Photometric stereo for strong specular highlights

Photometric stereo (PS) is a fundamental technique in computer vision known to produce 3-D shape with high accuracy. The setting of PS is defined by using several input images of a static scene taken from one and the same camera position but under varying illumination. The vast majority of studies in this 3-D reconstruction method assume orthographic projection for the camera model. In addition, they mainly consider the Lambertian reflectance model as the way that light scatters at surfaces. So, providing reliable PS results from real world objects still remains a challenging task. We address 3-D reconstruction by PS using a more realistic set of assumptions combining for the first time the complete Blinn-Phong reflectance model and perspective projection. To this end, we will compare two different methods of incorporating the perspective projection into our model. Experiments are performed on both synthetic and real world images. Note that our real-world experiments do not benefit from laboratory conditions. The results show the high potential of our method even for complex real world applications such as medical endoscopy images which may include high amounts of specular highlights

Deep Interactive Region Segmentation and Captioning

With recent innovations in dense image captioning, it is now possible to describe every object of the scene with a caption while objects are determined by bounding boxes. However, interpretation of such an output is not trivial due to the existence of many overlapping bounding boxes. Furthermore, in current captioning frameworks, the user is not able to involve personal preferences to exclude out of interest areas. In this paper, we propose a novel hybrid deep learning architecture for interactive region segmentation and captioning where the user is able to specify an arbitrary region of the image that should be processed. To this end, a dedicated Fully Convolutional Network (FCN) named Lyncean FCN (LFCN) is trained using our special training data to isolate the User Intention Region (UIR) as the output of an efficient segmentation. In parallel, a dense image captioning model is utilized to provide a wide variety of captions for that region. Then, the UIR will be explained with the caption of the best match bounding box. To the best of our knowledge, this is the first work that provides such a comprehensive output. Our experiments show the superiority of the proposed approach over state-of-the-art interactive segmentation methods on several well-known datasets. In addition, replacement of the bounding boxes with the result of the interactive segmentation leads to a better understanding of the dense image captioning output as well as accuracy enhancement for the object detection in terms of Intersection over Union (IoU).Comment: 17, pages, 9 figure

Improved collaborative filtering using clustering and association rule mining on implicit data

The recommender systems are recently becoming more significant due to their ability in making decisions on appropriate choices. Collaborative Filtering (CF) is the most successful and most applied technique in the design of a recommender system where items to an active user will be recommended based on the past rating records from like-minded users. Unfortunately, CF may lead to poor recommendation when user ratings on items are very sparse (insufficient number of ratings) in comparison with the huge number of users and items in user-item matrix. In the case of a lack of user rating on items, implicit feedback is used to profile a user’s item preferences. Implicit feedback can indicate users’ preferences by providing more evidences and information through observations made on users’ behaviors. Data mining technique, which is the focus of this research, can predict a user’s future behavior without item evaluation and can too, analyze his preferences. In order to investigate the states of research in CF and implicit feedback, a systematic literature review has been conducted on the published studies related to topic areas in CF and implicit feedback. To investigate users’ activities that influence the recommender system developed based on the CF technique, a critical observation on the public recommendation datasets has been carried out. To overcome data sparsity problem, this research applies users’ implicit interaction records with items to efficiently process massive data by employing association rules mining (Apriori algorithm). It uses item repetition within a transaction as an input for association rules mining, in which can achieve high recommendation accuracy. To do this, a modified preprocessing has been employed to discover similar interest patterns among users. In addition, the clustering technique (Hierarchical clustering) has been used to reduce the size of data and dimensionality of the item space as the performance of association rules mining. Then, similarities between items based on their features have been computed to make recommendations. Experiments have been conducted and the results have been compared with basic CF and other extended version of CF techniques including K-Means Clustering, Hybrid Representation, and Probabilistic Learning by using public dataset, namely, Million Song dataset. The experimental results demonstrate that the proposed technique exhibits improvements of an average of 20% in terms of Precision, Recall and Fmeasure metrics when compared to the basic CF technique. Our technique achieves even better performance (an average of 15% improvement in terms of Precision and Recall metrics) when compared to the other extended version of CF techniques, even when the data is very sparse

3D-Rekonstruktion durch die Verwendung der generalisierten Perspektive Photometric Stereo

Reconstruction of the 3D shape information is a fundamental problem in computer vision. Among different shape recovering technologies, photometric stereo is highlighted for its capability to produce high quality 3D reconstruction. This dissertation generalizes photometric stereo in different aspects towards creating a practical 3D reconstruction. The proposed techniques can be considered as a fundamental support to develop future cameras offering 3D shapes for various applications such as movie and video game industry, medical sciences, virtual reality, automotive driving and etc. The first generalization is developed for addressing specularities in 3D reconstructions and also involving the perspective projection. These attempts lead to remove the limitation of working with diffuse materials and confined projected scenes. We will prove the applicability of our approach using complex scenes like endoscopy images. In the second proposed approach, we will offer a real-time 3D reconstruction of micro-details with a more generalized reflectance model. Moreover, a recurrent optimization network will be provided. These innovations lead to presenting the 3D reconstruction of details which are even invisible to human eyes like micro-prints on the banknote. This information recovery can be used in various areas such as detecting security items on financial documents for fraud detection and also the quality control of any industrial productions including delicate details such as printed circuits. In the third proposed model, we develop a PS reconstruction technique using neural networks for the uncalibrated PS where the light direction is not available. Finally, for the first time, benefiting from deep neural networks and meta heuristic algorithms, we will devise an approach which can deliver high qualified 3D shape from the internet and out-door images, without any pre-necessary knowledge.Die Rekonstruktion von 3D-Gestaltinformationen ist eine fundamentale Aufgabenstellung im Bereich Computer-Vision. Unter den verschiedenen Techniken ist das sogenannte Photometrische Stereo Verfahren (PS) hervorzuheben, da es im Vergleich zu anderen Ansätzen ein höheres Potenzial für eine hoch genaue 3D-Rekonstruktion besitzt. Diese Arbeit befasst sich mit Verallgemeinerungen klassischer PS-Ansätze, die dieses Potenzial weiter ausschöpfen sollen. Die Entwicklungen können beispielsweise verwendet werden, um in Zukunft die 3D-Information in verschiedensten Bereichen für einen Anwender zu erschließen, wie etwa in der Medizin, der Film- und Videospieleindustrie, beim autonomen Fahren oder für Anwendungen in der virtuellen Realität. Die erste der Neuentwicklungen betrifft die systematische Verwendung von hellen Lichtreflexionen in der Berechnung der 3D-Rekonstruktion sowie die perspektivische Projektion. Hierdurch werden Beschränkungen üblicher Methoden, wie auf die Rekonstruktion diffus reflektierender Materialien und auf relativ weit entfernte Objekte, aufgehoben. Die Anwendbarkeit des entwickelten Ansatzes wird mittels der Rekonstruktion aus endoskopischen Bildern mit vielen hellen Lichtreflexionen untermauert. In einem weiteren Schritt wird gezeigt, wie man in Echtzeit eine 3D-Rekonstruktion auch von sehr feinen Details mittels eines verallgemeinerten Reflexionsmodells erreichen kann. Hierdurch werden hochaufgelöste Rekonstruktionen auch von Details erreicht, die für das menschliche Auge in Bildern unsichtbar sind. Hieraus ergeben sich viele potenzielle Anwendungsmöglichkeiten, etwa im Bereich der automatischen Detektion von Mikrodruck, die im Sicherheitsbereich Verwendung finden, oder in der industriellen Produktion für die Detektion sehr feiner Strukturen wie etwa gedruckter Schaltkreise. Als weiterer Beitrag der Arbeit wird ein Verfahren für das sogenannte nicht-kalibrierte PS entwickelt, bei dem neuronale Netzwerke verwendet werden, um die in diesem Fall fehlende Information der Beleuchtungsrichtung auszugleichen. Schließlich wird zum ersten Mal in der Literatur beschrieben, wie man basierend auf Metaheuristiken und tiefen neuronalen Netzen eine qualitativ hochwertige 3D-Rekonstruktion allein durch Bilder aus dem Internet oder natürliche Bilder ohne weiteres zusätzliches Wissen erlangt

& Sciences Publication Pvt. Ltd. Review on Various Kinds of Die Less Forming Methods

Abstract—With the increasing demands for low-volume and customer-made products, a die-less forming method, also called Incremental Sheet Metal Forming (ISMF), has become one of the leading research and development topics in the industry. Incremental Sheet Metal Forming (ISMF) is a recently invented die-less forming method that is quite different to the traditional methods. In ISMF, a piece of sheet metal is formed to the desired shape by a series of small incremental deformations. As it does not use dies, ISMF is effective for small batch production and prototypes. There are various kinds of die-less forming methods which can produce sheet metal parts without dies are proposed. This paper can help anyone who is interested in Incremental Sheet Metal forming with insight for future research direction. Index Terms — Die-less forming, Incremental sheet metal forming, Sheet metal parts

The Impact of Natural Elements on Environmental Comfort in the Iranian-Islamic Historical City of Isfahan

Cities directly influence microclimates. As the urbanization expands, and the green spaces diminish, the heat islands begin to emerge. An old technique used during the past centuries—in both hot and dry climates of the central cities of Iran—was the moderation of microclimates via water and plants. With a diachronic approach to the study of the historical Chahar Bagh Street in Isfahan, this paper investigates the impact of the structural changes on its microclimate in three different scenarios, i.e., the street with its features during the Safavid Era (from 1501 to 1736); the street in its current status; and finally a probable critical condition resulting from complete elimination of natural elements from the environment. The mixed strategy used in this study relies on logical reasoning and software-assisted evaluation for comparing the three scenarios. The predicted mean vote (PMV) model was used for measuring thermal comfort. The results indicate that the evaluated comfort-providing area in the Safavid scenario is 7–17 times more favorable than the others. Moreover, the temperature in the contemporary era was found to be 1.5 degrees Celsius cooler than that of the critical status scenario

An empirical study into model testability

Testability modeling has been performed for many years. Unfortunately, the modeling of a design for testability is often performed after the design is complete. This limits the functional use of the testability model to determining what level of test coverage is available in the design. This information may be useful to help assess whether a product meets a requirement to achieve a desired level of test coverage, but has little pro-active effect on making the design more testable. This paper investigates and presents a number of approaches for tackling this problem. Approaches are surveyed, achievements and main issues of each approach are considered. Investigation of that classification will help researchers who are working on model testability to deliver more applicable solutions

A framework for evaluation of enterprise architecture implementation methodologies

Enterprise Architecture (EA) Implementation Methodologies have become an important part of EA projects. Several implementation methodologies have been proposed, as a theoretical and practical approach, to facilitate and support the development of EA within an enterprise. A significant question when facing the starting of EA implementation is deciding which methodology to utilize. In order to answer this question, a framework with several criteria is applied in this paper for the comparative analysis of existing EA implementation methodologies. Five EA implementation methodologies including: EAP, TOGAF, DODAF, Gartner, and FEA are selected in order to compare with proposed framework. The results of the comparison indicate that those methodologies have not reached a sufficient maturity as whole due to lack of consideration on requirement management, maintenance, continuum, and complexities in their process. The framework has also ability for the evaluation of any kind of EA implementation methodologies