Pedestrian Reidentification Based on Feature Fusion and Metric Learning

The pedestrian recognition in public environment is influenced by the pedestrian environment and the dynamic characteristic boundary factors, so it is easy to produce the tracking error. In order to improve the ability of pedestrian re-identification in public environment, we need to carry out feature fusion and metric learning, and propose pedestrian re-identification based on feature fusion and metric learning. The geometric grid area model of pedestrian recognition in public environment is constructed, the method of fuzzy dynamic feature segmentation is used to reconstruct the dynamic boundary feature point of pedestrian recognition in public environment, the method of bottom-up modeling is used to design the dynamic area grid model of pedestrian recognition in public environment, the design of dynamic area grid model is three-dimensional grid area, the grayscale pixel set of pedestrian recognition dynamic constraint under public environment is extracted, the boundary feature fusion is carried out according to the distribution intensity of grayscale, the image fusion and enhancement information processing of pedestrian recognition under public environment, and the method of 3D dynamic constraint is used to realize the local motion planning of pedestrian recognition under public environment, and the recognition feature fusion and learning of pedestrian recognition under public environment is realized according to the result of contour segmentation. The simulation results show that the method is used for pedestrian recognition again in public environment, and the fuzzy judgment ability of pedestrian dynamic edge features is strong, which makes the error controlled below 10 mm, and the fluctuation of pedestrian recognition again is more stable, the recognition accuracy is higher and the robustness is better.

Lung Nodule Detection using Segmentation Approach for Computed Tomography Scan Images

Lung disorders have become really common in today’s world due to growing amount of air pollution, our increased exposure to harmful radiations and our unhealthy lifestyles. Hence, the diagnosis of lung disorders has become of paramount importance. The commonly used Thresholding approaches and morphological operations often fail to detect the peripheral pathology bearing areas. Hence, we present the segmentation approach of the lung tissue for computer aided diagnosis system. We use a novel technique for segmentation of lungs from CT scan (Computed Tomography) of the chest or upper torso. The accuracy of analysis and its implication majorly depends on the kind of segmentation technique used. Hence, it is important that the method used is highly reliable and is successful in nodule detection and classification. We use MATLAB and OpenCV libraries to apply segmentation on CT scan images to get the desired output. We have also created a working proprietary user interface called “PULMONIS” for the ease of doctors and patients to upload the CT scan images and get the output after the image processing is done in the backend. Keywords: Lung nodule detection, Image Processing, Computed Tomography, Image Segmentation, Lung Cancer, Contour Segmentation, MATLAB, OpenCV, Computer Vision.

Leo Ornstein

Στόχος της παρούσας έρευνας είναι να αναδειχθεί η ιστορική σημαντικότητα και στυλιστική ποικιλότητα της μουσικής του Leo Ornstein. Στη διάρκεια της διδακτορικής μου έρευνας, παρουσίασα και ερμήνευσα πολλά από τα πιο χαρακτηριστικά έργα του Ornstein για πιάνο. Επίσης, ερεύνησα και επεξεργάστηκα εκδοθέν και ανέκδοτο υλικό του έργου του. Ορόσημο στην έρευνά μου αποτελεί η απροσδόκητη ανακάλυψη μίας προηγουμένως άγνωστης αδημοσίευτης σονάτας για πιάνο του Ornstein, με τίτλο Sonata pour le Piano (1917). Εντόπισα την παρτιτούρα στη συλλογή “Leo Ornstein Papers” της Μουσικής Βιβλιοθήκης Irving S. Gilmore, του Πανεπιστημίου του Γέιλ (Yale University). Στην παρούσα διατριβή παρουσιάζω την πρώτη έκδοση και εκτενή ανάλυση αυτού του αξιοσημείωτου αδημοσίευτου έργου. Με βάση τα παραπάνω, προτείνω νέες μεθόδους ανάλυσης της μουσικής του Ornstein, χρησιμοποιώντας τα ακόλουθα: φθογγικά σύνολα, τροπική ανάλυση, contour segmentation και μοτιβικά κύτταρα, με σκοπό να προσδιορίσω τα βασικά στοιχεία του πιανιστικού του ιδιώματος. Η συγκριτική ανάλυση των έργων του για πιάνο και τα επαναλαμβανόμενα μοτίβα των πιανιστικών του συνθέσεων, καλύπτουν ένα εύρος από τον μοντερνισμό μέχρι τον νεορομαντισμό, εμποτισμένα με Εβραϊκά στοιχεία, και τα οποία καθορίζουν τα χαρακτηριστικά και την εξέλιξη του έργου του. Επιπλέον, για να αποκτήσω εις βάθος κατανόηση της ζωής, του έργου και της κληρονομιάς του Leo Ornstein, πήρα συνέντευξη από τον γιο του, Severo Ornstein, ο οποίος είναι ο πλέον σημαντικός συντηρητής, επιμελητής και εκδότης ολόκληρου του έργου του πατέρα του, αλλά και πρωτοπόρος ο ίδιος, καθώς επινόησε το “Mockingbird”, το πρώτο πρόγραμμα μουσικής σημειολογίας για υπολογιστή σε γραφικό περιβάλλον, το 1980. Τέλος, εξετάζω τους πολύπλευρους λόγους για την άνοδο του Ornstein στη φήμη, και την μετέπειτα απόσυρσή του από την διεθνή μουσική σκηνή, διερευνώντας την καριέρα του ως πιανίστα και συνθέτη και τη σχέση του με το κίνημα του πρώιμου μοντερνισμού στην Αμερική, κατά την περίοδο 1910–1920.

Evaluation and Implementation of Otsu and Active Contour Segmentation in Contrast-Enhanced Cardiac CT Images

The CT cardiac acquisition process is usually conducted by using an additional image with contrast medium that is injected inside the body and reconstructed by a radiologist using an integrated CT Scan software with the aim to find the morphology and volume dimension of the heart and coronary arteries. In fact, the data obtained from the hospital are raw data without segmented contour from a radiologist. For the purpose of automation, dataset is needed to be used as input data for further program development. This study is focused on the evaluation of the segmentation results of CT cardiac images using Otsu threshold and active contour algorithm with the aim to make a dataset for the heart volume quantification that can be used interactively as an alternative to integrated CT scan software. 2D contrast enhanced cardiac CT from 6 patients using image processing techniques was run on Matlab software. Of the 689 slices that was used, as many as (73.75 ± 19.41)%of CT cardiac slices have been segmented properly, (19.15 ± 19.61)%of the slices that were segmented included the spine bone, (1.36 ± 0.98)%of the slices did not include all region of the heart, (16.58 ± 15.26)%of the slices included other organs with the consistency from the measurement proven from inter-observer variability to produce r = 0,9941.The result is due to the geometry influence from the diameter of the patient’s body thickness that tends to be thin.

Multi-Feature Driven Active Contour Segmentation Model for Infrared Image With Intensity Inhomogeneity

Active contour model (ACM) is one of the most widely used image segmentation tools at present, but the existing methods only utilize single feature information of image to minimize the energy function, which is easy to cause false segmentations in infrared (IR) images. In this paper, we propose a multi-feature driven active contour segmentation model to handle IR images with intensity inhomogeneity. Firstly, an especially-designed signed pressure force (SPF) function is constructed by combining the global information calculated by global average gray information and the local multi-feature information calculated by local entropy, local standard deviation and gradient information. Then, we draw upon adaptive weight coefficient computed by local range to incorporate the afore-mentioned global term and local term. Next, the SPF function is substituted into the level set formulation (LSF) for further evolution. Finally, the LSF converges after a finite number of iterations and the IR image segmentation result is obtained from the corresponding convergence result. Experimental results demonstrate that the presented method outperforms typical models in terms of precision rate and overlapping rate in IR test images.