Signal/noise ratio at the visual recognition threshold of incomplete figures

We measured recognition thresholds of incomplete figure perception (the Gollin test). This test we regarded as a visual masking problem. Digital image processing permits us to measure the spatial properties and spatial frequency spectrum of the absent part of the image as the mask. Using a noise paradigm, we have measured the signal/noise ratio for Incomplete Figure. Recognition was worse with better spectral "similarity" between the figure and the "invisible" mask. At threshold, the spectrum of the fragmented image was equally similar to that of the "invisible" mask and complete image. We think the recognition thresholds for Gollin stimuli reflect the signal/noise ratio.     

Optical-geometric characteristics of fragmented figures and thresholds of their wholesome perception in repeated tests

Factors that lead to decreasing thresholds across repeated test sessions in Gollin incomplete figure test were investigated. In the first experiment, 4 tests with 5-day intervals and using same test figures, were conducted. Thresholds of recognition in each test and amplitude-frequency characteristics of test images were compared by correlation analysis. In the second investigation, 2 tests with 3-day intervals were conducted. In repeated tests, half figures were familiar to the subjects. In addition, half fragmented figures (both familiar an unfamiliar to the subjects) had such amplitude-frequency characteristics as in the first test. It was found that decreasing of thresholds was primary conditioned by optimization of mechanisms that work with statistical (not object) information.     

Optical-geometrical parameters and perception threshold of the fragmented contour figures

The thresholds of recognition of line drawings of common objects were measured using the Gollin-test procedure, in which separate random line fragments are displayed cumulatively up to the point of recognition. It was shown that the mean percentage of contour displayed at threshold recognition for different images was always about 12.5%, despite inter-subject variability between 5% and 25%. The comparative and spatial-frequency analysis of the geometrical parameters of images was carried out for different levels of fragmentation (before threshold, at threshold, and for the complete contour). The magnitude information of the Fourier domain image of figures was characterized by maximum at low and high levels of fragmentation, but at recognition threshold fragmentation it was characterized by minimum variability.     

Visual exploration and object recognition by lattice deformation

Mechanisms of explicit object recognition are often difficult to investigate and require stimuli with controlled features whose expression can be manipulated in a precise quantitative fashion. Here, we developed a novel method (called "Dots"), for generating visual stimuli, which is based on the progressive deformation of a regular lattice of dots, driven by local contour information from images of objects. By applying progressively larger deformation to the lattice, the latter conveys progressively more information about the target object. Stimuli generated with the presented method enable a precise control of object-related information content while preserving low-level image statistics, globally, and affecting them only little, locally. We show that such stimuli are useful for investigating object recognition under a naturalistic setting--free visual exploration--enabling a clear dissociation between object detection and explicit recognition. Using the introduced stimuli, we show that top-down modulation induced by previous exposure to target objects can greatly influence perceptual decisions, lowering perceptual thresholds not only for object recognition but also for object detection (visual hysteresis). Visual hysteresis is target-specific, its expression and magnitude depending on the identity of individual objects. Relying on the particular features of dot stimuli and on eye-tracking measurements, we further demonstrate that top-down processes guide visual exploration, controlling how visual information is integrated by successive fixations. Prior knowledge about objects can guide saccades/fixations to sample locations that are supposed to be highly informative, even when the actual information is missing from those locations in the stimulus. The duration of individual fixations is modulated by the novelty and difficulty of the stimulus, likely reflecting cognitive demand.     

Perceptual organization of local elements into global shapes in the human visual cortex

The question of how local image features on the retina are integrated into perceived global shapes is central to our understanding of human visual perception. Psychophysical investigations have suggested that the emergence of a coherent visual percept, or a "good-Gestalt", is mediated by the perceptual organization of local features based on their similarity. However, the neural mechanisms that mediate unified shape perception in the human brain remain largely unknown. Using human fMRI, we demonstrate that not only higher occipitotemporal but also early retinotopic areas are involved in the perceptual organization and detection of global shapes. Specifically, these areas showed stronger fMRI responses to global contours consisting of collinear elements than to patterns of randomly oriented local elements. More importantly, decreased detection performance and fMRI activations were observed when misalignment of the contour elements disturbed the perceptual coherence of the contours. However, grouping of the misaligned contour elements by disparity resulted in increased performance and fMRI activations, suggesting that similar neural mechanisms may underlie grouping of local elements to global shapes by different visual features (orientation or disparity). Thus, these findings provide novel evidence for the role of both early feature integration processes and higher stages of visual analysis in coherent visual perception.     

Age related size invariance in perception of illusory and fragmented contours

We investigated invariant perception to sizes of images. Observers were schoolmates of 7-17 years and adults. Two types of stimuli were used: fragmented line drawing of common objects and discs with deleted sectors, which represented illusory Kanizsa contours when discs were in particular positions. In experiments with fragmented images, we found an improvement in image recognition with observers' age, increasing up to 13-14 years. The probability of recognition of fragmented line drawings increased significantly with decreasing stimulus size for children of 7-12 years, indicating that size invariance at recognition for fragmented line drawings was absent in these children. However, size invariance was found for observers of 13-17 years and for adults in this task. At Kanizsa illusion appearance, the ratio of the separation between discs and disc diameter was smaller when we used larger disc diameters. This ratio increased with increasing age of observers. Obtained results provide evidence for the absence of size invariance when perceiving the Kanizsa illusion in our experimental conditions.     

Noise effect on the recognition of fragmented contour images

We compared the results of recognition of fragmented contour images in the presence of noise and without noise. Both the contour images and the visual noise were synthesized with Gabor elements. The spacing between fragments in contour images and between noise elements, as well as the sizes of images, varied irrespective of one another. The percentage of recognition did not depend on the size of stimuli, but it differed for various objects in the presence and absence of noise. The percentage of recognition was higher for images with lots of turns in the absence of noise and, on the contrary, for images with lengthy contours with a lightly varying curvature in the presence of noise. The thresholds of recognition in noise depended, in general, on the ratio of the spacing between the elements in noise to the spacing between contour fragments.     

Age-related size invariance in perception of illusory and fragmented contours

We investigated invariant perception to sizes of images. Observers were secondary school students aged 7–17 years and adults. Two types of stimuli were used: fragmented line drawing of common objects and discs with deleted sectors which represented illusory Kanizsa contours, when discs were in particular positions. In experiments with fragmented images, we found an improvement in image recognition with observer’s age, increasing up to 13–14 years. The probability of recognition of fragmented line drawings increased significantly with decreasing stimulus size for children aged 7–12 years, indicating that size invariance at recognition for fragmented line drawings was absent in these children. However, size invariance was found for observers aged 7–12 years and for adults in this task. Upon the Kanizsa illusion appearance, the ratio of the separation between discs and disc diameter was smaller when we used larger disc diameters. This ratio increased with increasing age of observers. The obtained results provide evidence for the absence of size invariance when perceiving the Kanizsa illusion under our experimental conditions.     

Recognition of fragmented images and mechanisms of memory

Analysis of the topography and parameters of event-related potentials (ERPs) recorded during the presentation of incomplete images with different fragmentation aided in study of the role of different cortical zones and the order of their involvement in the recognition process. The role of the frontal cortical areas at different stages of perception of fragmented images was established. The differences in the ERPs induced by recognized and unrecognized stimuli in the frontal and frontal-temporal derivations in the interval 30–83 ms were associated with the appearance of early positivity in response to recognized images and development of early negativity in response to unrecognized stimuli. The N300 component associated with recognition was stronger in these cortical zones during identification of images. A late positive complex appeared in the frontal areas earlier than in other areas. Involvement of the caudal visual areas in the recognition process was reflected by enhancement of the components P100, P250, and N400. Our results suggest that the frontal areas play the main role in the recognition of fragmented images because they are the structures that organize extraction of traces from long-term modality-specific memory using a system of afferent and efferent links and determine the strategy of information analysis necessary for the solution of a given task.     

Differences Help Recognition: A Probabilistic Interpretation

This paper presents a computational model to address one prominent psychological behavior of human beings to recognize images. The basic pursuit of our method can be concluded as that differences among multiple images help visual recognition. Generally speaking, we propose a statistical framework to distinguish what kind of image features capture sufficient category information and what kind of image features are common ones shared in multiple classes. Mathematically, the whole formulation is subject to a generative probabilistic model. Meanwhile, a discriminative functionality is incorporated into the model to interpret the differences among all kinds of images. The whole Bayesian formulation is solved in an Expectation-Maximization paradigm. After finding those discriminative patterns among different images, we design an image categorization algorithm to interpret how these differences help visual recognition within the bag-of-feature framework. The proposed method is verified on a variety of image categorization tasks including outdoor scene images, indoor scene images as well as the airborne SAR images from different perspectives.     

Rough-Fuzzy Clustering and Unsupervised Feature Selection for Wavelet Based MR Image Segmentation

Image segmentation is an indispensable process in the visualization of human tissues, particularly during clinical analysis of brain magnetic resonance (MR) images. For many human experts, manual segmentation is a difficult and time consuming task, which makes an automated brain MR image segmentation method desirable. In this regard, this paper presents a new segmentation method for brain MR images, integrating judiciously the merits of rough-fuzzy computing and multiresolution image analysis technique. The proposed method assumes that the major brain tissues, namely, gray matter, white matter, and cerebrospinal fluid from the MR images are considered to have different textural properties. The dyadic wavelet analysis is used to extract the scale-space feature vector for each pixel, while the rough-fuzzy clustering is used to address the uncertainty problem of brain MR image segmentation. An unsupervised feature selection method is introduced, based on maximum relevance-maximum significance criterion, to select relevant and significant textural features for segmentation problem, while the mathematical morphology based skull stripping preprocessing step is proposed to remove the non-cerebral tissues like skull. The performance of the proposed method, along with a comparison with related approaches, is demonstrated on a set of synthetic and real brain MR images using standard validity indices.     

Image-based multivariate profiling of drug responses from single cells

Quantitative analytical approaches for discovering new compound mechanisms are required for summarizing high-throughput, image-based drug screening data. Here we present a multivariate method for classifying untreated and treated human cancer cells based on approximately 300 single-cell phenotypic measurements. This classification provides a score, measuring the magnitude of the drug effect, and a vector, indicating the simultaneous phenotypic changes induced by the drug. These two quantities were used to characterize compound activities and identify dose-dependent multiphasic responses. A systematic survey of profiles extracted from a 100-compound compendium of image data revealed that only 10-15% of the original features were required to detect a compound effect. We report the most informative image features for each compound and fluorescence marker set using a method that will be useful for determining minimal collections of readouts for drug screens. Our approach provides human-interpretable profiles and automatic determination of on- and off-target effects.     

Motion detection in human vision: a unifying approach based on energy and features

Most studies of human motion perception have been based on the implicit assumption that the brain has only one motion-detection system, or at least that only one is operational in any given instance. We show, in the context of direction perception in spatially filtered two-frame random-dot kinematograms, that two quite different mechanisms operate simultaneously in the detection of such patterns. One mechanism causes reversal of the perceived direction (reversed-phi motion) when the image contrast is reversed between frames, and is highly dependent on the spatial-frequency content of the image. These characteristics are both signatures of detection based on motion energy. The other mechanism does not produce reversed-phi motion and is unaffected by spatial filtering. This appears to involve the tracking of unsigned complex spatial features. The perceived direction of a filtered dot pattern typically reflects a mixture of the two types of behaviour in any given instance. Although both types of mechanism have previously been invoked to explain the perception of motion of different types of image, the simultaneous involvement of two mechanisms in the detection of the same simple rigid motion of a pattern suggests that motion perception in general results from a combination of mechanisms working simultaneously on different principles in the same circumstances.     

Interhemispheric differences in the spatial-frequency description of images and the consequences for visual recognition

Visual perception of images transformed by spatial-frequency filtering was investigated in tachistoscopic experiments. Evidences have been received that the left hemisphere describes preferentially low harmonics of an image, and right hemisphere--the high ones. A hypothesis is suggested that these differences are based on different sizes of the receptive fields of cortical modules. Some consequences of the proposed model are discussed--the consequences for the invariance of visual recognition (innate invariance mechanisms in the left hemisphere and learned invariance in the right hemisphere) and for the methods of visual image classification (discriminant or structural methods in the left and right hemisphere respectively). Some data confirming these predictions are presented.     

Rapid Reconstruction of 3D Neuronal Morphology from Light Microscopy Images with Augmented Rayburst Sampling

Digital reconstruction of three-dimensional (3D) neuronal morphology from light microscopy images provides a powerful technique for analysis of neural circuits. It is time-consuming to manually perform this process. Thus, efficient computer-assisted approaches are preferable. In this paper, we present an innovative method for the tracing and reconstruction of 3D neuronal morphology from light microscopy images. The method uses a prediction and refinement strategy that is based on exploration of local neuron structural features. We extended the rayburst sampling algorithm to a marching fashion, which starts from a single or a few seed points and marches recursively forward along neurite branches to trace and reconstruct the whole tree-like structure. A local radius-related but size-independent hemispherical sampling was used to predict the neurite centerline and detect branches. Iterative rayburst sampling was performed in the orthogonal plane, to refine the centerline location and to estimate the local radius. We implemented the method in a cooperative 3D interactive visualization-assisted system named flNeuronTool. The source code in C++ and the binaries are freely available at http://sourceforge.net/projects/flneurontool/. We validated and evaluated the proposed method using synthetic data and real datasets from the Digital Reconstruction of Axonal and Dendritic Morphology (DIADEM) challenge. Then, flNeuronTool was applied to mouse brain images acquired with the Micro-Optical Sectioning Tomography (MOST) system, to reconstruct single neurons and local neural circuits. The results showed that the system achieves a reasonable balance between fast speed and acceptable accuracy, which is promising for interactive applications in neuronal image analysis.     

Visual recognition of age class and preference for infantile features: implications for species-specific vs universal cognitive traits in primates

Despite not knowing the exact age of individuals, humans can estimate their rough age using age-related physical features. Nonhuman primates show some age-related physical features; however, the cognitive traits underlying their recognition of age class have not been revealed. Here, we tested the ability of two species of Old World monkey, Japanese macaques (JM) and Campbell's monkeys (CM), to spontaneously discriminate age classes using visual paired comparison (VPC) tasks based on the two distinct categories of infant and adult images. First, VPCs were conducted in JM subjects using conspecific JM stimuli. When analyzing the side of the first look, JM subjects significantly looked more often at novel images. Based on analyses of total looking durations, JM subjects looked at a novel infant image longer than they looked at a familiar adult image, suggesting the ability to spontaneously discriminate between the two age classes and a preference for infant over adult images. Next, VPCs were tested in CM subjects using heterospecific JM stimuli. CM subjects showed no difference in the side of their first look, but looked at infant JM images longer than they looked at adult images; the fact that CMs were totally na?ve to JMs suggested that the attractiveness of infant images transcends species differences. This is the first report of visual age class recognition and a preference for infant over adult images in nonhuman primates. Our results suggest not only species-specific processing for age class recognition but also the evolutionary origins of the instinctive human perception of baby cuteness schema, proposed by the ethologist Konrad Lorenz.     

The relative contribution of local habitat and landscape context to metapopulation processes: a dynamic occupancy modeling approach

Changes in site occupancy across habitat patches have often been attributed to landscape features in fragmented systems, particularly when considering metapopulations. However, failure to include habitat quality of individual patches can mask the relative importance of local scale features in determining distributional changes. We employed dynamic occupancy modeling to compare the strength of local habitat variables and metrics of landscape patterns as drivers of metapopulation dynamics for a vulnerable, high‐elevation species in a naturally fragmented landscape. Repeat surveys of Bicknell's thrush Catharus bicknelli presence/non‐detection were conducted at 88 sites across Vermont, USA in 2006 and 2007. We used an organism‐based approach, such that at each site we measured important local‐scale habitat characteristics and quantified landscape‐scale features using a predictive habitat model for this species. We performed a principal component analysis on both the local and landscape features to reduce dimensionality. We estimated site occupancy, colonization, and extinction probabilities while accounting for imperfect detection. Univariate, additive, and interaction models of local habitat and landscape context were ranked using AICc scores. Both local and landscape scales were important in determining changes in occupancy patterns. An interaction between scales was detected for occupancy dynamics indicating that the relationship of the parameters to local‐scale habitat conditions can change depending on the landscape context and vice versa. An increase in both landscape‐ and local‐scale habitat quality increased occupancy and colonization probability while decreasing extinction risk. Colonization and extinction were both more strongly influenced by local habitat quality relative to landscape patterns. We also identified clear, qualitative thresholds for landscape‐scale features. Conservation of large habitat patches in high‐cover landscapes will help ensure persistence of Bicknell's thrushes, but only if local scale habitat quality is maintained. Our results highlight the importance of incorporating information beyond landscape characteristics when investigating patch occupancy patterns in metapopulations.     

New image dataset and new negative sample judgment method for crop pest recognition based on deep learning models

Crop pests are responsible for serious economic loss around the worldwide. Accurate recognition of pests is the key to pest control and is a considerable challenge in farming. Deep learning models have shown great promise in image recognition, drawing the attention of many agricultural experts. However, the lack of pest image datasets and the inexplicability of deep learning models have hindered the development of deep learning models in the field of pest recognition. Our work provides the following four contributions: (1) We constructed a new and more effective dataset, for crop pest recognition, named IP41 comprising 46,567 original images of crop pests in 41 classes. (2) We trained three different deep learning models based on IP41, using transfer learning combined with fine-tuning. The results of the three deep learning models exceeded 80.00% recognition. (3) A negative sample judgment method was proposed to exclude the uploaded pest-free images of the user. (4) We provided reasonable visual explanations for the most critical areas of the recognition layers by using the gradient-weighted class activation mapping method. This research suggests that the recognition process focuses more on image details than the image as a whole, and that overall difference is ignored to a certain extent. These results will be helpful to future research in the field of agricultural pest recognition     

Functional brain organization of global and local visual perception: an ERP study

Adult subjects were asked to recognize a hierarchical visual stimulus (a letter) while their attention was drawn to either the global or local level of the stimulus. Event-related potentials (ERP) and psychophysical indices (reaction time and percentage of correct responses) were measured. An analysis of psychophysical indices showed the global level precedence effect, i.e., the increase in a small letter recognition time when this letter is a part of incongruent stimulus. An analysis of ERP components showed level-related (global vs. local) differences in the timing and topography of the brain organization of perceptual processing and regulatory mechanisms of attention. Visual recognition at the local level was accompanied by (1) stronger activation of the visual associative areas (Pz and T6) at the stage of sensory features analysis (P1 ERP component), (2) involvement mainly of inferior temporal cortices of the right hemisphere (T6) at the stage of sensory categorization (P2 ERP component), and (3) involvement of prefrontal cortex of the right hemisphere at the stage of the selection of the relevant features of the target (N2 ERP component). Visual recognition at the global level was accompanied by (1) pronounced involvement of mechanisms of early sensory selection (N1 ERP component), (2) prevailing activation of parietal cortex of the right hemisphere (P4) at the stage of sensory categorization (P2 ERP component) as well as at the stage of the target stimulus identification (P3 ERP component). It is suggested that perception at the global level of the hierarchical stimulus is related primarily to the analysis of the spatial features of the stimulus in the dorsal visual system whereas the perception at the local level primarily involves an analysis of the object-related features in the ventral visual system.     

Statistical learning methods as a preprocessing step for survival analysis: evaluation of concept using lung cancer data

Background

Despite its superb lateral resolution, flat-panel-detector (FPD) based tomosynthesis suffers from low contrast and inter-plane artifacts caused by incomplete cancellation of the projection components stemming from outside the focal plane. The incomplete cancellation of the projection components, mostly due to the limited scan angle in the conventional tomosynthesis scan geometry, often makes the image contrast too low to differentiate the malignant tissues from the background tissues with confidence.

Methods

In this paper, we propose a new method to suppress the inter-plane artifacts in FPD-based tomosynthesis. If 3D whole volume CT images are available before the tomosynthesis scan, the CT image data can be incorporated into the tomosynthesis image reconstruction to suppress the inter-plane artifacts, hence, improving the image contrast. In the proposed technique, the projection components stemming from outside the region-of-interest (ROI) are subtracted from the measured tomosynthesis projection data to suppress the inter-plane artifacts. The projection components stemming from outside the ROI are calculated from the 3D whole volume CT images which usually have lower lateral resolution than the tomosynthesis images. The tomosynthesis images are reconstructed from the subtracted projection data which account for the x-ray attenuation through the ROI. After verifying the proposed method by simulation, we have performed both CT scan and tomosynthesis scan on a phantom and a sacrificed rat using a FPD-based micro-CT.

Results

We have measured contrast-to-noise ratio (CNR) from the tomosynthesis images which is an indicator of the residual inter-plane artifacts on the focal-plane image. In both cases of the simulation and experimental imaging studies of the contrast evaluating phantom, CNRs have been significantly improved by the proposed method. In the rat imaging also, we have observed better visual contrast from the tomosynthesis images reconstructed by the proposed method.

Conclusions

The proposed tomosynthesis technique can improve image contrast with aids of 3D whole volume CT images. Even though local tomosynthesis needs extra 3D CT scanning, it may find clinical applications in special situations in which extra 3D CT scan is already available or allowed.