Academic literature on the topic '080106 Image Processing'

Skin cancer is the most common cancer, accounting for 75% of all skin cancers worldwide. Malignant melanoma is the most invasive type of skin cancer, which is deadly. Some techniques have been investigated to diagnose skin diseases using skin tissue classification and diagnosis models and skin recognition approaches using colors based on image retrieval methods. In this regard, image processing techniques and classification methods are intelligent. The purpose of this method, diagnosing melanoma skin cancer using image processing. In the proposed method, after collecting the dataset, the boundary to separate the skin lesion from the background was specified. Then in the next step, the analysis was performed using Empirical wavelet transform (EWT). Then the color, texture, and shape features were extracted. In the next step, the feature was selected by Gray Wolf meta-heuristic algorithm using ranking models and the disease was classified into two categories, namely normal and abnormal. The database used in this study contains 594 dermatoscopic images with a resolution of 512 × 768 pixels, 476 images with normal spots, and 88 images with abnormal spots caused by melanoma. The evaluation results revealed that the proposed method had an accuracy of 97.25, indicating its significant performance compared to other methods. The contribution of the results of the proposed method can be very useful and valuable in the future for early detection of skin cancer.

The amount of data shared online today is increasing. Data security is therefore cited as a significant problem while processing data exchanges through the Internet. Everyone needs the security of their data during communication processes. The science and art of steganography is the concealment of one audio, message, video, or image by embedding another audio, message, video, or image in its place. It is employed to protect sensitive data against malicious assaults. In order to detect the numerous methods employed with digital steganography, this study seeks to identify the primary image-based mediums. As a result, in the spatial domain of the digital medium, the LSB approach was mostly employed, whereas in the transform domain, DTC and DWT were separated as the primary techniques. Due to its simplicity and large embedding capacity, the spatial domain was the most frequently used domain in digital steganography.

A general introduction to image segmentation is provided, including a detailed description of common classic techniques: Otsu’s threshold, k-means and fuzzy c-means clustering; and suggestions of ways in which these techniques have been subsequently modified for special situations. Additionally, a relatively new approach is described, which attempts to address certain exposed failings of the classic techniques listed by incorporating a spatial statistical analysis technique commonly used in geological studies. Results of different segmentation techniques are calculated for various images, and evaluated and compared, with deficiencies explained and suggestions for improvements made.

Autonomous navigation and locomotion of a mobile robot in natural environments remain a rather open issue. Several functionalities are required to complete the usual perception/decision/action cycle. They can be divided in two main categories : navigation (perception and decision about the movement) and locomotion (movement execution). In order to be able to face the large range of possible situations in natural environments, it is essential to make use of various kinds of complementary functionalities, defining various navigation and locomotion modes. Indeed, a number of navigation and locomotion approaches have been proposed in the literature for the last years, but none can pretend being able to achieve autonomous navigation and locomotion in every situation. Thus, it seems relevant to endow an outdoor mobile robot with several complementary navigation and locomotion modes. Accordingly, the robot must also have means to select the most appropriate mode to apply. This thesis proposes the development of such a navigation/locomotion mode selection system, based on two types of data: an observation of the context to determine in what kind of situation the robot has to achieve its movement and an evaluation of the behavior of the current mode, made by monitors which influence the transitions towards other modes when the behavior of the current one is considered as non satisfying. Hence, this document introduces a probabilistic framework for the estimation of the mode to be applied, some navigation and locomotion modes used, a qualitative terrain representation method (based on the evaluation of a difficulty computed from the placement of the robot's structure on a digital elevation map), and monitors that check the behavior of the modes used (evaluation of rolling locomotion efficiency, robot's attitude and configuration watching. . .). Some experimental results obtained with those elements integrated on board two different outdoor robots are presented and discussed.

Although robotics research has seen advances over the last decades robots are still not in widespread use outside industrial applications. Yet a range of proposed scenarios have robots working together, helping and coexisting with humans in daily life. In all these a clear need to deal with a more unstructured, changing environment arises. I herein present a system that aims to overcome the limitations of highly complex robotic systems, in terms of autonomy and adaptation. The main focus of research is to investigate the use of visual feedback for improving reaching and grasping capabilities of complex robots. To facilitate this a combined integration of computer vision and machine learning techniques is employed. From a robot vision point of view the combination of domain knowledge from both imaging processing and machine learning techniques, can expand the capabilities of robots. I present a novel framework called Cartesian Genetic Programming for Image Processing (CGP-IP). CGP-IP can be trained to detect objects in the incoming camera streams and successfully demonstrated on many different problem domains. The approach requires only a few training images (it was tested with 5 to 10 images per experiment) is fast, scalable and robust yet requires very small training sets. Additionally, it can generate human readable programs that can be further customized and tuned. While CGP-IP is a supervised-learning technique, I show an integration on the iCub, that allows for the autonomous learning of object detection and identification. Finally this dissertation includes two proof-of-concepts that integrate the motion and action sides. First, reactive reaching and grasping is shown. It allows the robot to avoid obstacles detected in the visual stream, while reaching for the intended target object. Furthermore the integration enables us to use the robot in non-static environments, i.e. the reaching is adapted on-the- fly from the visual feedback received, e.g. when an obstacle is moved into the trajectory. The second integration highlights the capabilities of these frameworks, by improving the visual detection by performing object manipulation actions.

Facial feature point detection is a key issue in facial image processing. One main challenge of facial feature point detection is the variation of facial structures due to expressions. This thesis aims to explore more accurate and robust facial feature point detection algorithms, which can facilitate the research on facial image processing, in particular the facial expression analysis. This thesis introduces a facial feature point detection system, where the Multilinear Principal Component Analysis is applied to extract the highly descriptive features of facial feature points. In addition, to improve the accuracy and efficiency of the system, a skin color based face detection algorithm is studied. The experiment results have indicated that this system is effective in detecting 20 facial feature points in frontal faces with different expressions. This system has also achieved a higher accuracy during the comparison with the state-of-the-art, BoRMaN.

Compression algorithms have tended to cater only for high compression ratios at reasonable levels of quality. Little work has been done to find optimal compression methods for high quality images where no visual distortion is essential. The need for such algorithms is great, particularly for satellite, medical and motion picture imaging. In these situations any degradation in image quality is unacceptable, yet the resolutions of the images introduce extremely high storage costs. Hence the need for a very low distortion image compression algorithm. An algorithm is developed to find a suitable compromise between hardware and software implementation. The hardware provides raw processing speed whereas the software provides algorithm flexibility. The algorithm is also optimised for the compression of high quality images with no visible distortion in the reconstructed image. The final algorithm consists of a Discrete Cosine Transform (DCT), quantiser, runlength coder and a statistical coder. The DCT is performed in hardware using the SGSThomson STV3200 Discrete Cosine Transform. The quantiser is specially optimised for use with high quality images. It utilises a non-uniform quantiser and is based on a series of lookup tables to increase the rate of computation. The run-length coder is also optimised for the characteristics exhibited by high-quality images. The statistical coder is an adaptive version of the Huffman coder. The coder is fast, efficient, and produced results comparable to the much slower arithmetic coder. Test results of the new compression algorithm are compared with those using both the lossy and lossless Joint Photographic Experts Group (JPEG) techniques. The lossy JPEG algorithm is based on the DCT whereas the lossless algorithm is based on a Differential Pulse Code Modulation (DPCM) algorithm. The comparison shows that for most high quality images the new algorithm compressed them to a greater degree than the two standard methods. It is also shown that, if execution speed is not critical, the final result can be improved further by using an arithmetic statistical coder rather than the Huffman coder.

Nous étudions dans cette thèse comment les réseaux de neurones profonds peuvent être utilisés dans différents domaines de la vision artificielle. La vision artificielle est un domaine interdisciplinaire qui traite de la compréhension d’images et de vidéos numériques. Les problèmes de ce domaine ont traditionnellement été adressés avec des méthodes ad-hoc nécessitant beaucoup de réglages manuels. En effet, ces systèmes de vision artificiels comprenaient jusqu’à récemment une série de modules optimisés indépendamment. Cette approche est très raisonnable dans la mesure où, avec peu de données, elle bénéficient autant que possible des connaissances du chercheur. Mais cette avantage peut se révéler être une limitation si certaines données d’entré n’ont pas été considérées dans la conception de l’algorithme. Avec des volumes et une diversité de données toujours plus grands, ainsi que des capacités de calcul plus rapides et économiques, les réseaux de neurones profonds optimisés d’un bout à l’autre sont devenus une alternative attrayante. Nous démontrons leur avantage avec une série d’articles de recherche, chacun d’entre eux trouvant une solution à base de réseaux de neurones profonds à un problème d’analyse ou de synthèse visuelle particulier. Dans le premier article, nous considérons un problème de vision classique: la détection de bords et de contours. Nous partons de l’approche classique et la rendons plus ‘neurale’ en combinant deux étapes, la détection et la description de motifs visuels, en un seul réseau convolutionnel. Cette méthode, qui peut ainsi s’adapter à de nouveaux ensembles de données, s’avère être au moins aussi précis que les méthodes conventionnelles quand il s’agit de domaines qui leur sont favorables, tout en étant beaucoup plus robuste dans des domaines plus générales. Dans le deuxième article, nous construisons une nouvelle architecture pour la manipulation d’images qui utilise l’idée que la majorité des pixels produits peuvent d’être copiés de l’image d’entrée. Cette technique bénéficie de plusieurs avantages majeurs par rapport à l’approche conventionnelle en apprentissage profond. En effet, elle conserve les détails de l’image d’origine, n’introduit pas d’aberrations grâce à la capacité limitée du réseau sous-jacent et simplifie l’apprentissage. Nous démontrons l’efficacité de cette architecture dans le cadre d’une tâche de correction du regard, où notre système produit d’excellents résultats. Dans le troisième article, nous nous éclipsons de la vision artificielle pour étudier le problème plus générale de l’adaptation à de nouveaux domaines. Nous développons un nouvel algorithme d’apprentissage, qui assure l’adaptation avec un objectif auxiliaire à la tâche principale. Nous cherchons ainsi à extraire des motifs qui permettent d’accomplir la tâche mais qui ne permettent pas à un réseau dédié de reconnaître le domaine. Ce réseau est optimisé de manière simultané avec les motifs en question, et a pour tâche de reconnaître le domaine de provenance des motifs. Cette technique est simple à implémenter, et conduit pourtant à l’état de l’art sur toutes les tâches de référence. Enfin, le quatrième article présente un nouveau type de modèle génératif d’images. À l’opposé des approches conventionnels à base de réseaux de neurones convolutionnels, notre système baptisé SPIRAL décrit les images en termes de programmes bas-niveau qui sont exécutés par un logiciel de graphisme ordinaire. Entre autres, ceci permet à l’algorithme de ne pas s’attarder sur les détails de l’image, et de se concentrer plutôt sur sa structure globale. L’espace latent de notre modèle est, par construction, interprétable et permet de manipuler des images de façon prévisible. Nous montrons la capacité et l’agilité de cette approche sur plusieurs bases de données de référence.
In the present thesis, we study how deep neural networks can be applied to various tasks in computer vision. Computer vision is an interdisciplinary field that deals with understanding of digital images and video. Traditionally, the problems arising in this domain were tackled using heavily hand-engineered adhoc methods. A typical computer vision system up until recently consisted of a sequence of independent modules which barely talked to each other. Such an approach is quite reasonable in the case of limited data as it takes major advantage of the researcher's domain expertise. This strength turns into a weakness if some of the input scenarios are overlooked in the algorithm design process. With the rapidly increasing volumes and varieties of data and the advent of cheaper and faster computational resources end-to-end deep neural networks have become an appealing alternative to the traditional computer vision pipelines. We demonstrate this in a series of research articles, each of which considers a particular task of either image analysis or synthesis and presenting a solution based on a ``deep'' backbone. In the first article, we deal with a classic low-level vision problem of edge detection. Inspired by a top-performing non-neural approach, we take a step towards building an end-to-end system by combining feature extraction and description in a single convolutional network. The resulting fully data-driven method matches or surpasses the detection quality of the existing conventional approaches in the settings for which they were designed while being significantly more usable in the out-of-domain situations. In our second article, we introduce a custom architecture for image manipulation based on the idea that most of the pixels in the output image can be directly copied from the input. This technique bears several significant advantages over the naive black-box neural approach. It retains the level of detail of the original images, does not introduce artifacts due to insufficient capacity of the underlying neural network and simplifies training process, to name a few. We demonstrate the efficiency of the proposed architecture on the challenging gaze correction task where our system achieves excellent results. In the third article, we slightly diverge from pure computer vision and study a more general problem of domain adaption. There, we introduce a novel training-time algorithm (\ie, adaptation is attained by using an auxilliary objective in addition to the main one). We seek to extract features that maximally confuse a dedicated network called domain classifier while being useful for the task at hand. The domain classifier is learned simultaneosly with the features and attempts to tell whether those features are coming from the source or the target domain. The proposed technique is easy to implement, yet results in superior performance in all the standard benchmarks. Finally, the fourth article presents a new kind of generative model for image data. Unlike conventional neural network based approaches our system dubbed SPIRAL describes images in terms of concise low-level programs executed by off-the-shelf rendering software used by humans to create visual content. Among other things, this allows SPIRAL not to waste its capacity on minutae of datasets and focus more on the global structure. The latent space of our model is easily interpretable by design and provides means for predictable image manipulation. We test our approach on several popular datasets and demonstrate its power and flexibility.

The input/output performance has always been the bottleneck problem of computer systems, and with multimedia applications, the problem has been intensified. Hierarchical storage systems provide extensive storage capacity for multimedia data at very economical cost, but the long access latency of tertiary storage devices makes them not attractive for multimedia systems. In this thesis, we present new storage and retrieval methods to handle multimedia data on hierarchical storage systems efficiently. First, we create a novel hierarchical storage organization to increase the storage system throughput. Second, we enhance the data migration method to reduce the multimedia stream response time. Third, we design a new bandwidth based placement method to store heterogeneous objects. Fourth, we demonstrate that disk performance is significantly enhanced using constant density recording disks. We have quantitatively analysed and compared the performance of magnetic disks and hierarchical storage systems in serving multimedia streams of requests. We have also earned out empirical studies which confirm our findings. Our new storage and retrieval methods are able to offer significant advantages and flexibility over existing methods.

To measure the quality of green vegetables in digital images, the colour appearance of the vegetable is one of the main factors. In general, green colour represents good quality and yellow colour represents poor quality empirically for green-vegetable. The colour appearance is mainly determined by its hue, however, the value of brightness and saturation affects the colour appearance under certain conditions. To measure the colour difference between green and yellow, a series of experiments have been designed to measure the colour difference under varying conditions. Five people were asked to measure the colour differences in different experiments. First, colour differences are measured as two of the values hue, brightness, and saturation are kept constant. Then, the previous results are applied to measure the colour difference as one of the values hue, brightness, and saturation is kept constant. Lastly, we develop a colour difference model from the different values of hue, brightness, and saturation. Such a colour difference model classifies the colours between green and yellow. A windows application is designed to measure the quality of leafy vegetables by using the colour difference model. The colours of such vegetables are classified to represent different qualities. The measurement by computer analysis conforms to that produced by human inspection.

Development of an automated method for classifying digital images using a combination of image quality modification and Bayesian classification is the subject of this thesis. The specific example is classification of images obtained by monitoring molten plastic in an extruder. These images were to be classified into two groups: the “with particle” (WP) group which showed contaminant particles and the “without particle” (WO) group which did not. Previous work effected the classification using only an adaptive Bayesian model. This work combines adaptive image quality modification with the adaptive Bayesian model. The first objective was to develop an off-line automated method for determining how to modify each individual raw image to obtain the quality required for improved classification results. This was done in a very novel way by defining image quality in terms of probability using a Bayesian classification model. The Nelder Mead Simplex method was then used to optimize the quality. The result was a “Reference Image Database” which was used as a basis for accomplishing the second objective. The second objective was to develop an in-line method for modifying the quality of new images to improve classification over that which could be obtained previously. Case Based Reasoning used the Reference Image Database to locate reference images similar to each new image. The database supplied instructions on how to modify the new image to obtain a better quality image. Experimental verification of the method used a variety of images from the extruder monitor including images purposefully produced to be of wide diversity. Image quality modification was made adaptive by adding new images to the Reference Image Database. When combined with adaptive classification previously employed, error rates decreased from about 10% to less than 1% for most images. For one unusually difficult set of images that exhibited very low local contrast of particles in the image against their background it was necessary to split the Reference Image Database into two parts on the basis of a critical value for local contrast. The end result of this work is a very powerful, flexible and general method for improving classification of digital images that utilizes both image quality modification and classification modeling.

This thesis focuses on Implicit Concept-based Image Indexing and Retrieval (ICIIR), and the development of a novel method for the indexing and retrieval of images. Image indexing and retrieval using a concept-based approach involves extraction, modelling and indexing of image content information. Computer vision offers a variety of techniques for searching images in large collections. We propose a method, which involves the development of techniques to enable components of an image to be categorised on the basis of their relative importance within the image in combination with filtered representations. Our method concentrates on matching subparts of images, defined in a variety of ways, in order to find particular objects. The storage of images involves an implicit, rather than an explicit, indexing scheme. Retrieval of images will then be achieved by application of an algorithm based on this categorisation, which will allow relevant images to be identified and retrieved accurately and efficiently. We focus on Implicit Concept-based Image Indexing and Retrieval, using fuzzy expert systems, density measure, supporting factors, weights and other attributes of image components to identify and retrieve images.