scan distinctness of image (DOI) techniques. It was found that the laminate surface finish was dependent on the amount of moisture in the aging test. Minor surface waviness occurred on the laminates exposed to the cyclic test, whereas, surface waviness, print through and DOI values were all significantly higher as the laminates absorbed larger quantities of moisture from the hygrothermal and hydrothermal tests. The water immersion test, which was the most detrimental to the surface finish of the painted laminates, produced dense blistering on the autoclave manufactured laminate surface whereas the out-of-autoclave laminate surface produced only a few. It was found that the out-of-autoclave laminate had high substrate surface roughness which resulted in improved paint adhesion and, therefore, prevented the formation of surface blistering with aging.

The effect of visual design and placement of intra-article navigation schemes on reading comprehension and website user perceptions

Multipage articles on websites require intra-article navigation schemes and, when well designed, such navigational schemes signal the topical structure of the article’s contents. This study examines the effect of the visual design and placement of intra-article navigation schemes on website users’ reading comprehension and user experience. Using an informational medical website, four treatments of an intra-article navigation scheme were varied in their level of visual distinctness from the site’s other navigation devices and in their placement in the web page. Significant differences were found for reading comprehension and perceived knowledge gained. Reading comprehension was highest with an intra-article navigation scheme that was most visually distinct as an article table of contents and that replaced the site’s navigation on the web page layout. Paradoxically, participants’ perceptions of the amount of knowledge they gained were rated lowest for this condition. Suggestions for navigation design are presented.

Axiomatic approach to computational attention

Here we describe, in terms of a decision problem, any situation in which a computational system will be forced to allocate attention at any time to one spatial location to improve the reconstruction fidelity on a neighborhood of the chosen point. The result is a rational model of computational attention in which a multi-bitrate attention map will provide us with the attention score for each spatial location at high and low quality versions of the image reconstruction. At any time a rational system should choose, even though without any outside knowledge, among alternative spatial locations in such a way as to avoid certain forms of behavioral inconsistency. We compare the performance between a rational approach of computational attention and various models for predicting visual target distinctness, using scenes that represent military vehicles in complex rural backgrounds.

Effects of Visual and Phonological Distinctness on Visual–verbal Paired Associate Learning in Dutch Dyslexic and Normal Readers

In three studies, the effects of visual and phonological distinctness on the visual–verbal paired associate learning of dyslexic and normal readers at the age of 10–12 were examined. We hypothesized that both groups would be equally affected by the visual distinctness of the pictures, whereas the learning performance of the dyslexic children would be more susceptible to the phonological distinctness of the verbal stimuli (words). As expected, in Study 1 we found that the visual distinctness of pictures had a similar effect on both groups. However, the results of Studies 2 and 3 on the effect of phonological distinctness did not support the hypothesis. Both reader groups were equally affected by the phonological distinctness of the words. In addition, we found that, although not consistently, dyslexic children tended to be worse in verbal learning, which could to a large extent be explained by their problems with phonological processing.

The relationship between information prioritization and visual distinctness in two progressive image transmission schemes

The goal of this paper is to analyze the relation between the visual distinctness and the priority assigned to the information in two different progressive transmission schemes: the Set Partitioning in Hierarchical Trees (SPIHT) and the Rational Embedded Wavelet Image Coding (REWIC). SPIHT assigns priority based on the mean squared-error (MSE) distortion measure. REWIC quantifies the visual distinctness through the expected increase in utility per coding bit, using a simple function of the Kullback–Leibler's information gain, called dist REWIC . We will show that in progressive transmission, the information selected through the increase in utility per coding bit corresponds to information with the largest visual relevance to a higher degree than the information selected on the basis of the MSE metric.

CORAL: collective rationality for the allocation of bits

The effectiveness of a coding method can be improved through a space-varying filterbank tree representation of the image, and this property can be conveniently exploited using appropriate bit alloca- tion strategies among the spatial segments of the image. We examine the conditions for achieving a rational agreement on the distribution problem by stating axioms that its solution must obey in absence of a priori knowledge about regions of interest. First, a measure of benefit avoiding certain forms of behavioral inconsistency is to be assigned to each possible bit allocation in such a way that each region's preference may be inferred between any two bit allocations from their respective benefits. Second, individual regions are to agree on an allocation of bits, which is then to be brought about by a joint strategy. But under what conditions is their agreement rational? We propose a characterization of rational agreement whose solution is an application of a general proce- dure for cooperative action, where each may benefit only on terms that permit proportionately equal benefits to others. Experimental results are given to evaluate the performance of the strategy of collective rationality for the allocation of bits (CORAL), based on a validated predictor for visual distinctness from digital imagery. © 2003 Society of Photo-Optical Instru-

Defining a target distinctness measure through a single-channel computational model of vision

The main goal of this paper is to define a computational measure for quantifying the visual distinctness of targets in complex natural scenes, using a single-channel vision model. The basic features incorporated in the defined target distinctness measure are: (i) an adaptive contrast sensitivity function; (ii) an estimation of image contrast masking and (iii) an estimation of a set of interest points in the image. Several experiments are performed to determine the relative contribution of each of these factors to the measure, using the correlation with human performance data as the evaluation function.

Rational systems exhibit moderate risk aversion with respect to “gambles” on variable-resolution compression

In an embedded wavelet scheme for progressive transmission, a tree structure naturally defines the spatial relationship on the hierarchical pyramid. Transform coefficients over each tree correspond to a unique local spatial region of the original image, and they can be coded bit-plane by bit-plane through successive-approximation quantization. After receiving the approximate value of some coefficients, the decoder can obtain a reconstructed image. We show a rational system for progressive transmission that, in absence of a priori knowledge about regions of interest, chooses at any truncation time among alternative trees for further transmission in such a way as to avoid certain forms of behavioral inconsistency. We prove that some rational transmission systems might exhibit aversion to risk involving gambles on tree-dependent quality of encoding while others favor taking such risks. Based on an acceptable predictor for visual distinctness from digital imagery, we demonstrate that, without any outside knowledge, risk-prone systems as well as those with strong risk aversion appear in capable of attaining the quality of reconstructions that can be achieved with moderate risk-averse behavior.

Image dataset for testing search and detection models

The TNO Human Factors Searchu2 image dataset consists of: a set of 44 high-resolution digital color images of different complex natural scenes, the ground truth corresponding to each of these scenes, and the results of psychophysical experiments on each of these images. The images in the Searchu2 dataset are a subset of a larger set that has been used in a visual search and detection experiment. Each scene (image) contains a single military vehicle that serves as a search target. The image dataset, an Excel file with the ground truth and observer data, and an extensive report describing the dataset are available on CD-ROM (requests by email to the first author). The dataset can be used to develop and validate digital metrics that compute the visual distinctness (contrast, conspicuity, or saliency) of targets in complex scenes, and models of human visual search and detection. The dataset has already been used in more than ten different studies in the literature, ranging from studies evaluating target detectability metrics to eye movement studies and attempts to model the human visual system. In addition to this work, eight other articles in this special section address the Searchu2 dataset.

Minimum error gain for predicting visual target distinctness

We present a new method for characterizing information about a target relative to its background. The resultant computational measures are then applied to quantify the visual distinctness of targets in complex natural backgrounds from digital imagery. A generalization of the Kullback-Leibler joint information gain over the optimal interest points of the target image is shown to correlate strongly with visual target dis- tinctness as estimated by human observers. Optimal interest points are defined as spatial locations of partially invariant features, which minimize the error probability between the target and the nontarget scenes; their significance is a function of the corresponding degree of congruence across scales and orientations. © 2001 Society of Photo-Optical Instrumentation Engineers. (DOI: 10.1117/1.1389064)

Information theoretic measure for visual target distinctness

It is of great benefit to have advance knowledge of human visual target acquisition performance for targets or other relevant objects. However, search performance inherently shows a large variance and depends strongly on prior knowledge of the perceived scene. A typical search experiment therefore requires a large number of observers to obtain statistically reliable data. Moreover, measuring target acquisition performance in field situations is usually impractical and often very costly or even dangerous. The paper presents a method for characterizing information of a target relative to its background. The resultant computational measures are then applied to quantify the visual distinctness of targets in complex natural backgrounds from digital imagery. A generalization of the Kullback-Leibler joint information gain of various random variables is shown to correlate strongly with visual target distinctness as estimated by human observers. Bootstrap methods for assessing statistical accuracy were used to produce this inference.

Integral opponent-colors features for computing visual target distinctness

This paper presents a computational target distinctness model to predict human visual search performance for a set of color images. Visual distinctness metrics can be used to compare and rank target detectability, and to quantify background or scene complexity. The subjective ranking induced by the psychophysically determined visual target distinctness for 64 human observers is adopted as the reference order. The ranking produced by the proposed measure and several computational models are compared with the subjective rank order.

Defining the notion of visual pattern for predicting visual target distinctness in a complex rural background

The goal of this paper is to analyze a new notion of visual pattern that captures the primary details for the task of quantifying the visual distinctness of targets in complex natural scenes. We show that given an objective function, which is defined as the mean of the fraction of correctly classified targets across a number of datasets and whose maximization is desired, the optimal notion of visual pattern in a complex natural background can be defined as congruence in a certain statistical structure at attentional points across a range of 2-D frequency bands. We draw this conclusion from several experiments in which the best definition of visual pattern is estimated based on the relation between the visual target distinctness measured by human observers and a compu- tational distance that applies a simple decision rule to the differences between segregated visual patterns. © 2000 Society of Photo-Optical Instrumen- tation Engineers. (S0091-3286(00)01802-X)

Computing visual target distinctness through selective filtering, statistical features, and visual patterns

This paper presents three computational visual distinctness measures, computed from image representational models based on selective filtering, statistical features, and visual patterns, respectively. They are applied to quantify the visual distinctness of targets in complex natural scenes. The measure that applies a simple decision rule to the distances between segregated visual patterns is shown (1) to predict human observer performance in search and detection tasks on complex natural imagery, and (2) to correlate strongly with visual target distinctness estimated by human observers.

The RGFF representational model: a system for the automatically learned partitioning of "visual patterns" in digital images

This paper describes a system for the automatically learned partitioning of visual patterns in 2D images, based on sophisticated band-pass filtering with fixed scale and orientation sensitivity. The visual patterns are defined as the features which have the highest degree of alignment in the statistical structure across different frequency bands. The analysis reorganizes the image according to an invariance constraint in statistical structure and consists of three stages: pre-attentive stage, integration stage, and learning stage. The first stage takes the input image and performs filtering with log-Gabor filters. Based on their responses, activated filters which are selectively sensitive to patterns in the image are short listed. In the integration stage, common grounds between several activated sensors are explored. The filtered responses are analyzed through a family of statistics. For any given two activated filters, a distance between them is derived via distances between their statistics. The third stage performs cluster partitioning for learning the subspace of log-Gabor filters needed to partition the image data. The clustering is based on a dissimilarity measure intended to highlight scale and orientation invariance of the responses. The technique is illustrated on real and simulated data sets. Finally, this paper presents a computational visual distinctness measure computed from the image representational model based on visual patterns. Experiments are performed to investigate its relation to distinctness as measured by human observers.

Computational visual distinctness metric

A new computational visual distinctness metric based on principles of the early human visual system is presented. The metric is applied to quantify (1) the visual distinctness of targets in complex natural scenes and (2) the perceptual differences between compressed and uncompressed images. The new metric is shown (1) to predict human observer performance in search and detection tasks on complex natural imagery, (2) to correlate with visual target distinctness estimated by human observers, and (3) to correlate with the quality factor of Joint Photographic Experts Group (JPEG) compressed imagery. © 1998 Society of Photo-Optical Instrumentation Engineers.

Visual conspicuity determines human target acquisition performance

A simple and efficient psychophysical procedure is presented to quantify the visual conspicuity of a target in a complex (natural) scene. Measurements can easily and quickly be performed in the field or in complex environments. Only a few observers (typically two to three) are needed to achieve sufficient accuracy. We show that this conspicuity measure predicts human visual search performance in realistic and military relevant complex scenarios. Also, conspicuity measured on photographic slides agrees with conspicuity measured in the field, which implies that the new conspicuity measure can be used in combination with photosimulation studies to optimize and evaluate the visual distinctness of displayed information.