High compression image and image sequence coding

The digital representation of an image requires a very large number of bits. This number is even larger for an image sequence. The goal of image coding is to reduce this number, as much as possible, and reconstruct a faithful duplicate of the original picture or image sequence. Early efforts in image coding, solely guided by information theory, led to a plethora of methods. The compression ratio reached a plateau around 10:1 a couple of years ago. Recent progress in the study of the brain mechanism of vision and scene analysis has opened new vistas in picture coding. Directional sensitivity of the neurones in the visual pathway combined with the separate processing of contours and textures has led to a new class of coding methods capable of achieving compression ratios as high as 100:1 for images and around 300:1 for image sequences. Recent progress on some of the main avenues of object-based methods is presented. These second generation techniques make use of contour-texture modeling, new results in neurophysiology and psychophysics and scene analysis

Investigation of the histopathological differences of radial artery used for coronary bypass surgery by electron microscopy in two different age groups

Aim: To investigate by electron microscopy whether there are any histopathological differences between the samples collected from the radial arteries used as grafts in the coronary bypass surgeries in two different age groups. Methods: Forty patients whose radial artery grafts were prepared for myocardial revascularization purposes were included in this study. The patients were divided into two groups of twenty each: patients over and under 55. All patients included in the study were evaluated preoperatively for the arterial circulation of the hand from which the radial artery graft would be harvested. The radial artery was dissected as a pedicle together with the surrounding venous structures by means of low-voltage electrocautery. It was left in its original anatomic site until the harvesting of the sample. The harvested samples were examined using a transmission electron microscope, and their photos were taken. Results: We found that the preoperative and intraoperative variables in both age groups were statistically similar except for age, and that the vessels in both groups were histologically normal, with minimal pathological changes. Conclusion: We concluded that radial artery is a graft that can be used in suitable cases in coronary bypass surgery with no age restrictions

Mevlânâ Müzesi Haziresindeki Mezar Taşlarından Örnekler

In this study, which was titled ‘’The Examples of Gravestones in the Mawlana Museum's Cemetery’’, 16 gravestones were studied from 71 gravestones in the cemetery. Due to the fact that it is not possible to examine 71 gravestones in this article, 16 gravestones, which are not repeated in terms of title, form and ornament features, are included in the catalog. All the samples in the catalog are documented by photographs. The inscriptions of the tombstones were read, and the title types, forms, and decoration features were examined and their place in the art of Turkish tombstone was determined. When Konya Mawlana Dervish Lodge was turned into a Museum, the gravestones on the grave of the Dervish Lodge were moved and their cemetery was destroyed. When the tombstones were removed to the museum, the head and footstones of most graves were mixed. Most of the stones in the Mawlana Museum are only the headstones. Accordingly, 10 of the tombstones in the present are soil tombstones, 2 are framed tombstones, 3 are cover-tombs and 1 is sarcophagus tombstone.Mevlânâ Müzesi haziresindeki mezar taşlarından örneklerin bulunduğu bu çalışmada, hazirede yer alan 71 adet mezar taşından 16 tanesi ele alınmıştır. 71 adet mezar taşının incelenmesinin bu makaleye sığdırılmasının mümkün olmaması nedeniyle, başlık, form ve süsleme özellikleri açısından tekrara girmeyen ve nitelikli olan 16 mezar taşı kataloğa dâhil edilmiştir. Katalogda bulunan örneklerin tümü fotoğraflanarak belgelenmiştir. Mezar taşlarının kitabeleri okunmuş, başlık tipleri, formları ve bezeme özellikleri ele alınarak Türk mezar taşı sanatı içindeki yerleri belirlenmeye çalışılmıştır. Konya Mevlânâ Dergâhı Müze'ye dönüştürüldüğünde dergâhın haziresinde bulunan mezar taşları yerlerinden sökülerek mezarlık alanı bozulmuştur. Mezar taşları müzeye kaldırıldığında bazı mezarların baş ve ayak taşları birbirine karışmıştır. Mevlânâ Müzesi'nde bulunan taşların büyük bir kısmı da yalnız baş taşıdır. Buna göre günümüzdeki hazirede yer alan mezar taşlarından 10 tanesi toprak mezar taşı, 2 tanesi çerçeveli mezar taşı, 3 tanesi kapak taşlı mezar ve 1 tanesi de Sandık(lahit) tipi mezar taşıdır

Hypothesis testing for evaluating a multimodal pattern recognition framework applied to speaker detection

Background: Speaker detection is an important component of many human-computer interaction applications, like for example, multimedia indexing, or ambient intelligent systems. This work addresses the problem of detecting the current speaker in audio-visual sequences. The detector performs with few and simple material since a single camera and microphone meets the needs. Method: A multimodal pattern recognition framework is proposed, with solutions provided for each step of the process, namely, the feature generation and extraction steps, the classification, and the evaluation of the system performance. The decision is based on the estimation of the synchrony between the audio and the video signals. Prior to the classification, an information theoretic framework is applied to extract optimized audio features using video information. The classification step is then defined through a hypothesis testing framework in order to get confidence levels associated to the classifier outputs, allowing thereby an evaluation of the performance of the whole multimodal pattern recognition system. Results: Through the hypothesis testing approach, the classifier performance can be given as a ratio of detection to false-alarm probabilities. Above all, the hypothesis tests give means for measuring the whole pattern recognition process effciency. In particular, the gain offered by the proposed feature extraction step can be evaluated. As a result, it is shown that introducing such a feature extraction step increases the ability of the classifier to produce good relative instance scores, and therefore, the performance of the pattern recognition process. Conclusion: The powerful capacities of hypothesis tests as an evaluation tool are exploited to assess the performance of a multimodal pattern recognition process. In particular, the advantage of performing or not a feature extraction step prior to the classification is evaluated. Although the proposed framework is used here for detecting the speaker in audiovisual sequences, it could be applied to any other classification task involving two spatio-temporal co-occurring signals

Object Detection and Matching with Mobile Cameras Collaborating with Fixed Cameras

A system is presented to detect and match any objects with mobile cameras collaborating with fixed cameras observing the same scene. No training data is needed. Various object descriptors are studied based on grids of region descriptors. Region descriptors such as histograms of oriented gradients and covariance matrices of different set of features are evaluated. A detection and matching approach is presented based on a cascade of descriptors outperforming previous approaches. The object descriptor is robust to any changes in illuminations, viewpoints, color distributions and image quality. Objects with partial occlusion are also detected. The dynamic of the system is taken into consideration to better detect moving objects. Qualitative and quantitative results are presented in indoor and outdoor urban scenes

A Master-Slave Approach for Object Detection and Matching with Fixed and Mobile Cameras

Typical object detection algorithms on mobile cameras suffer from the lack of a-priori knowledge on the object to be detected. The variability in the shape, pose, color distribution, and behavior affect the robustness of the detection process. In general, such variability is addressed by using a large training data. However, only objects present in the training data can be detected. This paper introduces a vision-based system to address such problem. A master-slave approach is presented where a mobile camera (the slave) can match any object detected by a fixed camera (the master). Features extracted by the master camera are used to detect the object of interest in the slave camera without the use of any training data. A single observation is enough regardless of the changes in illumination, viewpoint, color distribution and image quality. A coarse to fine description of the object is presented built upon image statistics robust to partial occlusions. Qualitative and quantitative results are presented in an indoor and an outdoor urban scene

A Master-Slave Approach to Detect and Match Objects Across Several Uncalibrated Moving Cameras

Most multi-camera systems assume a well structured environment to detect and match objects across cameras. Cameras need to be fixed and calibrated. In this work, a novel system is presented to detect and match any objects in a network of uncalibrated fixed and mobile cameras. A master-slave system is presented. Objects are detected with the mobile cameras (the slaves) given only their observations from the fixed cameras (the masters). No training stage and data are used. Detected objects are correctly matched across cameras leading to a better understanding of the scene. A cascade of dense region descriptors is proposed to describe any object of interest. Various region descriptors are studied such as color histogram, histogram of oriented gradients, Haar-wavelet responses, and covariance matrices of various features. The proposed approach outperforms existing work such as scale invariant feature transform (SIFT), or the speeded up robust features (SURF). Moreover, a sparse scan of the image plane is proposed to reduce the search space of the detection and matching process, approaching nearly real-time performance. The approach is robust to changes in illuminations, viewpoints, color distributions and image quality. Partial occlusions are also handled

Object Detection and Matching in a Mixed Network of Fixed and Mobile Cameras

This work tackles the challenge of detecting and matching objects in scenes observed simultaneously by fixed and mobile cameras. No calibration between the cameras is needed, and no training data is used. A fully automated system is presented to detect if an object, observed by a fixed camera, is seen by a mobile camera and where it is localized in its image plane. Only the observations from the fixed camera are used. An object descriptor based on grids of region descriptors is used in a cascade manner. Fixed and mobile cameras collaborate to confirm detection. Detected regions in the mobile camera are validated by analyzing the dual problem: analyzing their corresponding most similar regions in the fixed camera to check if they coincide with the object of interest. Experiments show that objects are successfully detected even if the cameras have significant change in image quality, illumination, and viewpoint. Qualitative and quantitative results are presented in indoor and outdoor urban scenes

Extraction of audio features specific to speech production for multimodal speaker detection

A method that exploits an information theoretic framework to extract optimized audio features using video information is presented. A simple measure of mutual information (MI) between the resulting audio and video features allows the detection of the active speaker among different candidates. This method involves the optimization of an MI-based objective function. No approximation is needed to solve this optimization problem, neither for the estimation of the probability density functions (pdf) of the features, nor for the cost function itself. The pdf are estimated from the samples using a non-parametric approach. The challenging optimization problem is solved using a global method: the Differential Evolution algorithm. Two information theoretic optimization criteria are compared and their ability to extract audio features specific to speech is discussed. Using these specific speech audio features, candidates video features are then classified as membership of the "speaker" or "non-speaker" class, resulting in a speaker detection scheme. As a result, our method achieves a speaker detection rate of 100% on home- grown test sequences, and of 85% on most commonly used sequences