Spatial Characteristics Of Image Research Articles

Synthetic aperture radar image aircraft detection based on target spatial imaging characteristics

With the development of artificial intelligence and synthetic aperture radar (SAR) imaging technology, methods based on deep learning have been gradually applied to SAR image aircraft detection. However, due to the special SAR imaging mechanism, the imaging results of aircraft targets in SAR images do not have clear features and appear as sparse scattering centers. In addition, airports contain strongly scattering buildings and objects, such as corridor bridges, which will bring huge interference to SAR aircraft target detection. To alleviate these problems, we propose an aircraft scattering information extraction and detection model (SIEDM) based on the spatial imaging characteristics of the targets. First, a scattering center feature extraction module is designed to extract the potential scattering centers of the aircraft targets in the SAR images and acquire the overall structural features of the aircraft targets. In addition, the adaptive noise suppression module is proposed to suppress background interference by learning global information. Finally, we conduct a series of experiments on an SAR image aircraft detection dataset. The experimental results show that the mean average precision of the aircraft SIEDM is 95.7%, which is better than other methods, and the proposed modules in this paper are effective.

A Study on the Cognition of Urban Spatial Image at Community Scale: A Case Study of Jinghu Community in Zhengzhou City

The community is the basic spatial unit for urban residents to live and rest. It is a crucial direction of city image research to explore people’s cognitive characteristics of community space image. Aiming at the lack of cognitive quantification of community spatial images, a new method that can quantify community spatial data into cognitive results is proposed. By employing spatial analysis tools, eleven spatial indicators from the perspective of community spatial form and spatial services are selected, and an image structure is constructed based on the characteristics of the indicator results. The results of multiple indicators are organized through the improved technique for order preference by similarity to ideal solution (TOPSIS) and overlay analysis method to produce a spatial image map of the community. The study displays that the spatial image characteristics of the community scale can be comprehensively expressed through three types of elements: district, path (edge), and node (landmark). These three types of elements constitute the image structure at the community scale and present apparent elements’ characteristics. This scrutiny is also aimed to demonstrate the construction and use process of the methodology and to provide new ideas for the cognitive research of urban spatial image at the community scale.

Noisy Low-Illumination Image Enhancement Based on Parallel Duffing Oscillator and IMOGOA

In complex environment, the captured images face several kinds of problems, including low illumination and intensive noise, which deteriorates image quality and has a great impact on the follow-up work. In this work, inspired by stochastic resonance theory, we design a model that considers the spatial characteristics of image and noise reduction and enhancement are simultaneously realized. The 8-neighborhood pixel extraction method and the Duffing oscillator model are used to parallel process the image, and then the image details are restored by homomorphic filter. In order to optimize the parameters of parallel Duffing oscillator model and homomorphic filter adaptively, multiobjective grasshopper optimization algorithm is introduced into the method. Sobol sequence and differential mutation operators are used to improve the optimization algorithm, and the fitness function is constructed by using peak signal-to-noise ratio and standard deviation. To verify the effectiveness of the proposed method, low-illumination image data with Gaussian noise is used for subjective and objective evaluation. The experimental results show that the proposed algorithm gives prominence to useful information, which has smaller color distortion and better visual quality.

Exploring the Spatial Image of Traditional Villages from the Tourists’ Hand-Drawn Sketches

As an important concept in cognitive psychology and behavioural geography, destination spatial image cognition has a significant impact on the quality of tourists’ experience, and on their behavioural intention. However, studies of spatial image cognition in small-scale traditional villages are limited. Therefore, the present study analyses the spatial image characteristics of four traditional villages of World Cultural Heritage sites in China through the use of tourists’ hand-drawn sketches, using a sample of 366 respondents to further explore the evolution process of cognitive map types and constituent elements with tourists’ stay days. Results indicate that the spatial cognitive map and landmarks are the main types and dominant elements of spatial image cognition, respectively. The tourists’ spatial cognitive process includes two sequences, as follows: the evolution sequence of dominant cognitive maps is “spatial + individual → spatial + individual + hybrid → spatial + individual”, while the evolution sequence of dominant cognition elements is “landmark + path + animal and plant → landmark + animal and plant + path”. This study extends the current destination spatial image cognition literature, and has substantial value for the destination in terms of developing traditional village sustainable tourism based on the tourists’ attitude, as obtained by the cognitive map method.

Spatial Relational Attention Using Fully Convolutional Networks for Image Caption Generation

Attention-based encoder–decoder framework has greatly improved image caption generation tasks. The attention mechanism plays a transitional role by transforming static image features into sequential captions. To generate reasonable captions, it is of great significance to detect spatial characteristics of images. In this paper, we propose a spatial relational attention approach to consider spatial positions and attributes. Image features are firstly weighted by the attention mechanism. Then they are concatenated with contextual features to form a spatial–visual tensor. The tensor is feature extracted by a fully convolutional network to produce visual concepts for the decoder network. The fully convolutional layers maintain spatial topology of images. Experiments conducted on the three benchmark datasets, namely Flickr8k, Flickr30k and MSCOCO, demonstrate the effectiveness of our proposed approach. Captions generated by the spatial relational attention method precisely capture spatial relations of objects.

МЕХАНІЗМ СПРИЙНЯТТЯ ПРОСТОРОВО-ЧАСОВОЇ ІНФОРМАЦІЇ В КОГНІТИВНОМУ ПРОСТОРІ ОСОБИСТОСТІ

The article is dedicated the psychological and pedagogical problem of the cognitive process of brain activity from the standpoint of neurophysiological understanding of the functional mechanisms of processing space-time information, their structure and properties. The spatial and temporal characteristics of the spatial-temporal image encoding mechanism are studied. Based on the fact that time and space are fundamental characteristics, it is concluded that the spatial and temporal characteristics of the spatial – temporal image processing mechanism are independent. This conclusion leads to the fact that the cognitive memory space must contain encoded information about spatial and temporal characteristics as components of the model representation of the mental image. In such a model, it is expected that each component of the space-time image will have its own "encoding" mechanism. The spatial characteristic of the image is encoded in the functional mode; the temporal characteristic is encoded in the time code. From the assumption that the spatial and temporal characteristics of the image processing mechanism are independent, it follows that the spatial and temporal representation of the mental image in the cognitive memory space is modeled by two components that are related by the dynamic characteristics of the spatial and temporal encoding mechanisms. The article analyzes the mechanism of timing of subjective (internal) time of cognitive activity. Biological processes determine the mechanism of chronometry of biological time. Biological time is a synchronizer of all processes in the biological system, its dynamic parameters, including parameters of temporary activation of neurons. This approach to studying the structure of the mechanism for creating and tracking time code allows us to explain the preservation of the time sequence of stored space-time information. The study of the cognitive space-time mechanism of memory is important for understanding the directions of development of the cognitive abilities of the individual to perceive space-time information and improving the technology of the educational process in the system of higher mathematical and professional education. Key words: the cognitive memory space, the space-time information, the functional mode, the time code, the chronometry of subjective time.

A study of an improved RCNN network model for surface defect detection algorithm of precision workpiece and its realisation

In this paper, an improved RCNN algorithm for surface defect detection of precision workpiece is proposed. During model training, the original image is processed by data amplification, which overcomes the phenomenon of model overfitting caused by too small data set. The fuzzy image is processed by the conditional generation antagonism network, which guarantees the authenticity of the image and eliminates the fuzziness of the image. On the basis of network selection, through network training and comparative analysis, ResNet101 was selected as the basic network for feature extraction. By combining the network performance, the number of convolutional layers (model depth), system efficiency and other factors, and taking advantage of the spatial characteristics of images, the irregular cross convolution kernel idea and the differentiated convolution kernel design method are adopted to realise the feature fusion of different convolutional layers. Embedded in the target Network Squeeze and Excitation (SE) module channel features fusion module, the Feature Pyramid Network (FPN) module, ROI Network module. The experimental results show that the improved network model is used to realise the location and classification of defects on the workpiece surface. The detection accuracy was 91% and the recall rate was 93%.

Multi-Objective Based Spatio-Temporal Feature Representation Learning Robust to Expression Intensity Variations for Facial Expression Recognition

Facial expression recognition (FER) is increasingly gaining importance in various emerging affective computing applications. In practice, achieving accurate FER is challenging due to the large amount of inter-personal variations such as expression intensity variations. In this paper, we propose a new spatio-temporal feature representation learning for FER that is robust to expression intensity variations. The proposed method utilizes representative expression-states (e.g., onset, apex and offset of expressions) which can be specified in facial sequences regardless of the expression intensity. The characteristics of facial expressions are encoded in two parts in this paper. As the first part, spatial image characteristics of the representative expression-state frames are learned via a convolutional neural network. Five objective terms are proposed to improve the expression class separability of the spatial feature representation. In the second part, temporal characteristics of the spatial feature representation in the first part are learned with a long short-term memory of the facial expression. Comprehensive experiments have been conducted on a deliberate expression dataset (MMI) and a spontaneous micro-expression dataset (CASME II). Experimental results showed that the proposed method achieved higher recognition rates in both datasets compared to the state-of-the-art methods.

Latent feature representation with depth directional long-term recurrent learning for breast masses in digital breast tomosynthesis

Characterization of masses in computer-aided detection systems for digital breast tomosynthesis (DBT) is an important step to reduce false positive (FP) rates. To effectively differentiate masses from FPs in DBT, discriminative mass feature representation is required. In this paper, we propose a new latent feature representation boosted by depth directional long-term recurrent learning for characterizing malignant masses. The proposed network is designed to encode mass characteristics in two parts. First, 2D spatial image characteristics of DBT slices are encoded as a slice feature representation by convolutional neural network (CNN). Then, depth directional characteristics of masses among the slice feature representations are encoded by the proposed depth directional long-term recurrent learning. In addition, to further improve the class discriminability of latent feature representation, we have devised three objective functions aiming to (a) minimize classification error, (b) minimize intra-class variation within the same class, and (c) preserve feature representation consistency in a central slice. Experimental results have demonstrated that the proposed latent feature representation achieves a higher level of classification performance in terms of receiver operating characteristic (ROC) curves and the area under the ROC curve values compared to performance with feature representation learned by conventional CNN and hand-crafted features.

Identifying spatial imaging biomarkers of glioblastoma multiforme for survival group prediction

Glioblastoma multiforme (GBM) is the most common malignant brain tumor in adults. Most GBMs exhibit extensive regional heterogeneity at tissue, cellular, and molecular scales, but the clinical relevance of the observed spatial imaging characteristics remains unknown. We investigated pretreatment magnetic resonance imaging (MRI) scans of GBMs to identify tumor subregions and quantify their image-based spatial characteristics that are associated with survival time. We quantified tumor subregions (termed habitats) in GBMs, which are hypothesized to capture intratumoral characteristics using multiple MRI sequences. For proof-of-concept, we developed a computational framework that used intratumoral grouping and spatial mapping to identify GBM tumor subregions and yield habitat-based features. Using a feature selector and three classifiers, experimental results from two datasets are reported, including Dataset1 with 32 GBM patients (594 tumor slices) and Dataset2 with 22 GBM patients, who did not undergo resection (261 tumor slices) for survival group prediction. In both datasets, we show that habitat-based features achieved 87.50% and 86.36% accuracies for survival group prediction, respectively, using leave-one-out cross-validation. Experimental results revealed that spatially correlated features between signal-enhanced subregions were effective for predicting survival groups (P < 0.05 for all three machine-learning classifiers). The quantitative spatial-correlated features derived from MRI-defined tumor subregions in GBM could be effectively used to predict the survival time of patients. 2 J. MAGN. RESON. IMAGING 2017;46:115-123.

Object-oriented change detection based on weighted polarimetric scattering differences on POLSAR images

Abstract. For wide application of change detection with SAR imagery, current processing technologies and methods are mostly based on pixels. It is difficult for pixel-based technologies to utilize spatial characteristics of images and topological relations of objects. Object-oriented technology takes objects as processing unit, which takes advantage of the shape and texture information of image. It can greatly improve the efficiency and reliability of change detection. Recently, with the development of polarimetric synthetic aperture radar (PolSAR), more backscattering features on different polarization state can be available for usage of object-oriented change detection study. In this paper, the object-oriented strategy will be employed. Considering the fact that the different target or target's state behaves different backscattering characteristics dependent on polarization state, an object-oriented change detection method that based on weighted polarimetric scattering difference of PolSAR images is proposed. The method operates on the objects generated by generalized statistical region merging (GSRM) segmentation processing. The merit of GSRM method is that image segmentation is executed on polarimetric coherence matrix, which takes full advantages of polarimetric backscattering features. And then, the measurement of polarimetric scattering difference is constructed by combining the correlation of covariance matrix and the difference of scattering power. Through analysing the effects of the covariance matrix correlation and the scattering echo power difference on the polarimetric scattering difference, the weighted method is used to balance the influences caused by the two parts, so that more reasonable weights can be chosen to decrease the false alarm rate. The effectiveness of the algorithm that proposed in this letter is tested by detection of the growth of crops with two different temporal radarsat-2 fully PolSAR data. First, objects are produced by GSRM algorithm based on the coherent matrix in the pre-processing. Then, the corresponding patches are extracted in two temporal images to measure the differences of objects. To detect changes of patches, a difference map is created by means of weighted polarization scattering difference. Finally, the result of change detection can be obtained by threshold determining. The experiments show that this approach is feasible and effective, and a reasonable choice of weights can improve the detection accuracy significantly.

Evaluation of temporal and spatial characteristics of 2D HYPR processing using simulations

Highly constrained back-projection (HYPR) is a data acquisition and reconstruction method that provides very rapid frame update rates and very high spatial resolution for a time series of images while maintaining a good signal-to-noise ratio and high image quality. In this study we used simulations to evaluate the temporal and spatial characteristics of images produced using the HYPR algorithm. The simulations demonstrate that spatial accuracy is well maintained in the images and the temporal changes in signal intensity are represented with high fidelity. The waveforms representing signal intensity as a function of time obtained from regions-of-interest placed in simulated objects track the true curves very well, with variations from the truth occurring only when objects with very different temporal behavior are very close to each other. However, even when objects with different temporal characteristics are touching, their influences on each other are small.

Reversible steganographic method using SMVQ approach based on declustering

Reversible steganography allows an original image to be completely restored after the extraction of hidden data embedded in a cover image. In this paper, we propose a reversible scheme for VQ-compressed images that is based on a declustering strategy and takes advantage of the local spatial characteristics of the image. The main advantages of our method are ease of implementation, low computational demands, and no requirement for auxiliary data. The experimental results show that the embedding capacity of the proposed method is controlled by the number of declustered groups and the spatial characteristics of the cover image. The results also show that the proposed method has the best embedding capacity and the least running time compared with other methods.

Image restoration for frame- and object-based video coding using an adaptive constrained least-squares approach

Especially at low bit-rates current DCT-based video coding standards suffer from the disadvantage that coarse quantization and a rigid block structure result in noticeable blocking and ringing noise. In this paper we propose a spatially adaptive method for reduction of these coding artifacts based on the principle of constrained least-squares image restoration. A strictly local filter is developed which adapts to the spatial image characteristics as well as to the coding conditions. Due to the small filter kernel, the method can be applied to frame as well as object-based video coding and is also suited for quality improvement in arbitrarily shaped MPEG-4 coded video material. The proposal is numerically scalable and yields visually pleasing results for intra- as well as interframe coded images. This is also reflected by consistent PSNR improvements between 0.2 and 1.3 dB. As post-processing technique, it is compatible to all existing image and video coding standards.

Anisotropy in Alphanumeric Reproduction Tolerances

The relative importance of spatial image characteristics in the reproduction of alphanumeric characters has been examined, and it is concluded that significant anisotropy exists. At low distortion levels, horizontal image degradation is somewhat less objectionable than vertical degradation, but at higher levels vertical distortion is significantly less objectionable. The upper and lower portions of characters are found to be much more important for legibility than are the central portions. Upper portions of characters carry more legibility information than the lower portions. The implications of these findings on image transmission systems are discussed.