Processing Of Local Features Research Articles

A method for multi-target segmentation of bud-stage apple trees based on improved YOLOv8

The bud stage is a crucial period in the growth and development of apple trees. Accurate detections of the physiological changes in the organs (branches, buds, leaves, and the connecting parts between buds and branches) are essential for the scientific management of orchards. In the field of intelligent orchard management, image segmentation is a fundamental method for obtaining the phenotypes of fruit tree organs, making it especially critical. To address this, we have created a dataset for apple tree organ segmentation during the bud stage and have incorporated several advanced convolutional network modules (ConvNeXt V2, Multi-scale Extended Attention Module (MSDA), Dynamic Snake Convolution (DSConv)) to enhance YOLOv8 and improve the accuracy of organ segmentation in complex natural environments. In the backbone network, we have integrated ConvNeXt V2 and MSDA modules to increase the extraction of contextual information and improve the network’s ability to recognize multi-scale and multi-shaped targets. Additionally, we have embedded DSConv in the network’s head and utilized deformable convolution to enable adaptive sampling of the feature map, capturing a wider range of context information and improving the local feature processing capability for objects of varying sizes, ultimately leading to improved segmentation accuracy. Our models significantly outperform existing models, achieving 82.58 % mean Precision (mP), 74.58 % mean Recall (mR), 77.94 % mean Dice(mDice), 64.91 % mean IoU(mIoU), and 79.75 % mean Average Precision (mAP). Ablation studies confirm the contributions of each module to intelligent orchard management and suggest potential benefits for precise agricultural decision-making and operations.

An attention-based progressive fusion network for pixelwise pavement crack detection

Detecting pavement cracks is a complex task in pavement maintenance. Convolutional neural network (CNN)-based approaches have been increasingly used for this purpose in recent years, but these approaches still have limitations, such as insufficient processing of local features, poor boundary detection ability and information loss issue. In this paper, a novel CNN-based model, named APF-Net, is presented. Specifically, to extract richer and more feature information, proposed model utilizes a progressive fusion module (PF) that enables effective feature enhancement by feature learning from cross-layer features, which operates progressively across adjacent network layers, enabling the crack segmentation model to extract more global and detail features from the crack images. Additionally, a hybrid multiple attention (HMA) module that incorporates both a spatial attention mechanism from two directions and a channel attention mechanism, is proposed, which could effectively capture the long-term dependence of crack features and enhance crack boundary detection. Moreover, proposed model employs a lightweight feature extraction block, named mobile inverted bottleneck convolution (MBC) block, which significantly reduces the model parameters and enables more efficient processing of the crack images. This approach results in a reduction of the computational load and faster detection of pavement cracks. The effectiveness and generalization of the proposed APF-Net have been evaluated through experiments on three publicly available crack datasets. The results demonstrate that the proposed model outperforms other state-of-the-art segmentation models in detecting pavement cracks while with 7.6M parameters and 5.0G FLOPs. Our proposed model can contribute to the development of automatic pavement crack detection systems, which can aid in the maintenance and preservation of roads and highways. The source code for proposed APF-Net is available at https://github.com/MMYZZU/APFNet.git.

MSS-UNet: A Multi-Spatial-Shift MLP-based UNet for skin lesion segmentation

Multilayer perceptron (MLP) networks have become a popular alternative to convolutional neural networks and transformers because of fewer parameters. However, existing MLP-based models improve performance by increasing model depth, which adds computational complexity when processing local features of images. To meet this challenge, we propose MSS-UNet, a lightweight convolutional neural network (CNN) and MLP model for the automated segmentation of skin lesions from dermoscopic images. Specifically, MSS-UNet first uses the convolutional module to extract local information, which is essential for precisely segmenting the skin lesion. We propose an efficient double-spatial-shift MLP module, named DSS-MLP, which enhances the vanilla MLP by enabling communication between different spatial locations through double spatial shifts. We also propose a module named MSSEA with multiple spatial shifts of different strides and lighter external attention to enlarge the local receptive field and capture the boundary continuity of skin lesions. We extensively evaluated the MSS-UNet on ISIC 2017, 2018, and PH2 skin lesion datasets. On three datasets, the method achieves IoU metrics of 85.01%±0.65, 83.65%±1.05, and 92.71%±1.03, with a parameter size and computational complexity of 0.33M and 15.98G, respectively, outperforming most state-of-the-art methods.The code is publicly available at https://github.com/AirZWH/MSS-UNet.

An attention-based dual-encoding network for fire flame detection using optical remote sensing

Automatic extraction of flame area plays an important role in forest fire detection, which can accurately understand the spatial distribution and development trend of forest fire, so as to effectively realize the protection of forest resources. However, due to the instability and spread of fires, and the complexity of the background, accurate early fire detection is extremely challenging. At the same time, the image pixel proportion of the flame area in early stage is much smaller than that in the background, which causes a serious class imbalance problem. With the fast development of deep learning, some achievements have been made in flame extraction, but there are still some deficiencies in the existing networks, such as limited feature representation, poor feature capturing ability on micro objects, insufficiency processing of local features, etc. This paper proposes an attention-based dual-encoding segmentation network, abbreviated as ADE-Net, for pixelwise early fire detection. To realize strong feature representation, a dual-encoding path, consisting of semantic units and spatial units, is introduced to extract richer features, and an attention fusion module (AFM) is introduced to fully integrate spatial and semantic information and achieve effective feature aggregation. In addition, faced with the class imbalance problem, a multi-attention fusion (MAF) module is introduced to obtain more discriminating features to make the segmentation network to focus on the key pixel areas. Furthermore, a feature enhancement module, named attention-guided enhancement (AGE) module, is proposed to enrich the feature representation of local feature maps. Finally, to realize better multi-scale global feature extraction and fusion, a global context fusion (GCF) module is proposed into the bottleneck layer for multi-scale feature enhancement. Experimental results show that the proposed ADE-Net has a good early fire detection ability from remote sensing images, and it has obtained a competitive advantage compared with advanced segmentation models.

CFFR-Net: A channel-wise features fusion and recalibration network for surgical instruments segmentation

Surgical instrument segmentation plays a crucial role in robot-assisted surgery by furnishing essential information about instrument location and orientation. This information not only enhances surgical planning but also augments the precision and safety of procedures. Despite promising strides in recent research on surgical instrument segmentation, accuracy still faces obstacles due to local feature processing limitations, surgical environment complexity, and instrument morphological variability. To address these challenges, we introduced the channel-wise features fusion and recalibration network (CFFR-Net). This network utilizes a dual-stream mechanism, combining a context-guided block and dense block for feature extraction. The context-guided block captures a variety of contextual information by using different dilation rates. Additionally, CFFR-Net employs a fusion mechanism that harmonizes context-guided and dense streams. This integration, along with the inclusion of Squeeze-and-Excitation attention, enhances both the precision and robustness of semantic instrument segmentation.We performed experiments using two publicly available datasets for surgical instrument segmentation: the Kvasir-instrument and Endovis2017 datasets. The results of these experiments were highly encouraging, as our proposed model exhibited remarkable performance on both datasets compared to the state-of-the-art methods. On the Kvasir-instrument set, our model achieved a Dice score of 95.84% and mean intersection over union (mIOU) value of 92.40%. Similarly, on the Endovis2017 set, it obtained a Dice score of 95.47% and mIOU value of 93.02%.

A deep segmentation network for crack detection with progressive and hierarchical context fusion

Accurate detection of crack defects in infrastructure is crucial to ensure their safety and extend their service life. However, the presence of complex backgrounds, various shapes and sizes of crack defects, incomplete discontinuous crack defects, and class imbalance makes this task challenging. Traditional image processing techniques are sensitive to image noise and may miss cracks due to weak textures, complex lighting conditions, and other similar objects on the pavement. In recent years, deep learning-based segmentation networks have been proposed for crack defect detection, but they still face challenges in high-precision crack segmentation due to insufficient local feature processing, information loss caused by pooling operations, and limited receptive fields. To address these issues, we propose an end-to-end deep crack segmentation network, called PHCF-Net, which incorporates progressive and hierarchical context fusion. Firstly, the proposed network consists of progressive context fusion (PCF) and hierarchical context fusion (HCF) blocks for effective aggregation of global and local context information. Secondly, a multi-scale context fusion (MCF) block is proposed for multi-scale context extraction and aggregation. Finally, in order to solve the information loss problem caused by pooling operations, a hierarchical context fusion (HCF) block is proposed for effective aggregation of the deep and shallow features. In addition, a multi-scale input unit is also applied to the proposed segmentation network to obtain more context information. To evaluate the performance of PHCF-Net, we have conducted experiments on two publicly available crack segmentation datasets and compared its performance with mainstream segmentation models. The results demonstrate that proposed PHCF-Net achieves better pixel-level crack detection results and outperforms other advanced segmentation models.

EEG-based seizure prediction via hybrid vision transformer and data uncertainty learning

Feature embeddings derived from continuous mapping using the deep neural network are critical for accurate classification in seizure prediction tasks. However, the embeddings of individual electroencephalogram (EEG) samples learned through modern decoding algorithms may contain ambiguous and noisy representations, owing to the susceptibility of weak EEG signals to interference from other signals unrelated to EEG activity. To address this issue, we consider data uncertainty learning (DUL), which models each representation of the EEG sample as a Gaussian or Laplacian distribution to mitigate potential noise interference and enhance model robustness. Moreover, data uncertainty learning for transformer architectures is seldom explored due to the limitation of multi-headed self-attention mechanisms in processing local features and the vanishing of potential top-level gradients. In this study, we introduce a novel hybrid visual transformer (HViT) architecture, which enhances the processing capability of localized features in the transformer through the convolutional neural network (CNN). Concretely, we learn the mean of the distribution using the HViT, and an additional branch is designed to capture the variance of the Gaussian distribution or the scale of the Laplacian distribution during training. We also propose a learnable manner to learn constraint coefficients in the loss functions for different patients, resulting in better optimization across patients. In addition, we introduce a simple uncertainty quantification method for each alarm of the k-of-n continuous prediction strategy by utilizing the continuity of the EEG signals. Empirical evaluations on two publicly available epilepsy datasets demonstrate the superiority of our DUL method and the effectiveness of the proposed HViT architecture.

TMA-Net: A Transformer-Based Multi-Scale Attention Network for Surgical Instrument Segmentation

The ability to accurately and automatically segment surgical instruments is one of the important prerequisites for reasonable and stable operation of surgical robots. The utilization of deep learning in medical image segmentation has gained widespread recognition in recent years, leading to the proposition of multiple network models designed for the segmentation of diverse medical images, among which the most effective one is U-Net and its variants. Nevertheless, these existing networks also have various drawbacks, such as limited contextual representation capability, insufficient local feature processing, etc. In order to solve the above problems so that more accurate surgical instrument segmentation performance can be obtained, a transformer-based multi-scale attention network is proposed, referred to as TMA-Net, for surgical instrument segmentation from endoscopic images to serve robot-assisted surgery. To enable more accurate extraction of image features, a dual-branch encoder structure is proposed to obtain stronger contexts. Further, to address the problem that the simple skip connection is insufficient for local feature processing, an attention feature fusion (AFF) module and an additive attention and concatenation (AAC) module are proposed for effective feature learning to filter out the irrelevant information in the low-level features. Furthermore, a multi-scale context fusion (MCF) block is introduced to enhance the local feature maps and capture multi-scale contextual information. The efficacy of proposed TMA-Net is demonstrated through experimentation on publicly available surgical instrument segmentation datasets, including Endovis2017 and UW-Sinus-Surgery-C/L. The results show that proposed TMA-Net outperforms existing methods in terms of surgical instrument segmentation accuracy.

Multi-scale triple-attention network for pixelwise crack segmentation

Currently, intelligent crack detection is of great value for the maintenance of infrastructure, of which the most significant kind in China is roads. For pavement defects, the pavement can be repaired and maintained in a timely manner with an accurate defect detection task, which significantly reduces the occurrence of hazards. However, the detection of pavement defects remains a great challenge owing to many difficulties, for example, complex backgrounds, microdefects, various defect shapes and sizes, class imbalance issues, etc. Recently, deep learning has demonstrated its superior performance on pixelwise image segmentation, but some issues still exist on demanding pixelwise image segmentation, for instance, limited receptive field, insufficiency processing of local features, information loss issue generated by pooling operations, etc. Based on all of the above issues, a multiscale triple-attention network, named MST-Net, is proposed for end-to-end pixelwise crack detection. First, a multiscale input strategy is applied to the proposed segmentation network to capture more context information. Meanwhile, it can capably reduce the effect of the information loss issue generated by pooling operations. Second, to realize effective feature representation of local features, an additive attention fusion (AAF) block is proposed to guide feature learning to capture both global and local contexts. In addition, faced with the crack detection task with class imbalance issues, a triple attention (TA) block is proposed to detect spatial, channel and pixel attention information to suppress the background and useless information, which is conducive to the characterization of microcracks. Finally, aiming at the limited receptive field, a multiscale feature aggregation unit is proposed for feature fusion to increase the detection ability of multiscale defects. To better guide network training, a deep supervision mechanism is also introduced to speed up the convergence of the proposed segmentation model and improve the performance of defect segmentation. The related evaluation and detection experiments are carried out on three public datasets on crack segmentation, and the comparison experiments with the mainstream segmentation models show that the proposed segmentation network achieves excellent performance on pixelwise crack detection.

DRA-Net: A Dual-Branch Residual Attention Network for Pixelwise Power Line Detection

The accurate extraction of transmission lines from UAV aerial photography is a necessary but challenging task for intelligent power inspection due to some factors, such as complex backgrounds, varying illumination, and class imbalance issue. Although there have been some research achievements in transmission line extraction, especially U-Net and its variant networks, there are still some shortcomings, such as insufficiency processing of local features, limited noise denoising ability, poor edge feature representation ability, etc. In this paper, we propose a dual-branch residual attention network, called DRA-Net, which introduces a dual-branch encoder composed of residual convolutional neural network (RCNN) branch and recurrent residual convolutional neural network (RRCNN) branch to extract richer semantic information and a context fusion block (CFB) to fuse temporal and spatial information effectively. In addition, an U-shape noise de-noising block (UND) is proposed to reduce interference from complex backgrounds and an edge enhancement block (EEB) is also proposed to strength the capacity of segmentation network to extract useful edge feature information from noise. Experiments demonstrate that proposed DRA-Net has obtained an excellent segmentation manifestation on remote sensing images, with a 93.26% Dice coefficient and 93.19% mIOU value on public power line dataset (PLD), and a 96.40% Dice coefficient and 96.04% mIOU value on self-built overhead power line (OPL) dataset.

An attention-guided network for surgical instrument segmentation from endoscopic images

Accurate surgical instrument segmentation can provide the precise location and pose information to the surgeons, assisting the surgeon to accurately judge the follow-up operation during the robot-assisted surgery procedures. Due to strong context extraction ability, there have been significant advances in research of automatic surgical instrument segmentation, especially U-Net and its variant networks. However, there are still some problems to affect segmentation accuracy, like insufficient processing of local features, class imbalance issue, etc. To deal with these problems, with the typical encoder–decoder structure, an effective surgical instrument segmentation network is proposed for providing an end-to-end detection scheme. Specifically, aimed at the problem of insufficient processing of local features, the residual path is introduced for the full feature extraction to strengthen the backward propagation of low-level features. Further, to achieve feature enhancement of local feature maps, a non-local attention block is introduced to insert into the bottleneck layer to acquire global contexts. Besides, to highlight the pixel areas of the surgical instruments, a dual-attention module (DAM) is introduced to make full use of the high-level features extracted from decoder unit and the low-level features delivered by the encoder unit to acquire the attention features and suppress the irrelevant features. To prove the effectiveness and superiority of the proposed segmentation model, experiments are conducted on two public surgical instrument segmentation data sets, including Kvasir-instrument set and Endovis2017 set, which could acquire a 95.77% Dice score and 92.13% mIOU value on Kvasir-instrument set, and simultaneously reach 95.60% Dice score and 92.74% mIOU value on Endovis2017 set respectively. Experimental results show that the proposed segmentation model realizes a superior performance on surgical instruments in comparison to other advanced models, which could provide a good reference for further development of intelligent surgical robots. The source code is provided at https://github.com/lyangucas92/Surg_Net.

Global-prior-guided fusion network for salient object detection

RGB image and depth map (RGB-D)-based salient object detection (SOD) has been well-studied in recent years, especially using deep neural networks. An RGB image provides rich local and semantic features, while the depth map provides global structural information. Many researchers have treated depth information as a supplement to RGB maps. However, depth maps in various datasets are not as precise as RGB information, as they are captured under various conditions. Therefore, thoroughly exploiting these features at different levels remains unresolved. Many cognitive theories, such as the topological perception theory, claim that global properties are prior to local ones and are important for human cognition. In this paper, we propose a novel global-prior-guided fusion network with global-prior extraction modules to fuse cross-modality features. Each module contains a cross attention guided by deeper global priors, and the global prior extracted by this module is used to guide the processing of local features in shallow layers. The global guided network first integrates the local and global cross features into the decoder of depth maps, and then the fused structural features of the decoder are finally fused into the saliency decoder. Experimental results show that our method outperformed other state-of-the-art methods in the RGB-D-based SOD task on seven datasets (i.e., DUT-RGBD, NJUD, LFSD, NLPR, RGBD135, SIP, and STERE) and in terms of most metrics. To thoroughly exploit the modules we designed, we extended our model to accomplish the tasks of RGB and video SOD with slight adaptions, and obtained results comparable to those of the state-of-the-art (SOTA) methods in both fields.

Towards a unified understanding of lateralized vision: A large-scale study investigating principles governing patterns of lateralization using a heterogeneous sample

While functional lateralization of the human brain has been a widely studied topic in the past decades, few studies to date have gone further than investigating lateralization of single, isolated processes. With the present study, we aimed to arrive at a more unified view by investigating lateralization patterns in face and word processing, and associated lower-level visual processing. We tested a large and heterogeneous participant group, and used a number of tasks that had been shown to produce replicable indices of lateralized processing of visual information of different types and complexity. Following Bayesian statistics, group-level analyses showed the expected right hemisphere (RH) lateralization for face, global form, low spatial frequency processing, and spatial attention, and left hemisphere (LH) lateralization for visual word and local feature processing. Compared to right-handed individuals, lateralization patterns of left-handed and especially those who are RH-dominant for language deviated from this ‘typical’ pattern. Our results support the notion that face and word processes come to be lateralized to homologue areas of the two hemispheres, under influence of the RH- and LH-specializations in global form, local feature, and low and high spatial frequency processing. As such, we present a more unified understanding of lateralized vision, providing evidence for the input asymmetry and causal complementarity principles of lateralized visual information processing. The absence of correlations between spatial attention and lateralization of the other processes supports the notion of their independent lateralization, conform the statistical complementarity principle.

The perception of emotional facial expressions by children with autism using hybrid multiple factorial design and eye-tracking

Children with autism spectrum disorder (ASD) have the deficits in emotional facial expression recognition and understanding, which is considered as the core source of their social communication disorder and stereotype behaviour. Previous studies have suggested that individuals with ASD have the deficits in the perception of static emotional facial expressions, but results are inconclusive. The use of 3D or dynamic facial stimuli that express emotions at different levels of intensities may produce more robust results, since these resemble the expression of emotions in daily life to a greater extent. In this study, 30 children (aged 2–7 years) with ASD and 51 age-matched children with typical development (TD) performed an emotion recognition (ER) task, in which morphs, presented on a computer screen, depicted facial expressions associated with two basic emotions (happiness and fear) at four levels of emotional intensity(25%–100%). Using a hybrid-design experiment and eye-tracking technology, we explored the influencing factors of ER in children with ASD and their interaction. In addition, we analyzed the gaze characteristics and processing patterns of children with ASD during the ER, and their impact on ER. The results showed that the type of subject, expression category, and intensity of expression had the main effect on recognition accuracy, and there was interaction among the factors that affected the emotional face recognition ability of the children with ASD. Moreover, children with ASD and TD both had an advantage when recognizing dynamic expressions rather than static expressions in 2D or 3D. The recognition accuracy of 3D expressions of children with TD was higher than that of 2D expressions, but children with ASD had the opposite result. The recognition accuracy of fearful expression for both ASD and TD groups was significantly better than that of happy expression. There was an interaction effect between gender and expression category, but age was not significant. In particular, girls’ facial expression recognition ability was superior to that of boys. The eye-tracking results showed that children with ASD have face-specific processing mechanisms: avoidance of eye contact, increased focus on the mouth and background information, and a preference for local feature processing. In conclusion, this study suggests that the material, category, and intensity of facial expression combines with the age and gender of subjects to affect the emotional expression recognition ability of children with ASD. Children with autism have the advantage of dynamic processing and mouth-directed gaze. In addition, dynamic facial expressions may be used as effective training and intervention materials for ER. Abnormal face processing and eye-tracking characteristics can help to understand the social disorders of ASD and provide behavioral characteristic indicators for early identification of ASD.

From coarse to fine: Interactive feature processing precedes local feature analysis in human face perception

Face perception depends on a dynamic interplay of a “holistic” Interactive Feature Processing (IFP) and a Local Feature Processing (LFP) style. However, it is unclear whether features are processed locally before they are integrated into a holistic percept (Fine-to-Coarse strategy), or whether local feature processing occurs only after a holistic percept is established (Coarse-to-Fine strategy). The present Event-Related Potentials study investigates whether IFP precedes LFP (Coarse-to-Fine) or vice versa (Fine-to-Coarse).Participants matched target features within face pairs (here the eye region), in which distracter features (nose and mouth) called for the same or a different response (congruent and incongruent, respectively). Psychophysical results replicated previous findings. That is, dissimilar target features are locally processed (LFP), which minimizes interference from surrounding incongruent distracters. Conversely, an IFP mode is elicited when similar target features are embedded in dissimilar contexts. In IFP mode, incongruent distracters do interfere with the processing of similar target features, thereby deteriorating task performance. Face inversion, which preserves input properties but disrupts high-level face perception, annihilated these incongruency effects.Psychophysical observations were reflected at the neural level. The IFP and LFP modes of face perception elicited distinct time-courses in occipito-temporal cortex. IFP was affected by inversion as soon as 176 ms post-stimulus onset (coinciding with the N170 peak). In contrast, the first robust indications of LFP occurred 120 ms later, at 296 ms. Thus, the contribution of IFP to high-level face perception appears to temporally precede LFP. Moreover, results showed that the IFP and LFP modes did not only operate in distinct time intervals, but also in different brain areas: activity associated with the IFP mode was right-lateralized, whereas the LPF mode engaged the left hemisphere.In sum, interactive “holistic” encoding of facial features temporally precedes their local analysis. This agrees with models suggesting a Coarse-to-Fine strategy for face perception, in line with generic descriptions of visual perception in which global scene analysis precedes the examination of local details.

The global precedence effect in English and Korean native speakers with Roman, Korean Hangul and Thai compound letters

The aim was to investigate whether native English speakers (Experiment 1) and native Korean speakers (Experiment 2) processed familiar letters in an analytic manner in comparison to unfamiliar letters or symbols. Participants performed a two-alternative-forced-choice identification task with Roman, Korean Hangul and Thai Navon compound letters (large letters made up of small letters). The English speakers were familiar with Roman script but not Korean or Thai, whereas the Korean speakers were familiar with Korean and Roman script but not Thai. The global precedence effect (GPE), an indication of holistic processing, is characterised by a global advantage (global processing is faster than local processing) and asymmetric congruence (global processing interferes with the processing of local features). Based on previous research, it was predicted that there would be a global precedence effect for unfamiliar but not familiar letters.Results from the English speakers did not support this prediction as we found a GPE for familiar Roman as well as unfamiliar Thai letters but not for unfamiliar Korean letters. In contrast, for the Korean speakers, we found support for the prediction as we found a GPE for Thai letters but not for familiar Korean and Roman letters. Based on this evidence, we propose that the Koreans are processing Korean and Roman letters in a more analytic manner than the English native speakers for Roman script. Due to the characteristics of Korean Hangul, Korean readers may be processing letters in a more analytic manner than the English readers.

Comparing the face inversion effect in crows and humans

Humans show impaired recognition of faces that are presented upside down, a phenomenon termed face inversion effect, which is thought to reflect the special relevance of faces for humans. Here, we investigated whether a phylogenetically distantly related avian species, the carrion crow, with similar socio-cognitive abilities to human and non-human primates, exhibits a face inversion effect. In a delayed matching-to-sample task, two crows had to differentiate profiles of crow faces as well as matched controls, presented both upright and inverted. Because crows can discriminate humans based on their faces, we also assessed the face inversion effect using human faces. Both crows performed better with crow faces than with human faces and performed worse when responding to inverted pictures in general compared to upright pictures. However, neither of the crows showed a face inversion effect. For comparative reasons, the tests were repeated with human subjects. As expected, humans showed a face-specific inversion effect. Therefore, we did not find any evidence that crows—like humans—process faces as a special visual stimulus. Instead, individual recognition in crows may be based on cues other than a conspecific’s facial profile, such as their body, or on processing of local features rather than holistic processing.

Natural scene recognition with increasing time-on-task: the role of typicality and global image properties.

Human observers can recognize natural images very effectively. Yet, in the literature there is a debate about the extent to which the recognition of natural images requires controlled attentional processing. In the present study we address this topic by testing whether natural scene recognition is affected by mental fatigue. Mental fatigue is known to particularly compromise high-level, controlled attentional processing of local features. Effortless, automatic processing of more global features of an image stays relatively intact, however. We conducted a natural image categorization experiment (N = 20) in which mental fatigue was induced by time-on-task (ToT). Stimuli were images from 5 natural scene categories. Semantic typicality (high or low) and the magnitude of 7 global image properties were determined for each image in separate rating experiments. Significant performance effects of typicality and global properties on scene recognition were found, but, despite a general decline in performance, these effects remained unchanged with increasing ToT. The findings support the importance of the global property processing in natural scene recognition and suggest that this process is insensitive to mental fatigue.

Preliminary evidence that different mechanisms underlie the anger superiority effect in children with and without Autism Spectrum Disorders.

Previous studies have demonstrated that angry faces capture humans' attention more rapidly than emotionally positive faces. This phenomenon is referred to as the anger superiority effect (ASE). Despite atypical emotional processing, adults and children with Autism Spectrum Disorders (ASD) have been reported to show ASE as well as typically developed (TD) individuals. So far, however, few studies have clarified whether or not the mechanisms underlying ASE are the same for both TD and ASD individuals. Here, we tested how TD and ASD children process schematic emotional faces during detection by employing a recognition task in combination with a face-in-the-crowd task. Results of the face-in-the-crowd task revealed the prevalence of ASE both in TD and ASD children. However, the results of the recognition task revealed group differences: In TD children, detection of angry faces required more configural face processing and disrupted the processing of local features. In ASD children, on the other hand, it required more feature-based processing rather than configural processing. Despite the small sample sizes, these findings provide preliminary evidence that children with ASD, in contrast to TD children, show quick detection of angry faces by extracting local features in faces.

Neural substrates of numerosity estimation in autism.

Visual skills, including numerosity estimation are reported to be superior in autism spectrum disorders (ASD). This phenomenon is attributed to individuals with ASD processing local features, rather than the Gestalt. We examined the neural correlates of numerosity estimation in adults with and without ASD, to disentangle perceptual atypicalities from numerosity processing. Fourteen adults with ASD and matched typically developed (TD) controls estimated the number of dots (80-150) arranged either randomly (local information) or in meaningful patterns (global information) while brain activity was recorded with magnetoencephalography (MEG). Behavioral results showed no significant group difference in the errors of estimation. However, numerical estimation in ASD was more variable across numerosities than TD and was not affected by the global arrangement of the dots. At 80-120 ms, MEG analyses revealed early significant differences (TD > ASD) in source amplitudes in visual areas, followed from 120 to 400 ms by group differences in temporal, and then parietal regions. After 400 ms, a source was found in the superior frontal gyrus in TD only. Activation in temporal areas was differently sensitive to the global arrangement of dots in TD and ASD. MEG data show that individuals with autism exhibit widespread functional abnormalities. Differences in temporal regions could be linked to atypical global perception. Occipital followed by parietal and frontal differences might be driven by abnormalities in the processing and conversion of visual input into a number-selective neural code and complex cognitive decisional stages. These results suggest overlapping atypicalities in sensory, perceptual and number-related processing during numerosity estimation in ASD.