Scale Space Image Research Articles

Ecological problems of the plant cover of the northeastern slope of Great Caucasus (within Azerbaijan)

Highlight the purpose. Plant cover of our republic, especially forest ecosystems, has lost its original state as a result of the influence of natural and anthropogenic factors. The study of the impact of these factors on biodiversity has been the focus of many experts. The settlement of the studied area since ancient times and the good development of agriculture have caused the degradation and erosion of the forest cover that covers large areas, as well as the vegetation that replaces each other in the altitude zones here. For this reason, the issues of protection and protection of vegetation are distinguished by their relevance. The main goal of our research is to protect the vegetation of forests that have changed their initial state. Objectives. Mountain geosystems of the northeastern slope of the Greater Caucasus are bordered by the Russian Federation to the north, northwest, and west, the Samur-Devachi plain to the east, and the watershed line of the Greater Caucasus Range and Atachay to the south and southeast. Methods. Field studies were organized to solve the issues raised during the research period. Short-term visual observations of the existing geoecological conditions were carried out, from topographic maps and other maps (landscape, vegetation, etc.) of various scales, as well as from 1:25000 scale aerial and space images in the fund of the Institute of Ecology of MAKA, “Google” and “Bing” space images with a resolution of 1 meter in the funds, taken in the visible zone of the spectrum, obtained from various satellites in 2020-2023 and based on the application of SASPlanet, ArcGIS processing programs were used. With the help of these materials, the vegetation degradation and erosion processes of the research area were studied. Results. The northeast part of the Great Caucasus differs from the other mountainous regions for its appropriation features. For a long time appropriation of the investigated zone by the various farming areas caused a serious change in the natural geosystems. Thus, the zones where a hypsometric height of 200-500 m are mainly replaced by agro-complexes, the parts within 500-1800 m turned into a life and rest objects together with the arable and pastures, an irregular use from the summer pastures in cattle-breeding in the zones higher than 1800 m was completed by exposing of the plant cover to transformation. During assimilation, activation of ecological problems like decreased plant productivity, intensification of the erosion process, acceleration of degradation, and so on activates a problem of rational use. Defining importance, sensitiveness of plant cover, and rational use ways as protection, refusal of usage, extensive use of protection, development, extensive use, improvement, and development were determined.

GLS–MIFT: A modality invariant feature transform with global-to-local searching

Accurate image matching is the basis for many information fusion-related applications. Conventional methods fail when handling multimodal image pairs with severe nonlinear radiation distortion (NRD) and geometric transformations. To address this problem, we present an effective method, termed modality-invariant feature transform with global-to-local searching (GLS–MIFT). First, we addressed scale changes by constructing the image scale space. Then, we obtained multi-scale, multi-orientation filtering results based on first-order Gaussian steerable filters to exploit their robustness to NRD. Next, we introduced a feature response aggregation model to synthesize the filtering results to generate a feature map, and used it to detect highly repeatable features. Additionally, we designed an adaptive partitioning descriptor to achieve rotation-invariant feature description, involving the following six steps: generation of statistical histograms of multi-orientation filtering values, synthesis of histograms on multiple scales, estimation and updating of the primary direction, determination of the sampling direction, normalization of the feature vector in sub-regions, and finally, obtaining the complete description vector. A convolutional image grouping strategy was used to enhance the rotational invariance of the method. We developed a new feature matcher based on the GLS strategy. Guided by the results of global searching stage, the local searching stage further improved the matching accuracy and reliability of the results. Our experimental results confirmed that GLS–MIFT achieved high-quality matching for a large-scale dataset of 1110 image pairs, with various multimodal image types from the fields of computer vision, medicine, and remote sensing. GLS–MIFT outperformed state-of-the-art methods including SIFT, RIFT, HOWP, OFM, MatchFormer, SemLA and GIFT in qualitative and quantitative evaluations. Our implementation and datasets are available at: https://github.com/Zhongli-Fan/GLS-MIFT.

Application of The Speed-Up Robust Features Method To Identify Signature Image Patterns On Single Board Computer

Through the development of a signature pattern recognition program on SBC Beagle-bone Black, this research seeks to determine how to differentiate between real and false signatures. Three techniques of gathering data were employed in this study: interviews, observations, and a review of the literature. The quick application development method is the approach that is applied. The rapid, efficient, and brief development cycle (RAD) is emphasized. This study uses a use-case diagram to illustrate the application's logic and data flow. In this study, OpenCV is used as a digital image processing library along with the C++ programming language and QT creator as an integrated development environment (IDE). This application was subjected to both accuracy and functional testing. The following conclusions are drawn from the findings of the investigation and testing that was done: Using the fast library approach for approximate nearest neighbors (FLANN) and the speeded-up robust features (SURF) feature extraction method, the signature pattern recognition program on the Beagle-bone black SBC can differentiate between real and fraudulent signatures. Through the processes of generating image scale space, feature localization, and feature description, the SURF approach extracts feature from signature images. This signature pattern recognition application is one of the digital image processing apps that can be run on the Beagle-bone Black single board computer. This indicates that the specifications of the SBC Beagle-bone Black for digital image processing are good.

A Coupled 3d Morphological Reconstruction Approach for Surface Microcrack in Si3 n4 Ceramic Bearing Roller Based on Adaptive Nano Feature Extraction & Multiscale Depth Fusion.

To extract the fuzzy contour features, tiny depth features of surface microcracks in the Si3 N4 ceramic bearings roller. An adaptive nano feature extraction and multiscale deep fusion coupling method is proposed, to sufficiently reconstruct the three-dimensional morphology characteristics of surface microcracks. Construct an adaptive nano feature extraction method, form the surface microcrack image scale space and the Gaussian difference pyramid function equation, realize the detection and matching of global feature points. The sparse point cloud is obtained. Through polar-line correction, depth estimation, and fusion of feature points on the surface microcracks image, a multiscale depth fusion matching cost pixel function is established to realize a dense point cloud reconstruction of surface microcracks. The reconstruction results show that the highest value of the local convex surface reconstructed by the dense point cloud reaches 1183nm, and the lowest local concave surface is accurate to 296nm. Compared with the measurement results of the confocal platform, the relative error of the reconstruction result is 24.6%. The overall feature-matching rate of the reconstruction reaches 93.3%. It provides a theoretical basis for the study of surface microcrack propagation mechanism and the prediction of bearing life.

Cross-View Image Synthesis From a Single Image With Progressive Parallel GAN

Cross-view image synthesis aims to synthesize a ground-view image covering the same geographic region for a given single aerial-view image (or vice versa). Existing approaches typically tackle this challenging task by relaxing the single-image constraint and using a ground-truth semantic map as additional input to aid synthesis. However, this is nearly infeasible in practice. In this paper, we investigate how to generate a detail-enriched and structurally accurate ground-level image from only a single aerial-level input image, in which there are no other prior knowledge except for the input image. Towards this goal, we propose a novel Progressive Parallel Generative Adversarial Network (PPGAN) that starts from generating low-resolution outputs and progressively produces ground images at higher resolutions as the network propagates forward. In this manner, our PPGAN decomposes the task into several manageable sub-tasks, which helps to generate detail-enriched and structurally accurate ground images. During progressive generation, the PPGAN employs a parallel generation paradigm that enables the generator to produce multi-resolution images in parallel, thereby avoiding excessive time cost on training. Furthermore, for effective information propagation across multi-resolution images, a feature fusion module (FFM) is devised to mitigate the domain gap between cross-level image features, which enables a balance of detail and structural information synthesis. Additionally, the proposed Channel-Space Attention Selection Module (CSASM) learns the mapping relationship between cross-view images in a larger scale space to enhance the quality of the output image. Quantitative and qualitative experiments demonstrate that, our method requires only one input image without the aid of additional inputs, but is capable of synthesizing detail-enriched and structurally accurate ground images and outperforms the existing state-of-the-art methods on two famous benchmarks.

Multi-Scale PIIFD for Registration of Multi-Source Remote Sensing Images

This paper aims at providing multi-source remote sensing images registered in geometric space for image fusion. Focusing on the characteristics and differences of multi-source remote sensing images, a feature-based registration algorithm is implemented. The key technologies include image scale-space for implementing multi-scale properties, Harris corner detection for keypoints extraction, and partial intensity invariant feature descriptor (PIIFD) for keypoints description. Eventually, a multi-scale Harris-PIIFD image registration algorithm framework is proposed. The experimental results of fifteen sets of representative real data show that the algorithm has excellent, stable performance in multi-source remote sensing image registration, and can achieve accurate spatial alignment, which has strong practical application value and certain generalization ability.

ColorEmo: Color Hunt with Affective Words

Color is a main channel to express emotions of an image. Selecting a good color scheme with color harmony and proper emotional expression is very important in many fields, such as design. There are many color palette tools to help users search, create, edit, save, and share color themes, but few tools are from the perspective of emotional expression and explicitly address the emotional effects of colors. In this paper, a color hunt system with affective words called ColorEmo is developed. Multiple types of input, including affective words, affective categories in image‐scale space, and main colors, are allowed for users with accurate emotional description. Based on the dataset with 428,924 color themes, the system provides rich candidates. The system is designed for user research experiments. The user‐friendly interactions are offered for easy color modifications. The affective matching and color harmony are evaluated in real time while changing colors. This system can be used in many scenarios of designs and applications.

No-reference Image Blur Assessment Based on Multi-scale Spatial Local Features

Blur is an important type of image distortion. How to evaluate the quality of blurred image accurately and efficiently is a research hotspot in the field of image processing in recent years. Inspired by the multi-scale perceptual characteristics of the human visual system (HVS), this paper presents a no-reference image blur/sharpness assessment method based on multi-scale local features in the spatial domain. First, considering various content has different sensitivity to blur distortion, the image is divided into smooth, edge, and texture regions in blocks. Then, the Gaussian scale space of the image is constructed, and the categorized contrast features between the original image and the Gaussian scale space images are calculated to express the blur degree of different image contents. To simulate the impact of viewing distance on blur distortion, the distribution characteristics of local maximum gradient of multi-resolution images were also calculated in the spatial domain. Finally, the image blur assessment model is obtained by fusing all features and learning the mapping from features to quality scores by support vector regression (SVR). Performance of the proposed method is evaluated on four synthetically blurred databases and one real blurred database. The experimental results demonstrate that our method can produce quality scores more consistent with subjective evaluations than other methods, especially for real burred images.

A Novel Harris Feature Detection-Based Registration for Remote Sensing Image

In view of the significant intensity difference between remote sensing image pairs, weak robustness, and insufficient key point correspondence, the novel remote sensing image registration method is proposed. Firstly, a nonlinear scale space is established by means of the anisotropic diffusion equation and fast explicit diffusion. Then, an improved gradient calculation method is used to calculate the gradient amplitude of the nonlinear scale-space image to establish the gradient amplitude space of the nonlinear scale space, and the multiscale Harris method is used to detect the feature points in the gradient amplitude space. The experimental results show that this feature extraction method can consider the boundaries and smoothness of objects and reduce the problem of gray-level difference to increase the number of feature points with potential of being correctly matched, and the distribution of feature points is relatively uniform. In addition, the improved gradient calculation method can effectively reduce the impact of nonlinear intensity differences on image registration. Overall, the algorithm can effectively solve the problem of registration difficulties caused by the significant grayscale difference between multisource remote sensing images and enhance the robustness. Compared with other advanced algorithms, this one has higher accuracy and more correct correspondence relations, and the registration performance has been significantly improved.

A Novel Keypoint Detector Combining Corners and Blobs for Remote Sensing Image Registration

Keypoint detection is a crucial step for feature-based image registration. The traditional detectors only extract one type of keypoint such as a corner or a blob, which is not quite beneficial to image registration. Accordingly, this letter presents a novel keypoint detector that aims to simultaneously extract corners and blobs. The proposed detector is named as Harris-Difference of Gaussian (DoG), which combines the advantages of the Harris-Laplace corner detector and the DoG blob detector. In the definition of Harris-DoG, we first build an image scale space and extract the corners by using the multiscale Harris detector. Then, these corners are screened by an automatic scale selection technique based on their DoG responses. This can make the corners robust to scale changes. Meanwhile, DoG also is used to detect the blobs in the image scale space by a nonmaxima suppression scheme. Finally, the scale invariant feature transform (SIFT) descriptors are computed for the detected corners and blobs, and they are applied together for image registration. The proposed Harris-DoG has been tested by using three pairs of multisensor remote sensing images. The experimental results show that Harris-DoG can effectively increase the number of correct matches and improve the registration accuracy compared with the state-of-the-art keypoint detectors.

Open Curvature Scale Space Matching for Coronary Artery Identification in X-Ray Angiographic Images

Identification of the coronary artery in sequence angiograms is an important issue in the analysis and treatment of coronary angiography data, due to the ambiguities in the coronary vessels resulting from cardiac motion and the differences in the projection angles in angiograms. In this paper, we propose a novel coronary artery identification method based on curvature scale space image matching. First, points on the centerline of each coronary artery branch are convolved with Gaussian kernel functions of different scales to obtain the corresponding curvature scale space (CSS) image during the curve evolution. Then, a CSS image matching method for open curves, which uses a combination of open feature vectors and closed feature vectors, is proposed. Finally, curve matching transitivity is used to solve the problem of the inability to recognize the coronary artery when the difference in the imaging angles is too large. The experimental results demonstrate that the proposed method can recognize every coronary artery in different views, even when the difference in the imaging angles is 29.8°.

RRAM Crossbar-Based In-Memory Computation of Anisotropic Filters for Image Preprocessingloa

Anisotropic-diffusion is a commonly used signal preprocessing technique that allows extracting meaningful local characteristics from a signal, such as edges in an image and can be used to support higher-level processing tasks, such as shape detection. This paper presents a fully scalable CMOS-RRAM architecture of an edge-aware-anisotropic filtering algorithm aimed at computer vision applications. The CMOS circuitry controls the scale-space image data to perform pseudo-parallel in-memory computing and nonlinear processing through RRAM crossbar. The arithmetic operations for in-memory computation of brightness gradients are efficiently accumulated to produce the enhanced image in several iterations. The proposed architecture uses single RRAM as a computing and storage element to perform both arithmetic operations and accumulations. Thanks to the in-memory computation, memory accesses and arithmetic operations are reduced by 64% and 92%, respectively, compared to traditional digital implementations. This, in turn, results in a potential reduction of power and area costs of about 75% and 85%, respectively. The processing time is also reduced by 67%.

Monitoring Coastline Dynamics of Alakol Lake in Kazakhstan Using Remote Sensing Data

Alakol Lake is one of the largest hydrologically closed lake located in Balkash-Alakol River Basin in southeast Kazakhstan. Having a coastline approximately at 490 km, Alakol Lake has faced multiple threats due to both natural and anthropogenic factors as a result of tectonic movements, geology, wind-wave conditions, growing tourism activities, fishing, and transport, etc. The present study aims to investigate the historical trends in coastline changes along Alakol Lake in Kazakhstan and estimate its change rate by using remote sensing data in particular scale-space images Landsat-5 TM, 7 ETM+, 8 OLI, and Sentinel-2A. Based on Landsat and Sentinel data, the modified normalized difference water index was calculated to demonstrate the coastline changes along Alakol Lake between 1990 and 2018. Moreover, the monitoring and analysis of coastline dynamics is based on the main morphometric characteristics of Alakol Lake including water surface area, coastline length, geomorphology of the coast, etc. Our results reveal that there is a continuous coastline retreat, depending on the coast types. For example, in the case of the denudation coasts, a land inundation was from 120 to 270 m between 1990 and 2018. In the case of the accumulative coast (mainly northeast, north, and northwest coasts) a land inundation was from 200 to 900 m. A vast area of agricultural land around Alakol Lake become flooded and lost. This study demonstrates the importance of monitoring coastline dynamics because it provides essential information for understanding the coastal response to contemporary nature and anthropogenic impacts.

BRAIN TUMOR DETECTION AND SEGMENTATION USING MULTISCALE INTUITIONISTIC FUZZY ROUGHNESS IN MR IMAGES

The magnetic resonance imaging technique is mostly used for visualizing and detecting brain tumor, which requires accurate segmentation of brain MR images into white matter, gray matter, cerebrospinal fluid, necrotic tissue, tumor, and edema. But brain image segmentation is a challenging task because of unknown noise and intensity inhomogeneity in brain MR images. This paper proposed a technique for the segmentation and the detection of a tumor, cystic component and edema in brain MR images using multiscale intuitionistic fuzzy roughness (MSIFR). Application of linear scale-space theory and intuitionistic fuzzy image representation deals with noise and intensity inhomogeneity in brain MR images. Intuitionistic fuzzy roughness calculated at proper scale is used to find optimum valley points for segmentation of brain MR images. The algorithm is applied to the real brain MR images from various hospitals and also to the benchmark set of the synthetic MR images from brainweb. The algorithm segments synthetic brain MR image into three regions, gray matter (GM), white matter (WM) and cerebrospinal fluid (CSF) and also separates tumor, cystic component and edema accurately in real brain MR images. The results of segmentation of proposed algorithm for synthetic images are compared with nonlocal fuzzy c-means (NLFCM), rough set based algorithms, intervalued possibilistic fuzzy c-means (IPFCM), robust modified Gaussian mixture model with rough set (RMGMMRS) and three algorithms, recursive bias corrected possibilistic fuzzy c-means (RBCPFCM), recursive bias corrected possibilistic neighborhood fuzzy c-means (RBCPNFCM) and recursive bias corrected separately weighted possibilistic neighborhood fuzzy c-means (RBCSPNFCM). The quantitative and qualitative evaluation demonstrates the superiority of the proposed algorithm.

Learning multi-scale and multi-order features from 3D local differences for visual smoke recognition

To overcome shortages of conventional hand-crafted features, we propose a learning based feature extraction method for visual smoke recognition. We first slide a 3D sampling window in the scale space of images to densely compute 3D local differences across scales, and learn a projection matrix from all 3D local differences of training images. Then the projection matrix is used to transform 3D local differences of each image to generate feature maps, which naturally contain both local and holistic information. To further generate robust descriptors, we process these feature maps in two encoding ways: within-map and between-map encodings. The within-map encoding way generates an Local Binary Pattern (LBP) map for each feature map, while the between-map way encodes pixel-wise values across different feature maps to generate only one LBP map, denoted as a cross-sign map, for every eight feature maps. To make the two encoding ways have the same contributions, we weight the histograms of cross-sign maps and LBP maps with different coefficients. Each computational layer includes calculation of 3D local differences, learning of projection matrices, and encoding of projected features. Several computational layers can be stacked on top of each other to present a hierarchical structure to extract multi-order and high-level features. Subsequent layers carry higher order information on variations of local pixel values, which is more discriminative but also more sensitive to noise. To make a tradeoff between discriminative ability and noise suppression, Taylor-like coefficients are proposed to weight histograms computed from different computational layers. Experimental results demonstrate that the proposed method achieves better performance than most existing handcrafted methods and learning based feature extraction methods on both smoke recognition and texture classification.

A local phase based invariant feature for remote sensing image matching

Local invariant features from computer vision community have recently been widely applied to the matching of remote sensing images. However, these features are mainly designed to handle geometric distortions, and are sensitive to complex radiometric differences between multisensor images. To address this issue, this paper proposes an effective local invariant feature that is sufficiently robust to both geometric and radiometric changes. The proposed feature is built based on the phase congruency model that is invariant to illumination and contrast variation. It consists of a feature detector named MMPC-Lap and a feature descriptor named local histogram of orientated phase congruency (LHOPC). MMPC-Lap is constructed by using the minimum moment of phase congruency for feature detection with an automatic scale location technique, which is used to detect stable keypoints in image scale space. Subsequently, LHOPC derives the feature descriptor for a keypoint by utilizing an extended phase congruency feature with an advanced descriptor configuration. Finally, correspondences are achieved by evaluating the similarity of the feature descriptors. The proposed MMPC-Lap and LHOPC have been evaluated under different imaging conditions (spectral, temporal, and scale changes). The results obtained on a variety of remote sensing images demonstrate its excellent performance with respect to the state-of-the-art local invariant features, especially for cases where there are complex radiometric differences.

Complexity Reduction by Modified Scale-Space Construction in SIFT Generation Optimized for a Mobile GPU

Scale-invariant feature transform (SIFT) is one of the most widely used local features for computer vision in mobile devices. A mobile graphic processing unit (GPU) is often used to run computer-vision applications using SIFT features, but the performance in such a case is not powerful enough to generate SIFT features in real time. This paper proposes an efficient scheme to optimize the SIFT algorithm for a mobile GPU. It analyzes the conventional scale-space construction step in the SIFT generation, finding that reducing the size of the Gaussian filter and the scale-space image leads to a significant speedup with only a slight degradation of the quality of the features. Based on this observation, the SIFT algorithm is modified and implemented for real-time execution. Additional optimization techniques are employed for a further speedup by efficiently utilizing both the CPU and the GPU in a mobile processor. The proposed SIFT generation scheme achieves a processing speed of 28.30 frames/s for an image with a resolution of $1280 \times 720$ running on a Galaxy S5 LTE-A device, thereby gaining a speedup by the factors of 114.78 and 4.53 over CPU- and GPU-only implementations, respectively.

Learning to Appreciate the Aesthetic Effects of Clothing

How do people describe clothing? The words like “formal”or "casual" are usually used. However, recent works often focus on recognizing or extracting visual features (e.g., sleeve length, color distribution and clothing pattern) from clothing images accurately. How can we bridge the gap between the visual features and the aesthetic words? In this paper, we formulate this task to a novel three-level framework: visual features(VF) - image-scale space (ISS) - aesthetic words space(AWS). Leveraging the art-field image-scale space served as an intermediate layer, we first propose a Stacked Denoising Autoencoder Guided by CorrelativeLabels (SDAE-GCL) to map the visual features to the image-scale space; and then according to the semantic distances computed byWordNet::Similarity, we map the most often used aesthetic words in online clothing shops to the image-scale space too. Employing upper body menswear images downloaded from several global online clothing shops as experimental data, the results indicate that the proposed three-level framework can help to capture the subtle relationship between visual features and aesthetic words better compared to several baselines. To demonstrate that our three-level framework and its implementation methods are universally applicable, we finally present some interesting analyses on the fashion trend of menswear in the last 10 years.

스케일 공간 도함수를 이용한 강인한 기술자 생성 기법

Requirement of effective image handling methods such as image retrieval has been increasing with the rising production and consumption of multimedia data. In this paper, a method of constructing more effective descriptor is proposed for robust keypoint based image retrieval. The proposed method uses information embedded in the first order and second order derivative images, in addition to the scale space image, for the descriptor construction. The performance of multi-image descriptor is evaluated in terms of the similarities in keypoints with a public domain image database that contains various image transformations. The proposed descriptor shows significant improvement in keypoint matching with minor increase of the length.

Recursive Coarse-to-Fine Localization for Fast Object Detection

Sliding window (SW) technique is one of common paradigms employed for object detection. However, the computational cost of this approach is so expensive because the detection window is scanned at all possible positions and scales. To overcome this problem, we propose a compact feature together with fast recursive coarse-to-fine object localization strategy. To build a compact feature, we project the Histograms of Oriented Gradient (HOG) features to linear subspace by Principal Component Analysis (PCA). We call this feature as PCA-HOG feature. The exploitation of the PCA-HOG feature not only helps the classifiers run faster but also still maintains the accuracy. In order to further speeding up the localization, we propose a recursive coarse-to-fine refinement to scan image. We scan image in both scale space and multi-resolution space from coarsest to finest resolutions. Only the best obtained hypothesis from the coarser resolution could be passed to finer resolution. Each resolution has its own linear Support Vector Machine (SVM) classifier and PCA-HOG features. Evaluation with INRIA dataset shows that our method achieves a significant speedup compared to standard sliding window and original HOG feature, while even get higher detection accuracy.