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Skin cancer is a perilous kind of cancer caused by damaged DNA and it leads to death. This damaged DNA causes uncontrolled proliferation of cells. Even though, the image analysis of lesions is highly difficult due to light reflections from skin surface, fluctuations at color lighting, variety of lesions' forms and sizes in skin cancer. Because of these issues, automatic recognition of skin cancer accurateness is decreased. Therefore, a Graph Convolutional Neural Network (GCNN) by ResNet 152 Transfer Learning Architype optimized with Chameleon Swarm Optimization Algorithm (GCNN-ResNet 152 TL-CSOA) is proposed at this manuscript for enhancing skin cancer detection with classification in medical image processing. Initially, the input images are taken from International Skin Imaging Collaboration (ISIC) of dermoscopic skin cancer imagery data set. Afterward, the input images are pre-processed utilizing trilateral filter method for removing noise. The pre-processed output is supplied to the process of feature extraction. Here, image features, like morphologic, gray scale statistic and Haralick texture features are extracted by Gray-Level Co-Occurrence Matrix window adaptive approach (GLCM-WAA) technique. After that, the GCNN-ResNet 152 TL classifies the skin cancer images into Actinic Keratosis, Basal Cell Carcinoma, Malignant Melanoma and Squamous Cell Carcinoma. Additionally, GCNN-ResNet 152 TL weight parameters is tuned by Chameleon Swarm Optimization Algorithm (CSOA). The simulation process is executed at Python tool. From simulation, the proposed approach attains 23.34%, 12.03%, 21.42% improved accuracy and 18.23%, 21.23%, 14.56% higher sensitivity compared with existing approaches. [ABSTRACT FROM AUTHOR]

The holistic understanding of the information contained in technical documents depends on the understanding of the document's individual modalities. These modalities are tables, graphics, diagrams, formulas, etc. and each of them is a standalone research topic that requires a different way of processing and understanding. These modalities, processed and combined with the document text, can introduce new techniques for visual question answering in documents. Thus, in this paper we present Pinakas, a methodology for automatic analysis of the internal tabular information that appears in technical documents and its modeling to stochastic Petri-net graphs. We focus only on tables that strictly abide by the IEEE format rules. The methodology presented here is divided into the following steps: (1) table detection, (2) table recognition, (3) table understanding. Qualitative results of Pinakas are demonstrated as proof of concept for the accurate extraction of information from three different types of tables. [ABSTRACT FROM AUTHOR]

In the medical image-processing field brain tumor segmentation is aquintessential task. Thereby early diagnosis gives us a chance of increasing survival rate. It will be way much complex and time consuming when comes to processing large amount of MRI images manually, so for that we need an automatic way of brain tumor image segmentation process. This paper aims to gives a comparative study of brain tumor segmentation, which are MRI-based. So recent methods of automatic segmentation along with advanced techniques gives us an improved result and can solve issue better than any other methods. Therefore, this paper brings comparative analysis of three models such as Deformable model of Fuzzy C-Mean clustering (DMFCM), Adaptive Cluster with Super Pixel Segmentation (ACSP) and Grey Wolf Optimization based ACSP (GWO_ACSP) and these are tested on CANCER IMAGE ACHRCHIEVE which is a preparation information base containing High Grade and Low-Grade astrocytoma tumors. Here boundaries including Accuracy, Dice coefficient, Jaccard score and MCC are assessed and along these lines produce the outcomes. From this examination the test consequences of Grey Wolf Optimization based ACSP (GWO_ACSP) gives better answer for mind tumor division issue. [ABSTRACT FROM AUTHOR]

Humans are capable of understanding the knowledge that is included in technical documents automatically by consciously combining the information that is presented in the document's individual modalities. These modalities are mathematical formulas, charts, tables, diagram images and etc. In this paper, we significantly enhance a previously presented technical document understanding methodology3 that emulates the way that humans also perceive information. More specifically, we make the original diagram understanding methodology adaptive to larger architectures with more complex structures and modules. The overall understanding methodology results in the generation of a Stochastic Petri-net (SPN) graph that describes the system's functionality. Finally, we conclude with the introduction of the hierarchical association of different diagram images from the same technical document. This processing step aims to provide a holistic understanding of all illustrated diagram information. [ABSTRACT FROM AUTHOR]

Recently, the methods based on Convolutional Neural Networks (CNNs) have achieved state-of-the-art performance in visual place recognition. CNN is a class of multilayer perceptrons, but unlike common multilayer perceptrons that it is not usually fully connected networks. It can acquire more general image features and make the image processing computationally manageable through filtering the connections by proximity. In this paper, we utilize the deep features generated by CNNs and the dynamic time warping (DTW) algorithm for image sequence place recognition. We propose a novel image similarity measurement, which is derived from cosine distance and can better distinguish match and mismatch. Meanwhile, we improve the DTW algorithm to design a local matching method that can reduce time complexity from O(n3) to O(n). To test the proposed method, four datasets (Nordland, Gardens Point, St. Lucia, and UoA datasets) are used as benchmarks; using two traverses in each dataset with one for reference and the other for testing. The results show high precision-recall characteristics of our method in the cases of severe appearance changes. Besides, our method achieves substantial improvements over the methods using the deep feature representations of a single image for recognition, which reflects that the spatiotemporal information contained in the image sequence is significant for the task of visual place recognition. Moreover, the proposed method also shows to outperform the classical sequence-based method SeqSLAM. [ABSTRACT FROM AUTHOR]

During the last years, big data has become the new emerging trend that increasingly attracting the attention of the R&D community in several fields (e.g., image processing, database engineering, data mining, artificial intelligence). Marine data is part of these fields which accommodates this growth, hence the appearance of marine big data paradigm that monitoring advocates the assessment of human impact on marine data. Nonetheless, supporting acoustic sounds classification is missing in such environment, with taking into account the diversity of such data (i.e., sounds of living undersea species, sounds of human activities, and sounds of environmental effects). To overcome this issue, we propose in this paper an approach that efficiently allowing acoustic diversity classification using machine learning techniques. The aim is to reach an automated support of marine big data analysis. We have conducted a set of experiments, using a real marine dataset, in order to validate our approach and show its effectiveness and efficiency. To do so, three machine learning techniques are employed: (i) classic machine learning models (i.e., k-nearest neighbor and support vector machine), (ii) deep learning based on convolutional neural networks, and (iii) transfer learning based on the reuse of pretrained models. [ABSTRACT FROM AUTHOR]

Subspace clustering analysis algorithms are often employed when dealing with high-dimensional data. As a representative approach, Low-Rank Representation (LRR) of data has achieved great success for subspace segmentation tasks in applications such as image processing. The traditional LRR-related methods consist of two separate tasks: first, the affinity graph construction by using lowrank minimization techniques, and then the spectral clustering, which is done on the affinity graph to get the final segmentation. Since these two steps are independent of each other, this method does not guarantee that the results obtained by the algorithm are globally optimal. In this paper, a method called Robust Structured Low-Rank Representation (RSLRR) is proposed, by integrating the two above mentioned tasks and solve a joint optimization problem. This paper also puts forward a method to solve the joint optimization problem, which can efficiently get both the segmentation and the structured low-rank representation. Experiments on several standard datasets show that, compared with other algorithms, the algorithm proposed in this paper can achieve better clustering results. [ABSTRACT FROM AUTHOR]

In response to the fast propagation of videos on the Internet, Content-Based Video Retrieval (CBVR) was introduced to help users find their desired items. Since most videos concern humans, human action retrieval was introduced as a new topic in CBVR. Most human action retrieval methods represent an action by extracting and describing its local features as more reliable than global ones; however, these methods are complex and not very accurate. In this paper, a low-complexity representation method that more accurately describes extracted local features is proposed. In this method, each video is represented independently from other videos. To this end, the motion information of each extracted feature is described by the directions and sizes of its movements. In this system, the correspondence between the directions and sizes of the movements is used to compare videos. Finally, videos that correspond best with the query video are delivered to the user. Experimental results illustrate that this method can outperform state-of-the-art methods. [ABSTRACT FROM AUTHOR]

In Finger-Knuckle-Print (FKP) recognition, feature extraction plays a very important role in the overall system performance. This paper merges two types of the histograms of oriented gradients (HOG)-based features extracted from reflectance and illumination images for FKP-based identification. The Adaptive Single Scale Retinex (ASSR) algorithm has been used to extract the illumination and the reflectance images from each FKP image. Serial feature fusion is used to form a large feature vector for each user, and extract the distinctive features in the higher-dimension vector space. Finally, the cosine similarity distance measure is used for classification. The Hong Kong Polytechnic University (PolyU) FKP database is used during all of the tests. Experimental results show that our proposed system achieves better results than other state-of-the-art system. [ABSTRACT FROM AUTHOR]

Accurate segmentation of human brain image is an essential step for clinical study of magnetic resonance imaging (MRI) images. However, vagueness and other ambiguity present between the brain tissues boundaries can lead to improper segmentation. Possibilistic fuzzy c-means (PFCM) algorithm is the hybridization of fuzzy c-means (FCM) and possibilistic c-means (PCM) algorithms which overcomes the problem of noise in the FCM algorithm and coincident clusters problem in the PCM algorithm. A major challenge posed in the PFCM algorithm for segmentation of ill-defined MRI image with noise is to take into account the ambiguity in the final localization of the feature vectors due to lack of qualitative information. This may lead to improper assignment of membership (typicality) value to their desired cluster. In this paper, we have proposed the possibilistic intuitionistic fuzzy c-means (PIFCM) algorithm for Atanassov's intuitionistic fuzzy sets (A-IFS) which includes the advantages of the PCM, FCM algorithms and A-IFS. Real and simulated MRI brain images are segmented to show the superiority of the proposed PIFCM algorithm. The experimental results demonstrate that the proposed algorithm yields better result. [ABSTRACT FROM AUTHOR]

The interest in image annotation and recommendation has been increased due to the ever rising amount of data uploaded to the web. Despite the many efforts undertaken so far, accuracy or efficiency still remain open problems. Here, a complete image annotation and tourism recommender system is proposed. It is based on the probabilistic latent semantic analysis (PLSA) and hypergraph ranking, exploiting the visual attributes of the images and the semantic information found in image tags and geo-tags. In particular, semantic image annotation resorts to the PLSA, exploiting the textual information in image tags. It is further complemented by visual annotation based on visual image content classification. Tourist destinations, strongly related to a query image, are recommended using hypergraph ranking enhanced by enforcing group sparsity constraints. Experiments were conducted on a large image dataset of Greek sites collected from Flickr. The experimental results demonstrate the merits of the proposed model. Semantic image annotation by means of the PLSA has achieved an average precision of 92% at 10% recall. The accuracy of content-based image classification is 82, 6%. An average precision of 92% is measured at 1% recall for tourism recommendation. [ABSTRACT FROM AUTHOR]

The three-dimensional reconstruction of an object from one gray-scale image is a basic problem in computer vision. This problem is known by Shape From Shading. It is considered as an ill-posed due to the ambiguity of the convexity or the concavity of the surface to be reconstructed. A method of solving the ambiguity based on the singular points of the image was proposed in this paper. It uses tabu search to determine the optimal solution of the solution space. The proposed method determines all 3D objects for an image in a short time. The proposed method has been tested on synthetic and real images, it was compared with another method of resolution. [ABSTRACT FROM AUTHOR]

Automatic facial landmark localization is a challenging problem for real world images because of face pose variations and occlusions. This paper proposes a unified framework to robustly locate facial landmarks under different poses and occlusions. Instead of explicitly modeling the statistical point distribution, we use a sparse linear combination to approximate the observed shape, and hence alleviate the multi-pose problem. In addition, we use the sparsity constraint to handle outliers caused by occlusions. We also model the initial misalignment and use convex optimization techniques to solve them simultaneously and efficiently. We evaluated the proposed method extensively on both synthetic and real data. The experimental results are promising on handling pose variations and occlusions. [ABSTRACT FROM AUTHOR]

The development of vision-based human activity recognition and analysis systems has been a matter of great interest to both the research community and practitioners during the last 20 years. Traditional methods that require a human operator watching raw video streams are nowadays deemed as at least ineffective and expensive. New, smart solutions in automatic surveillance and monitoring have emerged, propelled by significant technological advances in the fields of image processing, artificial intelligence, electronics and optics, embedded computing and networking, molding the future of several applications that can benefit from them, like security and healthcare. The main motivation behind it is to exploit the highly informative visual data captured by cameras and perform high-level inference in an automatic, ubiquitous and unobtrusive manner, so as to aid human operators, or even replace them. This survey attempts to comprehensively review the current research and development on vision-based human activity recognition. Synopses from various methodologies are presented in an effort to garner the advantages and shortcomings of the most recent state-of-the-art technologies. Also a first-level self-evaluation of methodologies is also proposed, which incorporates a set of significant features that best describe the most important aspects of each methodology in terms of operation, performance and others and weighted by their importance. The purpose of this study is to serve as a reference for further research and evaluation to raise thoughts and discussions for future improvements of each methodology towards maturity and usefulness. [ABSTRACT FROM AUTHOR]

A strong assumption to derive generalization guarantees in the standard PAC framework is that training (or source) data and test (or target) data are drawn according to the same distribution. Because of the presence of possibly outdated data in the training set, or the use of biased collections, this assumption is often violated in real-world applications leading to different source and target distributions. To go around this problem, a new research area known as Domain Adaptation (DA) has recently been introduced giving rise to many adaptation algorithms and theoretical results in the form of generalization bounds. This paper deals with self-labeling DA whose goal is to iteratively incorporate semi-labeled target data in the learning set to progressively adapt the classifier from the source to the target domain. The contribution of this work is three-fold: First, we provide the minimum and necessary theoretical conditions for a self-labeling DA algorithm to perform an actual domain adaptation. Second, following these theoretical recommendations, we design a new iterative DA algorithm, called GESIDA, able to deal with structured data. This algorithm makes use of the new theory of learning with (ε,γ,τ)-good similarity functions introduced by Balcan et al., which does not require the use of a valid kernel to learn well and allows us to induce sparse models. Finally, we apply our algorithm on a structured image classification task and show that self-labeling domain adaptation is a new original way to deal with scaling and rotation problems. [ABSTRACT FROM AUTHOR]

In this paper, we propose an automatic analyzing and transforming approach to L-system grammar extraction from real plants. Instead of using manually designed rules and cumbersome parameters, our method establishes the relationship between L-system grammars and the iterative trend of botanical entities, which reflect the endogenous factors that caused the plant branching process. To realize this goal, we use a digital camera to take multiple images of unfoliaged (leafless) plants and capture the topological and geometrical data of plant entities using image processing methods. The data then stored into specific data structures. A Hidden Markov based statistical model is then employed to reveal the hidden relations of plant entities which have been classified into categories based on their statistical properties extracted by a classic EM algorithm, the hidden relations have been integrated into the target L-system as grammars. Results show that our method is capable of automatically generating L-grammars for a given unfoliaged plant no matter what branching type it is belongs to. [ABSTRACT FROM AUTHOR]

We describe a wayfinding system for blind and visually impaired persons that uses a camera phone to determine the user's location with respect to color markers, posted at locations of interest (such as offices), which are automatically detected by the phone. The color marker signs are specially designed to be detected in real time in cluttered environments using computer vision software running on the phone; a novel segmentation algorithm quickly locates the borders of the color marker in each image, which allows the system to calculate how far the marker is from the phone. We present a model of how the user's scanning strategy (i.e. how he/she pans the phone left and right to find color markers) affects the system's ability to detect color markers given the limitations imposed by motion blur, which is always a possibility whenever a camera is in motion. Finally, we describe experiments with our system tested by blind and visually impaired volunteers, demonstrating their ability to reliably use the system to find locations designated by color markers in a variety of indoor and outdoor environments, and elucidating which search strategies were most effective for users. [ABSTRACT FROM AUTHOR]

This paper presents a language for image processing-analysis called ElKONES. The language grammar is derived from the C language with extensions for efficient and flexible image processing. The EIKONES language provides to the user flexibility and friendliness for image processing which are not available in other image processing tools or conventional languages or function libraries. The basic idea behind EIKONES, is the consideration of the image processing algorithms as objects and the appropriate development of a formal grammar for its actual implementation. EIKONES provides a high level language for image processing with features such as learning capability, statistical and region analysis, object tracking. The way in which image operations are specified facilitates a natural language interface for use in a voice-activated system because multiple image operations are mapped to a single image word. [ABSTRACT FROM AUTHOR]

This paper presents a new topological structure for use in the context of hierarchical radiosity combined with discontinuity meshing. This is most useful for a new strategy adopted for subdividing the elements of a scene consisting of convex polygons. The subdivision is done in a local optimization manner keeping the aspect ratios of produced polygons low. The generated meshes give high visual accuracy. [ABSTRACT FROM AUTHOR]

This paper addresses an important and practical problem in computer vision and pattern recognition -- qualitative target detection from aerial images. In particular, it discusses the problem of qualitative building detection based on a monocular aerial image. The approach proposed, due to its independence of site models or camera calibration information, complements the model based approaches developed in the rest of the research community of building detection from aerial images. Specifically, a knowledge supervised perceptual grouping (KSPG) system based on reinvestigation, and hypothesis generation and verification, is presented, and is shown to be reasonably robust in experiments using real data. [ABSTRACT FROM AUTHOR]

This paper presents a methodology for visually tracking, extracting and recognizing targets from a sequence of images (video). The methodology is based on the local-global (LG) graph as a combination of algorithms, such as fuzzy-like segmentation, edge detection, thinning, region growing, fractals, feature extraction, region-graph with attributes, etc., appropriately used for tracking, extracting and recognizing targets under various conditions, such as moving target — still camera, still camera — moving target, moving target — moving camera. The main contribution of this paper is the real-time combination of algorithms that provides a human-like feedback geometric approach of processing low resolution information in a sequence of consecutive images. Simulated results of the methodology are presented for synthetic and real images. [ABSTRACT FROM AUTHOR]

This paper discusses the research conducted on developing a Multi-Agent System (MAS) for solving an image classification task. The aim of this research is to equip agents in MAS with reusable autonomous capabilities. The system provides a flexible framework for developing the communication aspects within an agent-oriented architecture to program agents that dynamically acquire functionality at runtime using event based messaging. In this research agents are equipped with unique image processing capabilities and required to interact and cooperate to achieve the goal. Complementary research on a variety of agent tools (specifically JACK, JADE and CIAgent) and communication languages (ACL, KQML, FIPA and SOAP) has been reviewed to glean knowledge that enables these agents to adapt those capabilities. The system has generated encouraging results. [ABSTRACT FROM AUTHOR]