Objects In Natural Images Research Articles

Quantifying the role of perceived curvature in the processing of natural object images

Quantifying the role of perceived curvature in the processing of natural object images

Алгоритм проведения профилактических и лечебных курсов арктической ландшафтной терапии для купирования различных типов стресса.

In connection with the need of non-drug biotechnologies for stress relief in the extreme conditions of the Far North, an algorithm was created and a preliminary testing of the method of Arctic landscape therapy was carried out. The method is personalized, non-invasive and, thanks to the use of virtual and painted images of natural objects, can be used at any time of the year. Due to the steady decrease in situational and personal anxiety of patients, it contributes to the solution of a wide range of various social problems

TA-YOLO: a lightweight small object detection model based on multi-dimensional trans-attention module for remote sensing images

Object detection plays a vital role in remote sensing applications. Although object detection has achieved proud results in natural images, these methods are difficult to be directly applied to remote sensing images. Remote sensing images often have complex backgrounds and small objects, which results in a highly unbalanced distribution of foreground and complex background information. In order to solve the above problems, this paper proposes a multi-head channel and spatial trans-attention (MCSTA) module, which performs remote pixel interaction from the channel and spatial dimensions respectively to complete the attention feature capture function. It is a plug-and-play module that can be easily embedded in any other natural image object detection convolutional neural network, making it quickly applicable to remote sensing images. First, in order to reduce computational complexity and improve feature richness, we use a special linear convolution to obtain three projection features instead of the simple matrix multiplication transformation in Transformer. Second, we obtain trans-attention maps in different dimensions in a manner similar to the self-attention mechanism to capture the interrelationships of features in channels and spaces. In this process, we use a multi-head mechanism to perform parallel operations to improve speed. Furthermore, in order to avoid large-scale matrix operations, we specially designed an attention blocking mode to reduce computer memory usage and increase operation speed. Finally, we embedded the trans-attention module into YOLOv8, added a new detection head and optimized the feature fusion method, thus designing a lightweight small object detection model named TA-YOLO for remote sensing images. It has fewer parameters than the benchmark model YOLOv8, and its mAP on the PASCAL VOC and VisDrone data sets increased by 1.3% and 6.2% respectively. The experimental results prove the powerful function of the trans-attention module and the excellent performance of TA-YOLO.

Color and luminance processing in V1 complex cells and artificial neural networks.

Object recognition by natural and artificial visual systems benefits from the identification of object boundaries. A useful cue for the detection of object boundaries is the superposition of luminance and color edges. To gain insight into the suitability of this cue for object recognition, we examined convolutional neural network models that had been trained to recognize objects in natural images. We focused specifically on units in the second convolutional layer whose activations are invariant to the spatial phase of a sinusoidal grating. Some of these units were tuned for a nonlinear combination of color and luminance, which is broadly consistent with a role in object boundary detection. Others were tuned for luminance alone, but very few were tuned for color alone. A literature review reveals that V1 complex cells have a similar distribution of tuning. We speculate that this pattern of sensitivity provides an efficient basis for object recognition, perhaps by mitigating the effects of lighting on luminance contrast polarity. The absence of a contrast polarity-invariant representation of chromaticity alone suggests that it is redundant with other representations.

Satellite Imageries for Detection of Bangladesh’s Rural and Urban Areas Using YOLOv5 and CNN

In recent years, there have been significant advancements in object identification in natural photos. However, when applying natural image object recognition techniques directly to satellite images, the results are often unsatisfactory. This is primarily due to inherent disparities in the object scale and orientation caused by the omniscient viewpoint of satellite imagery. The distinguishing factors between rural and urban areas lie in the objects that cover them. Furthermore, the complex backdrop of satellite photos poses challenges in accurately extracting features, leading to the omission of small objects in many regions. The performance of object detection, which is crucial for area identification, is also affected by dense object overlap and occlusion. To address these aforementioned issues, we made modifications to the generalized one-stage detector YOLOv5, specifically tailored for satellite photos. For this research, we manually collected data from Google Earth, meticulously labeling them and subsequently verifying them with human annotators. We then preprocessed the data using computer vision techniques, such as resizing and normalization. Next, we employed YOLOv5 and transfer learning-based CNN architectures of InceptionV3, DenseNet201, and Xception to compare their performances. The goal was to accurately identify rural and urban areas from remote sensing images.

GPTR: Gestalt-Perception Transformer for Diagram Object Detection

Diagram object detection is the key basis of practical applications such as textbook question answering. Because the diagram mainly consists of simple lines and color blocks, its visual features are sparser than those of natural images. In addition, diagrams usually express diverse knowledge, in which there are many low-frequency object categories in diagrams. These lead to the fact that traditional data-driven detection model is not suitable for diagrams. In this work, we propose a gestalt-perception transformer model for diagram object detection, which is based on an encoder-decoder architecture. Gestalt perception contains a series of laws to explain human perception, that the human visual system tends to perceive patches in an image that are similar, close or connected without abrupt directional changes as a perceptual whole object. Inspired by these thoughts, we build a gestalt-perception graph in transformer encoder, which is composed of diagram patches as nodes and the relationships between patches as edges. This graph aims to group these patches into objects via laws of similarity, proximity, and smoothness implied in these edges, so that the meaningful objects can be effectively detected. The experimental results demonstrate that the proposed GPTR achieves the best results in the diagram object detection task. Our model also obtains comparable results over the competitors in natural image object detection.

A Dense-Aware Cross-splitNet for Object Detection and Recognition

Object detection and recognition is widely used in various fields and have become key technologies in computer vision. The distribution of objects in natural images can be roughly divided into densely stacked objects and scattered objects. Due to the incomplete attributes or features of some objects in densely stacked distributions, some object detectors have missed local area details or low detection accuracy. In this paper, we propose Cross-splitNet, a novel cross-split method for dense object detection and recognition based on candidate box generation. First, an adaptive feature extraction network is constructed. Different datasets are input into convolutional neural networks with various depths, the generalization of the model. Then, the proposed cross-split algorithm is introduced to guide the different deep networks to learn features of images with various densities, according to intermediate object density classification results. Finally, we adopt a feature pyramid network (FPN) subnet to perform multi-scale feature extraction while retaining lower-layer object information and physical characteristics. The model was trained on the COCO 17, VOC 12, and VOC 07 datasets, which contain a large number of object categories. Our network was compared with several two-stage detectors, and the results show that our model achieved an average precision ( <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">AP</i> ) of 0.819 at 22.9 frames per second ( <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">FPS</i> ) on the VOC 07+12 dataset. The mean average precision ( <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">mAP</i> ) of the object detection model with R50+R2-101 backbones on the COCO dataset was increased by 1.9%.

THINGSplus: New norms and metadata for the THINGS database of 1854 object concepts and 26,107 natural object images

To study visual and semantic object representations, the need for well-curated object concepts and images has grown significantly over the past years. To address this, we have previously developed THINGS, a large-scale database of 1854 systematically sampled object concepts with 26,107 high-quality naturalistic images of these concepts. With THINGSplus, we significantly extend THINGS by adding concept- and image-specific norms and metadata for all 1854 concepts and one copyright-free image example per concept. Concept-specific norms were collected for the properties of real-world size, manmadeness, preciousness, liveliness, heaviness, naturalness, ability to move or be moved, graspability, holdability, pleasantness, and arousal. Further, we provide 53 superordinate categories as well as typicality ratings for all their members. Image-specific metadata includes a nameability measure, based on human-generated labels of the objects depicted in the 26,107 images. Finally, we identified one new public domain image per concept. Property (M = 0.97, SD = 0.03) and typicality ratings (M = 0.97, SD = 0.01) demonstrate excellent consistency, with the subsequently collected arousal ratings as the only exception (r = 0.69). Our property (M = 0.85, SD = 0.11) and typicality (r = 0.72, 0.74, 0.88) data correlated strongly with external norms, again with the lowest validity for arousal (M = 0.41, SD = 0.08). To summarize, THINGSplus provides a large-scale, externally validated extension to existing object norms and an important extension to THINGS, allowing detailed selection of stimuli and control variables for a wide range of research interested in visual object processing, language, and semantic memory.

Натурморфные образы в славянской и тюркской эмотивной фразеологии: параллели и контрасты

The article is devoted to a comparative study of the naturmorphic images underlying the internal form of emotive phraseological units in Slavic and Turkic languages (Russian, Bulgarian, Polish, Czech, Tatar). These images are noted to correlate with the corresponding code of culture, which assumes a figurative interpretation of the realia of the world with consideration of knowledge about both living and inanimate nature. The revealed images of animals, birds, insects, plants, natural objects and phenomena, elements constitute a well-arranged system, the components of which are analyzed in terms of both frequency and universality/ethnospecificity. These images are actualized when describing a wide range of emotions (joy, sadness, love, resentment, shame, excitement, etc.), but most of them relate to negative states, primarily fear and anger. The most productive are zoomorphic images, as well as images of natural objects, phenomena and elements (such as the sky, thunder, fire, water, etc.), which often reveal universality due to external factors. The inner form of many emotive phraseological units is based on the metonymic principle, according to which emotions are described by referring to their bodily symptoms (chills, tears, facial expressions, etc.). The universality and ethnospecificity of images are proved to have linguistic and extralinguistic reasons.

Decahedra and Icosahedra Everywhere: The Anomalous Crystallization of Au and Other Metals at the Nanoscale (Crystal Research and Technology 4/2023)

Crystal Research and TechnologyVolume 58, Issue 4 2370007 Cover PictureFree Access Decahedra and Icosahedra Everywhere: The Anomalous Crystallization of Au and Other Metals at the Nanoscale (Crystal Research and Technology 4/2023) First published: 06 April 2023 https://doi.org/10.1002/crat.202370007AboutPDF ToolsRequest permissionExport citationAdd to favoritesTrack citation ShareShare Give accessShare full text accessShare full-text accessPlease review our Terms and Conditions of Use and check box below to share full-text version of article.I have read and accept the Wiley Online Library Terms and Conditions of UseShareable LinkUse the link below to share a full-text version of this article with your friends and colleagues. Learn more.Copy URL Share a linkShare onFacebookTwitterLinkedInRedditWechat Graphical Abstract The cover image of this issue refers to the Review article by Miguel José Yacaman and co-workers (article number 2200259). Micrographs of nanoparticles and images of natural objects with fivefold symmetry are arranged in juxtaposition to reveal striking similarities between the natural and the nano world. Although millions of times smaller than life, these particles somehow appear to belong in the natural world. The Review discusses several reasons for the stability of nanoparticles with fivefold symmetry, including the formation of twin boundaries, surface reconstruction faceting, and other factors. Volume58, Issue4April 20232370007 RelatedInformation

The Spatiotemporal Neural Dynamics of Object Recognition for Natural Images and Line Drawings.

Drawings offer a simple and efficient way to communicate meaning. While line drawings capture only coarsely how objects look in reality, we still perceive them as resembling real-world objects. Previous work has shown that this perceived similarity is mirrored by shared neural representations for drawings and natural images, which suggests that similar mechanisms underlie the recognition of both. However, other work has proposed that representations of drawings and natural images become similar only after substantial processing has taken place, suggesting distinct mechanisms. To arbitrate between those alternatives, we measured brain responses resolved in space and time using fMRI and MEG, respectively, while human participants (female and male) viewed images of objects depicted as photographs, line drawings, or sketch-like drawings. Using multivariate decoding, we demonstrate that object category information emerged similarly fast and across overlapping regions in occipital, ventral-temporal, and posterior parietal cortex for all types of depiction, yet with smaller effects at higher levels of visual abstraction. In addition, cross-decoding between depiction types revealed strong generalization of object category information from early processing stages on. Finally, by combining fMRI and MEG data using representational similarity analysis, we found that visual information traversed similar processing stages for all types of depiction, yet with an overall stronger representation for photographs. Together, our results demonstrate broad commonalities in the neural dynamics of object recognition across types of depiction, thus providing clear evidence for shared neural mechanisms underlying recognition of natural object images and abstract drawings.SIGNIFICANCE STATEMENT When we see a line drawing, we effortlessly recognize it as an object in the world despite its simple and abstract style. Here we asked to what extent this correspondence in perception is reflected in the brain. To answer this question, we measured how neural processing of objects depicted as photographs and line drawings with varying levels of detail (from natural images to abstract line drawings) evolves over space and time. We find broad commonalities in the spatiotemporal dynamics and the neural representations underlying the perception of photographs and even abstract drawings. These results indicate a shared basic mechanism supporting recognition of drawings and natural images.

Emergent color categorization in a neural network trained for object recognition.

Color is a prime example of categorical perception, yet it is unclear why and how color categories emerge. On the one hand, prelinguistic infants and several animals treat color categorically. On the other hand, recent modeling endeavors have successfully utilized communicative concepts as the driving force for color categories. Rather than modeling categories directly, we investigate the potential emergence of color categories as a result of acquiring visual skills. Specifically, we asked whether color is represented categorically in a convolutional neural network (CNN) trained to recognize objects in natural images. We systematically trained new output layers to the CNN for a color classification task and, probing novel colors, found borders that are largely invariant to the training colors. The border locations were confirmed using an evolutionary algorithm that relies on the principle of categorical perception. A psychophysical experiment on human observers, analogous to our primary CNN experiment, shows that the borders agree to a large degree with human category boundaries. These results provide evidence that the development of basic visual skills can contribute to the emergence of a categorical representation of color.

Rock Classification with Features Based on Higher Order Riesz Transform

Most modern algorithms use convolutional neural networks to classify image data of different kinds. While this approach is a good method to differentiate between natural images of objects, big datasets are needed for the training process. Another drawback is the demand for high computational power. We introduce a new approach which involves classic feature vectors with structural information based on higher order Riesz transform. Following this way we create a framework specialized for texture data like images of rock cross-sections. The key advantages are faster computations and more versatile choices of the underlying machine learning tools while maintaining a comparable accuracy in comparison with state-of-the-art algorithms.

Swin-Transformer-Enabled YOLOv5 with Attention Mechanism for Small Object Detection on Satellite Images

Object detection has made tremendous progress in natural images over the last decade. However, the results are hardly satisfactory when the natural image object detection algorithm is directly applied to satellite images. This is due to the intrinsic differences in the scale and orientation of objects generated by the bird’s-eye perspective of satellite photographs. Moreover, the background of satellite images is complex and the object area is small; as a result, small objects tend to be missing due to the challenge of feature extraction. Dense objects overlap and occlusion also affects the detection performance. Although the self-attention mechanism was introduced to detect small objects, the computational complexity increased with the image’s resolution. We modified the general one-stage detector YOLOv5 to adapt the satellite images to resolve the above problems. First, new feature fusion layers and a prediction head are added from the shallow layer for small object detection for the first time because it can maximally preserve the feature information. Second, the original convolutional prediction heads are replaced with Swin Transformer Prediction Heads (SPHs) for the first time. SPH represents an advanced self-attention mechanism whose shifted window design can reduce the computational complexity to linearity. Finally, Normalization-based Attention Modules (NAMs) are integrated into YOLOv5 to improve attention performance in a normalized way. The improved YOLOv5 is termed SPH-YOLOv5. It is evaluated on the NWPU-VHR10 dataset and DOTA dataset, which are widely used for satellite image object detection evaluations. Compared with the basal YOLOv5, SPH-YOLOv5 improves the mean Average Precision (mAP) by 0.071 on the DOTA dataset.

Detection with fast feature pyramids and lightweight convolutional neural network: a practical aircraft detector for optical remote images

Aircraft detection in optical remote sensing images has important practical value and attracts great attention in recent years. Due to some clustered backgrounds and various deformations like variability in viewpoints, scales, and directions, it is still a big challenge. In recent years, deep learning, especially convolutional neural networks (CNNs), has achieved remarkable success not only in natural image object detection tasks but also in remote sensing image object detection tasks. However, due to the limitations of the actual application deployment environment only limited processing units and storage space can be used, and most deep learning-based aircraft detection algorithms cannot work well. To this end, a unique aircraft detection framework in optical remote sensing images is proposed, which is based on fast feature pyramids and a specialized lightweight CNN. The algorithm can be summarized into two steps. In the first step, a highly reliable region proposal generation algorithm is designed to predict region proposals. In this phase, a prior square sliding window method and an efficient linear search algorithm are proposed. In the next step, a lightweight CNN is designed to achieve fast and accurate aircraft classification. The proposed model only requires about 237 KB of parameter storage and can infer in a poor central processing unit platform. Comprehensive experiments on three publicly optical remote sensing aircraft datasets demonstrate the superiority and effectiveness of the proposed method. It is also shown that the proposed method is light and fast in comparison with some state-of-the-art methods.

MKLM: a multiknowledge learning module for object detection in remote sensing images

ABSTRACT Object detection in optical remote sensing images has made remarkable progress on computer vision. Unlike objects in natural images, objects in remote sensing are generally more complex and diverse. Therefore, most methods design deeper networks to capture more distinguishing features for each object region while ignoring the semantic correlation between scenes and ground objects. In this work, we introduce a novel multiknowledge learning module (MKLM), which combines two kinds of knowledge to adaptively enhance the original feature representation of the basic detector by interacting with information. Specifically, the internal knowledge module is utilized to learn global and local contextual relationships (e.g. feature similarity, spatial location) given object proposals, and the external knowledge module learns human common-sense knowledge (e.g. co-occurrence, inherent properties) to impose constraints. Then, it achieves the purpose of updating the object state under the supervision of different knowledge guidance. Our MKLM is easy to extend and migrate and can be embedded in any detection paradigm. Reliable experiments on two challenging remote sensing image object detection datasets (DOTA and RSOD) show that the proposed method achieves superior detection performance compared to many state-of-the-art methods and baselines.

A Concentric Loop Convolutional Neural Network for Manual Delineation-Level Building Boundary Segmentation From Remote-Sensing Images

To date, accurate building footprint delineation in the surveying, mapping, and geographic information system (GIS) communities has been dependent on human labor. In this article, to address this issue, we propose a concentric loop convolutional neural network (CLP-CNN) method for the automatic segmentation of building boundaries from remote-sensing images. The proposed method consists of three components: 1) a boundary detector to extract coarse polygonal boundaries of individual regions of interest; 2) a concentric loop-shaped convolutional network with bidirectional pairing loss to fine-tune the vertices of the polygons; and 3) a refinement block, which removes redundant vertices and regularizes the boundaries to polygons at the manual delineation level. We also demonstrate that the proposed CLP-CNN method is applicable to other generic objects in natural images. Experiments on two building datasets confirmed that more than 77%/67% of the building polygons predicted by the proposed method are on par with the manual delineation level, representing a significant saving in the labor cost of manual annotation. In generic object boundary delineation tests performed on the Semantic Boundaries Dataset (SBD), the proposed method outperformed the most recent state-of-the-art methods by at least 3.1% in average precision (AP). Furthermore, compared with other vertex matching methods, the learning process of the proposed method converges faster. The source code will be available at <uri xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">http://gpcv.whu.edu.cn/data</uri> .

High-performing computational models of visual cortex are marked by high intrinsic dimensionality

A central goal of cognitive neuroscience is to understand the computational properties of neural representations in visual cortex. Above and beyond the information content or visual features encoded in neural populations, we wish to understand the encoding format itself and the computations it subserves. These questions are at the heart of research into computational properties such as sparsity, dimensionality, manifold geometry, invariance, and dynamics in both biological and artificial neural networks. Here we investigate the computational properties of representations across human visual cortex using fMRI encoding models. We first fit voxelwise encoding models to predict fMRI responses to images of natural objects and scenes using both hand-engineered feature detectors (e.g. edge detectors) and pre-trained and untrained convolutional neural networks (CNNs). We then performed statistical analyses of the encoding model regressors to explore how performance is affected by several computational properties, including mixed selectivity and intrinsic dimensionality. We searched for the properties that correlated best with encoding accuracy. We find evidence for high intrinsic dimensionality as an important driver of encoding model performance across the ventral and dorsal visual streams. Furthermore, we find that several mechanisms for increasing intrinsic dimensionality lead to similar improvements in encoding model performance, including division normalization, multiplicative interaction, random nonlinear projection, and supervised learning for image classification. Thus, it appears that intrinsic dimensionality itself best explains the improvements of these varied representational mechanisms. Together, our work explores a methodology for investigating the computational properties of neural representations through an ensemble of encoding models and statistical analyses of their computational properties. We use this approach to identify high intrinsic dimensionality as a key predictor of encoding model performance, suggesting that biological vision relies on representations that are highly efficient, and may allow for the linear separability of a large number of visual properties across different tasks.

THINGS-fMRI/MEG: A large-scale multimodal neuroimaging dataset of responses to natural object images

A detailed understanding of visual object representations in brain and behavior is fundamentally limited by the number of stimuli that can be presented in any one experiment. Ideally, the space of objects should be sampled in a representative manner, with (1) maximal breadth of the stimulus material and (2) minimal bias in the object categories. Such a dataset would allow the detailed study of object representations and provide a basis for testing and comparing computational models of vision and semantics. Towards this end, we recently developed the large-scale object image database THINGS of more than 26,000 images of 1,854 object concepts sampled representatively from the American English language (Hebart et al., 2019). Here we introduce THINGS-fMRI and THINGS-MEG, two large-scale brain imaging datasets using functional magnetic resonance imaging (fMRI) and magnetoencephalography (MEG). Over the course of 12 scanning sessions, 7 participants (fMRI: n = 3, MEG: n = 4) were presented with images from the THINGS database (fMRI: 8,740 images of 720 concepts, MEG: 22,448 images of 1,854 concepts) while they carried out an oddball detection task. To reduce noise, participants’ heads were stabilized and repositioned between sessions using custom head casts. To facilitate the use by other researchers, the data were converted to the Brain Imaging Data Structure format (BIDS; Gorgolewski et al., 2016) and preprocessed with fMRIPrep (Esteban et al., 2018). Estimates of the noise ceiling and general quality control demonstrate overall high data quality, with only small overall displacement between sessions. By carrying out a broad and representative multimodal sampling of object representations in humans, we hope this dataset to be of use for visual neuroscience and computational vision research alike.

Face-selective responses in combined EEG/MEG recordings with fast periodic visual stimulation (FPVS)

Fast periodic visual stimulation (FPVS) allows the recording of objective brain responses of human face categorization (i.e., generalizable face-selective responses) with high signal-to-noise ratio. This approach has been successfully employed in a number of scalp electroencephalography (EEG) studies but has not been used with magnetoencephalography (MEG) yet, let alone with combined MEG/EEG recordings and distributed source estimation. Here, we presented various natural images of faces periodically (1.2 Hz) among natural images of objects (base frequency 6 Hz) whilst recording simultaneous EEG and MEG in 15 participants. Both measurement modalities showed face-selective responses at 1.2 Hz and harmonics across participants, with high and comparable signal-to-noise ratio (SNR) in about 3 min of stimulation. The correlation of face categorization responses between EEG and two MEG sensor types was lower than between the two MEG sensor types, indicating that the two sensor modalities provide independent information about the sources of face-selective responses. Face-selective EEG responses were right-lateralized as reported previously, and were numerically but non-significantly right-lateralized in MEG data. Distributed source estimation based on combined EEG/MEG signals confirmed a more bilateral face-selective response in visual brain regions located anteriorly to the common response to all stimuli at 6 Hz and harmonics. Conventional sensor and source space analyses of evoked responses in the time domain further corroborated this result. Our results demonstrate that FPVS in combination with simultaneously recorded EEG and MEG may serve as an efficient localizer paradigm for human face categorization.