Objects Of Interest In Images Research Articles

A Method and Algorithm for Implementing the Mandelbrot Set for Processing Complex Structured Images

The purpose of research. In our life, fractals are ubiquitous, for example, in nature, there are a huge number of figures similar to themselves and built according to certain laws. Fractals are used in computer graphics, economics, radio engineering, physics and many other fields. The use of fractals in various fields of science can open up many new possibilities. Therefore, the development of a software product designed to visualize the Mandelbrot set is an urgent task. The purpose of the research is to develop a software product designed for processing complex structured images based on fractal methods and algorithms for visualizing the Mandelbrot set.Methods. The Java programming language was used to implement the software product in order to visualize the Mandelbrot set. The SQLite database was used as a local data store. Libraries were also used: JavaFX ‒ to create a user interface; SQLite JDBC 3.21.0 ‒ to work with the SQLite database; imgscalr ‒ to scale images; Apache Commons IO 2.6 to work with the file system; ControlsFX v8.40.14 ‒ to create a user interface.Results. The developed software product based on fractal methods makes it possible to significantly highlight the textural features of images, on the basis of which the clustering operation of selected objects of interest in images is performed in order to export them for further classification.Conclusion. During the implementation of the project, a software product for visualization of the Mandelbrot set was implemented, which can be used to model objects having a fractal shape, in relation to the processing of complexly structured images containing invisible hidden features of objects in the images.

Detection of Malignant Skin Lesions Based on Decision Fusion of Ensembles of Neural Networks.

Today, skin cancer, and especially melanoma, is an increasing and dangerous health disease. The high mortality rate of some types of skin cancers needs to be detected in the early stages and treated urgently. The use of neural network ensembles for the detection of objects of interest in images has gained more and more interest due to the increased performance of the results. In this sense, this paper proposes two ensembles of neural networks, based on the fusion of the decisions of the component neural networks for the detection of four skin lesions (basal cancer cell, melanoma, benign keratosis, and melanocytic nevi). The first system is based on separate learning of three neural networks (MobileNet V2, DenseNet 169, and EfficientNet B2), with multiple weights for the four classes of lesions and weighted overall prediction. The second system is made up of six binary models (one for each pair of classes) for each network; the fusion and prediction are conducted by weighted summation per class and per model. In total, 18 such binary models will be considered. The 91.04% global accuracy of this set of binary models is superior to the first system (89.62%). Separately, only for the binary classifications within the system was the individual accuracy better. The individual F1 score for each class and the global system varied from 81.36% to 94.17%. Finally, a critical comparison is made with similar works from the literature.

The Dahu graph-cut for interactive segmentation on 2D/3D images

Interactive image segmentation is an important application in computer vision for selecting objects of interest in images. Several interactive segmentation methods are based on distance transform algorithms. However, the most known distance transform, geodesic distance, is sensitive to noise in the image and to seed placement. Recently, the Dahu pseudo-distance, a continuous version of the minimum barrier distance (MBD), is proved to be more powerful than the geodesic distance in noisy and blurred images. This paper presents a method for combining the Dahu pseudo-distance with edge information in a graph-cut optimization framework and leveraging each’s complementary strengths. Our method works efficiently on both 2D/3D images and videos. Results show that our method achieves better performance than other distance-based and graph-cut methods, thereby reducing the user’s efforts.

Interactive Image Segmentation Based on Feature-Aware Attention

Interactive segmentation is a technique for picking objects of interest in images according to users’ input interactions. Some recent works take the users’ interactive input to guide the deep neural network training, where the users’ click information is utilized as weak-supervised information. However, limited by the learning capability of the model, this structure does not accurately represent the user’s interaction intention. In this work, we propose a multi-click interactive segmentation solution for employing human intention to refine the segmentation results. We propose a coarse segmentation network to extract semantic information and generate rough results. Then, we designed a feature-aware attention module according to the symmetry of user intention and image semantic information. Finally, we establish a refinement module to combine the feature-aware results with coarse masks to generate precise intentional segmentation. Furthermore, the feature-aware module is trained as a plug-and-play tool, which can be embedded into most deep image segmentation models for exploiting users’ click information in the training process. We conduct experiments on five common datasets (SBD, GrabCut, DAVIS, Berkeley, MS COCO) and the results prove our attention module can improve the performance of image segmentation networks.

DETECTION OF OBJECTS IN IMAGES USING AREA SELECTION

The aim of the work is to study the effectiveness of algorithms for detecting objects of interest in images obtained by remote surveillance systems. The main properties of objects in relation to the background are their compactness and isolation. The algorithms in question use multithresholding, which yields a set of binary slices. It allows to carry out morphological processing of objects on each slice for the purpose of the analysis of their geometrical characteristics and the subsequent selection. As a result, an adaptive object detection threshold can be set. The use of area selection can significantly reduce the number of false alarms during detection and use lower‑level thresholds, which increases the probability of correct detection of objects of interest. Results of processing of test models of images, and also results of detection of objects on the real image showing efficiency of the considered algorithm are presented.

FRWCAE: joint faster-RCNN and Wasserstein convolutional auto-encoder for instance retrieval

Based on the powerful feature extraction capability of deep convolutional neural networks, image-level retrieval methods have achieved superior performance compared to the hand-crafted features and indexing algorithms. However, people tend to focus on foreground objects of interest in images. Locating objects accurately and using object-level features for retrieval become the essential tasks of instance search. In this work, we propose a novel instance retrieval method FRWACE, which combines the Faster R-CNN framework for object-level feature extraction with a brand-new Wasserstein Convolutional Auto-encoder for dimensionality reduction. In addition, we propose a considerate category-first spatial re-rank strategy to improve instance-level retrieval accuracy. Extensive experiments on four large datasets Oxford 5K, Paris 6K, Oxford 105K and Paris 106K show that our approach has achieved significant performance compared to the state-of-the-arts.

Towards semantic segmentation of orthophoto images using graph-based community identification

We present an unsupervised framework that automatically detects objects of interest in images by formulating the general problem of semantic segmentation as community detection problem in graphs. The framework broadly follows a four-step procedure. First, we perform an over-segmentation of the original image using the well-known statistical region merging algorithm which presents the advantage of not requiring any quantization or colour space transformations. Second, we compute the feature descriptors of the resulting segmented regions. For encoding colour and other textural information, each region is described by an hybrid descriptor based on colour histograms and covariance matrix descriptor. Third, from the set of descriptors we construct different weighted graphs using various graph construction algorithms. Finally, the resulting graphs are then divided into groups or communities using a community detection algorithm based on spectral modularity maximization. This algorithm makes use of the eigenspectrum of matrices such as the graph Laplacian matrix and the modularity matrix which are more likely to reveal the community structure of the graph. Experiments conducted on large orthophotos depicting several zones in the region of Belfort city situated on the north-eastern of France provide promising results. The proposed framework can be used by semi-automatic approaches to handle the challenging problems of scene parsing.

Unsupervised Salient Object Detection via Inferring From Imperfect Saliency Models

Visual saliency detection has become an active research direction in recent years. A large number of saliency models, which can automatically locate objects of interest in images, have been developed. As these models take advantage of different kinds of prior assumptions, image features, and computational methodologies, they have their own strengths and weaknesses and may cope with only one or a few types of images well. Inspired by these facts, this paper proposes a novel salient object detection approach with the idea of inferring a superior model from a variety of previous imperfect saliency models via optimally leveraging the complementary information among them. The proposed approach mainly consists of three steps. First, a number of existing unsupervised saliency models are adopted to provide weak/imperfect saliency predictions for each region in the image. Then, a fusion strategy is used to fuse each image region's weak saliency predictions into a strong one by simultaneously considering the performance differences among various weak predictions and various characteristics of different image regions. Finally, a local spatial consistency constraint that ensures high similarity of the saliency labels for neighboring image regions with similar features is proposed to refine the results. Comprehensive experiments on five public benchmark datasets and comparisons with a number of state-of-the-art approaches can demonstrate the effectiveness of the proposed work.

Weakly Supervised Large Scale Object Localization with Multiple Instance Learning and Bag Splitting.

Localizing objects of interest in images when provided with only image-level labels is a challenging visual recognition task. Previous efforts have required carefully designed features and have difficulty in handling images with cluttered backgrounds. Up-scaling to large datasets also poses a challenge to applying these methods to real applications. In this paper, we propose an efficient and effective learning framework called MILinear, which is able to learn an object localization model from large-scale data without using bounding box annotations. We integrate rich general prior knowledge into a learning model using a large pre-trained convolutional network. Moreover, to reduce ambiguity in positive images, we present a bag-splitting algorithm that iteratively generates new negative bags from positive ones. We evaluate the proposed approach on the challenging Pascal VOC 2007 dataset, and our method outperforms other state-of-the-art methods by a large margin; some results are even comparable to fully supervised models trained with bounding box annotations. To further demonstrate scalability, we also present detection results on the ILSVRC 2013 detection dataset, and our method outperforms supervised deformable part-based model without using box annotations.

Automated image detection of the main bloodstream stem from CT angiography image

Abstract: Purpose of this paper is to design automatized system for detection of main bloodstream stem and to use its output for another processing. System is designed to get image ready for segmentation after using two images, obtained by examination with medical device called computer tomography, using method digital subtraction tomography. The methods for subtraction, normalization and filtration are used as pre-processing. The blood vessels are objects of interest in images, and this is why the designed system separate them from the background. Segmentation methods are used for displaying main bloodstream stem from blood vessels. This output can be used for another applications of larger project. Next applications will be finding blood vessels connected on main blood-stream stem and to separate them, using outputs for verification of therapy success or output that can be used after connection with database for comparison of therapy success in time.

A weakly supervised geodesic level set framework for interactive image segmentation

Interactive image segmentation is growingly useful for selecting objects of interest in images, facilitating spatially localized media manipulation especially on touch screen devices. We present a robust and efficient approach for segmenting image with less and intuitive user interaction. Our approach combines geodesic distance information with the flexibility of level set methods in energy minimization, leveraging the complementary strengths of each to promote accurate boundary placement and strong region connectivity while requiring less user interaction. We harness weakly supervised segment annotation to maximize the user-provided prior knowledge. This leads to a seed generation algorithm which enables image object segmentation without user-provided background seeds. We demonstrate that our approach is less sensitive to seed placement and better at edge localization, whilst requiring less user interaction, compared with the state-of-the-art methods.

Adaptive salient block-based image retrieval in multi-feature space

In this paper, a new method for object-based image retrieval is proposed. The technique is designed to adaptively and efficiently locate salient blocks in images. Salient blocks are used to represent semantically meaningful objects in images and to perform object-oriented annotation and retrieval. An algorithm is proposed to locate the most suitable blocks of arbitrary size representing the query concept or object of interest in images. To annotate single objects according to human perception, associations between several low-level patterns and semantic concepts are modelled by an optimised multi-descriptor space. The approach starts by dividing the image into blocks partitioned according to several different layouts. Then, a fitting block is selected according to a similarity metric acting on concept-specific multi-feature spaces. The similarity metric is defined as linear combination of single feature space metrics for which the corresponding weights are learned from a group of representative salient blocks using multi-objective optimisation. Relevance Feedback is seamlessly integrated in the retrieval process. In each iteration, the user selects images relevant to the query object, then the corresponding salient blocks in selected images are used as training examples. The proposed technique was thoroughly assessed and selected results are reported in this paper to demonstrate its performance.

Relevance-based Content Modeling and Object Retrieval from Multi-source Image Data

The problem of object retrieval for design automation based on semi-semantic representation of objects of interest in images is addressed in this article. The concept of an ordered set of salient feature vectors (SFVs) is introduced to concisely describe multi-source image data in different application areas. A system architecture is presented which combines statistical learning modules with multi-scale morphological modeling and analysis of image contents. In the presented approach, the object retrieval is based on establishing correspondence between two ordered sets of SFVs: a query reference image (or concise description of the object) and a database image. On a higher level, new rules of association are established between the design objects, based on the extracted SFVs and their spatial relations in images. Experiments with different types of images confirmed the utility of the proposed content modeling and proved the adequacy of the extraction accuracy of the SFVs.

A fast algorithm for the computation of axial moments and its application to the orthogonal fitting of curves

This paper describes a fast algorithm to compute local axial moments used in the detection of objects of interest in images. The basic idea is the elimination of redundant operations while computing axial moments for two neighboring angles of orientation. The main result is that the complexity of the recursive computation of axial moments becomes independent of the total number of computed moments at a given point, i.e., it is of the order O( N) where N is the size of the data set. This result is of great importance in computer vision since many feature extraction methods rely on the computation of axial moments. The use of this algorithm for fast object skeletonization in images by orthogonal regression fitting is described in detail, with the experimental results confirming the theoretical computational complexity.

A generic knowledge-guided image segmentation and labeling system using fuzzy clustering algorithms

Segmentation of an image into regions and the labeling of the regions is a challenging problem. In this paper, an approach that is applicable to any set of multifeature images of the same location is derived. Our approach applies to, for example, medical images of a region of the body; repeated camera images of the same area; and satellite images of a region. The segmentation and labeling approach described here uses a set of training images and domain knowledge to produce an image segmentation system that can be used without change on images of the same region collected over time. How to obtain training images, integrate domain knowledge, and utilize learning to segment and label images of the same region taken under any condition for which a training image exists is detailed. It is shown that clustering in conjunction with image processing techniques utilizing an iterative approach can effectively identify objects of interest in images. The segmentation and labeling approach described here is applied to color camera images and two other image domains are used to illustrate the applicability of the approach.