Features In Natural Scenes Research Articles

Cucumber pathogenic spores’ detection using the GCS-YOLOv8 network with microscopic images in natural scenes

Fungal diseases are the main factors affecting the quality and production of vegetables. Rapid and accurate detection of pathogenic spores is of great practical significance for early prediction and prevention of diseases. However, there are some problems with microscopic images collected in the natural environment, such as complex backgrounds, more disturbing materials, small size of spores, and various forms. Therefore, this study proposed an improved detection method of GCS-YOLOv8 (Global context and CARFAE and Small detector-optimized YOLOv8), effectively improving the detection accuracy of small-target pathogen spores in natural scenes. Firstly, by adding a small target detection layer in the network, the network’s sensitivity to small targets is enhanced, and the problem of low detection accuracy of the small target is effectively improved. Secondly, Global Context attention is introduced in Backbone to optimize the CSPDarknet53 to 2-Stage FPN (C2F) module and model global context information. At the same time, the feature up-sampling module Content-Aware Reassembly of Features (CARAFE) was introduced into Neck to enhance the ability of the network to extract spore features in natural scenes further. Finally, we used an Explainable Artificial Intelligence (XAI) approach to interpret the model’s predictions. The experimental results showed that the improved GCS-YOLOv8 model could detect the spores of the three fungi with an accuracy of 0.926 and a model size of 22.8 MB, which was significantly superior to the existing model and showed good robustness under different brightness conditions. The test on the microscopic images of the infection structure of cucumber down mildew also proved that the model had good generalization. Therefore, this study realized the accurate detection of pathogen spores in natural scenes and provided feasible technical support for early predicting and preventing fungal diseases.

Optimizing Fiducial Marker Placement for Improved Visual Localization

Adding fiducial markers to a scene is a well-known strategy for making visual localization algorithms more robust. Traditionally, these marker locations are selected by humans who are familiar with visual localization techniques. This paper explores the problem of automatic marker placement within a scene. Specifically, given a predetermined set of markers and a scene model, we compute optimized marker positions within the scene that can improve accuracy in visual localization. Our main contribution is a novel framework for modeling camera localizability that incorporates both natural scene features and artificial fiducial markers added to the scene. We present optimized marker placement (OMP), a greedy algorithm that is based on the camera localizability framework. We have also designed a simulation framework for testing marker placement algorithms on 3D models and images generated from synthetic scenes. We have evaluated OMP within this testbed and demonstrate an improvement in the localization rate by up to 20 percent on four different scenes.

Systematic reduction of the dimensionality of natural scenes allows accurate predictions of retinal ganglion cell spike outputs

The mammalian retina engages a broad array of linear and nonlinear circuit mechanisms to convert natural scenes into retinal ganglion cell (RGC) spike outputs. Although many individual integration mechanisms are well understood, we know less about how multiple mechanisms interact to encode the complex spatial features present in natural inputs. Here, we identified key spatial features in natural scenes that shape encoding by primate parasol RGCs. Our approach identified simplifications in the spatial structure of natural scenes that minimally altered RGC spike responses. We observed that reducing natural movies into 16 linearly integrated regions described ∼80% of the structure of parasol RGC spike responses; this performance depended on the number of regions but not their precise spatial locations. We used simplified stimuli to design high-dimensional metamers that recapitulated responses to naturalistic movies. Finally, we modeled the retinal computations that convert flashed natural images into one-dimensional spike counts.

Facial Expressions and Self-Reported Emotions When Viewing Nature Images.

Many studies have demonstrated that exposure to simulated natural scenes has positive effects on emotions and reduces stress. In the present study, we investigated emotional facial expressions while viewing images of various types of natural environments. Both automated facial expression analysis by iMotions’ AFFDEX 8.1 software (iMotions, Copenhagen, Denmark) and self-reported emotions were analyzed. Attractive and unattractive natural images were used, representing either open or closed natural environments. The goal was to further understand the actual features and characteristics of natural scenes that could positively affect emotional states and to evaluate face reading technology to measure such effects. It was predicted that attractive natural scenes would evoke significantly higher levels of positive emotions than unattractive scenes. The results showed generally small values of emotional facial expressions while observing the images. The facial expression of joy was significantly higher than that of other registered emotions. Contrary to predictions, there was no difference between facial emotions while viewing attractive and unattractive scenes. However, the self-reported emotions evoked by the images showed significantly larger differences between specific categories of images in accordance with the predictions. The differences between the registered emotional facial expressions and self-reported emotions suggested that the participants more likely described images in terms of common stereotypes linked with the beauty of natural environments. This result might be an important finding for further methodological considerations.

Asymmetrical cross-modal influence on neural encoding of auditory and visual features in natural scenes

Natural scenes contain multi-modal information, which is integrated to form a coherent perception. Previous studies have demonstrated that cross-modal information can modulate neural encoding of low-level sensory features. These studies, however, mostly focus on the processing of single sensory events or rhythmic sensory sequences. Here, we investigate how the neural encoding of basic auditory and visual features is modulated by cross-modal information when the participants watch movie clips primarily composed of non-rhythmic events. We presented audiovisual congruent and audiovisual incongruent movie clips, and since attention can modulate cross-modal interactions, we separately analyzed high- and low-arousal movie clips. We recorded neural responses using electroencephalography (EEG), and employed the temporal response function (TRF) to quantify the neural encoding of auditory and visual features. The neural encoding of sound envelope is enhanced in the audiovisual congruent condition than the incongruent condition, but this effect is only significant for high-arousal movie clips. In contrast, audiovisual congruency does not significantly modulate the neural encoding of visual features, e.g., luminance or visual motion. In summary, our findings demonstrate asymmetrical cross-modal interactions during the processing of natural scenes that lack rhythmicity: Congruent visual information enhances low-level auditory processing, while congruent auditory information does not significantly modulate low-level visual processing.

Analysis of the Relevance Environment between Marxist Philosophy and System Theory Based on Deep Learning.

In social science and natural science, MP (Marxist Philosophy) has played an active role in promoting its development, and MP also guides people's practice and understanding. There is an inevitable connection with system theory MP. In a sense, both system theory and PMbelong to methodology and both contain the viewpoints of movement and development. In this paper, various text features in natural scenes are discussed in detail, and the original vector is studied by using CNN (Convective Neural Network) of DL (Deep Learning), so as to construct a one-dimensional text vector and realize the mutual influence and continuous optimization of feature extraction and text clustering. The experimental results show that under the condition of calculating the current cosine similarity measure, the accuracy rate is the highest, reaching 93.67%. This algorithm can effectively improve its performance in text classification tasks on large data sets.

Image luminance changes contrast sensitivity in visual cortex

SUMMARYAccurate measures of contrast sensitivity are important for evaluating visual disease progression and for navigation safety. Previous measures suggested that cortical contrast sensitivity was constant across widely different luminance ranges experienced indoors and outdoors. Against this notion, here, we show that luminance range changes contrast sensitivity in both cat and human cortex, and the changes are different for dark and light stimuli. As luminance range increases, contrast sensitivity increases more within cortical pathways signaling lights than those signaling darks. Conversely, when the luminance range is constant, light-dark differences in contrast sensitivity remain relatively constant even if background luminance changes. We show that a Naka-Rushton function modified to include luminance range and light-dark polarity accurately replicates both the statistics of light-dark features in natural scenes and the cortical responses to multiple combinations of contrast and luminance. We conclude that differences in light-dark contrast increase with luminance range and are largest in bright environments.

Development of Natural Scene Representation in Primary Visual Cortex Requires Early Postnatal Experience

The development of the visual system is known to be shaped by early-life experience. To identify response properties that contribute to enhanced natural scene representation, we performed calcium imaging of excitatory neurons in the primary visual cortex (V1) of awake mice raised in three different conditions (standard-reared, dark-reared, and delayed-visual experience) and compared neuronal responses to natural scene features in relation to simpler grating stimuli that varied in orientation and spatial frequency. We assessed population selectivity in the V1 by using decoding methods and found that natural scene discriminability increased by 75% between the ages of 4 and 6weeks. Both natural scene and grating discriminability were higher in standard-reared animals than in those raised in the dark. This increase in discriminability was accompanied by a reduction in the number of neurons that responded to low-spatial-frequency gratings. At the same time, there was an increase in neuronal preference for natural scenes. Light exposure restricted to a 2- to 4-week window during adulthood did not induce improvements in natural scene or in grating stimulus discriminability. Our results demonstrate that experience reduces the number of neurons needed to effectively encode grating stimuli and that early visual experience enhances natural scene discriminability by directly increasing responsiveness to natural scene features.

Multiple-order diffractive engineered surface lenses.

A multiple-order diffractive engineered surface (MODE) lens is introduced, in which focal position change with wavelength exhibits both refractive and diffractive characteristics. Engineering calculations are provided that indicate Strehl ratio and encircled energy performance over a large range of focal length and aperture diameter design space. A prototype lens is designed and constructed for the astronomical R-band (589 nm to 727 nm) wavelength range. Test results show that measured full-width-at-half-maximum focal spot diameter is 2.1 times larger than the ideal Airy spot diameter, and focal position versus wavelength is nearly identical to the design. The 48 mm aperture diameter, f/3.12 prototype telescope exhibits angularly resolved features in natural scenes at 0.006°, with subtense of the Airy spot diameter at 0.002°. Applications include eventual use in large aperture, ultralightweight space telescopes.

Sensory cortex is optimized for prediction of future input.

Neurons in sensory cortex are tuned to diverse features in natural scenes. But what determines which features neurons become selective to? Here we explore the idea that neuronal selectivity is optimized to represent features in the recent sensory past that best predict immediate future inputs. We tested this hypothesis using simple feedforward neural networks, which were trained to predict the next few moments of video or audio in clips of natural scenes. The networks developed receptive fields that closely matched those of real cortical neurons in different mammalian species, including the oriented spatial tuning of primary visual cortex, the frequency selectivity of primary auditory cortex and, most notably, their temporal tuning properties. Furthermore, the better a network predicted future inputs the more closely its receptive fields resembled those in the brain. This suggests that sensory processing is optimized to extract those features with the most capacity to predict future input.

Experience-Dependent Specialization of Receptive Field Surround for Selective Coding of Natural Scenes

SummaryAt eye opening, neurons in primary visual cortex (V1) are selective for stimulus features, but circuits continue to refine in an experience-dependent manner for some weeks thereafter. How these changes contribute to the coding of visual features embedded in complex natural scenes remains unknown. Here we show that normal visual experience after eye opening is required for V1 neurons to develop a sensitivity for the statistical structure of natural stimuli extending beyond the boundaries of their receptive fields (RFs), which leads to improvements in coding efficiency for full-field natural scenes (increased selectivity and information rate). These improvements are mediated by an experience-dependent increase in the effectiveness of natural surround stimuli to hyperpolarize the membrane potential specifically during RF-stimulus epochs triggering action potentials. We suggest that neural circuits underlying surround modulation are shaped by the statistical structure of visual input, which leads to more selective coding of features in natural scenes.

Contour inflections are adaptable features

An object's shape is a strong cue for visual recognition. Most models of shape coding emphasize the role of oriented lines and curves for coding an object's shape. Yet inflection points, which occur at the junction of two oppositely signed curves, are ubiquitous features in natural scenes and carry important information about the shape of an object. Using a visual aftereffect in which the perceived shape of a contour is changed following prolonged viewing of a slightly different-shaped contour, we demonstrate a specific aftereffect for a contour inflection. Control conditions show that this aftereffect cannot be explained by adaptation to either the component curves or to the local orientation at the point of inflection. Further, we show that the aftereffect transfers weakly to a compound curve without an inflection, ruling out a general compound curvature detector as an explanation of our findings. We assume however that there are adaptable mechanisms for coding other specific forms of compound curves. Taken together, our findings provide evidence that the human visual system contains specific mechanisms for coding contour inflections, further highlighting their role in shape and object coding.

Multi-Scale Spatial Concatenations of Local Features in Natural Scenes and Scene Classification

How does the visual system encode natural scenes? What are the basic structures of natural scenes? In current models of scene perception, there are two broad feature representations, global and local representations. Both representations are useful and have some successes; however, many observations on human scene perception seem to point to an intermediate-level representation.In this paper, we proposed natural scene structures, i.e., multi-scale spatial concatenations of local features, as an intermediate-level representation of natural scenes. To compile the natural scene structures, we first sampled a large number of multi-scale circular scene patches in a hexagonal configuration. We then performed independent component analysis on the patches and classified the independent components into a set of clusters using the K-means method. Finally, we obtained a set of natural scene structures, each of which is characterized by a set of dominant clusters of independent components.We examined a range of statistics of the natural scene structures, compiled from two widely used datasets of natural scenes, and modeled their spatial arrangements at larger spatial scales using adjacency matrices. We found that the natural scene structures include a full range of concatenations of visual features in natural scenes, and can be used to encode spatial information at various scales. We then selected a set of natural scene structures with high information, and used the occurring frequencies and the eigenvalues of the adjacency matrices to classify scenes in the datasets. We found that the performance of this model is comparable to or better than the state-of-the-art models on the two datasets. These results suggest that the natural scene structures are a useful intermediate-level representation of visual scenes for our understanding of natural scene perception.

Statistics of natural scene structures and scene categorization

Humans can grasp the gist of complex natural scenes very quickly and can remember extraordinarily rich details in thousands of scenes viewed for a very brief period [1,2]. This amazing ability of rapid scene perception challenges both the traditional view of image-based, bottom-up visual processing [3] and recent models of scene categorization based on global visual features and features at low spatial frequency [4]. Low-level visual features such as edges, junctions, and various image gradients are insufficient for revealing the content of complex natural scenes. On the other hand, global visual features and features at low spatial frequency cannot encode the extraordinarily rich spatial concatenations of visual features in natural scenes. We proposed natural scene structures, i.e., multi-size, multi-scale, spatial concatenations of visual features, as the basic encoding units of natural scenes and scene categories. Natural scene structures convey various amount of information about scene identities and categories since general structures are shared by more scenes while specific structures are shared by only a few scenes. Thus, any natural scene and category can be represented by a probability distribution based on a set of natural scene structures and their spatial concatenations. These structural representations are robust against variations due to noises, occlusions, changes in scales, and other factors and require no isolation of objects or figure-background segmentation, nor computation of global scene features. To test this model of natural scenes, we compiled a large set of natural scene structures from a database of natural scenes and examined the information conveyed by the natural scene structures about natural scene categories. We then selected a set of natural scene structures with high information content, organized them into clusters, and developed a probabilistic model on the clusters of selected scene structures for each scene category. Finally, we categorized natural scenes by performing Bayesian inference based on these probability distributions. We found that the model achieved a high performance of categorization on a large dataset of natural scene categorizes. We also tested this model of natural scenes using human psychophysics. We constructed experimental stimuli that consisted of only the selected natural scene structures and asked human subjects to perform scene categorization. We either maintained or shuffled the spatial locations of the natural scene structures in the experimental stimuli. We found that the subject performance was significantly above chance even when the selected scene structures covered only a small portion of the scenes. Furthermore, shuffling the spatial locations of the scene structures significantly reduced the subject performance. These results support our statistical model of natural scenes using natural scene structures as encoding units.

Temporal and spatial adaptation of transient responses to local features

Interpreting visual motion within the natural environment is a challenging task, particularly considering that natural scenes vary enormously in brightness, contrast and spatial structure. The performance of current models for the detection of self-generated optic flow depends critically on these very parameters, but despite this, animals manage to successfully navigate within a broad range of scenes. Within global scenes local areas with more salient features are common. Recent work has highlighted the influence that local, salient features have on the encoding of optic flow, but it has been difficult to quantify how local transient responses affect responses to subsequent features and thus contribute to the global neural response. To investigate this in more detail we used experimenter-designed stimuli and recorded intracellularly from motion-sensitive neurons. We limited the stimulus to a small vertically elongated strip, to investigate local and global neural responses to pairs of local “doublet” features that were designed to interact with each other in the temporal and spatial domain. We show that the passage of a high-contrast doublet feature produces a complex transient response from local motion detectors consistent with predictions of a simple computational model. In the neuron, the passage of a high-contrast feature induces a local reduction in responses to subsequent low-contrast features. However, this neural contrast gain reduction appears to be recruited only when features stretch vertically (i.e., orthogonal to the direction of motion) across at least several aligned neighboring ommatidia. Horizontal displacement of the components of elongated features abolishes the local adaptation effect. It is thus likely that features in natural scenes with vertically aligned edges, such as tree trunks, recruit the greatest amount of response suppression. This property could emphasize the local responses to such features vs. those in nearby texture within the scene.

Discrimination of Features in Natural Scenes by a Dragonfly Neuron

Flying insects engage in spectacular high-speed pursuit of targets, requiring visual discrimination of moving objects against cluttered backgrounds. As a first step toward understanding the neural basis for this complex task, we used computational modeling of insect small target motion detector (STMD) neurons to predict responses to features within natural scenes and then compared this with responses recorded from an identified STMD neuron in the dragonfly brain (Hemicordulia tau). A surprising model prediction confirmed by our electrophysiological recordings is that even heavily cluttered scenes contain very few features that excite these neurons, due largely to their exquisite tuning for small features. We also show that very subtle manipulations of the image cause dramatic changes in the response of this neuron, because of the complex inhibitory and facilitatory interactions within the receptive field.

Emergence of visual saliency from natural scenes via context-mediated probability distributions coding.

Visual saliency is the perceptual quality that makes some items in visual scenes stand out from their immediate contexts. Visual saliency plays important roles in natural vision in that saliency can direct eye movements, deploy attention, and facilitate tasks like object detection and scene understanding. A central unsolved issue is: What features should be encoded in the early visual cortex for detecting salient features in natural scenes? To explore this important issue, we propose a hypothesis that visual saliency is based on efficient encoding of the probability distributions (PDs) of visual variables in specific contexts in natural scenes, referred to as context-mediated PDs in natural scenes. In this concept, computational units in the model of the early visual system do not act as feature detectors but rather as estimators of the context-mediated PDs of a full range of visual variables in natural scenes, which directly give rise to a measure of visual saliency of any input stimulus. To test this hypothesis, we developed a model of the context-mediated PDs in natural scenes using a modified algorithm for independent component analysis (ICA) and derived a measure of visual saliency based on these PDs estimated from a set of natural scenes. We demonstrated that visual saliency based on the context-mediated PDs in natural scenes effectively predicts human gaze in free-viewing of both static and dynamic natural scenes. This study suggests that the computation based on the context-mediated PDs of visual variables in natural scenes may underlie the neural mechanism in the early visual cortex for detecting salient features in natural scenes.

Dichotomy between luminance and disparity features at binocular fixations

Analysis of the statistics of natural scene features at observers' fixations can help us understand the mechanism of fixation selection and visual attention of the human vision system. Previous studies revealed that several low-level luminance features at fixations are statistically different from those at randomly selected locations. In our study, we conducted eye tracking experiments on naturalistic stereo images presented through a haploscope and found that fixated luminance contrast and luminance gradient are generally higher than randomly selected luminance contrast and luminance gradient, which agrees with previous literature, but the fixated disparity contrast and disparity gradient are generally lower than randomly selected disparity contrast and disparity gradient. We discuss the relevance of our findings in the context of the complexity of disparity calculations and the metabolic needs of disparity processing.

Task-specific modulation of memory for object features in natural scenes

The influence of visual tasks on short and long-term memory for visual features was investigated using a change-detection paradigm. Subjects completed 2 tasks: (a) describing objects in natural images, reporting a specific property of each object when a crosshair appeared above it, and (b) viewing a modified version of each scene, and detecting which of the previously described objects had changed. When tested over short delays (seconds), no task effects were found. Over longer delays (minutes) we found the describing task influenced what types of changes were detected in a variety of explicit and incidental memory experiments. Furthermore, we found surprisingly high performance in the incidental memory experiment, suggesting that simple tasks are sufficient to instill long-lasting visual memories.

Learning Midlevel Image Features for Natural Scene and Texture Classification

This paper deals with coding of natural scenes in order to extract semantic information. We present a new scheme to project natural scenes onto a basis in which each dimension encodes statistically independent information. Basis extraction is performed by independent component analysis (ICA) applied to image patches culled from natural scenes. The study of the resulting coding units (coding filters) extracted from well-chosen categories of images shows that they adapt and respond selectively to discriminant features in natural scenes. Given this basis, we define global and local image signatures relying on the maximal activity of filters on the input image. Locally, the construction of the signature takes into account the spatial distribution of the maximal responses within the image. We propose a criterion to reduce the size of the space of representation for faster computation. The proposed approach is tested in the context of texture classification (111 classes), as well as natural scenes classification (11 categories, 2037 images). Using a common protocol, the other commonly used descriptors have at most 47.7% accuracy on average while our method obtains performances of up to 63.8%. We show that this advantage does not depend on the size of the signature and demonstrate the efficiency of the proposed criterion to select ICA filters and reduce the dimension